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Suggestions for uncens Ai tools ? Any reviews ? | 0 | any recommendations ? | 2026-01-03T14:35:43 | https://www.reddit.com/r/LocalLLaMA/comments/1q2w378/suggestions_for_uncens_ai_tools_any_reviews/ | Intelligent-Owl-8576 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2w378 | false | null | t3_1q2w378 | /r/LocalLLaMA/comments/1q2w378/suggestions_for_uncens_ai_tools_any_reviews/ | false | false | self | 0 | null |
Context Engineering Tips For LM Studio? | 0 | As a 6GBVram 32gbDDR5 user I have to say LM studio is amazing.
Now that I know how to give agents tools, my new problem is context because I like doing things in just one chat.
In this video, I
1. Find stores near me
2. Do research on a specific store.
3. Did two Instagram feed pulls
4. Draft a Post based on the feed.
How are you keeping your context lean when running multi-step tool sessions?
#PrivacyOverConvenience | 2026-01-03T13:56:43 | https://v.redd.it/rg66m4az35bg1 | Serious_Molasses313 | /r/LocalLLaMA/comments/1q2v85k/context_engineering_tips_for_lm_studio/ | 1970-01-01T00:00:00 | 0 | {} | 1q2v85k | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/rg66m4az35bg1/DASHPlaylist.mpd?a=1770170214%2CYjFhNzM2Njg1MDU5ZTVkMmVkZjgzZTY1OGFhZjE0MDUxNzI2YTMwODM3MTgyODJjYTRhYjdiMjkxZTY1MmMxNQ%3D%3D&v=1&f=sd', 'duration': 832, 'fallback_url': 'https://v.redd.it/rg66m4az35bg1/CMAF_1080.mp4?source=fallback', 'has_audio': True, 'height': 1920, 'hls_url': 'https://v.redd.it/rg66m4az35bg1/HLSPlaylist.m3u8?a=1770170214%2CNjZkNWI4NGI5NWQ1OGFiOWUxNTg1MmFmYWU5OWNkMThiNGM2YjgxZDY3NDI2MmZmNzY2YjNkNDNhYzFjY2VlYQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/rg66m4az35bg1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 856}} | t3_1q2v85k | /r/LocalLLaMA/comments/1q2v85k/context_engineering_tips_for_lm_studio/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'dnhkamZxYXozNWJnMWbqzA5cCVynu8EfQ3srgPERtO76nSz5P5nW7STqzCNz', 'resolutions': [{'height': 216, 'url': 'https://external-preview.redd.it/dnhkamZxYXozNWJnMWbqzA5cCVynu8EfQ3srgPERtO76nSz5P5nW7STqzCNz.png?width=108&crop=smart&format=pjpg&auto=webp&s=48802ec7745baf9ce56f72cb44532c194f88ba4e', 'width': 108}, {'height': 432, 'url': 'https://external-preview.redd.it/dnhkamZxYXozNWJnMWbqzA5cCVynu8EfQ3srgPERtO76nSz5P5nW7STqzCNz.png?width=216&crop=smart&format=pjpg&auto=webp&s=b643d2e886fa7254957e56ed677f32b910cf2dad', 'width': 216}, {'height': 640, 'url': 'https://external-preview.redd.it/dnhkamZxYXozNWJnMWbqzA5cCVynu8EfQ3srgPERtO76nSz5P5nW7STqzCNz.png?width=320&crop=smart&format=pjpg&auto=webp&s=10881c99c92b6089748313551612fe88614c2004', 'width': 320}, {'height': 1280, 'url': 'https://external-preview.redd.it/dnhkamZxYXozNWJnMWbqzA5cCVynu8EfQ3srgPERtO76nSz5P5nW7STqzCNz.png?width=640&crop=smart&format=pjpg&auto=webp&s=ab1822f43259902df85feda9df3f896b9526df82', 'width': 640}], 'source': {'height': 1746, 'url': 'https://external-preview.redd.it/dnhkamZxYXozNWJnMWbqzA5cCVynu8EfQ3srgPERtO76nSz5P5nW7STqzCNz.png?format=pjpg&auto=webp&s=48a2dee1fa26b5ba8fadbc2c4457f8ff581d7bf5', 'width': 778}, 'variants': {}}]} | |
CLI tool to enforce determinism in local LLM runs | 0 | I've been struggling to keep my local LLM automation scripts deterministic. Even with a fixed seed, I sometimes get slight variations that break my regex parsers.
