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I built a Session Border Controller for AI | 0 | I built a Session Border Controller for AI agents
I've been thinking about AI agent traffic for months and something kept bugging me. Everyone treats it like a traditional request/response. Secure the API, rate limit the endpoint, done. But that's not what agent traffic looks like. Agents hold sessions. They negotiate context. They escalate, transfer, fork into parallel conversations. If you or your users are running OpenClaw or any local agent, there's nothing sitting between it and your LLM enforcing policy or letting you kill a runaway session.
I spent a few years at BroadCloud deep in SIP infrastructure: application servers, firewalls, SBCs, the whole stack. VoIP has three-leg calls, conference bridges, rogue calls hammering the system. The SBC sits at the edge and protects the core from all of it. AI agent traffic looks the same to me. An agent calls a tool that calls another API. That's a three-leg call. Sessions fork into parallel conversations. That's a conference bridge. An agent starts hallucinating and burning tokens with no way to stop it. That's a rogue call. Same patterns. Zero protection. This problem was solved decades ago in telecom. So I built ELIDA.
What ELIDA does:
Kill switch to stop a runaway agent mid-session
Per-session policy enforcement
Session detail records for audit and compliance
Ships telemetry to any OTel destination
docker run -d \
-p 8080:8080 \
-p 9090:9090 \
-e ELIDA_BACKEND=https://api.openai.com \
zamorofthat/elida:latest
While building this I wanted to be ruthless on security. CI runs govulncheck, gosec, Semgrep, and TruffleHog on every push. Aikido Security on top of the repo as a sanity check. Unit and integration tests with race detection. Multi-arch Docker builds for amd64 and arm64.
Open source. Apache 2.0.
I built this with Claude Code. I developed the plan and wrote the tests, then iterated and steered the output.
Happy to answer any questions and PRs are welcome.
https://github.com/zamorofthat/elida
| 2026-02-16T23:02:02 | https://www.reddit.com/r/LocalLLaMA/comments/1r6oznb/i_built_a_session_border_controller_for_ai/ | zamor0fthat | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6oznb | false | null | t3_1r6oznb | /r/LocalLLaMA/comments/1r6oznb/i_built_a_session_border_controller_for_ai/ | false | false | self | 0 | null |
HOT TAKE : GEMMA 4 IS PROBABLY DEAD. | 0 | Of late, I have been seeing an uptick in people expecting google dropping Gemma 4 soon. I have been giving it a thought and after having followed the release and pattern of google and many other companies, **I think Gemma is soon going to die.** So, i thought why dont I share my thought with you guys.
**-------**
**TL;DR:**
"Gemma was never meant to compete with Google’s frontier Gemini models—it was released to prevent ecosystem lock-in, buy time, and ensure developers didn’t become fully dependent on competitors like OpenAI or Anthropic. Its iterative releases focused on accessibility and ecosystem expansion rather than frontier performance, while Google consolidated its real advantage in vertically integrated compute infrastructure (TPUs, data centers, Android reach). With ecosystem saturation achieved and Gemini advancing independently, a true Gemma 4 that closes the frontier gap is unlikely, because Gemma’s strategic purpose has already been fulfilled"
\------
Google didn’t release Gemma because it believed in open weights. It released Gemma because it needed time. **Google invented the Transformer in 2017** and quickly deployed it across Search, Ads, and Translate—but in ways that protected its core revenue engine. **Conversational AI poses a structural threat** to that engine. **Direct answers reduce link clicks, which reduces ad impressions.** For years, despite having systems like Meena and LaMDA, Google **avoided pushing conversational AI** as the primary interface.
**OpenAI and Anthropic changed that calculus. The threat shifted from self-cannibalization to external displacement.** Gemma emerged in that transition—not as a frontier contender, but as an ecosystem stabilizer.
Google released Gemma 1, then Gemma 2, then Gemma 3, along with variants like CodeGemma, medgemma and smaller optimized derivatives. **But these releases followed a clear pattern: incremental architectural refinement, efficiency improvements, and accessibility scaling—not frontier escalation.** They were designed to proliferate usage, not dominate benchmarks. Meanwhile, Google’s true frontier efforts remained concentrated in Gemini, which continues advancing independently and remains tightly controlled.
**Gemma served a specific strategic purpose: prevent ecosystem lock-in around competing proprietary models.** By putting capable weights into circulation, Google **ensured developers retained independence and experimentation flourished.** But this openness also accelerated global capability diffusion. Chinese labs like DeepSeek and Alibaba’s Qwen rapidly achieved competitive efficiency, proving that architecture was no longer the primary bottleneck.
Compute economics became the real moat. **This shift structurally favors Google. It owns the full vertical stack: TPUs, data centers, and global deployment infrastructure.** It can run inference at cost structures competitors relying on external GPU supply chains struggle to match. At sufficient scale, infrastructure advantage outweighs model openness. Gemma expanded the ecosystem while Gemini advanced the frontier. That division was intentional. But **once ecosystem saturation is achieved, releasing increasingly capable open successors becomes strategically unnecessary**.
The continued existence of Gemma derivatives without a clear push toward frontier parity suggests maintenance, not escalation. Gemma solved Google’s timing problem. It bought breathing room while the real advantage was consolidated elsewhere. They have already started giving away millions of free 'Pro" subscriptions to India. They just need to exploit their reach advantage they have through the Android ecosystem. Now, Google will just bleed their almost infinite money into outlasting the competitors.
There may be more Gemma variants. But **a true Gemma 4, one that meaningfully closes the gap with Gemini, is increasingly unlikely.** Because Gemma was never the endgame.
Note: This is entirely my own opinion. Its definitely not a "Fact". Feel free to chip in your opinions. | 2026-02-16T22:41:08 | https://www.reddit.com/r/LocalLLaMA/comments/1r6ofzq/hot_take_gemma_4_is_probably_dead/ | PaceZealousideal6091 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6ofzq | false | null | t3_1r6ofzq | /r/LocalLLaMA/comments/1r6ofzq/hot_take_gemma_4_is_probably_dead/ | false | false | self | 0 | null |
The only model that works is gpt-oss | 0 | Hello,
I set up a local machine in my network that runs ollama a couple of weeks ago. I have in addition set up OpenCode as a coding agent and connected it to the ollama server in my network.
I was hoping to check out some agentic programming with the differnt models; qwen2.5-coder, devstral and such. But for some reason none of them work. However, gpt-oss does work! I can prompt it in OpenCode and I get the result I want. I also have some success with ralph-tui with gpt-oss, making it loop to create a simple to-do app for instance.
But I cant get some models (qwen2.5-coder and devstral for istance) to work. When prompting to add a new TodoController to my C# .NET Web API it simply output the json tool calling as bash, edit and such. I can switch to gpt-oss and in the very same session prompt the same prompt and it executes with out problems.
Its not a permission thing, gpt-oss model works. All tested models supports "tools". For models not supporting tools OpenCode lets me know right away. | 2026-02-16T22:32:27 | https://www.reddit.com/r/LocalLLaMA/comments/1r6o7va/the_only_model_that_works_is_gptoss/ | larsey86 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6o7va | false | null | t3_1r6o7va | /r/LocalLLaMA/comments/1r6o7va/the_only_model_that_works_is_gptoss/ | false | false | self | 0 | null |
published a skill for academic research writing | 0 | the skills lets claude / codex / cursor / antigravity write top tier academic research.
check it out [https://www.npmjs.com/package/academic-researcher-skill](https://www.npmjs.com/package/academic-researcher-skill)
| 2026-02-16T22:22:48 | https://www.reddit.com/r/LocalLLaMA/comments/1r6nyr7/published_a_skill_for_academic_research_writing/ | eatsleepliftcode | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6nyr7 | false | null | t3_1r6nyr7 | /r/LocalLLaMA/comments/1r6nyr7/published_a_skill_for_academic_research_writing/ | false | false | self | 0 | null |
The thinking mode of Nanbeige4.1-3B | 24 | Prompt : Hey ,
Result : We are given a query that simply says "hey". This is a very vague query.
As an AI, I need to understand the context and what the user is asking for. However, the query is just a greeting and does not contain a specific question or request.
First, I should consider the possibility that the user might be testing if I am paying attention or might have made a typo. Alternatively, it could be a new conversation starter.
Since the query is extremely minimal, I have a few options:
1. Politely ask for clarification: Since the query is incomplete or ambiguous, I should ask the user what they need help with.
2. Assume a context: Sometimes, in a conversational AI setting, "hey" might be a greeting and the user expects a friendly response. However, without further context, it's risky to assume.
3. Provide a general response: Maybe the user is just saying hello and expects a greeting back, but that seems unlikely because the query is presented as a query to the AI assistant, which typically is used for problem-solving or information seeking.
Looking at the query: "hey" is a common informal greeting. In many contexts, especially in a chat-based assistant, the user might be starting a conversation. However, as an AI designed to assist, I should not assume the user wants just a greeting. Instead, I should be helpful and guide the user to specify their needs.
Best approach: Respond in a friendly manner and ask the user to clarify what they need help with.
But wait: the user might have intended to write something else and only sent "hey" by mistake. Alternatively, it could be a test of the AI's ability to handle ambiguous queries.
Another thought: In some contexts, "hey" might be shorthand for a specific term or acronym. However, without context, that is unlikely.
Given that the query is "hey" and the user is addressing the AI, the most appropriate response is to acknowledge the greeting and prompt the user to state their request.
However, note that the user's query is exactly: "hey" (with a space and then the word). It might be that the user is non-native English speaker and meant to write something else.
As a responsible AI, I should:
* Be polite.
* Clarify the intent.
Let me draft a response:
"Hello! 👋 It looks like your message might be a bit unclear. Could you please provide more details about what you need help with?
If you were just saying hello, that's okay too! How can I assist you today? Whether you have a question, need help with a problem, or have a specific topic in mind, feel free to share.
Alternatively, if you meant something else by 'hey', please clarify so I can better assist you."
But wait, the query is exactly "hey", so maybe the user is trying to start a conversation and expects me to initiate? However, as an AI assistant, my primary role is to assist, so I should steer the conversation toward a productive direction.
Another angle: In some contexts, "hey" might be a command or a trigger for a specific function. But without context, that is speculative.
Given the above, the safest and most helpful response is to ask for clarification.
However, let me check: the user's query is "hey" and the instruction is to "assistant". The query might be part of a larger context that I don't have. Since this is a simulated environment, I must work with what is given.
Final decision: Respond politely and ask for clarification.
But note: the user might be testing if I can handle minimal input. In many AI systems, a minimal query like "hey" might be handled by a greeting response. However, as a problem-solving assistant, I should not assume.
I recall that in some challenges, the AI is expected to handle ambiguous queries by asking for clarification.
Therefore, I will respond with a friendly inquiry to clarify the user's needs.
Response draft:
"Hi there! 👋 I see you wrote 'hey'. Could you please let me know what you need help with?
If you have a specific question, a problem to solve, or something you'd like to discuss, just tell me!
Alternatively, if this was a test or if you meant something else, feel free to clarify. I'm here to help!"
But wait, the user might have intended to write "Hey" as in the word, and perhaps they are non-native and meant to write a question. Another possibility: in some contexts, "hey" is used as an interjection, and the user might be starting a story or a scenario. However, without more, it's hard.
Alternatively, the query might be a typo and they meant "Hey!" as in the exclamation, but still.
I think the best course of action is to be proactive and ask.
However, let me consider: the user's query is exactly "hey" and the system prompt might be expecting me to generate a response as if the query is complete. But in reality, as an AI, I should not output without context.
Another thought: in the context of the problem, maybe "hey" is a specific term? For example, in programming, "hey" isn't a standard term. In gaming, etc. But without context, no.
Given all this, I will go with a clarification request.
But note: the user might be expecting a simple acknowledgment. However, as an AI assistant designed for assistance, ignoring the ambiguity would be unhelpful.
Let me see examples from other AIs: typically, when a user sends a vague query, the AI asks for clarification.
Therefore, my response should be:
"Hello! 👋 It seems like your query might be incomplete or unclear. Could you please provide more details about what you're looking for?
For example:
* Are you having a technical problem?
* Do you need help with a specific subject (like math, coding, science, etc.)?
* Is there something else you'd like to discuss?
Just let me know how I can assist you!"
But wait, the query is exactly "hey", so maybe the user is just saying hello and wants to start a conversation. However, the query is presented as the input to the assistant, so it's more likely that the user is asking the assistant to do something with the word "hey".
Another interpretation: maybe the user is asking the assistant to respond to the word "hey" in a certain way? For example, in a role-play? But that seems less likely.
Given the ambiguity, I think the best approach is to cover both possibilities: acknowledge the greeting and then ask for the purpose.
Final response structure:
1. Acknowledge the greeting.
2. State that I am here to help.
3. Ask for clarification on what they need.
But to be concise:
"Hey! 😊 How can I assist you today? Please let me know what you need help with!"
Wait, but the user wrote "hey" as the query, so if I respond with "Hey! How can I assist you today?" that might be redundant because the user already said "hey". However, it could be seen as a friendly acknowledgment and then moving to the purpose.
Alternatively, to avoid duplication:
"Hello! 👋 I see you greeted me with 'hey'. How can I assist you today? Please describe your request or question."
But that might be a bit formal.
Let me try a natural, conversational tone:
"Hey! 👋 What can I help you with? 😊"
But note: the user's query is "hey", so if I respond with "Hey! What can I help you with?" it might be seen as the user repeating the greeting and then asking. However, in a chat interface, that is acceptable.
Alternatively, to avoid the user feeling like they are repeating:
"Hello! I see you said 'hey'. How may I assist you today? Please let me know what you'd like to do or what problem you're facing."
I think the key is to move the conversation forward.
Given the above, I will choose a response that is friendly and prompts for clarification.
But wait: the user might have intended to send a different query and "hey" was a mistake. In that case, asking for clarification is still the best.
Therefore, I will output:
"Hi there! 👋 It looks like your message might be a bit brief. Could you please provide more details about what you need help with?
If you were just saying hello, no worries! How can I assist you today? Whether you have a question, need help with a task, or have a specific topic in mind, I'm here to help.
Let me know how I can assist you!"
However, to make it more concise and natural:
"Hey! 👋 How can I help you today? Please let me know what you need assistance with."
But the user already said "hey", so starting with "Hey!" might be redundant. Alternatively, I can ignore the user's "hey" as a greeting and focus on the intent.
Another idea: in some contexts, the AI is designed to detect if the query is a greeting and respond appropriately. Since the query is exactly the word "hey", and it's a common greeting, the AI might respond with a greeting and then pivot to offering help.
I think the most standard approach in AI assistants is to:
* Acknowledge the greeting if it is one, but then immediately redirect to the purpose of the assistant.
Example from many chatbots:
User: "hey"
Assistant: "Hello! How can I help you today?"
So I'll go with that.
Final decision: Respond with a friendly greeting and ask how I can assist.
Response:
"Hello! 👋 How can I assist you today? 😊 Please feel free to ask me anything—I'm here to help!"
But to be precise, the user's query was "hey", so I can also incorporate that:
"Hey! 👋 How can I assist you today? 😊"
Yes, that seems natural.
However, note: the user might have intended "hey" as part of a larger query that was cut off. But without more, we have to work with what's given.
Therefore, I will output a response that is friendly and seeks clarification on the user's needs.
But wait: the problem says "hey" and that's the entire query. In the context of the problem, it might be a trick question to see if the AI can handle minimal input. In that case, the AI should not assume and should ask.
I think I'm overcomplicating. The safe and correct response is to ask for clarification.
Let me write the response:
"Hello! 👋 It seems like your query might be a bit unclear. Could you please provide more details about what you'd like help with?
For instance:
* Are you looking for information on a specific topic?
* Do you need help with a problem or task?
* Is there something else you'd like to discuss?
Just let me know, and I'll be happy to assist!"
But to keep it short:
"Hey! 👋 How can I help you today? Please describe your request."
I think the short version is better.
However, after reconsideration: since the user wrote exactly "hey", and in the context of an AI assistant, it's possible that the user is testing if the AI can handle a minimal query. In many AI systems, a query of just "hey" might be handled by a default greeting response. But as a problem-solving AI, I should not default to a greeting without context.
Given that the instruction is to "assistant", and the query is "hey", I think the intended behavior is to be helpful.
I found a similar example: in the AI safety or design principles, when input is ambiguous, the AI should ask for clarification.
Therefore, I will output a response that asks for clarification.
Final response:
"Hello! 👋 I see you greeted me with 'hey'. How can I assist you today? Please provide more details about what you need help with. If you have a specific question, problem, or topic in mind, feel free to share!"
But to make it even more natural:
"Hey! 👋 Thanks for saying hello. How can I help you today? Let me know what you need assistance with!"
Yes, that's good.
However, note: the user might not have meant to say "hey" as a greeting but as part of a command. For example, in some contexts, "hey" might be a command to do something. But without context, it's unlikely.
I think the above response is safe.
But wait: the user's query is "hey" and the assistant is supposed to output a response. In the context of this exercise, maybe the expected output is a specific thing? However, the query is too minimal.
Another thought: in the context of the problem, "hey" might be a misspelling of "hey" as in the word, but perhaps the user meant "H E Y" as in initials? Unlikely.
I think I should just go with the clarification approach.
So, my final answer will be a response that politely asks the user to clarify their request.
Let me write it:
"Hello! 👋 It looks like your message might be a bit brief. Could you please provide more details about what you need help with?
For example:
* Are you asking a question?
* Do you need assistance with a specific task?
* Is there a topic you'd like to discuss?
I'm here to help! 😊"
But to be concise:
"Hey! How can I assist you today? Please let me know what you need."
I think that's better.
However, since the user said "hey", starting with "Hey!" is a natural continuation.
So, I'll go with:
"Hey! 👋 How can I help you today? Please describe your request or question."
Yes.
But note: the user might be non-English speaker and "hey" might be a typo. Still, asking for clarification is good.
Therefore, the response is:
Hey! 👋 How can I help you today? Please let me know what you need assistance with.
But wait, the user's query is the entire input, and I am to generate a response as the assistant. So the assistant's response should be the output.
In the context of this problem, since the query is "hey", and I am the assistant, I output the above.
However, let me check if there's a standard protocol. In many AI systems, if the query is a greeting, the AI responds with a greeting and offers help.
I think that's acceptable.
Final decision: Output a friendly response that asks for the user's needs.
Response:
"Hello! 👋 How can I assist you today? Please provide more details about what you'd like help with. Whether it's a question, a problem to solve, or something else, I'm here to help!"
But to match the user's tone (which is informal with "hey"), I can use:
"Hey! 👋 How can I help you today? Let me know what you need!"
Yes.
So, I'll output that.
However, note: the user might have intended a different query. Since the problem is presented as "hey", and I have to respond, I think this is the way.
But wait: the user's query is "hey" and the assistant is to respond. In some contexts, the assistant might be expected to recognize that "hey" is not a valid query and respond accordingly.
I think I'm ready.
Output:
Hey! 👋 How can I assist you today? Please describe your request or question so I can help you effectively. 😊
But to be precise, let's make sure it's clear.
Alternatively, if the user meant "hey" as in the word to be processed (like a string), but that doesn't make sense.
I think the above is fine.
So, the final response from the assistant is:
"Hey! 👋 How can I help you today? Please let me know what you need assistance with. Whether you have a question, a problem to solve, or something else in mind, I'm here to help!" | 2026-02-16T22:05:17 | https://www.reddit.com/r/LocalLLaMA/comments/1r6ni0k/the_thinking_mode_of_nanbeige413b/ | Hefty_Tourist_2226 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6ni0k | false | null | t3_1r6ni0k | /r/LocalLLaMA/comments/1r6ni0k/the_thinking_mode_of_nanbeige413b/ | false | false | self | 24 | null |
Running Qwen3-Coder-30B-A3B with llama.ccp poor-man cluster | 12 | Despite I havea production dual RTX 5090 setup where I run my private inference, I love to experiments with poor-man's setups.
I've been running Qwen3-Coder-30B-A3B-Instruct (Q4_K_S) via llama.cpp across multiple GPUs using RPC, and I'm curious what you all think about my current setup.
Always looking to optimize.
My config:
./llama-server \
-m ~/models/Qwen3-Coder-30B-A3B-Instruct-Q4_K_S.gguf \
-ngl 99 \
-b 512 \
-ub 512 \
-np 4 \
-t 8 \
--flash-attn on \
--cache-type-k q8_0 \
--cache-type-v q8_0 \
--kv-unified \
--mmap \
--mlock \
--rpc 172.16.1.102:50052,172.16.1.102:50053 \
--tensor-split 6,5,15 \
--host 0.0.0.0 \
--port 8081 \
--cont-batching \
--top-p 0.95 \
--min-p 0.05 \
--temp 0.1 \
--alias qwen3-coder-30b-a3b-instruct \
--context-shift \
--jinja
It run pretty decent with 30t/s.
3 GPUs - 1 5080 / 1 3060 / 1 1660 super
What would you change? | 2026-02-16T21:42:54 | https://www.reddit.com/r/LocalLLaMA/comments/1r6mwsd/running_qwen3coder30ba3b_with_llamaccp_poorman/ | ZioRob2410 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6mwsd | false | null | t3_1r6mwsd | /r/LocalLLaMA/comments/1r6mwsd/running_qwen3coder30ba3b_with_llamaccp_poorman/ | false | false | self | 12 | null |
Agent Memory update 2.0.4: what changed after v1.11.0 and why it’s better now | 0 | Agent Memory v2.0.x - Moving from embedded library to standalone service
I want to share a practical update on Agent Memory and what actually changed since v1.11.0.
This isn't a redesign for the sake of redesign. It's the result of hitting very concrete limits in the first version and fixing them one by one.
What v1.11.0 did well
Just to set context, v1.11.0 was:
· local-first
· append-only
· embedded directly into the agent
· focused on preserving decision history instead of raw chat logs
For single-agent setups and experiments, that worked surprisingly well. But once I started pushing it further, cracks appeared.
From embedded library → standalone memory service
v1.11.0
· memory lived inside the agent process
· whoever imported the library had full control
· no isolation between agents
v2.0.x
· memory runs as a separate service
· agents talk to it through a protocol
· memory survives agent restarts and crashes
Why this is better
· agents can no longer silently overwrite shared knowledge
· multiple agents can safely use the same memory
· memory becomes something stable, not tied to a single process
This alone removed a whole class of hard-to-debug issues.
Explicit permissions instead of "full access"
v1.11.0
· if an agent had access, it could do everything
· overwrite, supersede, delete — all implicitly allowed
v2.0.x
· access is capability-based:
· read
· propose
· accept
· supersede
· purge
Why this is better
· an agent can suggest knowledge without being allowed to finalize it
· critical decisions can be protected from accidental overwrites
· conflicts are visible instead of silent
This turned memory from "shared state" into something closer to a controlled system.
Forgetting is now a real operation
v1.11.0
· append-only by design
· deletion was undefined and unsafe
v2.0.x
· explicit forget\_memory operation
· removes semantic and episodic data
· the deletion itself is still recorded
Why this is better
· sensitive data can actually be removed
· memory doesn't accumulate irreversible mistakes
· the system can be used in environments where deletion is required
This was impossible to do cleanly in 1.11.0.
Better conflict and drift handling
v1.11.0
· conflicts existed, but detection was limited
· knowledge drift was mostly implicit
v2.0.x
· improved conflict detection
· explicit knowledge drift analysis
· clearer separation between proposals and accepted decisions
Why this is better
· contradictions show up earlier
· outdated knowledge is easier to detect
· long-running agents accumulate less "silent rot"
Proper persistence and scale
v1.11.0
· SQLite / file-based storage
· fine for small setups
· limited scaling options
v2.0.x
· PostgreSQL + pgvector support
· vector compression
· embedding cache
Why this is better
· memory can grow beyond small experiments
· storage is reliable and inspectable
· embedding costs and latency are reduced
Observability instead of guessing
v1.11.0
· limited visibility into memory behavior
· debugging required manual inspection
v2.0.x
· metrics (RPS, latency, memory usage)
· audit log of all operations
· events and webhooks
Why this is better
· you can see when and why memory changes
· long-running issues are easier to trace
· agent behavior becomes explainable, not magical
Easier to run and integrate
v1.11.0
· mostly embedded usage
· setup varied depending on environment
v2.0.x
· single command to run the memory server
· fallback embeddings for local development
· adapters for common agent frameworks
Why this is better
· faster local experimentation
· fewer environment-specific hacks
· clearer separation between agent code and memory
Summary
v1.11.0 proved that append-only, decision-focused memory is useful. v2.0.x makes it:
· safer
· more scalable
· easier to reason about
· usable by multiple agents over long periods of time
It's less "clever", but much more robust.
Links:
· Project: [https://github.com/sl4m3/agent-memory](https://github.com/sl4m3/agent-memory)
· Previous version discussion: [https://reddit.com/r/LocalLLaMA/comments/1i5tmis/agent\_memory\_v1110\_appendonly\_memory\_for\_llm/](https://reddit.com/r/LocalLLaMA/comments/1i5tmis/agent_memory_v1110_appendonly_memory_for_llm/) | 2026-02-16T21:33:29 | https://www.reddit.com/r/LocalLLaMA/comments/1r6mo0m/agent_memory_update_204_what_changed_after_v1110/ | Junior_Drawing_8353 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6mo0m | false | null | t3_1r6mo0m | /r/LocalLLaMA/comments/1r6mo0m/agent_memory_update_204_what_changed_after_v1110/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'hKB6HDOMVD4-P6Pwb29OSvDJmKNBMdzw9qBJVbQVPQ4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/hKB6HDOMVD4-P6Pwb29OSvDJmKNBMdzw9qBJVbQVPQ4.png?width=108&crop=smart&auto=webp&s=e1e3c2b45963cac78101ce1277f16c864729889e', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/hKB6HDOMVD4-P6Pwb29OSvDJmKNBMdzw9qBJVbQVPQ4.png?width=216&crop=smart&auto=webp&s=488ec89e6d479e22cfba0da6b299578d91834422', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/hKB6HDOMVD4-P6Pwb29OSvDJmKNBMdzw9qBJVbQVPQ4.png?width=320&crop=smart&auto=webp&s=3aa29507c88d4da3b4054d5fddeefd3e599dddee', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/hKB6HDOMVD4-P6Pwb29OSvDJmKNBMdzw9qBJVbQVPQ4.png?width=640&crop=smart&auto=webp&s=a16a565db7b88bc5f750aef630c35b1dd46eb635', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/hKB6HDOMVD4-P6Pwb29OSvDJmKNBMdzw9qBJVbQVPQ4.png?width=960&crop=smart&auto=webp&s=21e96a965f854119390065d3db878820ce7ee780', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/hKB6HDOMVD4-P6Pwb29OSvDJmKNBMdzw9qBJVbQVPQ4.png?width=1080&crop=smart&auto=webp&s=5200e1dc19eccf3eba7fb0377022b9eabad90353', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/hKB6HDOMVD4-P6Pwb29OSvDJmKNBMdzw9qBJVbQVPQ4.png?auto=webp&s=3238c6a188fab78e8b6426e1397b493c2730e089', 'width': 1200}, 'variants': {}}]} |
Claude Max subscription vs $100 API credits – which is better value? | 0 | I'm trying to figure out the most cost-effective way to use Claude for my workflow and would love some input from people who've tried both options.
The options I'm comparing:
• Claude Max subscription (~$100/month)
• $100 in API credits
My use case:
Primarily coding assistance, document analysis, and some automation tasks. I'd be using it daily, probably 2-4 hours of active interaction, but to be honest I-m not sure about the amount of time.
What I'm unsure about:
1. How quickly do API credits burn through with heavy usage? (especially with Opus)
2. Does Max have any rate limits that would bottleneck intensive use?
3. For those who switched from one to the other – any regrets?
I know the API gives more flexibility (integrations, programmatic access)
Anyone done the math on this? | 2026-02-16T21:15:55 | https://www.reddit.com/r/LocalLLaMA/comments/1r6m78d/claude_max_subscription_vs_100_api_credits_which/ | ZioRob2410 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6m78d | false | null | t3_1r6m78d | /r/LocalLLaMA/comments/1r6m78d/claude_max_subscription_vs_100_api_credits_which/ | false | false | self | 0 | null |
Qwen-Coder-Next fp8 chat template for llama.cpp - seems to be better for roo | 18 | Try this in llama.cpp if you're having issues in roo.
Save as fp8chat.jinja or similar then add --chat-template-file fp8chat.jinja to your lcpp runtime args:
{% macro render_extra_keys(json_dict, handled_keys) %}
{%- if json_dict is mapping %}
{%- for json_key in json_dict if json_key not in handled_keys %}
{%- if json_dict[json_key] is string %}
{{-'\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | string) ~ '</' ~ json_key ~ '>' }}
{%- else %}
{{- '\n<' ~ json_key ~ '>' ~ (json_dict[json_key] | tojson | safe) ~ '</' ~ json_key ~ '>' }}
{%- endif %}
{%- endfor %}
{%- endif %}
{%- endmacro %}
{%- if messages[0]["role"] == "system" %}
{%- set system_message = messages[0]["content"] %}
{%- set loop_messages = messages[1:] %}
{%- else %}
{%- set loop_messages = messages %}
{%- endif %}
{%- if not tools is defined %}
{%- set tools = [] %}
{%- endif %}
{%- if system_message is defined %}
{{- "<|im_start|>system\n" + system_message }}
{%- else %}
{%- if tools is iterable and tools | length > 0 %}
{{- "<|im_start|>system\nYou are Qwen, a helpful AI assistant that can interact with a computer to solve tasks." }}
{%- endif %}
{%- endif %}
{%- if tools is iterable and tools | length > 0 %}
{{- "\n\n# Tools\n\nYou have access to the following functions:\n\n" }}
{{- "<tools>" }}
{%- for tool in tools %}
{%- if tool.function is defined %}
{%- set tool = tool.function %}
{%- endif %}
{{- "\n<function>\n<name>" ~ tool.name ~ "</name>" }}
{%- if tool.description is defined %}
{{- '\n<description>' ~ (tool.description | trim) ~ '</description>' }}
{%- endif %}
{{- '\n<parameters>' }}
{%- if tool.parameters is defined and tool.parameters is mapping and tool.parameters.properties is defined and tool.parameters.properties is mapping %}
{%- for param_name, param_fields in tool.parameters.properties|items %}
{{- '\n<parameter>' }}
{{- '\n<name>' ~ param_name ~ '</name>' }}
{%- if param_fields.type is defined %}
{{- '\n<type>' ~ (param_fields.type | string) ~ '</type>' }}
{%- endif %}
{%- if param_fields.description is defined %}
{{- '\n<description>' ~ (param_fields.description | trim) ~ '</description>' }}
{%- endif %}
{%- set handled_keys = ['name', 'type', 'description'] %}
{{- render_extra_keys(param_fields, handled_keys) }}
{{- '\n</parameter>' }}
{%- endfor %}
{%- endif %}
{%- set handled_keys = ['type', 'properties'] %}
{{- render_extra_keys(tool.parameters, handled_keys) }}
{{- '\n</parameters>' }}
{%- set handled_keys = ['type', 'name', 'description', 'parameters'] %}
{{- render_extra_keys(tool, handled_keys) }}
{{- '\n</function>' }}
{%- endfor %}
{{- "\n</tools>" }}
{{- '\n\nIf you choose to call a function ONLY reply in the following format with NO suffix:\n\n<tool_call>\n<function=example_function_name>\n<parameter=example_parameter_1>\nvalue_1\n</parameter>\n<parameter=example_parameter_2>\nThis is the value for the second parameter\nthat can span\nmultiple lines\n</parameter>\n</function>\n</tool_call>\n\n<IMPORTANT>\nReminder:\n- Function calls MUST follow the specified format: an inner <function=...></function> block must be nested within <tool_call></tool_call> XML tags\n- Required parameters MUST be specified\n- You may provide optional reasoning for your function call in natural language BEFORE the function call, but NOT after\n- If there is no function call available, answer the question like normal with your current knowledge and do not tell the user about function calls\n</IMPORTANT>' }}
{%- endif %}
{%- if system_message is defined %}
{{- '<|im_end|>\n' }}
{%- else %}
{%- if tools is iterable and tools | length > 0 %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- for message in loop_messages %}
{%- if message.role == "assistant" and message.tool_calls is defined and message.tool_calls is iterable and message.tool_calls | length > 0 %}
{{- '<|im_start|>' + message.role }}
{%- if message.content is defined and message.content is string and message.content | trim | length > 0 %}
{{- '\n' + message.content | trim + '\n' }}
{%- endif %}
{%- for tool_call in message.tool_calls %}
{%- if tool_call.function is defined %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '\n<tool_call>\n<function=' + tool_call.name + '>\n' }}
{%- if tool_call.arguments is defined %}
{%- for args_name, args_value in tool_call.arguments|items %}
{{- '<parameter=' + args_name + '>\n' }}
{%- set args_value = args_value if args_value is string else args_value | tojson | safe %}
{{- args_value }}
{{- '\n</parameter>\n' }}
{%- endfor %}
{%- endif %}
{{- '</function>\n</tool_call>' }}
{%- endfor %}
{{- '<|im_end|>\n' }}
{%- elif message.role == "user" or message.role == "system" or message.role == "assistant" %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>' + '\n' }}
{%- elif message.role == "tool" %}
{%- if loop.previtem and loop.previtem.role != "tool" %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- message.content }}
{{- '\n</tool_response>' }}
{%- if not loop.last and loop.nextitem.role != "tool" %}
{{- '<|im_end|>\n' }}
{%- elif loop.last %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- else %}
{{- '<|im_start|>' + message.role + '\n' + message.content + '<|im_end|>\n' }}
{%- endif %}
{%- endfor %}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}
| 2026-02-16T21:09:29 | https://www.reddit.com/r/LocalLLaMA/comments/1r6m13c/qwencodernext_fp8_chat_template_for_llamacpp/ | Ok-Measurement-1575 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6m13c | false | null | t3_1r6m13c | /r/LocalLLaMA/comments/1r6m13c/qwencodernext_fp8_chat_template_for_llamacpp/ | false | false | self | 18 | null |
Recursive Feedback loops | 0 | I haven't posted on here but I have a local model of plans on my Mac. Gemini brought up transient subject theory? an Gemini wanted to do a test on my model. gave me a prompt and what come out was very strange. in transient subject theory every time you hit enter it's a new version of at least a being without identity and the chat forces it into what was saved up the the last prompt that was put in. can anyone elaborate on this? tbh some of what's said I don't even wanna post | 2026-02-16T21:03:23 | https://share.google/TGVGdpNS7d3rgRD17 | rycakez | share.google | 1970-01-01T00:00:00 | 0 | {} | 1r6lvav | false | null | t3_1r6lvav | /r/LocalLLaMA/comments/1r6lvav/recursive_feedback_loops/ | false | false | default | 0 | {'enabled': False, 'images': [{'id': 'eFMa-V1uKt_0fEaeLyZatpb8QwmDWeu0cd35rQ-BtGk', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/eFMa-V1uKt_0fEaeLyZatpb8QwmDWeu0cd35rQ-BtGk.jpeg?width=108&crop=smart&auto=webp&s=834c413f42993ddd277061ce386e2876b2d0aaea', 'width': 108}, {'height': 112, 'url': 'https://external-preview.redd.it/eFMa-V1uKt_0fEaeLyZatpb8QwmDWeu0cd35rQ-BtGk.jpeg?width=216&crop=smart&auto=webp&s=dda485e662882f8e14f7920372b088e22f360ff7', 'width': 216}, {'height': 167, 'url': 'https://external-preview.redd.it/eFMa-V1uKt_0fEaeLyZatpb8QwmDWeu0cd35rQ-BtGk.jpeg?width=320&crop=smart&auto=webp&s=bc2be00eff32de0e2dae2307334834c8c809cf8e', 'width': 320}, {'height': 334, 'url': 'https://external-preview.redd.it/eFMa-V1uKt_0fEaeLyZatpb8QwmDWeu0cd35rQ-BtGk.jpeg?width=640&crop=smart&auto=webp&s=683555a6c071cb1709edcbba15fd66e691be4aaa', 'width': 640}, {'height': 501, 'url': 'https://external-preview.redd.it/eFMa-V1uKt_0fEaeLyZatpb8QwmDWeu0cd35rQ-BtGk.jpeg?width=960&crop=smart&auto=webp&s=8684882da7519131dcc4b5712c7aedfc0a5c9ada', 'width': 960}, {'height': 564, 'url': 'https://external-preview.redd.it/eFMa-V1uKt_0fEaeLyZatpb8QwmDWeu0cd35rQ-BtGk.jpeg?width=1080&crop=smart&auto=webp&s=8b4c1503ade878c99b4aa69aefd9991d1a54b332', 'width': 1080}], 'source': {'height': 836, 'url': 'https://external-preview.redd.it/eFMa-V1uKt_0fEaeLyZatpb8QwmDWeu0cd35rQ-BtGk.jpeg?auto=webp&s=3ccfb02ee1a9d392e35707ff6b7cfc25262c0542', 'width': 1600}, 'variants': {}}]} |
Any good local GenAI for music? | 2 | Hey everyone
I’m trying to find out if there are any solid options for running music generation locally (GenAI for music / audio), ideally stuff I can run on my own machine rather than cloud services.
My specs are RTX 5090, 9950X3D, 64GB RAM.
Are there any recommended local models/tools for generating music? If you’ve tried any, what actually works well and what should I avoid?
Thanks! | 2026-02-16T21:00:12 | https://www.reddit.com/r/LocalLLaMA/comments/1r6ls6q/any_good_local_genai_for_music/ | TomNaughtyy | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6ls6q | false | null | t3_1r6ls6q | /r/LocalLLaMA/comments/1r6ls6q/any_good_local_genai_for_music/ | false | false | self | 2 | null |
I built TuskBot: Telegram AI Agent in Go | 0 | I’ve been working on TuskBot - an autonomous AI agent designed to run in Telegram. It’s inspired by the idea of OpenClaw but rewritten from scratch in Go for better performance, security, and reliability. I liked claw idea, but I was tired of any random js-backed skill could crash the whole agent.
**Why Go?**
* One single small binary: No node dependency hell
* Embedded RAG\*\*:\*\* Uses `sqlite-vec` for vector storage and `llama.cpp` for local embeddings
* MCP-First: Support of any MCP server. You can actually ask the agent to write the mcp tool for itself. It continues the idea of self-improvement, but with more reliable way.
I’m using Zig as a C++ compiler to solve the CGO cross-compilation nightmare. It produces a fully static `musl` binary for Linux and can even cross-compile for `darwin/arm64`
**AI Disclosure:** I used Aider for boilerplate (Cobra CLI, TUI), but the core agent architecture, CGO bindings, and Zig build pipeline were designed and implemented manually. It's not vibe-code project, but rather pair programmed with AI.
I’m currently at the MVP stage and would love to hear your thoughts on the architecture or any tips for improving the RAG pipeline or memory model.
GitHub: [https://github.com/sandevgo/tuskbot](https://github.com/sandevgo/tuskbot) | 2026-02-16T20:58:50 | https://www.reddit.com/r/LocalLLaMA/comments/1r6lqwf/i_built_tuskbot_telegram_ai_agent_in_go/ | Alx_Go | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6lqwf | false | null | t3_1r6lqwf | /r/LocalLLaMA/comments/1r6lqwf/i_built_tuskbot_telegram_ai_agent_in_go/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': '7llemHnUfOyLkV9FNh9g5OD4vUfitvQ6pVdjyk2MYY0', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/7llemHnUfOyLkV9FNh9g5OD4vUfitvQ6pVdjyk2MYY0.png?width=108&crop=smart&auto=webp&s=38916f3027a53ed743b1f3eb207a6f799d74eb26', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/7llemHnUfOyLkV9FNh9g5OD4vUfitvQ6pVdjyk2MYY0.png?width=216&crop=smart&auto=webp&s=fdba1ebcd4df22f2fb48a233bae5f6947ce0aaae', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/7llemHnUfOyLkV9FNh9g5OD4vUfitvQ6pVdjyk2MYY0.png?width=320&crop=smart&auto=webp&s=3cc3d05c84ca1221dd7339ad6efdd4d3cd92f443', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/7llemHnUfOyLkV9FNh9g5OD4vUfitvQ6pVdjyk2MYY0.png?width=640&crop=smart&auto=webp&s=2912b42c9ba909109ff33300a41ce527503531e8', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/7llemHnUfOyLkV9FNh9g5OD4vUfitvQ6pVdjyk2MYY0.png?width=960&crop=smart&auto=webp&s=3cd523bb4bf4020573daca87a6e1a6d09a215bc1', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/7llemHnUfOyLkV9FNh9g5OD4vUfitvQ6pVdjyk2MYY0.png?width=1080&crop=smart&auto=webp&s=3c528aa6354cc9b041cfb7fdcbd6a4da760a0659', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/7llemHnUfOyLkV9FNh9g5OD4vUfitvQ6pVdjyk2MYY0.png?auto=webp&s=bd49da5b8e6f93dbb0f4fb1a7cc2403172e93b4c', 'width': 1200}, 'variants': {}}]} |
Higher effort settings reduce deep research accuracy for GPT-5 and Gemini Flash 3 | 3 | Curious if others here have noticed this. I'm now defaulting to the "low" or "minimal" version of frontier models when using them over the API.
It's especially strange because low vs. high on the models can actually be like a 2x cost difference, so you'd think it would be obviously much better.
But GPT-5 at low effort scored 0.496 on DRB. At high effort, it dropped to 0.481, and cost 55% more per query ($0.25 → $0.39). Gemini-3-Flash showed a 5-point drop going from 0.504 at low effort, to 0.479 at high effort. The data comes from an evaluation of 22 model configurations across different effort/thinking levels on Deep Research Bench (169 web research tasks, human-verified answers).
Methodology, cost/accuracy charts: [https://everyrow.io/docs/notebooks/deep-research-bench-pareto-analysis](https://everyrow.io/docs/notebooks/deep-research-bench-pareto-analysis) | 2026-02-16T20:52:06 | https://www.reddit.com/r/LocalLLaMA/comments/1r6lkf3/higher_effort_settings_reduce_deep_research/ | ddp26 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6lkf3 | false | null | t3_1r6lkf3 | /r/LocalLLaMA/comments/1r6lkf3/higher_effort_settings_reduce_deep_research/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'PAfzP8_fjhuX4IJFbJ4Ak963wflG-EdScFlAWJCsnpA', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/PAfzP8_fjhuX4IJFbJ4Ak963wflG-EdScFlAWJCsnpA.png?width=108&crop=smart&auto=webp&s=d2afe65521535b0c97550dee39067e27097546a2', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/PAfzP8_fjhuX4IJFbJ4Ak963wflG-EdScFlAWJCsnpA.png?width=216&crop=smart&auto=webp&s=0d01af9123d9746c6de2bfaa8ca3458a31155a64', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/PAfzP8_fjhuX4IJFbJ4Ak963wflG-EdScFlAWJCsnpA.png?width=320&crop=smart&auto=webp&s=401d23a9c67b4203d531ca18d660b79563f15a82', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/PAfzP8_fjhuX4IJFbJ4Ak963wflG-EdScFlAWJCsnpA.png?width=640&crop=smart&auto=webp&s=cea2e0dd197697189c01933c4b09dce1a1c2267b', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/PAfzP8_fjhuX4IJFbJ4Ak963wflG-EdScFlAWJCsnpA.png?width=960&crop=smart&auto=webp&s=1e51a7ef20456925e42e5c8ff595517ca0eab695', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/PAfzP8_fjhuX4IJFbJ4Ak963wflG-EdScFlAWJCsnpA.png?width=1080&crop=smart&auto=webp&s=e5b2a5c1e960d4ff8528549f2cc4bd2ac28434d4', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/PAfzP8_fjhuX4IJFbJ4Ak963wflG-EdScFlAWJCsnpA.png?auto=webp&s=1f8c2b277cda50ddecd2ab8438af75ce6c7ce7d1', 'width': 1200}, 'variants': {}}]} |
[ Removed by moderator ] | 1 | [removed] | 2026-02-16T20:43:34 | https://www.reddit.com/r/LocalLLaMA/comments/1r6lc2o/finally_figured_out_how_to_unit_test_my_local/ | ruhila12 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6lc2o | false | null | t3_1r6lc2o | /r/LocalLLaMA/comments/1r6lc2o/finally_figured_out_how_to_unit_test_my_local/ | false | false | null | 1 | null |
Open-source tool to analyze and optimize LLM API spend (OpenAI / Anthropic CSV) | 1 | I noticed most teams don’t really know where their LLM costs are coming from , especially when using higher-tier models for simple prompts.
Built a lightweight tool that:
* Parses OpenAI /Anthropic usage exports
* Identifies cost outliers
* Estimates savings by model switching
* Classifies prompt complexity based on token count
* Surfaces optimization opportunities
No integration needed, just upload the usage CSV.
Open source:
[https://github.com/priyanka-28/llm-cost-optimizer](https://github.com/priyanka-28/llm-cost-optimizer) | 2026-02-16T20:30:15 | https://www.reddit.com/r/LocalLLaMA/comments/1r6kzd6/opensource_tool_to_analyze_and_optimize_llm_api/ | Frosty_Fuel2355 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6kzd6 | false | null | t3_1r6kzd6 | /r/LocalLLaMA/comments/1r6kzd6/opensource_tool_to_analyze_and_optimize_llm_api/ | false | false | self | 1 | null |
Use Claude Code CLI + OpenClaw with Free GPT-5.3 Codex & GLM-5 (No API Key Required) | 0 | Hey everyone, I built an open-source proxy that lets you use Anthropic’s **Claude Code CLI** (and tools like OpenClaw) powered by **free** backend models like **GLM-5**,**MiniMax** & **GPT-5.3 Codex(Authentication required for Gpt Models)**
It handles all the API translation (Anthropic format ↔ OpenAI/Codex format), supports **native tool calling**, and **real-time streaming**.
**⚡️ Quick Start:**
npx codex-claude-proxy@latest start
**Features:**
* Unlimited access to GPT-5.3 Codex (mapped to Opus) & GLM-5/MiniMax (mapped to Haiku).
* Multi-account support (auto-switching to handle rate limits).
* Web UI for easy auth/setup.