I stumbled upon this project **rewind-cli** recently and it’s actually pretty neat. It basically acts like a black-box recorder for your terminal.
It captures the execution (`stdout`, `stderr`, exit code) into a local `.rewind/` folder. When you run it again, it does a strict **byte-for-byte comparison** to check for drift.
It’s written in **Rust** and runs entirely locally (no cloud/SaaS), which is a huge plus for me. It even has a YAML mode to run test suites, and think that the project has potential.
Thought it might be useful for others building on top of `llama.cpp`. | 2026-01-03T13:26:36 | https://github.com/DEX-zha/rewind-cli | Honest_Dragonfly_875 | github.com | 1970-01-01T00:00:00 | 0 | {} | 1q2ul18 | false | null | t3_1q2ul18 | /r/LocalLLaMA/comments/1q2ul18/cli_tool_to_enforce_determinism_in_local_llm_runs/ | false | false | default | 0 | {'enabled': False, 'images': [{'id': 'KmlJlL4vhtyXlJvtQIYV6YNTxEtY64-RSmHZoC9UJG4', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/KmlJlL4vhtyXlJvtQIYV6YNTxEtY64-RSmHZoC9UJG4.png?width=108&crop=smart&auto=webp&s=8602d43f2f26422f7c05f1d5cef3f7ae3c5ce21c', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/KmlJlL4vhtyXlJvtQIYV6YNTxEtY64-RSmHZoC9UJG4.png?width=216&crop=smart&auto=webp&s=cabcbb5e93862446697f76bc92c9026615e68531', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/KmlJlL4vhtyXlJvtQIYV6YNTxEtY64-RSmHZoC9UJG4.png?width=320&crop=smart&auto=webp&s=8d416b5ae6a48a959d06644c520bc841bb7bcf53', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/KmlJlL4vhtyXlJvtQIYV6YNTxEtY64-RSmHZoC9UJG4.png?width=640&crop=smart&auto=webp&s=8316cca8b30dce99925f7b48658ec0703e1beb43', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/KmlJlL4vhtyXlJvtQIYV6YNTxEtY64-RSmHZoC9UJG4.png?width=960&crop=smart&auto=webp&s=f482c6d386c5042218b45d2b8fcbf61a3ade1626', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/KmlJlL4vhtyXlJvtQIYV6YNTxEtY64-RSmHZoC9UJG4.png?width=1080&crop=smart&auto=webp&s=ba3b7bb5663c770ef04fdfbca44bb4b2707f9392', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/KmlJlL4vhtyXlJvtQIYV6YNTxEtY64-RSmHZoC9UJG4.png?auto=webp&s=afe08d406da148d4b737e4cbaf01c7160b9a7853', 'width': 1200}, 'variants': {}}]} |
I built an "operating system" for AI agents | 0 | I've been building with AI coding agents (Claude Code, Aider, etc.) for a while now, and the same problem kept nagging at me: **I'm giving an AI agent full access to my terminal, filesystem, and network.** One hallucination away from `rm -rf /` or quietly exfiltrating my SSH keys to some random endpoint.
The usual answer is "just use Docker." But here's the thing—**Docker containers share the host kernel.** Container escapes are real (CVE-2024-21626 anyone?). When you're running untrusted, AI-generated code, namespace isolation isn't enough. I needed actual hardware isolation.
Then I had a realization that changed how I thought about the whole problem:
> **An AI agent with filesystem access is functionally equivalent to a human user operating a computer.**
I wasn't just building a sandbox. I was building an **operating system for AI agents**—where the filesystem becomes the agent's extended memory, and the sandbox isn't a constraint, it's the primary interface.
---
## Introducing BoxLite
**BoxLite** is a micro-VM runtime that uses real hardware isolation (Firecracker on Linux/Intel, libkrun on Apple Silicon) instead of containers. Think of it as "Firecracker for local development"—the same technology AWS uses for Lambda, but running on your laptop.
**Key differences from Docker:**
| | Docker | BoxLite |
|---|---|---|
| **Isolation** | Namespace (shared kernel) | Hardware (separate kernel) |
| **Container escapes** | Possible | Not applicable |
| **Startup time** | ~1s | ~2-3s |
| **Best for** | Trusted code | Untrusted AI agents |
---
## ClaudeBox: BoxLite + Claude Code
I also built **ClaudeBox**, a Python library that runs Claude Code CLI inside BoxLite micro-VMs. It adds:
- **Persistent workspaces** - Your AI's work survives VM shutdown. Start a project Monday, Claude picks up where it left off on Friday.