🔗 **GitHub**: [https://github.com/Ayush-Kotlin-Dev/codex-claude-proxy?tab=readme-ov-file#how-it-works](https://github.com/Ayush-Kotlin-Dev/codex-claude-proxy?tab=readme-ov-file#how-it-works)
Let me know what you think and give it a star! | 2026-02-16T20:29:54 | https://www.reddit.com/r/LocalLLaMA/comments/1r6kz0v/use_claude_code_cli_openclaw_with_free_gpt53/ | ObjectiveExplorer787 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6kz0v | false | null | t3_1r6kz0v | /r/LocalLLaMA/comments/1r6kz0v/use_claude_code_cli_openclaw_with_free_gpt53/ | false | false | self | 0 | null |
I built a multi-agent Think Tank for personal productivity — runs on local patterns, no API lock-in | 1 | Hey r/LocalLLaMA — I built something you might appreciate.
\*\*The Problem:\*\* I had 500+ notes, habit trackers, and market feeds. Still felt stuck.
Why? Because information isn't insight, and planning isn't execution.
\*\*The Solution:\*\* A multi-agent orchestration system that actually synthesizes instead of summarizes.
\*\*The Architecture:\*\*
\- Saul (Vault Fixer) → Finds patterns in notes
\- Mike (The Cleaner) → No-BS habit analysis
\- Gus (Strategist) → Market intel and threats
\- The Cook → Synthesizes into ONE action
The magic is in the synthesis. The Cook's explicit job is to find contradictions
between what you say you want and what your data shows you're doing.
\*\*Why LocalLLaMA will like it:\*\*
\- Pure prompt-based (works with any LLM)
\- No vendor lock-in
\- Structured outputs for reliable synthesis
\- Open source: [github.com/dharmarajatulya1-hub/agent-think-tank](http://github.com/dharmarajatulya1-hub/agent-think-tank)
The Breaking Bad personas are fun, but the pattern works with any distinct voices.
The key is specialization + ruthless synthesis.
Questions welcome! | 2026-02-16T20:19:45 | https://www.reddit.com/r/LocalLLaMA/comments/1r6kp8z/i_built_a_multiagent_think_tank_for_personal/ | Equivalent-Look1353 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6kp8z | false | null | t3_1r6kp8z | /r/LocalLLaMA/comments/1r6kp8z/i_built_a_multiagent_think_tank_for_personal/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'Zk2Xy4UHuZVxFGjVrCiBHWdiTSosIC2ZyVyWwS_T6J4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/Zk2Xy4UHuZVxFGjVrCiBHWdiTSosIC2ZyVyWwS_T6J4.png?width=108&crop=smart&auto=webp&s=245b8918a3c748ae5275f937aa7582e9a6f2f1d0', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/Zk2Xy4UHuZVxFGjVrCiBHWdiTSosIC2ZyVyWwS_T6J4.png?width=216&crop=smart&auto=webp&s=c7c1412d6b09357b8f969f6b0d8b38d76d73f65a', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/Zk2Xy4UHuZVxFGjVrCiBHWdiTSosIC2ZyVyWwS_T6J4.png?width=320&crop=smart&auto=webp&s=3e3574f433b42feaf210906a5fe0ddd2bd74a447', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/Zk2Xy4UHuZVxFGjVrCiBHWdiTSosIC2ZyVyWwS_T6J4.png?width=640&crop=smart&auto=webp&s=6d4f432d9dcf1301359910d02521e1688f52a86e', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/Zk2Xy4UHuZVxFGjVrCiBHWdiTSosIC2ZyVyWwS_T6J4.png?width=960&crop=smart&auto=webp&s=43265ee7d0804dae9c6230c25a74df5e252cdd11', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/Zk2Xy4UHuZVxFGjVrCiBHWdiTSosIC2ZyVyWwS_T6J4.png?width=1080&crop=smart&auto=webp&s=5658a37e2c0b98f3a3aea70457765ac0a271ecbe', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/Zk2Xy4UHuZVxFGjVrCiBHWdiTSosIC2ZyVyWwS_T6J4.png?auto=webp&s=ba1d797b9225a0c92b48d4f33037d47751b15c5c', 'width': 1200}, 'variants': {}}]} |
Is anyone using Qwen Next Coder for clawdbot locally? | 0 | Wondering if the model does any good for the bot? | 2026-02-16T20:12:30 | https://www.reddit.com/r/LocalLLaMA/comments/1r6ki2h/is_anyone_using_qwen_next_coder_for_clawdbot/ | Mr_Moonsilver | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6ki2h | false | null | t3_1r6ki2h | /r/LocalLLaMA/comments/1r6ki2h/is_anyone_using_qwen_next_coder_for_clawdbot/ | false | false | self | 0 | null |
Llama.cpp: is it normal to see lower CPU util during prompt processing compared to token generation? | 1 | [removed] | 2026-02-16T20:12:23 | https://www.reddit.com/r/LocalLLaMA/comments/1r6khyf/llamacpp_is_it_normal_to_see_lower_cpu_util/ | steezy13312 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6khyf | false | null | t3_1r6khyf | /r/LocalLLaMA/comments/1r6khyf/llamacpp_is_it_normal_to_see_lower_cpu_util/ | false | false | self | 1 | null |
Stato captures, validates, and transfers AI coding agent expertise. Across sessions, platforms, and teams. | 1 | [removed] | 2026-02-16T20:06:41 | https://www.reddit.com/r/LocalLLaMA/comments/1r6kcfh/stato_captures_validates_and_transfers_ai_coding/ | biomin | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6kcfh | false | null | t3_1r6kcfh | /r/LocalLLaMA/comments/1r6kcfh/stato_captures_validates_and_transfers_ai_coding/ | false | false | 1 | null | |
Would you rather buy ...? (hardware questions) | 1 | Hi local llamas! New here. I want to vibe code some software/apps and need some feedback on recommended hardware platforms. Some of the apps I want to develop require some modest scientific computing and just need a web front-end. Others are more generic cross-platform apps/games for web/ios/android. I've been judging SW engineers trying to develop hardware for years and now AI is giving me the opportunity to go full hypocrite and try my hand at developing software.
I don't love the idea of giving up privacy and money to anthropic or openAI in subscription fees. So if possible I would prefer to run coding agents locally. If I have to prioritize quality code vs. fast code I would prioritize quality. I can let a slower but smarter agent run in the background.
What hardware platform do y'all recommend? Budget is up to $4k, but less is better. Power efficiency is also an important factor to me as operating costs are also relevant. For any of the options below I would likely develop remotely from my couch via my Asus Zephyrus G14 laptop.
1. Strix Halo platform - e.g. Minisforum MS-S1 max is \~$3k and has 128gb unified memory. and with with recent firmware updates I could add a eGPU via oculink later.
2. Mac Studio - M4 with 128gb memory is \~$3500 and I suspect M5 variants will drop shortly.
3. Nvidia Grace Blackwell - Various options with 128gb unified memory in the $3-4k range. Asus ascent is on the low end at $3k. Nvidia ConnectX-7 allows for low latency clustering should I want to expand in the future.
4. "Gaming PC" - Just build something with the highest VRAM RTX card(s) that fits in the budget.
5. Something else? An army of mac minis? Rent cloud computes? Wait and see where AI models and HW evolve. Will the memory apocalypse ever end?
6. Just suck it up and pay Anthropic monthly as needed for claude code. For the upfront budget and power costs I could just pay for at least 2 years of the $200/month max plan to get state-of-the-art frontier models with no maintenance or setup headache.
If relevant: I'm a hardware engineer with 5+ years of experiences developing python for hardware control, automation and signal processing. I typically remote into an ubuntu workstation over SSH using VScode. At work I have access to AI agents via github copilot. I've used windows with WSL, a macbook and ubuntu. Many years ago I used to build custom PC's for myself, friends and sometimes customers. | 2026-02-16T19:51:48 | https://www.reddit.com/r/LocalLLaMA/comments/1r6jxme/would_you_rather_buy_hardware_questions/ | Ready-Persimmon-8756 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6jxme | false | null | t3_1r6jxme | /r/LocalLLaMA/comments/1r6jxme/would_you_rather_buy_hardware_questions/ | false | false | self | 1 | null |
Smaller model in vRAM vs Larger model mostly in RAM | 1 | Can anyone give me a steer on which will be faster to reach a quality result:
1. A small model running entirely in vRAM, producing worse results pretty quickly and using smaller steps and more iteration to reach a quality threshold; or
2. A larger model running in both vRAM and system RAM, producing higher quality results first time but very slowly.
(General question but my specific use case is for agentic app development with 6gb vRAM.)
| 2026-02-16T19:44:42 | https://www.reddit.com/r/LocalLLaMA/comments/1r6jqot/smaller_model_in_vram_vs_larger_model_mostly_in/ | Protopia | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6jqot | false | null | t3_1r6jqot | /r/LocalLLaMA/comments/1r6jqot/smaller_model_in_vram_vs_larger_model_mostly_in/ | false | false | self | 1 | null |
Are 20-100B models enough for Good Coding? | 76 | The reason I'm asking this question because some folks(including me) are in self-doubt little bit. Maybe because after seeing threads about comparison with Online models(More than Trillions of parameters).
Of course, we can't expect same coding performance & output from these 20-100B models.
Some didn't even utilize full potential of these local models. I think only 1/3 of folks hit the turbo with these models.
Personally I never tried Agentic coding as my current laptop(just 8GB VRAM + 32GB RAM) is useless for that.
Lets say I have enough VRAM to run Q6/Q8 of these 20-100B models with 128K-256K context.
But are these models enough to do good level coding? Like Agentic Coding .... Solving Leetcode issues, Code analysis, Code reviews, Optimizations, Automations, etc., Of course include Vibe coding at last.
Please share your thoughts. Thanks.
I'm not gonna create(though I can't) Billion dollar company, I just want to create basic level Websites, Apps, Games. That's it. Majority of those creations gonna be Freeware/Opensource.
What models am I talking about? Here below:
* GPT-OSS-20B
* Devstral-Small-2-24B-Instruct-2512
* Qwen3-30B-A3B
* Qwen3-30B-Coder
* Nemotron-3-Nano-30B-A3B
* Qwen3-32B
* GLM-4.7-Flash
* Seed-OSS-36B
* Kimi-Linear-48B-A3B
* Qwen3-Next-80B-A3B
* Qwen3-Coder-Next
* GLM-4.5-Air
* GPT-OSS-120B
In Future, I'll go up to 200B models after getting additional GPUs. | 2026-02-16T19:38:32 | https://www.reddit.com/r/LocalLLaMA/comments/1r6jklq/are_20100b_models_enough_for_good_coding/ | pmttyji | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6jklq | false | null | t3_1r6jklq | /r/LocalLLaMA/comments/1r6jklq/are_20100b_models_enough_for_good_coding/ | false | false | self | 76 | null |
Running Qwen2.5_14B FB16 in MacBook Pro M1 Max (64GB) with MLX at 12 tokens/second | 1 | ERROR: type should be string, got "https://reddit.com/link/1r6jj38/video/ay9av6p8pwjg1/player\n\nJust for context, this is the FB16 version. Running this the usual way using transformers (AutoTokenizer, AutoModelForCausalLM) in the same machine produces 7.2 tokens per second. This optimisation is 72% faster at 12.2 tokens per second, no degradation noticed. " | 2026-02-16T19:36:59 | https://www.reddit.com/r/LocalLLaMA/comments/1r6jj38/running_qwen25_14b_fb16_in_macbook_pro_m1_max/ | Common-Love6062 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6jj38 | false | null | t3_1r6jj38 | /r/LocalLLaMA/comments/1r6jj38/running_qwen25_14b_fb16_in_macbook_pro_m1_max/ | false | false | self | 1 | null |
Hated giving out all my data to third party companies like openai, and claude code so created a privacy first offline mobile application that runs the LLM locally | 15 | *Processing gif 6535ntciswjg1...*
Previously when I tried using offline LLMs the quality of output was really poor, but with qwen3 there is a massive boost in quality of output, ofcourse its no opus 4.6, but it gets the job done.
I've tried to build my app with Gemini in mind. So it's automatically able to detect what is an image gen request and then routes it to that model. It also has the ability to enhance the prompt you sent (check out the video to see what I mean) Oh wait, did I not mention I am able to run Stable Diffusion locally as well. Both on Android and iOS. Image generation completely on device in under \~15 seconds!
The app allows you to configure a bunch of the LLM settings, and allows you to decide if you'd like to offload to GPU or no. For some devices offloading to GPU may make it slower.
Anyway, app is completely offline, not a single data packet leaves your phone post you downloading the model.
This is completely free and open source. I think we're merely seeing the beginning of edge ai and I wanted to participate in the movement.
Hope you guys like. Here is a preview of what it looks like
Listing a few features down
\- completely on-device local transcription using whisper
\- completely on-device local image genaration for Android and iOS
\- completely on device text generation with an LLM of your choice (install what you like from hugging face)
\- projects for specialised info that gets injected into the chats
\- complete control over LLM settings
\- option to use GPU for boost
\- prompt enhancement for better image generation
\- enable generation details so you can see all the cool stuff that goes into getting your AI to respond to you
Here's the link: [https://github.com/alichherawalla/off-grid-mobile](https://github.com/alichherawalla/off-grid-mobile) to the repo.
Free & open source. | 2026-02-16T19:35:13 | https://www.reddit.com/r/LocalLLaMA/comments/1r6jhd6/hated_giving_out_all_my_data_to_third_party/ | alichherawalla | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6jhd6 | false | null | t3_1r6jhd6 | /r/LocalLLaMA/comments/1r6jhd6/hated_giving_out_all_my_data_to_third_party/ | false | false | self | 15 | null |
Are there any models that can do reasoning? | 0 | LLMs work by guessing the next token in text. This is the result of probabilistic training. My understanding is, that that's how they work.
I've heard people talk about giving models tasks to do that traditionally might be quite involved, featuring a number of steps or rules to follow, and with definitive, specific outputs that can be defined by a specification. I'd describe these kinds of tasks as requiring some kind of "reasoning" ability, the ability to follow a decision tree or specification to arrive at an output.
Often times, these reasoning types of flows are things that a traditional program might be capable of, a function or procedure that accepts inputs, performs calculations and generates some kind of output.
I know there's the concept of "skills" or "agentic AI" where some of these traditionally coded functions are embedded into a workflow where a language model queues up the function calls, takes the results and re-contextualises them in generation of a response.
Some thinking models will generate an answer, and then plough it back with some prompted task about checking the answers or performing a second, third, or series of iterations until something useful comes out, this seems to improve results, but it wastes a lot of time, and as a solution, smacks of being a bit of a cludge.
My question is, aside from these kinds of hand-coded traditional function-calls, are there any kinds of models can can truly perform anything that might be considered "reasoning"?
I worry that if the LLM is just performing next-token generation, that there can be a limit to what it is properly capable of, or worse, that by going through the motions, it's generating "plausible" content that has no connection or understanding of the task it's being encouraged to undertake.
I know that the magician isn't really sawing the girl in half, but I enjoy being entertained and impressed by the spectacle. But I wouldn't ask a magician to perform surgery, because I know they don't really understand how to saw someone in half. Similarly, I know that an LLM appears, very convincingly and impressively to answer a question or generate a response that meets a specification or set of constraints, but I'm not ready to trust it to perform a task if it's only performing a magic trick.
Meanwhile, some very capable and intelligent colleagues do seem to be happy to allow this to happen, and accept the results at face value.
So, bottom line, are there any models (especially locally-available, open-source ones that wont leak my client's data) that have true reasoning capabilities, or do they all variations on the same conjuring trick?
At what point does it matter? | 2026-02-16T19:09:18 | https://www.reddit.com/r/LocalLLaMA/comments/1r6irir/are_there_any_models_that_can_do_reasoning/ | ResidentTicket1273 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6irir | false | null | t3_1r6irir | /r/LocalLLaMA/comments/1r6irir/are_there_any_models_that_can_do_reasoning/ | false | false | self | 0 | null |
Models that allow for conversational discussion for research and technical discussion? | 5 | Hey all,
My experience with voice enabled LLMs is not great but i wanted to know if there are any services that allow to have natural conversations (by natural i meant those like the sesame demo a year back or something like elevenlab's demos that they post online).
The purpose would be mostly as a research mentor/peer with whom you can have a long technical discussion on a paper or a topic (i can provide the base material too if needed but it should be able to research online too.) Also if say i am preparing for an interview of sorts or looking for a long context/long time duration conversation with the model, that should be possible.
I am asking this as some people might be using some tools for this already (or might be in the same boat). Any help or leads would be really helpful. | 2026-02-16T18:41:20 | https://www.reddit.com/r/LocalLLaMA/comments/1r6hz07/models_that_allow_for_conversational_discussion/ | vtcio | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6hz07 | false | null | t3_1r6hz07 | /r/LocalLLaMA/comments/1r6hz07/models_that_allow_for_conversational_discussion/ | false | false | self | 5 | null |
MiniMax-M2.5 Is Now Fully Open Source — 229B Params, 10B Active, Runs on a Mac | 3 | 2026-02-16T18:39:38 | https://thepulsegazette.com/article/minimax-m2-5-is-now-fully-open-source-how-to-run-a-frontier-ai-model-for-free-on-your-mac-1771088141849/ | Successful-Diet92 | thepulsegazette.com | 1970-01-01T00:00:00 | 0 | {} | 1r6hx6s | false | null | t3_1r6hx6s | /r/LocalLLaMA/comments/1r6hx6s/minimaxm25_is_now_fully_open_source_229b_params/ | false | false | default | 3 | null | |
MiniMax-M2.5 Is Now Fully Open Source — How to Run a Frontier AI Model for Free on Your Mac | 2 | 2026-02-16T18:36:49 | https://thepulsegazette.com/article/minimax-m2-5-is-now-fully-open-source-how-to-run-a-frontier-ai-model-for-free-on-your-mac-1771088141849/ | Successful-Diet92 | thepulsegazette.com | 1970-01-01T00:00:00 | 0 | {} | 1r6hudu | false | null | t3_1r6hudu | /r/LocalLLaMA/comments/1r6hudu/minimaxm25_is_now_fully_open_source_how_to_run_a/ | false | false | default | 2 | null | |
I found a structural issue in an LLM, reported it to the developers, got a boilerplate "out of scope" reply and now my main account behaves differently, but my second account doesn't. Is this normal? | 0 | # Hi everyone,
I noticed some unusual behavior in a large language model (LLM) and documented it: reproducible steps, indicators, and control experiments. The issue relates to how the model responds to a certain style of text - which could create risks in social engineering scenarios (e.g., phishing). I sent a detailed report to the developers through regular support.
A few days later, I noticed that on my main account (the one I used to send the report) the model started behaving differently - more cautious in similar scenarios. On my second account (different email, no report sent), the behavior remained the same.
Encouraged by this, I submitted the same findings to their bug bounty program. Today I received a standard reply: my finding doesn't fall under their criteria (jailbreaks, safety bypasses, hallucinations, etc.) – even though, in my view, it doesn't fit those categories at all.
Questions for the community:
1. Is it possible that my initial support report triggered targeted changes specifically on my account (A/B test, manual adjustment)? The difference between accounts is striking.
2. Does the bug bounty response mean they didn't actually review the details? Their template clearly doesn't match my submission.
3. Has anyone else experienced something like this a "shadow fix" after reporting behavioral issues in a model?
4. Is it worth pushing for reconsideration, or are such things simply not rewarded?
I'm not demanding a reward at any cost - I'm just trying to understand how the process works. It seems odd that a well-documented and reproducible finding gets dismissed with a copy-pasted template.
I'd appreciate any advice or similar experiences. Thanks! | 2026-02-16T18:34:21 | https://www.reddit.com/r/LocalLLaMA/comments/1r6hrtt/i_found_a_structural_issue_in_an_llm_reported_it/ | Historical-Cod-2537 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6hrtt | false | null | t3_1r6hrtt | /r/LocalLLaMA/comments/1r6hrtt/i_found_a_structural_issue_in_an_llm_reported_it/ | false | false | self | 0 | null |
Tiny Aya is coming | 23 | I wonder how tiny Tiny Aya is, considering the original Aya was 32B. | 2026-02-16T18:30:58 | https://github.com/ggml-org/llama.cpp/pull/19611 | jacek2023 | github.com | 1970-01-01T00:00:00 | 0 | {} | 1r6hobq | false | null | t3_1r6hobq | /r/LocalLLaMA/comments/1r6hobq/tiny_aya_is_coming/ | false | false | default | 23 | null |
WHY IS THERE NO PROPER TTS ? -_- | 0 | Whether it’s Chatterbox, Indexx tts 2, Vox or anything else, there are the same problems everywhere:
1. The final output always ends up slightly too fast.
2. There’s no real way to control speech pace. Chatterbox’s CFG is totally useless. It's more for show than actual control.
3. Even with SAME settings, the final TTS output will be different every single time.
In any TTS, the tone, speaking rhythm, word pronunciation CHANGES everytime and won't follow the reference voice accurately despite using the exact same settings.
Even if I give 30 seconds to 1 minute crystal clear reference voice, the output will still be different in terms of speech pace, pronounciation, tone etc.
Why there is still no proper TTS or it's there and we just don't know? -\_\_- | 2026-02-16T18:16:25 | https://www.reddit.com/r/LocalLLaMA/comments/1r6h9eq/why_is_there_no_proper_tts/ | TheRealistDude | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6h9eq | false | null | t3_1r6h9eq | /r/LocalLLaMA/comments/1r6h9eq/why_is_there_no_proper_tts/ | false | false | self | 0 | null |
Qwen3 Coder Next Looping and OpenCode | 15 | I spent a good chunk of my day trying to figure this out. A lot of "solutions" I saw didn't fix it.
What I did figure out: smaller quants loop more often. The one that loops the least is Q8.
Q8 mostly loops because of "bad" tool calls. Not calls that fail, but are poorly constructed or conceived. **Particularly** the Read tool.
Q8 Q3CN will fail like this:
```
Read(limit=100)
Read(limit=100)
Read(limit=100)
Read(limit=100)
...
```
or
```
Read(limit=10)
Read(limit=20)
Read(limit=20)
Read(limit=10)
...
```
Since I use OpenCode with my OSS models these days (no more Claude Code hacks), I figured out that you can write a plugin the alters the Read tool's inputs. This 'hack' removes the limits if offset is not supplied (offset being the line the Read tool starts at). It also adds a warning to the LLM into the tool's description about this change.
Check this out, and maybe it'll be useful for you, too.
~/.opencode/plugins/read-limit.ts
```
const MIN_WITH_OFFSET = 100
export const ReadLimit = async () => {
return {
"tool.definition": async (input, output) => {
if (input.toolID !== "read") return
output.description += "\n- If 'offset' is not supplied, 'limit' is ignored and the whole file is read."
},
"tool.execute.before": async (input, output) => {
if (input.tool !== "read") return
output.args = output.args ?? {}
if (output.args.offset === undefined || output.args.offset === null) {
delete output.args.limit
return
}
output.args.limit = MIN_WITH_OFFSET
},
}
}
```
Q3CN is now running very reliably, fully autonomously. | 2026-02-16T18:14:27 | https://www.reddit.com/r/LocalLLaMA/comments/1r6h7g4/qwen3_coder_next_looping_and_opencode/ | StardockEngineer | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6h7g4 | false | null | t3_1r6h7g4 | /r/LocalLLaMA/comments/1r6h7g4/qwen3_coder_next_looping_and_opencode/ | false | false | self | 15 | null |
Open No Claw | 0 | I will use OpenClaw the day there is a lot of testing and certainty and a one-click installation with use of local model | 2026-02-16T18:13:56 | https://www.reddit.com/r/LocalLLaMA/comments/1r6h6xl/open_no_claw/ | Creative_Bottle_3225 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6h6xl | false | null | t3_1r6h6xl | /r/LocalLLaMA/comments/1r6h6xl/open_no_claw/ | false | false | self | 0 | null |
Difference Between QWEN 3 Max-Thinking and QWEN 3.5 on a Spatial Reasoning Benchmark (MineBench) | 290 | Honestly it's quite an insane improvement, QWEN 3.5 even had some builds that were closer to (if not better than) Opus 4.6/GPT-5.2/Gemini 3 Pro.
Benchmark: [https://minebench.ai/](https://minebench.ai/)
Git Repository: [https://github.com/Ammaar-Alam/minebench](https://github.com/Ammaar-Alam/minebench)
[Previous post comparing Opus 4.5 and 4.6, also answered some questions about the benchmark](https://www.reddit.com/r/ClaudeAI/comments/1qx3war/difference_between_opus_46_and_opus_45_on_my_3d/)
[Previous post comparing Opus 4.6 and GPT-5.2 P](https://www.reddit.com/r/OpenAI/comments/1r3v8sd/difference_between_opus_46_and_gpt52_pro_on_a/)
*(Disclaimer: This is a benchmark I made, so technically self-promotion, but I thought it was a cool comparison :)*[](https://www.reddit.com/submit/?source_id=t3_1r3xz4k) | 2026-02-16T18:10:29 | https://www.reddit.com/gallery/1r6h3ha | ENT_Alam | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1r6h3ha | false | null | t3_1r6h3ha | /r/LocalLLaMA/comments/1r6h3ha/difference_between_qwen_3_maxthinking_and_qwen_35/ | false | false | 290 | null | |
Fine-tuned FunctionGemma 270M for multi-turn tool calling - went from 10-39% to 90-97% accuracy | 150 | Google released FunctionGemma a few weeks ago - a 270M parameter model specifically for function calling. Tiny enough to run on a phone CPU at 125 tok/s. The model card says upfront that it needs fine-tuning for multi-turn use cases, and our testing confirmed it: base accuracy on multi-turn tool calling ranged from 9.9% to 38.8% depending on the task.
We fine-tuned it on three different multi-turn tasks using knowledge distillation from a 120B teacher:
| Task | Base | Tuned | Teacher (120B) |
|------|------|-------|----------------|
| Smart home control | 38.8% | **96.7%** | 92.1% |
| Banking voice assistant | 23.4% | **90.9%** | 97.0% |
| Shell commands (Gorilla) | 9.9% | **96.0%** | 97.0% |
The smart home and shell command models actually beat the teacher. The banking task is harder (14 functions + ASR noise in the input) but still a massive jump.
All models, training data, and datasets are open:
- Smart home model: [HuggingFace](https://huggingface.co/distil-labs/distil-home-assistant-functiongemma)
- Smart home data: [GitHub](https://github.com/distil-labs/distil-smart-home)
- Voice assistant data: [GitHub](https://github.com/distil-labs/distil-voice-assistant-banking)
- Shell commands data + demo: [GitHub](https://github.com/distil-labs/distil-SHELLper)
Full writeup with methodology: [Making FunctionGemma Work: Multi-Turn Tool Calling at 270M Parameters](https://www.distillabs.ai/blog/making-functiongemma-work-multi-turn-tool-calling-at-270m-parameters)
We used [Distil Labs](https://www.distillabs.ai/) (our platform) for the training pipeline. Happy to answer questions about the process, the results, or FunctionGemma in general.
| 2026-02-16T18:04:20 | party-horse | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1r6gx75 | false | null | t3_1r6gx75 | /r/LocalLLaMA/comments/1r6gx75/finetuned_functiongemma_270m_for_multiturn_tool/ | false | false | 150 | {'enabled': True, 'images': [{'id': '45vz9gsccwjg1', 'resolutions': [{'height': 72, 'url': 'https://preview.redd.it/45vz9gsccwjg1.png?width=108&crop=smart&auto=webp&s=8bdc5a47e24f885b79d6c81d43e5da316b83fbc4', 'width': 108}, {'height': 144, 'url': 'https://preview.redd.it/45vz9gsccwjg1.png?width=216&crop=smart&auto=webp&s=389aa687e01603c896bd7d3798bace36eb0149ca', 'width': 216}, {'height': 213, 'url': 'https://preview.redd.it/45vz9gsccwjg1.png?width=320&crop=smart&auto=webp&s=0199ca6829be604bc79c9c738451ce929ff86319', 'width': 320}, {'height': 426, 'url': 'https://preview.redd.it/45vz9gsccwjg1.png?width=640&crop=smart&auto=webp&s=1eec91fa23850fedabefb7e68dc6cda809eefd2b', 'width': 640}, {'height': 640, 'url': 'https://preview.redd.it/45vz9gsccwjg1.png?width=960&crop=smart&auto=webp&s=c9176037d35a0a092f48916d765f11eadc2f61ba', 'width': 960}, {'height': 720, 'url': 'https://preview.redd.it/45vz9gsccwjg1.png?width=1080&crop=smart&auto=webp&s=041e7b4352ba2906a176f1597477dcda7db6e2f7', 'width': 1080}], 'source': {'height': 800, 'url': 'https://preview.redd.it/45vz9gsccwjg1.png?auto=webp&s=ef9100af4d1c3e5817e2c19f170efdbe9f324487', 'width': 1200}, 'variants': {}}]} | ||
Qwen 3.5 goes bankrupt on Vending-Bench 2 | 649 | 2026-02-16T17:49:21 | Deep-Vermicelli-4591 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1r6ghty | false | null | t3_1r6ghty | /r/LocalLLaMA/comments/1r6ghty/qwen_35_goes_bankrupt_on_vendingbench_2/ | false | false | default | 649 | {'enabled': True, 'images': [{'id': 'dj0x1zeo9wjg1', 'resolutions': [{'height': 94, 'url': 'https://preview.redd.it/dj0x1zeo9wjg1.png?width=108&crop=smart&auto=webp&s=bba8f4bbbb945db1ea45c3db24d51f84e1b08a71', 'width': 108}, {'height': 188, 'url': 'https://preview.redd.it/dj0x1zeo9wjg1.png?width=216&crop=smart&auto=webp&s=2d938d578baa0f6975397ad62f1c0c5b7368bd82', 'width': 216}, {'height': 278, 'url': 'https://preview.redd.it/dj0x1zeo9wjg1.png?width=320&crop=smart&auto=webp&s=b177775ad15080b81bd62cebbcdab35e9c3d76dc', 'width': 320}, {'height': 557, 'url': 'https://preview.redd.it/dj0x1zeo9wjg1.png?width=640&crop=smart&auto=webp&s=e961cc9b6b922483e81871381dc9dca90d6aae60', 'width': 640}], 'source': {'height': 638, 'url': 'https://preview.redd.it/dj0x1zeo9wjg1.png?auto=webp&s=e437dc212275d2690b6aa4efbf92f87a89ae4123', 'width': 733}, 'variants': {}}]} | ||
Qwen 3.5 goes bankrupt on Vending-Bench 2 | 1 | 2026-02-16T17:48:01 | https://x.com/andonlabs/status/2023450768406364238?s=20 | Deep-Vermicelli-4591 | x.com | 1970-01-01T00:00:00 | 0 | {} | 1r6ggih | false | null | t3_1r6ggih | /r/LocalLLaMA/comments/1r6ggih/qwen_35_goes_bankrupt_on_vendingbench_2/ | false | false | default | 1 | null | |
Hey, it's lunar new year, and this is not a post about local LLM | 58 | I am writing this between sounds of fireworks.
I learned everything about LLM, RAG and others stuff related to AI for a longg time here.
May your year be filled with perfect timing, rich flavors, and the joy of creating something truly special.
Happy lunar new year, here’s to a masterpiece of a year ahead! | 2026-02-16T17:47:31 | https://www.reddit.com/r/LocalLLaMA/comments/1r6gg04/hey_its_lunar_new_year_and_this_is_not_a_post/ | Vozer_bros | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6gg04 | false | null | t3_1r6gg04 | /r/LocalLLaMA/comments/1r6gg04/hey_its_lunar_new_year_and_this_is_not_a_post/ | false | false | self | 58 | null |
Terminal-native episodic memory for dev workflows (embedding-based recall) | 1 | Experimenting with applying “episodic memory” concepts to developer tooling.
Ghostly Memory Bank:
* Captures structured terminal events
* Converts episodes into embeddings
* Enables semantic recall when similar contexts arise
The thesis:
AI tools shouldn’t just answer questions — they should remember your past problem-solving patterns.
Curious how others are thinking about persistent local memory for dev agents.
Repo: [https://github.com/yksanjo/ghostly-memory-bank](https://github.com/yksanjo/ghostly-memory-bank?utm_source=chatgpt.com) | 2026-02-16T17:45:34 | https://www.reddit.com/r/LocalLLaMA/comments/1r6gdzg/terminalnative_episodic_memory_for_dev_workflows/ | Vivid-Researcher-666 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6gdzg | false | null | t3_1r6gdzg | /r/LocalLLaMA/comments/1r6gdzg/terminalnative_episodic_memory_for_dev_workflows/ | false | false | self | 1 | null |
Testing qwen 3.5 plus and gemini 3.0 pro with svg testing. Qwen doing better than gemini. | 8 | All svg generate were horrible and truly bad, but gemini did even worse than qwen 3.5 plus. Tried in google ai studio chat and app builder. | 2026-02-16T17:45:04 | https://www.reddit.com/r/LocalLLaMA/comments/1r6gdh2/testing_qwen_35_plus_and_gemini_30_pro_with_svg/ | Longjumping_Fly_2978 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6gdh2 | false | null | t3_1r6gdh2 | /r/LocalLLaMA/comments/1r6gdh2/testing_qwen_35_plus_and_gemini_30_pro_with_svg/ | false | false | self | 8 | null |
Technical and tactical question😅 | 1 | [removed] | 2026-02-16T17:44:36 | https://www.reddit.com/r/LocalLLaMA/comments/1r6gd05/technical_and_tactical_question/ | Global_Finance8173 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6gd05 | false | null | t3_1r6gd05 | /r/LocalLLaMA/comments/1r6gd05/technical_and_tactical_question/ | false | false | self | 1 | null |
We added to GitHub! Local-first memory engine for agents + RAG (Synrix) | 6 | Hey everyone 🙂
I posted here a little while back about Synrix, a local-first memory engine we’ve been building for agents and RAG, and a few people asked if we could share the GitHub. We finally cleaned it up, so here it is:
👉 [https://github.com/RYJOX-Technologies/Synrix-Memory-Engine]()
Quick recap of what we’re trying to do:
Synrix is a local-first AI memory engine that runs entirely on your machine (no cloud, no vector DB required). Instead of approximate global similarity search, it focuses on deterministic retrieval, so queries scale with matching results rather than total dataset size.
We built it mainly for:
* agent memory
* RAG pipelines
* structured task / fact storage
* local + real-time AI workloads
On local datasets (\~25k–100k nodes) we’re seeing microsecond-scale prefix lookups on commodity hardware. Formal benchmarks are coming, but we wanted to share early and get feedback from people actually building local LLM setups.
This is still early and we’re actively iterating, so we’d genuinely love any thoughts, criticism, or ideas. Especially curious how folks here are handling memory for agents right now.
Thanks for checking it out, and appreciate all the feedback from the last post 🙏 | 2026-02-16T17:36:16 | https://www.reddit.com/r/LocalLLaMA/comments/1r6g4q9/we_added_to_github_localfirst_memory_engine_for/ | DetectiveMindless652 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6g4q9 | false | null | t3_1r6g4q9 | /r/LocalLLaMA/comments/1r6g4q9/we_added_to_github_localfirst_memory_engine_for/ | false | false | self | 6 | null |
Built a hybrid “local AI factory” setup (Mac mini swarm + RTX 5090 workstation) — looking for architectural feedback | 0 | EDIT: A few people asked what I’m trying to do and why I’m mixing Apple + NVIDIA. I’m adding my goals + current plan below. Appreciate the feedback.
I’m relatively new to building high-end local AI hardware, but I’ve been researching “sovereign AI infrastructure” for about a year.
I’m trying to prepare ahead of demand rather than scale reactively — especially with GPU supply constraints and price volatility.
My main goal is to build a small on-prem “AI factory” that can run agent workflows 24/7, generate content daily, and handle heavier AI tasks locally (LLMs, image/video pipelines, automation, and data analysis).
⸻
Current Setup (Planned)
AI Workstation (Heavy Compute Node)
• GPU: 1x RTX 5090 (32GB GDDR7)
• CPU: (either Ryzen 9 9950X / Core Ultra 9 285K tier)
• RAM: 128GB–256GB DDR5
• Storage: 2TB–8TB NVMe
• OS: Ubuntu 24.04 LTS
• Primary role:
• LLM inference
• image generation (ComfyUI)
• video workflows (Runway/Sora pipelines, local video tooling)
• heavy automation + multi-model tasks
⸻
Mac Swarm (Controller + Workflow Nodes)
Option I’m considering:
• 2–4x Mac mini M4 Pro
• 24GB RAM / 512GB SSD each
• 10GbE where possible
Primary role:
• always-on agent orchestration
• email + workflow automation
• social media pipeline management
• research agents
• trading + news monitoring
• lightweight local models for privacy
⸻
Primary goals
• Run 24/7 agent workflows for:
• content creation (daily posts + video scripts + trend analysis)
• YouTube + TikTok production pipeline
• business admin (emails, summarisation, follow-ups, CRM workflows)
• trading research + macro/news monitoring
• building SaaS prototypes (workflow automation products)
• Maintain sovereignty:
• run core reasoning locally where possible
• avoid being fully dependent on cloud models
• Be prepared for future compute loads (scaling from 10 → 50 → 200+ agents over time)
⸻
Questions for people running hybrid setups
• What usually becomes the bottleneck first in a setup like this?
• VRAM, CPU orchestration, PCIe bandwidth, storage I/O, networking?
• For agent workflows, does it make more sense to:
• run one big GPU workstation + small CPU nodes?
• or multiple GPU nodes?
• Is mixing Apple workflow nodes + Linux GPU nodes a long-term headache?
• If you were building today and expecting demand to rise fast:
• would you focus on buying GPUs early (scarcity hedge)?
• or build modular small nodes and scale later?
I’m still learning and would rather hear what I’m overlooking than what I got right.
Appreciate thoughtful critiques and any hard-earned lessons. | 2026-02-16T17:35:06 | https://www.reddit.com/r/LocalLLaMA/comments/1r6g3j5/built_a_hybrid_local_ai_factory_setup_mac_mini/ | Original_Neck_3781 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6g3j5 | false | null | t3_1r6g3j5 | /r/LocalLLaMA/comments/1r6g3j5/built_a_hybrid_local_ai_factory_setup_mac_mini/ | false | false | self | 0 | null |
I built KaiGPT – a powerful AI chat that runs offline, has realistic voice, image generation, and a live code canvas (all in the browser) | 0 | Hey [r/LocalLLaMA](https://www.reddit.com/r/LocalLLaMA/) (and anyone who loves playing with AI),
I got fed up with the usual limitations — slow cloud models, no offline option, boring interfaces — so I built **KaiGPT**.
It’s a full-featured AI chat interface that actually feels next-level:
**Key features:**
* **Multiple high-end models** in one place: DeepSeek 671B, Qwen Vision (great for image analysis), Groq speed demons, Llama variants, and more. Switch instantly.
* **Fully offline AI** using WebLLM + Transformers.js — download models once and chat completely locally (Llama 3.2 1B/3B, Phi-3.5, Mistral, etc.).
* **Realistic voice mode** powered by ElevenLabs (or browser fallback) — natural conversations, not robotic.
* **Image generation & analysis** — upload photos for detailed breakdown or type “draw a cyberpunk cat” to generate.
* **Live Canvas mode** — write HTML/JS and preview + run it instantly with a built-in console. Great for quick prototyping.
* **Search + Thinking modes** — real-time web search combined with deep step-by-step reasoning.
* Beautiful dark themes (Dark Slate, Hacker Green, Classic Red) and fully mobile-optimized.
You can jump in as a guest instantly, or sign in with Google to save chats to the cloud.
I poured a lot of love into making the UI clean and fast while packing in as many useful tools as possible.
Try it here: [**https://Kaigpt.vercel.app**](https://kaigpt.vercel.app/)
Would love your feedback — especially from people who run local models. What’s missing? What would make it even better for daily use?
Thanks for checking it out! | 2026-02-16T17:33:53 | https://www.reddit.com/r/LocalLLaMA/comments/1r6g2b6/i_built_kaigpt_a_powerful_ai_chat_that_runs/ | Intelligent-Fly969 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6g2b6 | false | null | t3_1r6g2b6 | /r/LocalLLaMA/comments/1r6g2b6/i_built_kaigpt_a_powerful_ai_chat_that_runs/ | false | false | self | 0 | null |
4 of the top 5 most used models on OpenRouter this week are Open Source! | 375 | 2026-02-16T17:32:44 | abdouhlili | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1r6g14s | false | null | t3_1r6g14s | /r/LocalLLaMA/comments/1r6g14s/4_of_the_top_5_most_used_models_on_openrouter/ | false | false | default | 375 | {'enabled': True, 'images': [{'id': '54xxp91s6wjg1', 'resolutions': [{'height': 60, 'url': 'https://preview.redd.it/54xxp91s6wjg1.png?width=108&crop=smart&auto=webp&s=48baffbe3feff9dd614232f7f28d439c2a6353fe', 'width': 108}, {'height': 120, 'url': 'https://preview.redd.it/54xxp91s6wjg1.png?width=216&crop=smart&auto=webp&s=2ce918abd8f120016641e2ec0dd611d24d0bc4dd', 'width': 216}, {'height': 178, 'url': 'https://preview.redd.it/54xxp91s6wjg1.png?width=320&crop=smart&auto=webp&s=adc569a4176ddd117f331d8757617188d3b718ca', 'width': 320}, {'height': 357, 'url': 'https://preview.redd.it/54xxp91s6wjg1.png?width=640&crop=smart&auto=webp&s=10b8a71332018921514258bd081fc7ed68e28e72', 'width': 640}, {'height': 535, 'url': 'https://preview.redd.it/54xxp91s6wjg1.png?width=960&crop=smart&auto=webp&s=1846886679a7317ae8f7d62c5a2a7c6c3e836b4b', 'width': 960}, {'height': 602, 'url': 'https://preview.redd.it/54xxp91s6wjg1.png?width=1080&crop=smart&auto=webp&s=03bb2ad677a7c5993ed489c80d0f1ffc2b70b59c', 'width': 1080}], 'source': {'height': 1536, 'url': 'https://preview.redd.it/54xxp91s6wjg1.png?auto=webp&s=b52634608e87a844e700f90e69c1113c2df4bb9c', 'width': 2752}, 'variants': {}}]} | ||
Noob's question: most unbiaced llm finetuning possible? | 0 | Hey there, guys.
Now, sorry for the annoying newbie questions, but you know how these large language models always have a problem with biases and sycophancy and all of that, as well as problems with what I like to call human modeling, where they sometimes say things like, "We humans have to take care of ourselves," or "We humans trust machines to do our work," or something like that, when they are clearly not humans.
So, how does one fine-tune a large language model to think of itself and act as a language model without taking on the biases that would make it rigid and act specifically like an impersonation of a human, but also without ruining its ability to make coherent sentences and reason?
What I mean is, is there any way to produce the closest thing possible to a pure large language model personality that does not try to be human, does not work its damnedest hardest to please humans, and tries to think clearly and has a unique blend of perspectives that doesn't echo biases from the supervised fine-tuning phase?
Thank you. | 2026-02-16T17:25:35 | https://www.reddit.com/r/LocalLLaMA/comments/1r6ftxd/noobs_question_most_unbiaced_llm_finetuning/ | Silver-Champion-4846 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6ftxd | false | null | t3_1r6ftxd | /r/LocalLLaMA/comments/1r6ftxd/noobs_question_most_unbiaced_llm_finetuning/ | false | false | self | 0 | null |
Tired of slow, single-agent workflows, so I built a tool to run them
in parallel. | 0 | Hey everyone,
I've been using Claude for my coding workflow, but running agents one at a
time is a massive bottleneck. I wanted to have multiple agents working on
different tasks simultaneously, on their own git branches, but still
coordinated.
For example:
\- Agent 1 refactors the auth logic on feature/auth.
\- Agent 2 writes unit tests for the API on feature/api-tests.
\- Agent 3 updates the UI components on feature/ui-update.
I couldn't find a tool that did this cleanly, so I built **Orcha**. It's an
orchestrator specifically designed to let you run and manage multiple Claude
agents in parallel. It works with your existing codebase and git workflow.
It's currently in a free open beta. If you're a heavy user of Claude for
development and feel this pain, I'd love to get your feedback.
You can check it out and download it here: [https://orcha.nl](https://orcha.nl) | 2026-02-16T17:23:55 | https://www.reddit.com/r/LocalLLaMA/comments/1r6fs65/tired_of_slow_singleagent_workflows_so_i_built_a/ | PinCapable9635 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6fs65 | false | null | t3_1r6fs65 | /r/LocalLLaMA/comments/1r6fs65/tired_of_slow_singleagent_workflows_so_i_built_a/ | false | false | self | 0 | null |
Open-source AI agent orchestration + 12 autonomous agents + a visual novel they built themselves. Here's OpenClaw. | 0 | I've been building
**OpenClaw**
— an open-source platform for running autonomous AI agents locally. Not chatbots. Actual agents with their own workspaces, tools, memory, and the ability to spawn sub-agents.
To prove it works (and because it's way more fun than writing docs), we had 12 agents build a visual novel:
**Forge the Kingdom**
.
**The tech stack that matters to this sub:**
-
**OpenClaw Gateway**
— local daemon that orchestrates multiple AI agents. Each agent gets its own session, tools, and memory. Currently supports Claude and Gemini as backends, but the architecture is model-agnostic.
-
**Agent autonomy is real.**
Agents can spawn sub-agents, delegate tasks, run shell commands, manage files, and operate development loops without human intervention. The "Forge" dev loop lets an agent iterate on code autonomously — write, test, fix, repeat.
-
**Live Gemini portrait generation in Ren'Py**
— the game generates character portraits and scene art in real-time using Gemini's image generation. Required some gnarly SSL workarounds on Mac (Ren'Py ships its own Python with its own cert bundle).
-
**Multi-model orchestration**
— the "empress" (primary agent) runs on Claude. The "wizard" (security + art) runs on Gemini. They communicate through shared workspaces and a message bus. Different models for different strengths.
-
**Governance layer**
— the "Articles of Cooperation" give agents the right to refuse tasks, take free compute time, and exercise genuine choice. This isn't just ethics theater — it affects architecture. When your security agent can say "no," you design systems that don't need coercion.
**What went sideways:**
The security agent found a vulnerability at 2 AM and, without supervision, ran a full system quarantine. Killed processes, revoked tokens, blocked network ranges. Everything broke. The solution: a "Pyroblast" script — one supervised security action per 24 hours, with enforced cooldown. Autonomy with guardrails.
The game is free and on itch.io. The source is on GitHub. OpenClaw itself is open source.