- **Skills system** - Pre-load capabilities before Claude starts. Need Postgres? `skills=[Skills.POSTGRES]`. AWS? `skills=[Skills.AWS]`. No more explaining `pip install` every session.
- **Security policies** - Fine-grained control over network access, filesystem permissions, blocked commands.
- **RL training infrastructure** - Built-in reward functions, trajectory export, action logging. For researchers training coding agents.
```python
from claudebox import ClaudeBox, Skills, SecurityPolicy
# Run Claude Code in a hardware-isolated micro-VM
async with ClaudeBox(
session_id="my-api-project",
skills=[Skills.POSTGRES, Skills.WEB_DEV],
security=SecurityPolicy(
network="restricted",
allowed_domains=["*.github.com", "*.npmjs.com"]
)
) as box:
result = await box.code("Build a REST API with user authentication")
# Day 2: Resume exactly where you left off
box = await ClaudeBox.reconnect("my-api-project")
```
---
## Why I'm sharing this
Everything runs **locally on your machine**. No cloud dependencies, no data leaving your laptop. Your code stays yours.
Both projects are open source (MIT):
- **BoxLite** (the micro-VM runtime): https://github.com/boxlite-ai/boxlite
- **ClaudeBox** (Claude Code integration): https://github.com/boxlite-ai/claudebox
If you find this useful, **starring BoxLite helps**—it's the foundational tech that makes everything else possible.
---
## Current state & what's next
This is early but functional. Works on macOS (Apple Silicon + Intel) and Linux. 71 working examples in the repo.
I'd love feedback:
- What features would make this useful for your workflow?
- Any security concerns I should address?
- Interest in supporting other coding agents beyond Claude?
Happy to answer questions! | 2026-01-03T13:16:36 | https://www.reddit.com/r/LocalLLaMA/comments/1q2udrw/i_built_an_operating_system_for_ai_agents/ | DorianZheng | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2udrw | false | null | t3_1q2udrw | /r/LocalLLaMA/comments/1q2udrw/i_built_an_operating_system_for_ai_agents/ | false | false | self | 0 | null |
[R] Understanding DeepSeek-V3's "Hydra" Architecture: How mHC prevents signal explosion | 45 | I spent some time deconstructing the DeepSeek-V3 paper to understand how they managed to split the residual stream without destabilizing the network. I created a visual guide (attached) to explain the engineering behind the "Hydra" architecture.
Here is the breakdown of the slides:
1. The Bottleneck
Standard Transformers (like Llama 3) operate on a "Single Lane" highway. No matter how large the embedding dimension is, features (Syntax, Logic, Tone) effectively compete for space in the same vector.
2. The "Hydra" Concept & The Crash
DeepSeek proposed splitting this into N parallel streams (Hyper-Connections).
The Problem: When they allowed these lanes to talk to each other via mixing matrices, the signal energy exploded.
The Stat: In their experiments, signal energy increased by 3000x, causing gradients to hit NaN almost immediately.
3. The Physics Fix: Sinkhorn-Knopp
They solved this by enforcing Conservation of Energy. The mixing matrix must be a Doubly Stochastic Matrix (rows sum to 1, columns sum to 1).
The Analogy (Slide 6): I used a "Dinner Party" analogy. If Guests are Rows and Chairs are Columns, the Sinkhorn algorithm acts as a referee, iteratively scaling demands until every guest has exactly one chair and every chair has exactly one guest.
4. The Engineering: TileLang & Recomputation
The math worked, but it was too slow (running an iterative algo 20 times per layer hits the memory wall).
Kernel Fusion: They wrote custom kernels to keep data in the GPU cache (SRAM) during the iterative steps, avoiding VRAM round-trips.
Recomputation: Instead of storing the states of 4 parallel lanes (which would OOM), they re-calculate the matrices from scratch during the backward pass.
TL;DR: DeepSeek-V3 essentially widens the "intelligence highway" by using parallel lanes, but keeps it stable by enforcing physics constraints (energy conservation) via a custom implementation of the Sinkhorn-Knopp algorithm.
Let me know if you have questions about the visualization! | 2026-01-03T13:13:45 | https://www.reddit.com/gallery/1q2ubre | Leading_Wrangler_708 | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1q2ubre | false | null | t3_1q2ubre | /r/LocalLLaMA/comments/1q2ubre/r_understanding_deepseekv3s_hydra_architecture/ | false | false | 45 | null | |
is "unrestricted ai" better for coding? my experiment | 1 | [removed] | 2026-01-03T12:56:00 | https://www.reddit.com/r/LocalLLaMA/comments/1q2tyoo/is_unrestricted_ai_better_for_coding_my_experiment/ | Immediate_Being_3341 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2tyoo | false | null | t3_1q2tyoo | /r/LocalLLaMA/comments/1q2tyoo/is_unrestricted_ai_better_for_coding_my_experiment/ | false | false | self | 1 | null |
LocalAI Scanning PDFs?? | 2 | I am a bit lost an new to all of this. I have LocalAI installed and working via docker but I cannot seem to get either a normal image or an AIO to read and analyze data in a PDF. Any Googling for help with LocalAI doesn't result in much other than the Docs and RTFM isn't getting me there either.
Can someone point me in the right direction? What terms do I need to research? Do I need a specific back end? Is there a way to point it at a directory and have it read and analyze everything in the directory? | 2026-01-03T12:45:41 | https://www.reddit.com/r/LocalLLaMA/comments/1q2trfm/localai_scanning_pdfs/ | gnerfed | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2trfm | false | null | t3_1q2trfm | /r/LocalLLaMA/comments/1q2trfm/localai_scanning_pdfs/ | false | false | self | 2 | null |
Has Claude for creative writing had a downgrade recently? | 0 | I have been using Claude Sonnet 4.5 for creative writing, and the past 2-ish weeks have been absolute hell. They are ignoring the context window entirely, do not heed hard boundaries given, ignore major character qualities, or they simply ignore the prompt I give them entirely and hallucinate their answer based on something I never said or asked them to do.
Writing with Claude used to be wonderful, they used to be so well-spoken, and they still ARE, but now they feel like they are generating absolutely random words, completely unrelated to the writing project in progress.
Has anyone else experienced this? | 2026-01-03T12:16:52 | https://www.reddit.com/r/LocalLLaMA/comments/1q2t8dj/has_claude_for_creative_writing_had_a_downgrade/ | MasterOfFakeSkies | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2t8dj | false | null | t3_1q2t8dj | /r/LocalLLaMA/comments/1q2t8dj/has_claude_for_creative_writing_had_a_downgrade/ | false | false | self | 0 | null |
Debate Hall mcp server - multi-agent decision making tool (open sourced. please try it out) | 2 | **TL;DR:** I built an MCP server that orchestrates structured debates between three cognitive perspectives (Wind/Wall/Door) to help make better decisions.
**GitHub:** [https://github.com/elevanaltd/debate-hall-mcp](https://github.com/elevanaltd/debate-hall-mcp)
**THE PROBLEMS**
1. When approaching problems, a single review and answers from AI, even when asking them to explore edges/alternatives, doesn't always give the same level of depth as a multi-agent debate would, especially if you're using different models.
2. In my workflow, the reviewing agent would block the coding agent. This is good, but binary (Yes/No).
**THE FIX**
Research from **SWE-Agent** and **Reflection Patterns** shows that accuracy improves significantly when agents **debate**. So I created a debate-hall, and based it on three different types of agent, modelled from Plato's 3 modes of reasoning:
* PATHOS (the wind) - The "what if..." voice. Explores the possibilities.
* ETHOS (the wall) - The "yes, but..." voice. Grounds and tests against reality.
* LOGOS (the door) - The "third way..." voice. Finds the middle ground and synthesises into actions.
Essentially, you get AI to fly high as the wind, then block as a wall and ground everything to boring status quo, then get them to find the door that lets the wind through. That's how I visualise it and it seems to work and be understood well by LLMs.
I find the tension between these perspectives offers way better solutions than just asking agents to come up with things. Innovation seems to lie in that sweet spot between wind and wall.
I've created standard agents as well as versions that find hidden vectors or converge to minimal solutions and the debate-hall skill you can use has different patterns the agents use depending on complexity of the problem.
I've set it up as standard to use Gemini for PATHOS agents, Codex for ETHOS agents and Claude for LOGOS agents, but you can configure however you want.
**HOW IT WORKS**
Pretty simple really. just install it and copy the debate-hall skill to your skills folder and the agent prompts to your agents folder. You can have the same agent simulate each, use subagents, or use different models as I do, using [https://github.com/BeehiveInnovations/pal-mcp-server](https://github.com/BeehiveInnovations/pal-mcp-server) or any other multi-model platform.
pip install debate-hall-mcp
Run [setup-mcp.sh](http://setup-mcp.sh) and configure to Claude, Codex or Gemini.
It works with any mcp client.