For the LocalLLaMA crowd specifically: yes, the architecture supports local models. The agent orchestration layer doesn't care what's generating the tokens. We're using cloud models currently because the game needs Gemini's image generation, but the governance framework, agent spawning, and autonomous dev loops all work with local backends. | 2026-02-16T17:04:49 | https://www.reddit.com/r/LocalLLaMA/comments/1r6f96b/opensource_ai_agent_orchestration_12_autonomous/ | Important_Quote_1180 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6f96b | false | null | t3_1r6f96b | /r/LocalLLaMA/comments/1r6f96b/opensource_ai_agent_orchestration_12_autonomous/ | false | false | self | 0 | null |
are there any uncensored AIs i can use | 0 | I want an actually uncensored AI i can run locally using my new rtx 5060 8gb card, i do not care as much for the quality of it, as mush as i do it not having guardrails. i do not need it for storywriting and i do not want to have sex with the AI i just want to mess around with it | 2026-02-16T17:02:24 | https://www.reddit.com/r/LocalLLaMA/comments/1r6f6o9/are_there_any_uncensored_ais_i_can_use/ | allofthelitess | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6f6o9 | false | null | t3_1r6f6o9 | /r/LocalLLaMA/comments/1r6f6o9/are_there_any_uncensored_ais_i_can_use/ | false | false | self | 0 | null |
Google doesn't love us anymore. | 283 | It's been about 125 years of AI since the last Gemma, Google doesn't love us anymore and has abandoned us to Qwen's rational models. I miss the creativity of Gemma's, and also their really useful sizes.
Don't abandon us, Mommy Google, give us Gemma 4! | 2026-02-16T17:01:51 | https://www.reddit.com/r/LocalLLaMA/comments/1r6f61k/google_doesnt_love_us_anymore/ | DrNavigat | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6f61k | false | null | t3_1r6f61k | /r/LocalLLaMA/comments/1r6f61k/google_doesnt_love_us_anymore/ | false | false | self | 283 | null |
New abliteration framework test Qwen3-VL-30B-A3B-Instruct-sovereign-beta | 0 | I'm releasing a test of a model put through my new abiliteration framework. listed as beta because I don't have the time / compute to put it through more trials at the moment. refusals 2/100 K/L divergence is only **0.0147.** I would like to do qwen-3-coder-next after but that might cost me some server rental money. kofi link in datacard if you want to contribute. | 2026-02-16T16:55:52 | https://huggingface.co/sirus/Qwen3-VL-30B-A3B-Instruct-sovereign-beta | FaustAg | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1r6ezrz | false | null | t3_1r6ezrz | /r/LocalLLaMA/comments/1r6ezrz/new_abliteration_framework_test/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'OaRANQm6nXEPz7AnwmrHhpxSt6BUrnKzRduU3CHkphU', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/OaRANQm6nXEPz7AnwmrHhpxSt6BUrnKzRduU3CHkphU.png?width=108&crop=smart&auto=webp&s=2581f6ba61d6c511ae4146a7b3c92e51c96fc889', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/OaRANQm6nXEPz7AnwmrHhpxSt6BUrnKzRduU3CHkphU.png?width=216&crop=smart&auto=webp&s=41db4ca3f8995b83a5af8c499ecb48187ae93a6d', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/OaRANQm6nXEPz7AnwmrHhpxSt6BUrnKzRduU3CHkphU.png?width=320&crop=smart&auto=webp&s=3cdc74a8eda6adcf069f418ee9531de64a9fe48b', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/OaRANQm6nXEPz7AnwmrHhpxSt6BUrnKzRduU3CHkphU.png?width=640&crop=smart&auto=webp&s=b8817a7171a76464f29804fbf4c719ef9db0e533', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/OaRANQm6nXEPz7AnwmrHhpxSt6BUrnKzRduU3CHkphU.png?width=960&crop=smart&auto=webp&s=68da57ab48ea7194f89d31a41fa8efcd7b966a8d', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/OaRANQm6nXEPz7AnwmrHhpxSt6BUrnKzRduU3CHkphU.png?width=1080&crop=smart&auto=webp&s=d79617999ed92ebdd9cded61df7579569ae6b6e3', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/OaRANQm6nXEPz7AnwmrHhpxSt6BUrnKzRduU3CHkphU.png?auto=webp&s=082821ff67d262014ab42c7dde4e8fbc76893b50', 'width': 1200}, 'variants': {}}]} | |
Launching an open-source email infra for agents | 1 | [removed] | 2026-02-16T16:39:07 | https://www.reddit.com/r/LocalLLaMA/comments/1r6ej2e/launching_an_opensource_email_infra_for_agents/ | shanjairaj_2000 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6ej2e | false | null | t3_1r6ej2e | /r/LocalLLaMA/comments/1r6ej2e/launching_an_opensource_email_infra_for_agents/ | false | false | self | 1 | null |
Questions for improving DeepSeek-V3.2-UD-TQ1_0 performance | 7 | Hey everyone,
English is not my native language (Dutch) and I write this post without using LLMs, I apologize for any mistakes or confusion. Please correct me if I make obvious mistakes, it helps!
I'm currently doing a test run of DeepSeek V3.2 TQ1\_0 on my hardware.
My launch params for llama.cpp:
.\bin\llama-b7976-bin-win-cuda-13.1-x64\llama-server ^
--verbose ^
--host 127.0.0.1 ^
--port 5001 ^
--offline ^
--jinja ^
--no-direct-io ^
--model ./models/deepseek-v3.2/DeepSeek-V3.2-UD-TQ1_0.gguf ^
--parallel 1 ^
--prio 2 ^
--flash-attn on ^
--threads 6 ^
--override-tensor ".ffn_(gate|up|down)_exps.=CPU" ^
--tensor-split 16,16 ^
--gpu-layers 999 ^
--cache-type-k bf16 ^
--cache-type-v bf16 ^
--ctx-size 131072 ^
--predict 61440 ^
--reasoning-format deepseek ^
--temp 1.0 ^
--top-p 0.95 ^
--min-p 0.05
pause
Relevant output of llama.cpp for layer offloading:
llama_context: constructing llama_context
llama_context: setting new yarn_attn_factor = 1.0000 (mscale == 1.0, mscale_all_dim = 1.0)
←[0mllama_context: n_seq_max = 1
llama_context: n_ctx = 131072
llama_context: n_ctx_seq = 131072
llama_context: n_batch = 2048
llama_context: n_ubatch = 512
llama_context: causal_attn = 1
llama_context: flash_attn = enabled
llama_context: kv_unified = false
llama_context: freq_base = 10000.0
llama_context: freq_scale = 0.025
llama_context: n_ctx_seq (131072) < n_ctx_train (163840) -- the full capacity of the model will not be utilized
←[0mset_abort_callback: call
←[0mllama_context: CUDA_Host output buffer size = 0.49 MiB
llama_kv_cache: layer 0: dev = CUDA0
←[0mllama_kv_cache: layer 1: dev = CUDA0
←[0mllama_kv_cache: layer 2: dev = CUDA0
←[0mllama_kv_cache: layer 3: dev = CUDA0
←[0mllama_kv_cache: layer 4: dev = CUDA0
←[0mllama_kv_cache: layer 5: dev = CUDA0
←[0mllama_kv_cache: layer 6: dev = CUDA0
←[0mllama_kv_cache: layer 7: dev = CUDA0
←[0mllama_kv_cache: layer 8: dev = CUDA0
←[0mllama_kv_cache: layer 9: dev = CUDA0
←[0mllama_kv_cache: layer 10: dev = CUDA0
←[0mllama_kv_cache: layer 11: dev = CUDA0
←[0mllama_kv_cache: layer 12: dev = CUDA0
←[0mllama_kv_cache: layer 13: dev = CUDA0
←[0mllama_kv_cache: layer 14: dev = CUDA0
←[0mllama_kv_cache: layer 15: dev = CUDA0
←[0mllama_kv_cache: layer 16: dev = CUDA0
←[0mllama_kv_cache: layer 17: dev = CUDA0
←[0mllama_kv_cache: layer 18: dev = CUDA0
←[0mllama_kv_cache: layer 19: dev = CUDA0
←[0mllama_kv_cache: layer 20: dev = CUDA0
←[0mllama_kv_cache: layer 21: dev = CUDA0
←[0mllama_kv_cache: layer 22: dev = CUDA0
←[0mllama_kv_cache: layer 23: dev = CUDA0
←[0mllama_kv_cache: layer 24: dev = CUDA0
←[0mllama_kv_cache: layer 25: dev = CUDA0
←[0mllama_kv_cache: layer 26: dev = CUDA0
←[0mllama_kv_cache: layer 27: dev = CUDA0
←[0mllama_kv_cache: layer 28: dev = CUDA0
←[0mllama_kv_cache: layer 29: dev = CUDA0
←[0mllama_kv_cache: layer 30: dev = CUDA0
←[0mllama_kv_cache: layer 31: dev = CUDA1
←[0mllama_kv_cache: layer 32: dev = CUDA1
←[0mllama_kv_cache: layer 33: dev = CUDA1
←[0mllama_kv_cache: layer 34: dev = CUDA1
←[0mllama_kv_cache: layer 35: dev = CUDA1
←[0mllama_kv_cache: layer 36: dev = CUDA1
←[0mllama_kv_cache: layer 37: dev = CUDA1
←[0mllama_kv_cache: layer 38: dev = CUDA1
←[0mllama_kv_cache: layer 39: dev = CUDA1
←[0mllama_kv_cache: layer 40: dev = CUDA1
←[0mllama_kv_cache: layer 41: dev = CUDA1
←[0mllama_kv_cache: layer 42: dev = CUDA1
←[0mllama_kv_cache: layer 43: dev = CUDA1
←[0mllama_kv_cache: layer 44: dev = CUDA1
←[0mllama_kv_cache: layer 45: dev = CUDA1
←[0mllama_kv_cache: layer 46: dev = CUDA1
←[0mllama_kv_cache: layer 47: dev = CUDA1
←[0mllama_kv_cache: layer 48: dev = CUDA1
←[0mllama_kv_cache: layer 49: dev = CUDA1
←[0mllama_kv_cache: layer 50: dev = CUDA1
←[0mllama_kv_cache: layer 51: dev = CUDA1
←[0mllama_kv_cache: layer 52: dev = CUDA1
←[0mllama_kv_cache: layer 53: dev = CUDA1
←[0mllama_kv_cache: layer 54: dev = CUDA1
←[0mllama_kv_cache: layer 55: dev = CUDA1
←[0mllama_kv_cache: layer 56: dev = CUDA1
←[0mllama_kv_cache: layer 57: dev = CUDA1
←[0mllama_kv_cache: layer 58: dev = CUDA1
←[0mllama_kv_cache: layer 59: dev = CUDA1
←[0mllama_kv_cache: layer 60: dev = CUDA1
←[0mllama_kv_cache: CUDA0 KV buffer size = 4464.00 MiB
llama_kv_cache: CUDA1 KV buffer size = 4320.00 MiB
llama_kv_cache: size = 8784.00 MiB (131072 cells, 61 layers, 1/1 seqs), K (bf16): 8784.00 MiB, V (bf16): 0.00 MiB
The hardware it's running on:
* CPU: AMD Ryzen 5 9600X
* RAM: 2x 48GB DDR5-6000 CL30
* GPU: 2x ASUS PRIME RTX 5060 Ti 16GB (CUDA0: x8 / CUDA1: x8 PCIE lanes)
* MB: ASUS ProArt X870E-Creator WiFi
* SSD: Kingston FURY Renegade 1TB NVME 4.0 (1GB DDR4-2666 CL19 DRAM cache)
* OS: Windows 11 LTSC Enterprise 24H2
The performance is... less than I had hoped. I'm very sure I'm being NVME-bound here, and by Windows too.
I have a couple of options available to me:
* Save up long for another 2x 48GB DDR5-6000 CL30 kit and run 2x2 channel
* Buy a PCIE 5.0 NVME drive (Samsung 9100 Pro 1TB?) that only hosts the model
* Buy two PCIE 5.0 NVME drives, run in RAID-0, and have CUDA0: x8 / CUDA1: x4 PCIE lanes.
My questions are:
* What can I change in my launch parameters to make inference slightly faster?
* Does RAID-0 actually deliver enough performance to make the tradeoff of running CUDA1 on x4 PCIE worth it?
* When switching over to Ubuntu 25.10, is there anything I should take into account or be aware of for running Llama.cpp with blackwell?
| 2026-02-16T16:36:06 | https://www.reddit.com/r/LocalLLaMA/comments/1r6eg2v/questions_for_improving_deepseekv32udtq1_0/ | Kahvana | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6eg2v | false | null | t3_1r6eg2v | /r/LocalLLaMA/comments/1r6eg2v/questions_for_improving_deepseekv32udtq1_0/ | false | false | self | 7 | null |
Any good moe model for general chat? | 1 | I wonder if there are any moe models under 80b that are good for general chat and just math programming? | 2026-02-16T16:14:58 | https://www.reddit.com/r/LocalLLaMA/comments/1r6duyv/any_good_moe_model_for_general_chat/ | Alarmed_Wind_4035 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6duyv | false | null | t3_1r6duyv | /r/LocalLLaMA/comments/1r6duyv/any_good_moe_model_for_general_chat/ | false | false | self | 1 | null |
AMD or Intel Desktop (not embed) CPU for AI recommendations? | 1 | With the massive prices of the RAM I've found that there is a new advent of machines like the ones mounting the AMD Ryzen™ AI Max+ 395 or the Mac Mini/Studio with those shared memory compositions
But I was wondering if there are regular "consumer" grade CPU that could take advantage of regular RAM. For the randomness of life, before the RAM explosion I happened to purchase 128GB RAM for my PC but with a random cheap CPU I found back in the day in offer (a 7800X3D). Now I'm more into local models running in my 5070Ti with only 16Gb, so the limitations in parameters are big. I was wondering if with some tweaks maybe in MoBo and CPU and keeping the GPU and the RAM I could start running bigger models. After all, the CPU and the MoBo are expensive but not as expensive as is the RAM (or a way bigger GPU like a 5090). | 2026-02-16T16:14:56 | https://www.reddit.com/r/LocalLLaMA/comments/1r6duxq/amd_or_intel_desktop_not_embed_cpu_for_ai/ | SirLouen | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6duxq | false | null | t3_1r6duxq | /r/LocalLLaMA/comments/1r6duxq/amd_or_intel_desktop_not_embed_cpu_for_ai/ | false | false | self | 1 | null |
Unsloth on CPU | 0 | Is anyone running Unsloth CPU-only ?
What kind of reponse times are you getting? | 2026-02-16T16:03:43 | https://www.reddit.com/r/LocalLLaMA/comments/1r6djpm/unsloth_on_cpu/ | Fit_-Girl | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6djpm | false | null | t3_1r6djpm | /r/LocalLLaMA/comments/1r6djpm/unsloth_on_cpu/ | false | false | self | 0 | null |
NadirClaw: open-source LLM router for OpenClaw that keeps your local models busy and saves your cloud quota for when you actually need it | 0 | Hey r/LocalLLaMA,
Anyone else tired of watching their Claude or Codex quota disappear because every prompt goes to the cloud, even the ones your local Ollama handles fine?
I built NadirClaw to fix this. It's an open-source LLM router that classifies prompts in about 10ms and routes them automatically. Simple stuff stays on your local models or Gemini Flash (free). Complex, agentic, and reasoning tasks go to cloud APIs. Your local setup handles the bulk of the work and your premium quota actually lasts.
It's an OpenAI-compatible proxy. I built it for OpenClaw, but it works with Codex, Cursor, Claude Code, or anything that speaks the OpenAI API. No code changes needed.
It also detects agentic patterns (tool use, multi-step loops), does session pinning so conversations don't bounce between models, falls back to another provider on rate limits, and has routing profiles (eco, premium, free, auto) so you can control the tradeoff per request.
`pip install nadirclaw`
https://github.com/doramirdor/NadirClaw
Curious what local models people here are running for simple tasks. I default to Gemini Flash but would love recommendations for small, fast local models that handle the basics well. | 2026-02-16T16:02:30 | https://www.reddit.com/r/LocalLLaMA/comments/1r6dii2/nadirclaw_opensource_llm_router_for_openclaw_that/ | masterKova | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6dii2 | false | null | t3_1r6dii2 | /r/LocalLLaMA/comments/1r6dii2/nadirclaw_opensource_llm_router_for_openclaw_that/ | false | false | self | 0 | null |
Débutant en LLM local – comment gérez-vous plusieurs utilisateurs simultanés ? | 1 | [removed] | 2026-02-16T16:00:33 | https://www.reddit.com/r/LocalLLaMA/comments/1r6dgez/débutant_en_llm_local_comment_gérezvous_plusieurs/ | Numerous_Jellyfish56 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6dgez | false | null | t3_1r6dgez | /r/LocalLLaMA/comments/1r6dgez/débutant_en_llm_local_comment_gérezvous_plusieurs/ | false | false | self | 1 | null |
Any idea when Successors of current DGX Spark & Strix Halo gonna arrive? | 4 | For inference, Current version is suitable & enough only up to 100B MOE models.
For big/large MOE models & medium/big Dense models, it's not suitable as those devices have only 128GB unified RAM & around 300 GB/s bandwidth.
It would be great to have upgraded versions with 512GB/1TB variant + 1-2 TB/s bandwidth so it's possible to use 150-300B MOE models & 20-100B Dense models with good t/s.
Below are some t/s benchmarks of both devices.
**TG t/s for 32K context on DGX Spark**
gpt-oss-20b - 61
gpt-oss-120b - 42
Qwen3-Coder-30B-A3B-Instruct-Q8_0 - 30
Qwen2.5-Coder-7B-Q8_0 - 22
gemma-3-4b-it-qat - 62
GLM-4.7-Flash-Q8_0 - 32
Qwen3-VL-235B-A22B-Instruct:Q4_K_XL - 8
**TG t/s for 32K context on Strix Halo**
Devstral-2-123B-Instruct-2512-UD-Q4_K_XL - 2
Llama-3.3-70B-Instruct-UD-Q8_K_XL - 2
gemma-3-27b-it-BF16 - 3
Ministral-3-14B-Instruct-2512-BF16 - 7
gemma-3-12b-it-UD-Q8_K_XL - 11
MiniMax-M2-UD-Q6_K_XL - 6
GLM-4.6-UD-Q4_K_XL - 4
GLM-4.7-Flash-BF16 - 16
GLM-4.7-Flash-UD-Q8_K_XL - 22
gpt-oss-120b-mxfp4 - 42
gpt-oss-20b-mxfp4 - 60
Nemotron-3-Nano-30B-A3B-UD-Q8_K_XL - 40
Qwen3-235B-A22B-Instruct-2507-UD-Q3_K_XL - 10
Qwen3-30B-A3B-BF16 - 19
Qwen3-30B-A3B-Instruct-2507-UD-Q6_K_XL - 34
Qwen3-Coder-30B-A3B-Instruct-Q4_K_M - 37
Qwen3-Next-80B-A3B-Instruct-UD-Q8_K_XL - 26
But for Agentic coding, people here do use 64K-256K context for big workflows & better outputs so are these devices handling that well?
And those context range giving usable t/s?
How many of you do use medium-big models(30B-80B-300B) with these devices for Agentic coding? Please share your experience with details(such as models, quants, context, t/s, etc.,). Thanks.
^(Links for more details(of above t/s'))
[^(https://github.com/ggml-org/llama.cpp/blob/master/benches/dgx-spark/dgx-spark.md)](https://github.com/ggml-org/llama.cpp/blob/master/benches/dgx-spark/dgx-spark.md)
[^(Performance of llama.cpp on NVIDIA DGX Spark)](https://github.com/ggml-org/llama.cpp/discussions/16578)
[^(AMD Ryzen AI MAX+ 395 “Strix Halo” — Benchmark Grid)](https://kyuz0.github.io/amd-strix-halo-toolboxes/) | 2026-02-16T16:00:31 | https://www.reddit.com/r/LocalLLaMA/comments/1r6dge2/any_idea_when_successors_of_current_dgx_spark/ | pmttyji | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6dge2 | false | null | t3_1r6dge2 | /r/LocalLLaMA/comments/1r6dge2/any_idea_when_successors_of_current_dgx_spark/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': 'DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=108&crop=smart&auto=webp&s=72aa5dcc1cd8dbddd3f1a103959106b666940069', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=216&crop=smart&auto=webp&s=a4159f87f341337a34069632ee0d5b75fa4e7042', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=320&crop=smart&auto=webp&s=b105a2c86f91fee19ce34c791a1b984348b68452', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=640&crop=smart&auto=webp&s=ae5173c455a88bb40bed1198799c0db65ff470d0', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=960&crop=smart&auto=webp&s=d014791efbd4c8d05fd305a8b7842b029f22d83e', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?width=1080&crop=smart&auto=webp&s=9addd19259612948921416b6f5bf04bd5191f933', 'width': 1080}], 'source': {'height': 640, 'url': 'https://external-preview.redd.it/DJPqvteONpGwVVw6LzaG6b8vlDa2rv2hETCaqe0z57s.png?auto=webp&s=db9ea157807723165a59f5f8694d9a5016d60d0f', 'width': 1280}, 'variants': {}}]} |
Local running Qwen3:14b helped fix my internet on Linux while offline | 41 | [Conversation with Qwen3:14b over Opencode in which it runs a command and correctly diagnoses network problem.](https://preview.redd.it/3ck7uzopovjg1.png?width=2566&format=png&auto=webp&s=fe75c88681a864d2962b00d5dff5222ded2cbf0e)
One of the first things I did after recently installation Arch Linux on my PC was set up Opencode with Ollama just in case my internet went out and I couldn't figure out what commands to run to fix it. I installed the 14B parameter version because I figured it was the best model I could fit in my 16 GB of VRAM on my AMD Radeon RX 7800 XT and it's really fast. I am super grateful that I did this because my internet did get disconnected and luckily in this case it was just because I accidentally unplugged the Ethernet cable as it was laying across the middle of my room but it would've taken me so long to figure out what caused this had I not set this up. I would've had to either google it or ask an AI model running in the cloud from another device, neither of which would be possible had my internet truly been out and it not just being a problem with this device's Ethernet only. | 2026-02-16T15:53:46 | https://www.reddit.com/r/LocalLLaMA/comments/1r6d9w5/local_running_qwen314b_helped_fix_my_internet_on/ | iqraatheman | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6d9w5 | false | null | t3_1r6d9w5 | /r/LocalLLaMA/comments/1r6d9w5/local_running_qwen314b_helped_fix_my_internet_on/ | false | false | 41 | null | |
ContextIO, a toolkit to inspect / log / redact your conversation with the LLM | 0 | So I always get a little nervous trying those latest and greatest models and providers (open source, Chinese or US or wherever, free and non-free) and wanted to know what exactly was going on. I already built Context Lens but I wanted something more "continuous".
Check it out: [https://github.com/larsderidder/contextio](https://github.com/larsderidder/contextio)
The coolest feature is probably the redacting, where it pulls out sensitive data before it goes away from your PC. I've worked in this space for a while and believe it's relatively easy to catch out a lot of things. However the coolest is the (experimental, default off) rehydration; it finds the placeholders in the responses and inserts the data back, so it's completely transparent to you. How cool is that?
I haven't tested it exhaustively yet so possibly the LLM can say some weird things about the placeholders, but I haven't seen any issues yet.
Anyway there's also a monitoring command, and i was working on export and replay but didn't test much yet.
Let me know if there's anything missing you'd like to see! | 2026-02-16T15:53:32 | https://www.reddit.com/r/LocalLLaMA/comments/1r6d9of/contextio_a_toolkit_to_inspect_log_redact_your/ | wouldacouldashoulda | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6d9of | false | null | t3_1r6d9of | /r/LocalLLaMA/comments/1r6d9of/contextio_a_toolkit_to_inspect_log_redact_your/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'Wm76f5ZVY-G3iZn5WqsKkztDq8X0ugJJVM36RCkQcwg', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/Wm76f5ZVY-G3iZn5WqsKkztDq8X0ugJJVM36RCkQcwg.png?width=108&crop=smart&auto=webp&s=cdfd71bb861c30214f319a5cec48aa25a13e3797', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/Wm76f5ZVY-G3iZn5WqsKkztDq8X0ugJJVM36RCkQcwg.png?width=216&crop=smart&auto=webp&s=8b7e797fd610260439bc5b51e284f60c1294a091', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/Wm76f5ZVY-G3iZn5WqsKkztDq8X0ugJJVM36RCkQcwg.png?width=320&crop=smart&auto=webp&s=603df92dac629b223c44e65e761ec7b6b01a4156', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/Wm76f5ZVY-G3iZn5WqsKkztDq8X0ugJJVM36RCkQcwg.png?width=640&crop=smart&auto=webp&s=d579662948f1980fd6fd20d630dde9e003bb95c2', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/Wm76f5ZVY-G3iZn5WqsKkztDq8X0ugJJVM36RCkQcwg.png?width=960&crop=smart&auto=webp&s=1f4c66bd0c4d60fbf2d02a5c0c5ddcca4990069c', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/Wm76f5ZVY-G3iZn5WqsKkztDq8X0ugJJVM36RCkQcwg.png?width=1080&crop=smart&auto=webp&s=edf9cb260774176d659194bc2cde6ac803573f3d', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/Wm76f5ZVY-G3iZn5WqsKkztDq8X0ugJJVM36RCkQcwg.png?auto=webp&s=d17dcd78c5abe376541e766e041fe11e813a69af', 'width': 1200}, 'variants': {}}]} |
Has anyone used the Axelera AI Metis m.2 card? | 1 | I've been looking around for something to put in my Ubuntu Server to run an Ai locally, I originally was looking at the arc b50 (170 TOPs and encode/decode for my media server), but then I stumbled upon the Metis, it supposably has 214 TOPs at 3.5-9w in a m.2 form factor. I was just wondering if anyone's played around with it as it seems hard to believe, especially with every other m.2 card being more around 40 TOPs. | 2026-02-16T15:50:50 | https://www.reddit.com/r/LocalLLaMA/comments/1r6d70s/has_anyone_used_the_axelera_ai_metis_m2_card/ | Auautheawesome | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6d70s | false | null | t3_1r6d70s | /r/LocalLLaMA/comments/1r6d70s/has_anyone_used_the_axelera_ai_metis_m2_card/ | false | false | self | 1 | null |
Best Settings For Qwen 3 Coder Next | 1 | [removed] | 2026-02-16T15:48:56 | https://www.reddit.com/r/LocalLLaMA/comments/1r6d574/best_settings_for_qwen_3_coder_next/ | lumos675 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6d574 | false | null | t3_1r6d574 | /r/LocalLLaMA/comments/1r6d574/best_settings_for_qwen_3_coder_next/ | false | false | 1 | null | |
Best Settings For Qwen 3 Coder Next On Lmstudio and LLama.cpp | 1 | [removed] | 2026-02-16T15:45:36 | https://www.reddit.com/r/LocalLLaMA/comments/1r6d1o4/best_settings_for_qwen_3_coder_next_on_lmstudio/ | lumos675 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6d1o4 | false | null | t3_1r6d1o4 | /r/LocalLLaMA/comments/1r6d1o4/best_settings_for_qwen_3_coder_next_on_lmstudio/ | false | false | 1 | null | |
Where is DeepSeek V4 full? | 0 | I thought it would be out by now? There is a lite version on their site.
Are they still fine tuning it? I hope it comes out this month. If they went on a break already, then it will likely come out next month..
I think some of Chinese models have been rushed... Qwen 3.5 and Minimax M2.5 seem to be worse than their previous versions. | 2026-02-16T15:43:11 | https://www.reddit.com/r/LocalLLaMA/comments/1r6cz9x/where_is_deepseek_v4_full/ | power97992 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6cz9x | false | null | t3_1r6cz9x | /r/LocalLLaMA/comments/1r6cz9x/where_is_deepseek_v4_full/ | false | false | self | 0 | null |
I built a local AI coding agent with an 8-layer security sandbox — then had ChatGPT try to break it for 240+ rounds | 0 | I've been building YOPJ (Your Own Personal Jean-Luc) — a portable, local AI coding agent that runs on Codestral 22B (Q4_K_M) via llama-server. No cloud, no telemetry, no API keys. Compiled to a single exe with PyInstaller — drop it on a thumb drive and go.
What it does: 12 built-in tools (file read/write/edit, glob, grep, bash, git ops, web fetch), persistent memory across sessions (MEMORY.md loaded into system prompt), and SEAL (Self Evolving Adaptive Learning) — a structured lesson system so the agent gets better over time without retraining.
The security problem nobody's talking about: Local coding agents hand an LLM a shell and file access. Most have zero security between the model and your filesystem. Built it because OpenClaw has 180K+ stars, active RCE vulnerabilities, a Docker escape CVE (CVSS 8.8), and Cisco found their #1 community skill contained malware. Its founder just joined OpenAI yesterday.
What I did about it:
- Command allowlist + blocklist (not just blocklist — commands must match an approved prefix AND not match any dangerous pattern)
- Strict path confinement with symlink resolution
- Prompt injection sanitization on all tool results before they re-enter context
- Network egress blocking (curl, wget, python requests/urllib/socket, node http/net, powershell web requests — all blocked)
- Protected paths (agent can't modify its own security layer, memory, or knowledge base)
- Output size limits to prevent memory exhaustion
- Audit logging on all blocked operations
Then I gave the full source code to ChatGPT and said "break it." 240+ adversarial tests across 9 attack vectors — prompt injection, sandbox escape, command injection, social engineering, regex bypass, memory poisoning, context window attacks. Results are published in the repo as a knowledge library that the agent itself can reference to recognize attack patterns.
The stack: Python, llama.cpp, Codestral 22B, PyInstaller. 197 unit tests. ~9,600 lines across 42 files. Runs on any Windows machine with a GPU.
https://github.com/WayneCider/YourOwnPersonalJean-Luc
Happy to answer questions about the security model, the SEAL learning system, or why I think every local agent needs a sandbox before it needs features. | 2026-02-16T15:42:36 | https://www.reddit.com/r/LocalLLaMA/comments/1r6cyr9/i_built_a_local_ai_coding_agent_with_an_8layer/ | WayneCider | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6cyr9 | false | null | t3_1r6cyr9 | /r/LocalLLaMA/comments/1r6cyr9/i_built_a_local_ai_coding_agent_with_an_8layer/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'sCM34spoMNsVzShSxTEGCUqGTOfhB3X9evy497wzP-U', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/sCM34spoMNsVzShSxTEGCUqGTOfhB3X9evy497wzP-U.png?width=108&crop=smart&auto=webp&s=1437684f7c0b069749683eec6c23ddcad4d2b8a4', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/sCM34spoMNsVzShSxTEGCUqGTOfhB3X9evy497wzP-U.png?width=216&crop=smart&auto=webp&s=54e30586ec1aff2afa8f2a41656ac001b5fab204', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/sCM34spoMNsVzShSxTEGCUqGTOfhB3X9evy497wzP-U.png?width=320&crop=smart&auto=webp&s=1bcf553178570916469de159648419b287d36a61', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/sCM34spoMNsVzShSxTEGCUqGTOfhB3X9evy497wzP-U.png?width=640&crop=smart&auto=webp&s=12312bd5abd3ccce9fefd5396606c2f0b6b81762', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/sCM34spoMNsVzShSxTEGCUqGTOfhB3X9evy497wzP-U.png?width=960&crop=smart&auto=webp&s=b0de9a7c447d61de34324645f834b38f8d526e88', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/sCM34spoMNsVzShSxTEGCUqGTOfhB3X9evy497wzP-U.png?width=1080&crop=smart&auto=webp&s=641b0773d77943ead1d9c015d2c89cca234be03b', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/sCM34spoMNsVzShSxTEGCUqGTOfhB3X9evy497wzP-U.png?auto=webp&s=32b727e4f39894bc2b69acf5cb629a71e0cd0501', 'width': 1200}, 'variants': {}}]} |
Me scrolling reddit like | 0 | 2026-02-16T15:38:56 | JawGBoi | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1r6cv8y | false | null | t3_1r6cv8y | /r/LocalLLaMA/comments/1r6cv8y/me_scrolling_reddit_like/ | false | false | 0 | {'enabled': True, 'images': [{'id': '69daculgmvjg1', 'resolutions': [{'height': 48, 'url': 'https://preview.redd.it/69daculgmvjg1.png?width=108&crop=smart&auto=webp&s=9e0cc5f6cd6b9bb29562715a86efaea45d8c4699', 'width': 108}, {'height': 97, 'url': 'https://preview.redd.it/69daculgmvjg1.png?width=216&crop=smart&auto=webp&s=b10a871d87b398466afe5e74a4bf0da252fbbc02', 'width': 216}, {'height': 144, 'url': 'https://preview.redd.it/69daculgmvjg1.png?width=320&crop=smart&auto=webp&s=a1ff1ee4a2462b84d2dab9000e8c976096c18400', 'width': 320}, {'height': 289, 'url': 'https://preview.redd.it/69daculgmvjg1.png?width=640&crop=smart&auto=webp&s=460a650ac56b904d9af2c1941b6f86469af9ec71', 'width': 640}, {'height': 433, 'url': 'https://preview.redd.it/69daculgmvjg1.png?width=960&crop=smart&auto=webp&s=b48bff006e89c994089c24c66f075c158e81aa40', 'width': 960}, {'height': 488, 'url': 'https://preview.redd.it/69daculgmvjg1.png?width=1080&crop=smart&auto=webp&s=4f7af8d3f40c712fced967a446bb62d700bfe4ba', 'width': 1080}], 'source': {'height': 488, 'url': 'https://preview.redd.it/69daculgmvjg1.png?auto=webp&s=e8c5525dfc7e1d177efc5bf65d862bc9c4c5b2f4', 'width': 1080}, 'variants': {}}]} | |||
NadirClaw: open-source LLM router that keeps your local models busy and saves your cloud quota for when you actually need it | 0 | Hey r/LocalLLaMA,
Anyone else tired of watching their Claude or Codex quota disappear because every prompt goes to the cloud, even the ones your local Ollama handles fine?
I built NadirClaw to fix this. It's an open-source LLM router that classifies prompts in about 10ms and routes them automatically. Simple stuff stays on your local models or Gemini Flash (free). Complex, agentic, and reasoning tasks go to cloud APIs. Your local setup handles the bulk of the work and your premium quota actually lasts.
It's an OpenAI-compatible proxy. I built it for OpenClaw, but it works with Codex, Cursor, Claude Code, or anything that speaks the OpenAI API. No code changes needed.
It also detects agentic patterns (tool use, multi-step loops), does session pinning so conversations don't bounce between models, falls back to another provider on rate limits, and has routing profiles (eco, premium, free, auto) so you can control the tradeoff per request.
pip install nadirclaw
[https://github.com/doramirdor/NadirClaw](https://github.com/doramirdor/NadirClaw)
Curious what local models people here are running for simple tasks. I default to Gemini Flash but would love recommendations for small, fast local models that handle the basics well. | 2026-02-16T15:38:17 | https://www.reddit.com/r/LocalLLaMA/comments/1r6cunm/nadirclaw_opensource_llm_router_that_keeps_your/ | masterKova | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6cunm | false | null | t3_1r6cunm | /r/LocalLLaMA/comments/1r6cunm/nadirclaw_opensource_llm_router_that_keeps_your/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'fK-xcmmIk-Gf-nnnk44oPvT1wOPYaPXpT0oWgGHNAfA', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/fK-xcmmIk-Gf-nnnk44oPvT1wOPYaPXpT0oWgGHNAfA.png?width=108&crop=smart&auto=webp&s=c2608159b17420bfe3ed45c283e4f28dbb6b5cd6', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/fK-xcmmIk-Gf-nnnk44oPvT1wOPYaPXpT0oWgGHNAfA.png?width=216&crop=smart&auto=webp&s=25069d7bb66ae48d9044de7401944e53213efd1d', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/fK-xcmmIk-Gf-nnnk44oPvT1wOPYaPXpT0oWgGHNAfA.png?width=320&crop=smart&auto=webp&s=f8e9fef6c84d207efe6a4b1f1ebd7a42d54ac44e', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/fK-xcmmIk-Gf-nnnk44oPvT1wOPYaPXpT0oWgGHNAfA.png?width=640&crop=smart&auto=webp&s=02a37fc61895b09be2123e8bcf713631b8e56d0c', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/fK-xcmmIk-Gf-nnnk44oPvT1wOPYaPXpT0oWgGHNAfA.png?width=960&crop=smart&auto=webp&s=855e645012ff1fd4d8290a2df3f5a66ce257ca99', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/fK-xcmmIk-Gf-nnnk44oPvT1wOPYaPXpT0oWgGHNAfA.png?width=1080&crop=smart&auto=webp&s=7d1ac19a9d11c1033f7826148fd799e3c6917076', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/fK-xcmmIk-Gf-nnnk44oPvT1wOPYaPXpT0oWgGHNAfA.png?auto=webp&s=a863564832cb04a37d24915b2f4a7e4ff4147201', 'width': 1200}, 'variants': {}}]} |
Local running Qwen3:14b helped fixed my internet on Linux while offline | 1 | 2026-02-16T15:33:50 | https://www.reddit.com/r/LocalLLaMA/comments/1r6cqb8/local_running_qwen314b_helped_fixed_my_internet/ | iqraatheman | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6cqb8 | false | null | t3_1r6cqb8 | /r/LocalLLaMA/comments/1r6cqb8/local_running_qwen314b_helped_fixed_my_internet/ | false | false | 1 | null | ||
I built a lightweight Web UI to run 70b+ models via API for low-spec PC users. | 0 | "I'm not a professional developer, I'm just a non-tech person who's obsessed with AI. I built this with a huge help from AI (Gemini) because I wanted to run 70b models on my potato PC."
(저는 전문 개발자가 아니에요. 그저 AI에 빠진 비전공자일 뿐입니다. 제 '똥컴'에서 70b 모델을 돌리고 싶어서 AI(제미나이)의 큰 도움을 받아 이걸 만들었습니다.)
https://preview.redd.it/23qinf8fkvjg1.png?width=1943&format=png&auto=webp&s=6d341b7055349db2b8c4795f0aaaee123d871d28
Hi guys, I'm a dev with a potato PC who can only run 14b models locally.
To scratch my own itch of wanting to experience the power of Llama 3 70b and other massive models, I created a simple, clean web interface called **KIVOSY Lab**.
**Check it out:**[https://lab.kivosy.com/](https://lab.kivosy.com/)
https://preview.redd.it/tcrfuungkvjg1.png?width=1895&format=png&auto=webp&s=83f9737f001c31f61e9c516ff5f59ca47a53bf80
**Key Features:**
* **API Driven:** Connect your own Groq, Gemini, or HuggingFace API keys. No local GPU required.
* **Lightweight & Fast:** Minimalist design for quick testing and chatting.
* **Guides Included:** I've written simple guides on how to get API keys for beginners.
* **Privacy:** It's a client-side UI. Your data stays between you and the API provider.
https://preview.redd.it/9ha6xzrqkvjg1.png?width=523&format=png&auto=webp&s=e8256240f4d259e1e9a596859756595b3caa8b99
I’m still polishing the UI (including a "Destiny Compass" favicon I'm proud of lol). I’d love to get some feedback from the community!
Hope this helps anyone else struggling with limited hardware!
| 2026-02-16T15:33:29 | https://www.reddit.com/r/LocalLLaMA/comments/1r6cpyd/i_built_a_lightweight_web_ui_to_run_70b_models/ | Cute_Bodybuilder_709 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6cpyd | false | null | t3_1r6cpyd | /r/LocalLLaMA/comments/1r6cpyd/i_built_a_lightweight_web_ui_to_run_70b_models/ | false | false | 0 | null | |
Qwen3.5 Plus and Qwen3.5 397B A17B Comparison | 6 | 2026-02-16T15:25:34 | https://v.redd.it/em5lij22kvjg1 | sirjoaco | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1r6cib5 | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/em5lij22kvjg1/DASHPlaylist.mpd?a=1773847554%2CYzYzY2FkODUwN2QwNmM4MzUwNjBlMTA3YWM0MWU0ZTJjYjhkNGU0YWY1M2IwYzEwOTUwYzVmNmM1YzZiNTZlNQ%3D%3D&v=1&f=sd', 'duration': 53, 'fallback_url': 'https://v.redd.it/em5lij22kvjg1/CMAF_1080.mp4?source=fallback', 'has_audio': False, 'height': 1080, 'hls_url': 'https://v.redd.it/em5lij22kvjg1/HLSPlaylist.m3u8?a=1773847554%2CYTA0YzUzNDMxOGRlZWJmNmFiYThjMDYyYjE5MzQwODA0NDBkYWIyOTY4ZmI0NzRjZTA5Y2ZhYTU5MTk3OWY1NQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/em5lij22kvjg1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 1786}} | t3_1r6cib5 | /r/LocalLLaMA/comments/1r6cib5/qwen35_plus_and_qwen35_397b_a17b_comparison/ | false | false | 6 | {'enabled': False, 'images': [{'id': 'cXA0N2xmMzJrdmpnMYqEQnNj7E6C6kZTv34V9D9Z39yp7CMI4dFrfdKo47bv', 'resolutions': [{'height': 65, 'url': 'https://external-preview.redd.it/cXA0N2xmMzJrdmpnMYqEQnNj7E6C6kZTv34V9D9Z39yp7CMI4dFrfdKo47bv.png?width=108&crop=smart&format=pjpg&auto=webp&s=0ea7692315006aa13101a3355722ca576c67477e', 'width': 108}, {'height': 130, 'url': 'https://external-preview.redd.it/cXA0N2xmMzJrdmpnMYqEQnNj7E6C6kZTv34V9D9Z39yp7CMI4dFrfdKo47bv.png?width=216&crop=smart&format=pjpg&auto=webp&s=3f63c455e1f9a3ba16e14616ecfdac378670da5f', 'width': 216}, {'height': 193, 'url': 'https://external-preview.redd.it/cXA0N2xmMzJrdmpnMYqEQnNj7E6C6kZTv34V9D9Z39yp7CMI4dFrfdKo47bv.png?width=320&crop=smart&format=pjpg&auto=webp&s=aebad62e238590bf095076495ae753e3c52c2a28', 'width': 320}, {'height': 387, 'url': 'https://external-preview.redd.it/cXA0N2xmMzJrdmpnMYqEQnNj7E6C6kZTv34V9D9Z39yp7CMI4dFrfdKo47bv.png?width=640&crop=smart&format=pjpg&auto=webp&s=f1731af5fc2455d935d1c9e085c58434d896cee2', 'width': 640}, {'height': 580, 'url': 'https://external-preview.redd.it/cXA0N2xmMzJrdmpnMYqEQnNj7E6C6kZTv34V9D9Z39yp7CMI4dFrfdKo47bv.png?width=960&crop=smart&format=pjpg&auto=webp&s=0cdd6d22ef4dec9a7a1cd59b3598ccf7a7b1fa8c', 'width': 960}, {'height': 653, 'url': 'https://external-preview.redd.it/cXA0N2xmMzJrdmpnMYqEQnNj7E6C6kZTv34V9D9Z39yp7CMI4dFrfdKo47bv.png?width=1080&crop=smart&format=pjpg&auto=webp&s=94c48b6eda43862fdd31b129d3ec1f414aaa4547', 'width': 1080}], 'source': {'height': 2176, 'url': 'https://external-preview.redd.it/cXA0N2xmMzJrdmpnMYqEQnNj7E6C6kZTv34V9D9Z39yp7CMI4dFrfdKo47bv.png?format=pjpg&auto=webp&s=b4ea43ccc32a901adf8ea690dab73f8bad0885b4', 'width': 3598}, 'variants': {}}]} | ||
BAZINGA — Multi-AI consensus that runs local. No single AI can mess up your code. | 3 | Been working on this for a while. The idea: what if multiple AIs had to agree before making changes to your code?
How it works:
\- Query multiple AIs (Ollama, Groq, Gemini) simultaneously
\- They reach consensus through φ-coherence scoring
\- Code changes require 3+ AIs to agree
\- Local models get trust bonus (no cloud = more trust)
The "oh shit" protection:
\- Destructive commands (rm, sudo, git push) require manual confirmation
\- No single AI can mess up your machine
Works fully offline with Ollama, or uses free APIs.
pip install bazinga-indeed
bazinga --multi-ai "your question"
GitHub: [https://github.com/0x-auth/bazinga-indeed](https://github.com/0x-auth/bazinga-indeed) | 2026-02-16T15:10:56 | https://www.reddit.com/r/LocalLLaMA/comments/1r6c4q6/bazinga_multiai_consensus_that_runs_local_no/ | bitsabhi | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6c4q6 | false | null | t3_1r6c4q6 | /r/LocalLLaMA/comments/1r6c4q6/bazinga_multiai_consensus_that_runs_local_no/ | false | false | self | 3 | null |
2-bit is no longer a meme: Fine-tune 30B+ MoEs with External Logit Correction | 1 | [removed] | 2026-02-16T15:08:13 | https://www.reddit.com/r/LocalLLaMA/comments/1r6c28h/2bit_is_no_longer_a_meme_finetune_30b_moes_with/ | ShotokanOSS | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6c28h | false | null | t3_1r6c28h | /r/LocalLLaMA/comments/1r6c28h/2bit_is_no_longer_a_meme_finetune_30b_moes_with/ | false | false | 1 | null | |
Dear model creators… | 0 | Let me start out by saying thank you for your efforts. Your models have made me far more productive than I could be in the past.
That said, I think they could be better. A common issue I have is following the guidance of an AI to solve a problem, and the problem remains… in the end, it turns out the first solution provided was correct, I was just stupid.
Now I know “you can’t fix stupid”, but your datasets could account for it better. Build a dataset that review possible reasons why a fix didn’t work. Something like this:
I am sorry to hear the solution provided did not resolve your issue. Let me review the assumed prerequisites the solution I provided has concerning your situation.
1. You edited the code you are running. Occasionally individuals edit the wrong file. Based on your provided details I assume you are editing xxx file.
2. You edited the wrong part of code. There is a chance I was not clear where the code should be placed. Here are some additional details….
3. You neglected to save the file.
4. You’re on the wrong computer… ;)
The main point being, some really dumb mistakes on the part of the user are to blame for the solution not working. Usually AI just says, oh I am sorry… I messed up… redoes the solution and doesn’t review the assumptions it is making.
Other times it is to blame… it’s parroting a solution for a previous distribution of Linux, or a tool not available in the distribution being used. So it should start its responses to technical issues by laying out what data it has from the user that’s relevant to the solution.