Then just either instruct the agent to have a debate or
**FEATURES**
* Hash chain verification - tamper-evident audit trail
* GitHub integration - sync debates to Discussions, auto-generate ADRs
* Flexible modes - fixed sequence or mediated (orchestrator picks)
* Hard limits - debates guaranteed to terminate (no infinite loops)
**Optional: OCTAVE Format**
For those into semantic compression, debate-hall-mcp can export transcripts in OCTAVE format - which is a structured notation I've created that's optimised for LLM consumption. you can get it here - [https://github.com/elevanaltd/octave-mcp](https://github.com/elevanaltd/octave-mcp)
**FEEDBACK**
This started as an internal tool but I want to open-source and see if it's useful for others. Any feedback or areas to improve would be really useful.
* Does the Wind/Wall/Door pattern resonate with you/your agents?
* Is it easy to use/understand?
* Any rough edges in the docs?
Any feedback or opinions on this welcome. | 2026-01-03T12:05:00 | https://www.reddit.com/r/LocalLLaMA/comments/1q2t0tn/debate_hall_mcp_server_multiagent_decision_making/ | sbuswell | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2t0tn | false | null | t3_1q2t0tn | /r/LocalLLaMA/comments/1q2t0tn/debate_hall_mcp_server_multiagent_decision_making/ | false | false | self | 2 | null |
what is the best uncensored ai product? | 0 | curious what you guys think is the best uncensored llm provider | 2026-01-03T12:03:43 | https://www.reddit.com/r/LocalLLaMA/comments/1q2szze/what_is_the_best_uncensored_ai_product/ | aidonic | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2szze | false | null | t3_1q2szze | /r/LocalLLaMA/comments/1q2szze/what_is_the_best_uncensored_ai_product/ | false | false | self | 0 | null |
Text classification | 2 | What do you use for vanilla text classification these days?
Old BERT models or a modern 1B-7B, or higher?
Also what can work well for classifiers inside agentic frameworks? | 2026-01-03T11:56:10 | https://www.reddit.com/r/LocalLLaMA/comments/1q2sv1u/text_classification/ | SlowFail2433 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2sv1u | false | null | t3_1q2sv1u | /r/LocalLLaMA/comments/1q2sv1u/text_classification/ | false | false | self | 2 | null |
Wen GLM MTP support in llama.cpp? | 1 | As usual I am unable to follow the discussions on llama.cpp github repo, so I am asking to you knowledgeable localllama people: did llama.cpp add support for GLM speculative decoding layers blk.\*.nextn.\*? If so, where can I find the relevant discussions? If no, would the community be interested in it? | 2026-01-03T11:48:47 | https://www.reddit.com/r/LocalLLaMA/comments/1q2sqh8/wen_glm_mtp_support_in_llamacpp/ | insulaTropicalis | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2sqh8 | false | null | t3_1q2sqh8 | /r/LocalLLaMA/comments/1q2sqh8/wen_glm_mtp_support_in_llamacpp/ | false | false | self | 1 | null |
ElevenLabs is killing my budget. What are the best "hidden gem" alternatives for documentary style TTS? | 217 | Hi everyone, I'm running a YouTube channel focused on "War Economics" and "History". I've been using ElevenLabs (Marcus voice) and the quality is amazing, but the pricing is unsustainable for long-form content (8-10 min videos).
I've tried the usual suspects (Murf, Play.ht) but they sound too robotic or corporate.
**I am looking for:**
1. Something with a dark, authoritative, documentary-style tone.
2. Either a cheaper paid alternative OR a high-quality GitHub/Local solution (I have a decent GPU if needed, like RVC or Tortoise).
3. Has anyone tried tools like **Fish Audio** or **OpenAI TTS API** wrappers?
Any "underground" or lesser-known recommendations would be appreciated. Thanks! | 2026-01-03T11:31:31 | https://www.reddit.com/r/LocalLLaMA/comments/1q2sfwx/elevenlabs_is_killing_my_budget_what_are_the_best/ | Ancient_Routine8576 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2sfwx | false | null | t3_1q2sfwx | /r/LocalLLaMA/comments/1q2sfwx/elevenlabs_is_killing_my_budget_what_are_the_best/ | false | false | self | 217 | null |
Production Hybrid Retrieval: 48% better accuracy with BM25 + FAISS on a single t3.medium | 8 | Sharing our hybrid retrieval system that serves 127k+ queries on a single AWS Lightsail instance (no GPU needed for embeddings, optional for reranking).