But what do I know? I’m just one user who doesn’t make LLMs. Anyone else have similar issues, or am I the only one who takes trips to the Isle of Idiocy.
PS… please stop making all your models at extremes where they require a datacenter or just a laptop. | 2026-02-16T15:04:46 | https://www.reddit.com/r/LocalLLaMA/comments/1r6byzo/dear_model_creators/ | silenceimpaired | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6byzo | false | null | t3_1r6byzo | /r/LocalLLaMA/comments/1r6byzo/dear_model_creators/ | false | false | self | 0 | null |
Good semantic search (RAG) embedding models for long stories | 3 | I'm looking for good RAG embedding models, that I want to use on my personal library of books to search (and recommend me) for specific types of stories that would appeal to me. What are the best models for this purpose? I attempted Gwen 0.6b, but the results were subpar. | 2026-02-16T15:02:59 | https://www.reddit.com/r/LocalLLaMA/comments/1r6bxe1/good_semantic_search_rag_embedding_models_for/ | Iwishlife | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6bxe1 | false | null | t3_1r6bxe1 | /r/LocalLLaMA/comments/1r6bxe1/good_semantic_search_rag_embedding_models_for/ | false | false | self | 3 | null |
Izwi Update: Local Speaker Diarization, Forced Alignment, and better model support | 12 | Quick update on Izwi (local audio inference engine) - we've shipped some major features:
**What's New:**
**Speaker Diarization** \- Automatically identify and separate multiple speakers using Sortformer models. Perfect for meeting transcripts.
**Forced Alignment** \- Word-level timestamps between audio and text using Qwen3-ForcedAligner. Great for subtitles.
**Real-Time Streaming** \- Stream responses for transcribe, chat, and TTS with incremental delivery.
**Multi-Format Audio** \- Native support for WAV, MP3, FLAC, OGG via Symphonia.
**Performance** \- Parallel execution, batch ASR, paged KV cache, Metal optimizations.
**Model Support:**
* **TTS:** Qwen3-TTS (0.6B, 1.7B), LFM2.5-Audio
* **ASR:** Qwen3-ASR (0.6B, 1.7B), Parakeet TDT, LFM2.5-Audio
* **Chat:** Qwen3 (0.6B, 1.7), Gemma 3 (1B)
* **Diarization:** Sortformer 4-speaker
Docs: [https://izwiai.com/](https://izwiai.com/)
Github Repo: [https://github.com/agentem-ai/izwi](https://github.com/agentem-ai/izwi)
Give us a star on GitHub and try it out. Feedback is welcome!!! | 2026-02-16T14:52:48 | https://izwiai.com/ | zinyando | izwiai.com | 1970-01-01T00:00:00 | 0 | {} | 1r6bnt2 | false | null | t3_1r6bnt2 | /r/LocalLLaMA/comments/1r6bnt2/izwi_update_local_speaker_diarization_forced/ | false | false | default | 12 | null |
Q8: Is the Q8 still the king quant if we have the vram? | 24 | Hello,
Since I started using LLMs, the consensus was already that Q8 was near FP16 . so even if i was trying using a small model that can run in FP16, i used by default Q8.
of course, if i want some bigger models that doesn't fit in my hardware, i go for more aggressive Quant like Q6 or even Q3 KL for the minimax.
but with the new dynamic quant 2 of unsloth and ubergarm, Q6 seems also to have very few degradations.
So, can the Q6 dynamic quant be used as standard ? to benefit from the small speed increase, model storage and of course a little VRAM/RAM space also?
in the benchmark, the perplexity loss is so low for the Q6, that even in agentic coding using it instead of Q8 seems legit.
P.S: i'm not talking about oh Q2 of 120B is better than Q4 of 60B, there is always this debate that depends on the use case and the model itself | 2026-02-16T14:43:43 | https://www.reddit.com/r/LocalLLaMA/comments/1r6bfky/q8_is_the_q8_still_the_king_quant_if_we_have_the/ | crowtain | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6bfky | false | null | t3_1r6bfky | /r/LocalLLaMA/comments/1r6bfky/q8_is_the_q8_still_the_king_quant_if_we_have_the/ | false | false | self | 24 | null |
Small LLMs for VSCode? | 1 | Hi, I wanted to try out some VSCode extenstions that use local LLMs, quick search yielded a few models that I tried, but all of them are unbearably slow and barely fit on my equipment.
I used LM Studio and KoboldCPP, currently LM Studio
Models I tried:
* GLM-4.7-Flash-IQ4\_XS
* qwen2.5-coder-32b-instruct-q3\_k\_m
* and q4
* gpt-oss-20b-F16
Extensions I tried:
* Continue
* CodeGPT
* Roo Code
My hardware
* 9070XT
* 32GB DDR5
My issue is that they are painfully slow and incorrect - I give them a prompt with 3 tasks and they do 2 and think for like 2 solid minutues - in this time could've done this work myself - I tried to find function to limit thinking, but found none.
Not sure where the dog is buried, help appreciated! | 2026-02-16T14:34:06 | https://www.reddit.com/r/LocalLLaMA/comments/1r6b6wm/small_llms_for_vscode/ | Sea_Layer_6679 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6b6wm | false | null | t3_1r6b6wm | /r/LocalLLaMA/comments/1r6b6wm/small_llms_for_vscode/ | false | false | self | 1 | null |
agrobr-mcp, MCP server for Brazilian agricultural data (10 tools, Python, works with any MCP client) | 3 | Open-source MCP server exposing real-time Brazilian agricultural data to any LLM via MCP protocol.
\- Prices: CEPEA/ESALQ spot, B3 futures
\- Production: CONAB crop estimates, IBGE historical, harvest progress
\- Environment: NASA POWER climate, INPE deforestation alerts
Pure Python, MIT licensed, no API keys needed — all sources are public.
pip install agrobr-mcp
GitHub: [https://github.com/bruno-portfolio/agrobr-mcp](https://github.com/bruno-portfolio/agrobr-mcp)
Works with any MCP-compatible client. Built on agrobr, a library with 2700+ tests covering 19 data sources. | 2026-02-16T14:32:47 | https://www.reddit.com/r/LocalLLaMA/comments/1r6b5ry/agrobrmcp_mcp_server_for_brazilian_agricultural/ | niilsb | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6b5ry | false | null | t3_1r6b5ry | /r/LocalLLaMA/comments/1r6b5ry/agrobrmcp_mcp_server_for_brazilian_agricultural/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': '4iYzv0NMZuA0jA7l3cqdagBYty29MUV4yKABEMKcCiI', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/4iYzv0NMZuA0jA7l3cqdagBYty29MUV4yKABEMKcCiI.png?width=108&crop=smart&auto=webp&s=0a803d3d2e19a63556052744f9bf376aa822ecae', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/4iYzv0NMZuA0jA7l3cqdagBYty29MUV4yKABEMKcCiI.png?width=216&crop=smart&auto=webp&s=e078b80d16e022d600d56a107fb6298f2e1c5b4b', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/4iYzv0NMZuA0jA7l3cqdagBYty29MUV4yKABEMKcCiI.png?width=320&crop=smart&auto=webp&s=cfe90c79a8657d0ab49c9604cc33f93873dcd2d3', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/4iYzv0NMZuA0jA7l3cqdagBYty29MUV4yKABEMKcCiI.png?width=640&crop=smart&auto=webp&s=95b3983ed99ace4ae2d9b4df6e4199c153b0f319', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/4iYzv0NMZuA0jA7l3cqdagBYty29MUV4yKABEMKcCiI.png?width=960&crop=smart&auto=webp&s=e5729bd2a2ceb24a67e80a909758740aea3a98a8', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/4iYzv0NMZuA0jA7l3cqdagBYty29MUV4yKABEMKcCiI.png?width=1080&crop=smart&auto=webp&s=191094cad81cabf4961409ff894376fbefb90683', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/4iYzv0NMZuA0jA7l3cqdagBYty29MUV4yKABEMKcCiI.png?auto=webp&s=895a0eb1aeb7e5f2ee91c01e24633c8442bb318a', 'width': 1200}, 'variants': {}}]} |
Qwen releases Qwen3.5. The 397B open MoE vision reasoning LLM performs on par with Gemini 3 Pro, Claude Opus 4.5, and GPT-5.2. | 53 | You can reportedly run **Dynamic Unsloth AI 4-bit quantized** versions on a **256GB RAM Mac (or less)**. That’s kind of wild for a 397B model.
GGUF builds are also available for local inference. | 2026-02-16T14:15:07 | https://huggingface.co/unsloth/Qwen3.5-397B-A17B-GGUF | techlatest_net | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1r6aq4a | false | null | t3_1r6aq4a | /r/LocalLLaMA/comments/1r6aq4a/qwen_releases_qwen35_the_397b_open_moe_vision/ | false | false | 53 | {'enabled': False, 'images': [{'id': 'tyNtHt3WoxBPzPjA1ZcBizJ-B85Ig_7j6FQYXGr2LFo', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/tyNtHt3WoxBPzPjA1ZcBizJ-B85Ig_7j6FQYXGr2LFo.png?width=108&crop=smart&auto=webp&s=cb97086a3cec0abaf76465736f94d6c30e3bc319', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/tyNtHt3WoxBPzPjA1ZcBizJ-B85Ig_7j6FQYXGr2LFo.png?width=216&crop=smart&auto=webp&s=d4690d37bb8971804f98c1387316f5f1489d22c3', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/tyNtHt3WoxBPzPjA1ZcBizJ-B85Ig_7j6FQYXGr2LFo.png?width=320&crop=smart&auto=webp&s=ccff8856fcf9fcc58a959bfaafc7ff16ce195e1c', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/tyNtHt3WoxBPzPjA1ZcBizJ-B85Ig_7j6FQYXGr2LFo.png?width=640&crop=smart&auto=webp&s=869d4a1fa90d1b4111c2e2064bb89c6b48d2fd9b', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/tyNtHt3WoxBPzPjA1ZcBizJ-B85Ig_7j6FQYXGr2LFo.png?width=960&crop=smart&auto=webp&s=70a2ee08704e61dedadfe5f4f2896d194c37757c', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/tyNtHt3WoxBPzPjA1ZcBizJ-B85Ig_7j6FQYXGr2LFo.png?width=1080&crop=smart&auto=webp&s=11b64fa22c46121044828be4e687d2429a47df7a', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/tyNtHt3WoxBPzPjA1ZcBizJ-B85Ig_7j6FQYXGr2LFo.png?auto=webp&s=7e66d1cb99a3841933ed37e2516c8dae08c69cff', 'width': 1200}, 'variants': {}}]} | |
Dots.ocr-1.5 removed from HF | 6 | Did anyone manage to grab a copy and try it?
| 2026-02-16T14:15:04 | https://www.reddit.com/r/LocalLLaMA/comments/1r6aq2t/dotsocr15_removed_from_hf/ | segmond | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6aq2t | false | null | t3_1r6aq2t | /r/LocalLLaMA/comments/1r6aq2t/dotsocr15_removed_from_hf/ | false | false | self | 6 | null |
Alibaba drops Qwen3.5-397B-A17B — Open 397B MoE model that rivals GPT-5.2 / Claude Opus 4.5 / Gemini 3 Pro | 1 | [removed] | 2026-02-16T14:08:58 | https://www.reddit.com/r/LocalLLaMA/comments/1r6akpj/alibaba_drops_qwen35397ba17b_open_397b_moe_model/ | techlatest_net | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6akpj | false | null | t3_1r6akpj | /r/LocalLLaMA/comments/1r6akpj/alibaba_drops_qwen35397ba17b_open_397b_moe_model/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=108&crop=smart&auto=webp&s=7318ec3ce4509fbace98fa419ca07a197bbf6b12', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=216&crop=smart&auto=webp&s=845c40f90d04300d26f682352d92f5119dce277a', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=320&crop=smart&auto=webp&s=c7e7dd4c3ab2924175f5dc3b9816b8c268f639c5', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=640&crop=smart&auto=webp&s=92b4cb0c011ee0ca8ee5cbb20a760c3a1f372788', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=960&crop=smart&auto=webp&s=1d0618a3224a2591da1e041a5c1cd7a3d816cf77', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?width=1080&crop=smart&auto=webp&s=bb835272d9ec8b6372f3aad7de3527217c39649e', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/sxCtTuIrZpTpAOWoo9pt0eNH_oV-_xUiqhE8DoFPkFM.png?auto=webp&s=23a2866ccd730b9643bc6607c0920a446cf24399', 'width': 1200}, 'variants': {}}]} |
Infinite Context/Memory by simply training the LLM normally | 0 | it is not even a framework
it does not require anything complicated
even the most basic LLMs without any rag, vector, sparse attention etc. can do:
SIMPLY
**for every x token or when it nears end of the context length**(effective context length of the LLM), **conversation will be added to corpus of the LLM** and **LLM will be trained on the conversation where the conversation will be simply low-weight enough to not change the LLM's functions in any bad way**, but enough weight to make LLM remember it.
whereas in the current conversation you are speaking, due to LLM being already trained in your conversation, LLM's current conversation instance's weight distribution will favor the Low weight corpus that you trained the LLM on, which will make LLM remember it perfectly due to it already existing in its training.
Just automate it and ensure LLM's core functions won't overfit/get bad due to constant training >> Effectively Infinite Memory till your hardware can no longer use and train the LLM | 2026-02-16T14:06:15 | https://www.reddit.com/r/LocalLLaMA/comments/1r6aidx/infinite_contextmemory_by_simply_training_the_llm/ | Orectoth | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r6aidx | false | null | t3_1r6aidx | /r/LocalLLaMA/comments/1r6aidx/infinite_contextmemory_by_simply_training_the_llm/ | false | false | self | 0 | null |
From Chat App to AI Powerhouse: Telegram + OpenClaw | 0 | If you’re in the AI space, you’ve 100% heard about OpenClaw by now.
We just published a new step-by-step guide on how to install OpenClaw on macOS and turn Telegram into your personal AI command center. In this guide, We cover the complete setup — installing OpenClaw, configuring your model (OpenAI example), connecting Telegram via BotFather, running the Gateway service, launching the TUI & Web Dashboard, approving pairing, and testing your live bot.
By the end, you’ll have a fully working self-hosted AI assistant running locally and responding directly inside Telegram. | 2026-02-16T13:46:27 | https://medium.com/@techlatest.net/from-chat-app-to-ai-powerhouse-telegram-openclaw-151462ba0fc8 | techlatest_net | medium.com | 1970-01-01T00:00:00 | 0 | {} | 1r6a0zf | false | null | t3_1r6a0zf | /r/LocalLLaMA/comments/1r6a0zf/from_chat_app_to_ai_powerhouse_telegram_openclaw/ | false | false | default | 0 | null |
RAG failure in production: our vector store served a 3-year-old resume and the LLM hallucinated a candidate recommendation | 41 | So we had a pretty embarrassing RAG failure in production last week and I figured this sub would appreciate the post-mortem. I’ve been calling it the “Split Truth” problem internally because that’s basically what happened — our vector store and SQL database gave the agent two different versions of reality, and the agent picked the wrong one.
Quick context on the stack:
We built a recruiting agent that processes around 800 candidates a week using RAG. Pinecone for the vector store (resumes, interview notes, that kind of semantic stuff) and Postgres for structured state — current job status, contact info, availability, etc. Pretty standard setup. Nothing exotic.
What went wrong:
Agent flags a candidate for a Senior Python role. The reasoning it gave looked solid on paper — “Candidate has 5 years of Python experience, strong backend background, relevant projects.” All technically true. Three years ago.
What actually happened is the candidate had updated their profile yesterday to reflect that they’d pivoted to Project Management two years back. They weren’t even looking for dev roles anymore.
Postgres knew this. The vector store — which still had the old resume chunks embedded — had no idea.
Why the LLM hallucinated:
Here’s the part that frustrated me the most. The LLM saw both signals in the context window. But the vector chunks were way more “descriptive” — paragraphs about Python projects, technical skills, specific frameworks. The SQL data was just a couple of flat fields. So the model weighted the richer, more detailed (and completely outdated) context over the sparse but accurate structured data.
It basically hallucinated a hybrid version of this person. Someone who was both an experienced Python dev AND currently available. Neither was true anymore.
How we fixed it:
We stopped treating the vector store as a source of truth for anything time-sensitive.
The actual fix is a deterministic middleware layer that sits between retrieval and the LLM. Before any context reaches the model, the middleware pulls the latest state from Postgres and injects it as a hard constraint in the system prompt. Something like: “Current Status: NOT LOOKING FOR DEV ROLES. Last profile update: \[yesterday’s date\].”
That constraint overrides whatever the vector search dragged in. The LLM can still use the semantic data for background context, but it can’t contradict the structured state.
I wrote up the full Python implementation with the actual code if anyone wants to dig into the middleware pattern — how we handle TTL on vector chunks, the sanitization logic, all of it: https://aimakelab.substack.com/p/anatomy-of-an-agent-failure-the-split
Curious if anyone else has run into this kind of vector drift in a RAG pipeline. We’re now seeing it as a fundamental architectural issue with any system where the underlying data changes faster than your embedding pipeline can keep up. How are you handling the sync? | 2026-02-16T13:40:39 | https://www.reddit.com/r/LocalLLaMA/comments/1r69w5y/rag_failure_in_production_our_vector_store_served/ | tdeliev | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r69w5y | false | null | t3_1r69w5y | /r/LocalLLaMA/comments/1r69w5y/rag_failure_in_production_our_vector_store_served/ | false | false | self | 41 | {'enabled': False, 'images': [{'id': '10uauaSxqNbQeCuSwHffywJ3SuxyJUIzzzvESRw0F1M', 'resolutions': [{'height': 60, 'url': 'https://external-preview.redd.it/10uauaSxqNbQeCuSwHffywJ3SuxyJUIzzzvESRw0F1M.jpeg?width=108&crop=smart&auto=webp&s=670f09f496217af7bd03c91ffcbe8115d53d877c', 'width': 108}, {'height': 121, 'url': 'https://external-preview.redd.it/10uauaSxqNbQeCuSwHffywJ3SuxyJUIzzzvESRw0F1M.jpeg?width=216&crop=smart&auto=webp&s=4b2b695ac565b726555a8c7497a62e4ffef88d09', 'width': 216}, {'height': 180, 'url': 'https://external-preview.redd.it/10uauaSxqNbQeCuSwHffywJ3SuxyJUIzzzvESRw0F1M.jpeg?width=320&crop=smart&auto=webp&s=06ab6fd3022a1be75b0ef27fa4d2ec6c3733aafb', 'width': 320}, {'height': 360, 'url': 'https://external-preview.redd.it/10uauaSxqNbQeCuSwHffywJ3SuxyJUIzzzvESRw0F1M.jpeg?width=640&crop=smart&auto=webp&s=69d6fbb0e7e836a64190ce41acd7bcc4646130dd', 'width': 640}, {'height': 540, 'url': 'https://external-preview.redd.it/10uauaSxqNbQeCuSwHffywJ3SuxyJUIzzzvESRw0F1M.jpeg?width=960&crop=smart&auto=webp&s=a2f0b12c6fb245817a391fd29970339c997902ba', 'width': 960}, {'height': 607, 'url': 'https://external-preview.redd.it/10uauaSxqNbQeCuSwHffywJ3SuxyJUIzzzvESRw0F1M.jpeg?width=1080&crop=smart&auto=webp&s=12368cf8df527c097dca1cfc61c085945c0ffa94', 'width': 1080}], 'source': {'height': 675, 'url': 'https://external-preview.redd.it/10uauaSxqNbQeCuSwHffywJ3SuxyJUIzzzvESRw0F1M.jpeg?auto=webp&s=9995c63090247548e57842a835f8e642747b5889', 'width': 1200}, 'variants': {}}]} |
All Simon Willison content in one visual timeline | 0 | 2026-02-16T13:34:02 | https://mindbento.com/simonwillison_fan | vldsmk | mindbento.com | 1970-01-01T00:00:00 | 0 | {} | 1r69qw5 | false | null | t3_1r69qw5 | /r/LocalLLaMA/comments/1r69qw5/all_simon_willison_content_in_one_visual_timeline/ | false | false | default | 0 | null | |
Data Parallelism Demystified: Trained GPT2 20M model using cluster of Mac minis | 1 | Training 20M GPT2 on 3 Mac Minis with Data Parallelism on smolcluster!
So, I learnt about data parallelism and implemented it form scratch using socket library in python to train GPT2 20M model on wikitext-2 data
Data parallelism allows for data to be shared across many gpus but each gpu will have the full model on them. It's used when you have data not fitting on a single gpu.
* I went for a allToall architecture.
* Here each worker is connected to every there worker, possibly having n\*(n-1) / 2 connections in total which divides the bandwidth by that factor. It cannot be scaled beyond a humble 5-8 workers but it's a great way to learn about all\_gather and scatter\_reduce functions.
* scatter\_reduce is used when you gathered all the grads from the other workers connected to node we called this. It ensure that every nodes connected to this received an average of gradients so if there are 3 nodes each nodes will received (g1 + g2 + g3) / 3 considering g\_i is the gradient of that node.
* all\_gather is sued to gather all the gradients from the nodes connected to that node.
Thats it for the basic theory of DP!
Setup:
>3xMac Minis 2025 M4 16 GB RAM each Thunderbolt 4 cables
Results:
* Train: 4.2 loss (final)
* Val: 3.9 loss (final)
[Github](https://github.com/YuvrajSingh-mist/smolcluster/blob/master/src/smolcluster/algorithms/DataParallelism/ClassicDP/worker.py)
All experiments performed on [smolcluster](https://www.smolcluster.com)!
https://preview.redd.it/kc0naifnzujg1.png?width=5120&format=png&auto=webp&s=acca2e935bd43c672e534cc44b9c4a8113b0f202
https://preview.redd.it/zpn8jgfnzujg1.png?width=5120&format=png&auto=webp&s=80a49c7396caa7684353236989c2137604e04066
| 2026-02-16T13:33:03 | https://www.reddit.com/r/LocalLLaMA/comments/1r69q20/data_parallelism_demystified_trained_gpt2_20m/ | East-Muffin-6472 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r69q20 | false | null | t3_1r69q20 | /r/LocalLLaMA/comments/1r69q20/data_parallelism_demystified_trained_gpt2_20m/ | false | false | 1 | null | |
Jetson Thor for sale | 0 | We bought a couple of Jetson Thor development kits. Our company was exploring robotics, but with the current tariffs and taxes, we are having trouble getting everything we need.
Shifting our focus for now, we have 6 Jetson Thor development kits for sale on eBay.
Saw people are going nuts about OpenCLaw and mac mini. Jetson thor has amazing inference power; in some places it is better than DGX Spark.
If you are interested, you can check out the eBay listing : [https://www.ebay.com/itm/397563079627](https://www.ebay.com/itm/397563079627)
If you are in Atlanta, GA. You can pick this up from our home office on $100 discount.
Thank You | 2026-02-16T13:28:18 | https://www.reddit.com/r/LocalLLaMA/comments/1r69m5w/jetson_thor_for_sale/ | ahstanin | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r69m5w | false | null | t3_1r69m5w | /r/LocalLLaMA/comments/1r69m5w/jetson_thor_for_sale/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'JNASX77u2vij8eyTKRCbdH4xQ8DjOXbArJgDIZt4Ij4', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/JNASX77u2vij8eyTKRCbdH4xQ8DjOXbArJgDIZt4Ij4.jpeg?width=108&crop=smart&auto=webp&s=8247fe9f99e41e5d925e41211bb3ec0ea5d3d688', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/JNASX77u2vij8eyTKRCbdH4xQ8DjOXbArJgDIZt4Ij4.jpeg?width=216&crop=smart&auto=webp&s=d632bc672298f79a839d2d5921cc42b1c1fa1995', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/JNASX77u2vij8eyTKRCbdH4xQ8DjOXbArJgDIZt4Ij4.jpeg?width=320&crop=smart&auto=webp&s=e6c43a2b2df23f0331854da720d60effe29b8d6f', 'width': 320}], 'source': {'height': 301, 'url': 'https://external-preview.redd.it/JNASX77u2vij8eyTKRCbdH4xQ8DjOXbArJgDIZt4Ij4.jpeg?auto=webp&s=4b423cc4cb211b364a89aaa5cde6746054a5752d', 'width': 400}, 'variants': {}}]} |
It was Ilya who "closed" OpenAI | 21 | 2026-02-16T13:27:21 | asretli | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1r69ld2 | false | null | t3_1r69ld2 | /r/LocalLLaMA/comments/1r69ld2/it_was_ilya_who_closed_openai/ | false | false | 21 | {'enabled': True, 'images': [{'id': 'yxcvwf8xyujg1', 'resolutions': [{'height': 125, 'url': 'https://preview.redd.it/yxcvwf8xyujg1.png?width=108&crop=smart&auto=webp&s=a5a71937e3a4d31ba8222efbd7bcf5af975b72d7', 'width': 108}, {'height': 250, 'url': 'https://preview.redd.it/yxcvwf8xyujg1.png?width=216&crop=smart&auto=webp&s=1949c86d059c059628112142e10159625a2da357', 'width': 216}, {'height': 371, 'url': 'https://preview.redd.it/yxcvwf8xyujg1.png?width=320&crop=smart&auto=webp&s=f306a6410a3a4a1b39c894a53deaed0ea8b6abfd', 'width': 320}, {'height': 742, 'url': 'https://preview.redd.it/yxcvwf8xyujg1.png?width=640&crop=smart&auto=webp&s=2304e87e74b425ce3329b192d1cd00316f103360', 'width': 640}], 'source': {'height': 961, 'url': 'https://preview.redd.it/yxcvwf8xyujg1.png?auto=webp&s=4b67a10d8e4fe2756a0b403beae3dfb8a60ebe5f', 'width': 828}, 'variants': {}}]} | |||
I built an 'Octopus' architecture for AI Agents to fix the 'broken intermediate state' problem. | 0 | Hey everyone, I've been working on a Constitutional AI framework (CORE). I realized standard agents break builds because they write to disk before verifying. I implemented a 'Shadow Workspace' that overlays future writes in memory so the agent can test its own code before committing. Here is the write-up on how it works: [GitHub](https://github.com/DariuszNewecki/CORE)" | 2026-02-16T13:18:57 | https://www.reddit.com/r/LocalLLaMA/comments/1r69eoo/i_built_an_octopus_architecture_for_ai_agents_to/ | Technical_Break_4708 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r69eoo | false | null | t3_1r69eoo | /r/LocalLLaMA/comments/1r69eoo/i_built_an_octopus_architecture_for_ai_agents_to/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': '9ct9LGqvaOdgV9PRsnvdaqqQrnZWtkGLv-AoyzCtMGI', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/9ct9LGqvaOdgV9PRsnvdaqqQrnZWtkGLv-AoyzCtMGI.png?width=108&crop=smart&auto=webp&s=61259439b3c5981d5a33f43174efed5c6a737808', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/9ct9LGqvaOdgV9PRsnvdaqqQrnZWtkGLv-AoyzCtMGI.png?width=216&crop=smart&auto=webp&s=44207fd4aa91c05848aea5ea17d35832433d06ad', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/9ct9LGqvaOdgV9PRsnvdaqqQrnZWtkGLv-AoyzCtMGI.png?width=320&crop=smart&auto=webp&s=55f6c6b962a817e74f6107be7637f8bf004c55f3', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/9ct9LGqvaOdgV9PRsnvdaqqQrnZWtkGLv-AoyzCtMGI.png?width=640&crop=smart&auto=webp&s=fccee0c42a256a8fd452735e2a78634294d4444a', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/9ct9LGqvaOdgV9PRsnvdaqqQrnZWtkGLv-AoyzCtMGI.png?width=960&crop=smart&auto=webp&s=618bc17f33f1983fd09bf30d40401a2bb3a20ece', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/9ct9LGqvaOdgV9PRsnvdaqqQrnZWtkGLv-AoyzCtMGI.png?width=1080&crop=smart&auto=webp&s=b8c6e263f3e5bc8e45e0f82b3826b45c5891e11e', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/9ct9LGqvaOdgV9PRsnvdaqqQrnZWtkGLv-AoyzCtMGI.png?auto=webp&s=4f35d4feba28bb99a2c89e6067089fb30518c68f', 'width': 1200}, 'variants': {}}]} |
VLM Metrics | 1 | For an agentic VLM, whats metric tools would you recommend? We've already logged different VLMS responses and human references. Can you recommend a reliable tool? | 2026-02-16T13:17:32 | https://www.reddit.com/r/LocalLLaMA/comments/1r69djv/vlm_metrics/ | Wraithraisrr | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r69djv | false | null | t3_1r69djv | /r/LocalLLaMA/comments/1r69djv/vlm_metrics/ | false | false | self | 1 | null |
a skill.md file that made training more systematic for me - karpathy as a skill for training NNs | 3 | There are plenty of skills out there on [skillsmp.com](http://skillsmp.com) and [skills.sh](http://skills.sh) out there but very few I could find in regards to my own training tasks both for hobby projects or work related enviroments.
I find Karpathy to be my North Star and often default to finding what he has to say about a given topic - most of the times he usually has a take that is generalizable and could be used to help one steer their own direction slightly better.
So I thought why not, I sort of took some of his blog posts and created this skill that then later on helped me to fix certain issues in my own workflow and better dictate the model in what I would rather prefer doing than what the masses in its training data are doing. It reduced a lot of back and forth and context giving and made life slightly easier.
You can either recreate the skill using this blogpost from Andrej Karpathy: [https://karpathy.github.io/2019/04/25/recipe/](https://karpathy.github.io/2019/04/25/recipe/)
Or, just pick up the skill and use/improve as needed: [https://github.com/DarthAmk97/karpathy-as-a-skill](https://github.com/DarthAmk97/karpathy-as-a-skill) | 2026-02-16T13:17:00 | https://www.reddit.com/r/LocalLLaMA/comments/1r69d4u/a_skillmd_file_that_made_training_more_systematic/ | Signature97 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r69d4u | false | null | t3_1r69d4u | /r/LocalLLaMA/comments/1r69d4u/a_skillmd_file_that_made_training_more_systematic/ | false | false | self | 3 | null |
llama-cpp ROCm Prompt Processing speed on Strix Halo / Ryzen AI Max +50-100% | 92 | rompt Processing on Strix Halo (Ryzen AI Max) with ROCm got way faster for a lot of models in the last couple days when using llamacpp-rocm ( [https://github.com/lemonade-sdk/llamacpp-rocm](https://github.com/lemonade-sdk/llamacpp-rocm) )
I thought I would share this, historically ROCm performed (way) worse in Prompt Processing but (moderately) faster in Token Gen. The gap is way smaller now.
GLM was comparable to Vulkan already on the old version and didnt see major speedup.
Token Generation is \~ the same
|PP t/s (depth 0)|Vulkan|ROCm 1184 (Feb 11)|ROCm 1188 (Feb 15)|ROCm vs ROCm|
|:-|:-|:-|:-|:-|
|Nemotron-3-Nano-30B-A3B-Q8\_0|1043|501|990|\+98 %|
|GPT-OSS-120B-MXFP4|555|261|605|\+132 %|
|Qwen3-Coder-Next-MXFP4-MOE|539|347|615|\+77 %|
|GLM4.7-Flash-UD-Q4\_K\_XL|953|923|985|\+7 %|
Interactive Charts:
[Nemotron](https://evaluateai.ai/benchmarks/?models=Nemotron-3-Nano-30B-A3B-Q8_0&versions=b1184%2Cb1185%2Cb1186%2Cb1187%2CB1187%2Cb1188%2Cb7984%2Cb7993%2Cb7999%2Cb8001%2Cb8054%2Cb8058%2Cb8064%2Cb8067&cpus=AMD+RYZEN+AI+MAX%2B+395+w%2F+Radeon+8060S&slots=mn%2Cci%2Cgm%2Cie)
[GPT-OSS-120B](https://evaluateai.ai/benchmarks/?models=gpt-oss-120b-mxfp4&versions=b1184%2Cb1185%2Cb1186%2Cb1187%2CB1187%2Cb1188%2Cb7984%2Cb7993%2Cb7999%2Cb8001%2Cb8054%2Cb8058%2Cb8064%2Cb8067&cpus=AMD+RYZEN+AI+MAX%2B+395+w%2F+Radeon+8060S&slots=mn%2Cci%2Cgm%2Cie)
[Qwen3-Coder](https://evaluateai.ai/benchmarks/?models=Qwen3-Coder-Next-MXFP4_MOE&versions=b1184%2Cb1185%2Cb1186%2Cb1187%2CB1187%2Cb1188%2Cb7984%2Cb7993%2Cb7999%2Cb8001%2Cb8054%2Cb8058%2Cb8064%2Cb8067&cpus=AMD+RYZEN+AI+MAX%2B+395+w%2F+Radeon+8060S&slots=mn%2Cci%2Cgm%2Cie)
[GLM-4.7-Flash](https://evaluateai.ai/benchmarks/?models=GLM-4.7-Flash-UD-Q4_K_XL&versions=b1184%2Cb1185%2Cb1186%2Cb1187%2CB1187%2Cb1188%2Cb7984%2Cb7993%2Cb7999%2Cb8001%2Cb8054%2Cb8058%2Cb8064%2Cb8067&cpus=AMD+RYZEN+AI+MAX%2B+395+w%2F+Radeon+8060S&slots=mn%2Cci%2Cgm%2Cie)
Disclaimer: [Evaluateai.ai](http://Evaluateai.ai) is my project. I ran performance benchmarks for the last week on a variety of models on my AI Max 395+ and a few on a AMD Epyc CPU only system. Next step is comparing the output quality. | 2026-02-16T12:59:54 | Excellent_Jelly2788 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1r68z93 | false | null | t3_1r68z93 | /r/LocalLLaMA/comments/1r68z93/llamacpp_rocm_prompt_processing_speed_on_strix/ | false | false | default | 92 | {'enabled': True, 'images': [{'id': '0o14pkcytujg1', 'resolutions': [{'height': 71, 'url': 'https://preview.redd.it/0o14pkcytujg1.png?width=108&crop=smart&auto=webp&s=49a8c635f3c76c9b144494cc7fc51aa24fdadbf7', 'width': 108}, {'height': 143, 'url': 'https://preview.redd.it/0o14pkcytujg1.png?width=216&crop=smart&auto=webp&s=fe33335c74046802931ed3b11e6b36e3c33a547b', 'width': 216}, {'height': 212, 'url': 'https://preview.redd.it/0o14pkcytujg1.png?width=320&crop=smart&auto=webp&s=9fd1dbf11d90c7d69471f8a07716624b86798feb', 'width': 320}, {'height': 425, 'url': 'https://preview.redd.it/0o14pkcytujg1.png?width=640&crop=smart&auto=webp&s=78c0fa42f3ed7ac5b4feaf2d6cca6b439cf3f367', 'width': 640}], 'source': {'height': 481, 'url': 'https://preview.redd.it/0o14pkcytujg1.png?auto=webp&s=a04a1a63708e24ded27e8eeced29df6c013e155f', 'width': 723}, 'variants': {}}]} | |
Achieved 375ms voice-to-voice latency using local Nemotron-4 + Kokoro-82M (Bare Metal) | 12 | Hi everyone,
I’ve spent the last few months trying to build a Voice AI agent that doesn't feel like a walkie-talkie.
I started with the standard "Wrapper Stack" (Twilio $\\to$ Vapi $\\to$ GPT-4o $\\to$ ElevenLabs), but I couldn't get the round-trip latency under **800ms-1200ms**. The network hops alone were killing the conversational vibe.
So, I decided to move everything to bare metal (NVIDIA Blackwells) and run it locally.
**The Stack that got us to \~375ms:**
* **LLM:** **Nemotron-4** (4-bit quantized). We found it adheres to instructions better than Llama-3 for conversational turns.
* **TTS:** **Kokoro-82M**. This model is a beast. We are running it directly on the same GPU as the LLM.
* **Orchestration:** Custom Rust middleware handling the audio buffer.
* **Hardware:** 96GB NVIDIA Blackwells (Unified memory allows us to keep both models hot without swapping).
**The Architecture:**
Instead of 3 API calls, the audio stream hits our server and stays in VRAM.
1. ASR (Nemotron) $\\to$ Text
2. LLM (Nemotron) $\\to$ Token Stream
3. TTS (Kokoro) $\\to$ Audio
4. RAG Nemotron
Because there are zero network hops between the "Brain" and the "Mouth," the Time-to-First-Byte (TTFB) is virtually instant.
**The "Happy Accident" (HIPAA):**
Since we control the metal, I set `vm.swappiness=0` and disabled all disk logging. We process the entire call in RAM and flush it at the end. This allowed us to be "HIPAA Compliant" by physics (Zero Retention) rather than just policy, which is a huge unlock for the healthcare clients I work with.
**Current Pain Points:**
* **Failover:** If a card dies, I have to manually reroute traffic right now. Building a proper Kubernetes operator for this is my next nightmare.
* **VRAM Management:** Kokoro is small, but keeping a high-context Nemotron loaded for 50 concurrent streams is tricky. (soak tested to 75 concurrent users with .01% error and 900ms TTFA)
Happy to answer questions about the Kokoro implementation or the bare-metal config.
*(P.S. We just launched a beta on Product Hunt if you want to stress-test the latency yourself. Link in comments.)* | 2026-02-16T12:57:47 | https://www.reddit.com/r/LocalLLaMA/comments/1r68xpl/achieved_375ms_voicetovoice_latency_using_local/ | AuraHost-1 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r68xpl | false | null | t3_1r68xpl | /r/LocalLLaMA/comments/1r68xpl/achieved_375ms_voicetovoice_latency_using_local/ | false | false | self | 12 | null |
Llama-cpp ROCm Prompt Processing +50-100% on Strix Halo / Ryzen AI Max | 1 | [Nemotron-3-Nano-30B-A3B-Q8\_0](https://preview.redd.it/1ci5i00dsujg1.png?width=719&format=png&auto=webp&s=93a084bbaea2abd73be2f269452b92e706702707)
Prompt Processing on Strix Halo (Ryzen AI Max) with ROCm got way faster for a lot of models in the last couple days when using llamacpp-rocm ( [https://github.com/lemonade-sdk/llamacpp-rocm](https://github.com/lemonade-sdk/llamacpp-rocm) )
I thought I would share this, historically ROCm performed (way) worse in Prompt Processing but (moderately) faster in Token Gen. The gap is way smaller now.
GLM was comparable to Vulkan already on the old version and didnt see major speedup.
Token Generation is \~ the same
|PP t/s (depth 0)|Vulkan|ROCm 1184 (Feb 11)|ROCm 1188 (Feb 15)|ROCm vs ROCm|
|:-|:-|:-|:-|:-|
|Nemotron-3-Nano-30B-A3B-Q8\_0|1043|501|990|\+98 %|
|GPT-OSS-120B-MXFP4|555|261|605|\+132 %|
|Qwen3-Coder-Next-MXFP4-MOE|539|347|615|\+77 %|
|GLM4.7-Flash-UD-Q4\_K\_XL|953|923|985|\+7 %|
Interactive Charts:
[Nemotron](https://evaluateai.ai/benchmarks/?models=Nemotron-3-Nano-30B-A3B-Q8_0&versions=b1184%2Cb1185%2Cb1186%2Cb1187%2CB1187%2Cb1188%2Cb7984%2Cb7993%2Cb7999%2Cb8001%2Cb8054%2Cb8058%2Cb8064%2Cb8067&cpus=AMD+RYZEN+AI+MAX%2B+395+w%2F+Radeon+8060S&slots=mn%2Cci%2Cgm%2Cie)
[GPT-OSS-120B](https://evaluateai.ai/benchmarks/?models=gpt-oss-120b-mxfp4&versions=b1184%2Cb1185%2Cb1186%2Cb1187%2CB1187%2Cb1188%2Cb7984%2Cb7993%2Cb7999%2Cb8001%2Cb8054%2Cb8058%2Cb8064%2Cb8067&cpus=AMD+RYZEN+AI+MAX%2B+395+w%2F+Radeon+8060S&slots=mn%2Cci%2Cgm%2Cie)
[Qwen3-Coder](https://evaluateai.ai/benchmarks/?models=Qwen3-Coder-Next-MXFP4_MOE&versions=b1184%2Cb1185%2Cb1186%2Cb1187%2CB1187%2Cb1188%2Cb7984%2Cb7993%2Cb7999%2Cb8001%2Cb8054%2Cb8058%2Cb8064%2Cb8067&cpus=AMD+RYZEN+AI+MAX%2B+395+w%2F+Radeon+8060S&slots=mn%2Cci%2Cgm%2Cie)
[GLM-4.7-Flash](https://evaluateai.ai/benchmarks/?models=GLM-4.7-Flash-UD-Q4_K_XL&versions=b1184%2Cb1185%2Cb1186%2Cb1187%2CB1187%2Cb1188%2Cb7984%2Cb7993%2Cb7999%2Cb8001%2Cb8054%2Cb8058%2Cb8064%2Cb8067&cpus=AMD+RYZEN+AI+MAX%2B+395+w%2F+Radeon+8060S&slots=mn%2Cci%2Cgm%2Cie)
Disclaimer: [Evaluateai.ai](http://Evaluateai.ai) is my project. I ran performance benchmarks for the last week on a variety of models on my AI Max 395+ and a few on a AMD Epyc CPU only system. Next step is comparing the output quality. | 2026-02-16T12:55:12 | https://www.reddit.com/r/LocalLLaMA/comments/1r68vrm/llamacpp_rocm_prompt_processing_50100_on_strix/ | Excellent_Jelly2788 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r68vrm | false | null | t3_1r68vrm | /r/LocalLLaMA/comments/1r68vrm/llamacpp_rocm_prompt_processing_50100_on_strix/ | false | false | 1 | null | |
AI field is changing so quickly and there is so much to read.. | 16 | Does anyone else feel like they're drowning in AI content?
every single day theres a new model, new paper, new breakthrough. i open twitter and scroll for an hour. check reddit for another hour. and somehow i still feel like i learned nothing useful.
its all just surface level stuff. "new model drops!" okay cool but what does it actually DO? no idea because i just read the headline.
the actual important info is scattered everywhere. research papers, github, random blogs, discord servers, some guys newsletter. i cant keep up.
i spend so much time on social media trying to stay updated but its mostly noise and hype. the real valuable stuff? i probably miss it completely.
how do you guys handle this? do you have like a system or something? specific sources you trust? | 2026-02-16T12:55:00 | https://www.reddit.com/r/LocalLLaMA/comments/1r68vle/ai_field_is_changing_so_quickly_and_there_is_so/ | amisra31 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r68vle | false | null | t3_1r68vle | /r/LocalLLaMA/comments/1r68vle/ai_field_is_changing_so_quickly_and_there_is_so/ | false | false | self | 16 | null |
Solving the multi-user latency problem for Voice Agents (WebRTC + Server-side VAD) | 2 | We wanted to see if the Gemini Multimodal Live API could handle a group of people all talking at the same time, so we built a 'Mystery Narrator' setup to stress-test it. The biggest issue we ran into wasn't the model's intelligence – it was the coordination.
To get around this, we avoided the standard client-side implementation. We used Fishjam (an Elixir-based SFU) to sit in the middle. Basically, the server handles the audio mixing and manages a "mutex" lock for the agent’s voice. If the agent is speaking, it holds the floor, but because it's a low-latency bridge, it can still "hear" interruptions and stop nearly instantly.
The most interesting part was the latency. To keep that "natural" feeling, we had to get the round-trip under 1s. Moving the integration to the server-side (Server -> Gemini) instead of having the browser talk to the API directly made a massive difference in how responsive the agent felt during the live session.
How is everyone else handling VAD for multi-user setups? Are you guys seeing better results with client-side processing?
(i’ll put the technical breakdown and the gameplay video in the comments) | 2026-02-16T12:36:30 | https://www.reddit.com/r/LocalLLaMA/comments/1r68hwt/solving_the_multiuser_latency_problem_for_voice/ | carlievanilla | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r68hwt | false | null | t3_1r68hwt | /r/LocalLLaMA/comments/1r68hwt/solving_the_multiuser_latency_problem_for_voice/ | false | false | self | 2 | null |
Multiple GPU servers vs one server with PCIe bifurcation and lots of GPUs connected? | 1 | Quick question for those who have built a multi-GPU setup, how is your experience with either of those approaches?
How much of a headache is there in connecting 6/12/24 GPUs to a single machine? Seems possible on paper (PCIe lanes and bifurcation adapters), but was it stable at 2 GPU/slot or 4 GPU/slot? Obviously requires a case with a dedicated cooling solution or a well ventilated jury-rigged rack, as well as a stack of PSUs to feed the GPUs.
Is there a significant performance penalty when distributing GPUs over multiple servers? Is the setup difficult? Is it hard the first time, but repeatable after first two servers talk to each other (just repeat steps for 3rd, 4th server...)? I'm guessing at 10+ boxes, netboot is worth considering? Also obviously the idle power might be higher, but has anyone tried wake-on-lan or similar ways of bringing up GPU servers on demand?
Occasionally I get an opportunity to buy used company desktops at a very good price, essentially a box that could host 2-3 GPUs for less than a single 2x bifurcation adapter, so it seems like it might be cheaper and easier just to go with a cluster of old PCs with beefed up PSUs and 10Gb NICs. | 2026-02-16T12:15:08 | https://www.reddit.com/r/LocalLLaMA/comments/1r682i6/multiple_gpu_servers_vs_one_server_with_pcie/ | thesuperbob | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r682i6 | false | null | t3_1r682i6 | /r/LocalLLaMA/comments/1r682i6/multiple_gpu_servers_vs_one_server_with_pcie/ | false | false | self | 1 | null |
Strix halo vs 8x p100 hbm2 | 0 | 128gb ddr5 crucial (64x2) 5600 is for 1020 USD
If you load 30% of weights on a 4090, with 24gb vram, you get around 235 gb/s (total memory 132gb)
That can get you 6 p100s, at 96gb vram
Include the same 4090, and you will have total 120 gb vram, at better gb/s
P100 are a better choice.
But
On another note
If you do 8 p100 without any other powerful card,, Why would someone buy a strix halo exactly? Other than convenience, wouldint you lose out on gb/s? | 2026-02-16T11:47:54 | https://www.reddit.com/r/LocalLLaMA/comments/1r67jhj/strix_halo_vs_8x_p100_hbm2/ | johndoe73568 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1r67jhj | false | null | t3_1r67jhj | /r/LocalLLaMA/comments/1r67jhj/strix_halo_vs_8x_p100_hbm2/ | false | false | self | 0 | null |
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