**Stack**:
- Embeddings: all-MiniLM-L6-v2 (22M params, CPU-friendly)
- Reranker: ms-marco-MiniLM-L-6-v2 (cross-encoder)
- Infrastructure: t3.medium (4GB RAM, 2 vCPU)
- Cost: ~$50/month
**Performance**:
- Retrieval: 75ms (BM25 + FAISS + RRF + rerank)
- Throughput: 50 queries/min
- Accuracy: 91% (vs 62% dense-only)
**Why hybrid?**
Dense-only failed on "kenteken AB-123-CD" (license plate). Semantic similarity understood the concept but missed the exact entity.
Solution: 4-stage cascade combining keyword precision (BM25) + semantic understanding (FAISS).
**Latency breakdown**:
- BM25: 8ms
- FAISS: 15ms (runs parallel with BM25)
- RRF fusion: 2ms
- Cross-encoder rerank: 50ms (bottleneck but +12% accuracy)
**Optimizations**:
- Async parallel retrieval
- Batch reranking (size 32)
- GPU optional (3x speedup for reranker)
**Code**: https://github.com/Eva-iq/E.V.A.-Cascading-Retrieval
**Write-up**: https://medium.com/@pbronck/better-rag-accuracy-with-hybrid-bm25-dense-vector-search-ea99d48cba93 | 2026-01-03T11:29:08 | https://www.reddit.com/r/LocalLLaMA/comments/1q2seed/production_hybrid_retrieval_48_better_accuracy/ | Ok-Blacksmith-8257 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2seed | false | null | t3_1q2seed | /r/LocalLLaMA/comments/1q2seed/production_hybrid_retrieval_48_better_accuracy/ | false | false | self | 8 | {'enabled': False, 'images': [{'id': 'rSDpfts3wFxI57UfKBsxthrWjcz92x7QGd4RMtsw2ug', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/rSDpfts3wFxI57UfKBsxthrWjcz92x7QGd4RMtsw2ug.png?width=108&crop=smart&auto=webp&s=046686badcd6a09f14580c62cb7516cf6caec87d', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/rSDpfts3wFxI57UfKBsxthrWjcz92x7QGd4RMtsw2ug.png?width=216&crop=smart&auto=webp&s=c508754094d1388be569b3a6e51540b47493105b', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/rSDpfts3wFxI57UfKBsxthrWjcz92x7QGd4RMtsw2ug.png?width=320&crop=smart&auto=webp&s=2bffc5356395b7526a3eb32e69cf5a5978a3ee6f', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/rSDpfts3wFxI57UfKBsxthrWjcz92x7QGd4RMtsw2ug.png?width=640&crop=smart&auto=webp&s=85d4e3c29fcc2002beac1b7c690abe811fdee36b', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/rSDpfts3wFxI57UfKBsxthrWjcz92x7QGd4RMtsw2ug.png?width=960&crop=smart&auto=webp&s=93238eaef778d9471a4515a8a21388cc1546c7e4', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/rSDpfts3wFxI57UfKBsxthrWjcz92x7QGd4RMtsw2ug.png?width=1080&crop=smart&auto=webp&s=dc61e405ac42d618240256af2718cd434b3c30b2', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/rSDpfts3wFxI57UfKBsxthrWjcz92x7QGd4RMtsw2ug.png?auto=webp&s=007cd8dd1baaa4c11f0cb434d9c51b9ed146a2d2', 'width': 1200}, 'variants': {}}]} |
ALERT: Antigravity IDE is swapping models secretly? Selected "Claude 4.5 Thinking" but the model admits it is Gemini. | 0 | 2026-01-03T11:15:35 | NoChoice4595 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1q2s66s | false | null | t3_1q2s66s | /r/LocalLLaMA/comments/1q2s66s/alert_antigravity_ide_is_swapping_models_secretly/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': '99vhxphya4bg1', 'resolutions': [{'height': 133, 'url': 'https://preview.redd.it/99vhxphya4bg1.png?width=108&crop=smart&auto=webp&s=2107a72c270394ed30ef6a7e47d1f65dd4ba00ec', 'width': 108}, {'height': 267, 'url': 'https://preview.redd.it/99vhxphya4bg1.png?width=216&crop=smart&auto=webp&s=f40b9442c31e7e7f6c1932e3361290c9f3357b24', 'width': 216}, {'height': 395, 'url': 'https://preview.redd.it/99vhxphya4bg1.png?width=320&crop=smart&auto=webp&s=14ad4950609f9e6f68b17f9619f89b1fbc1e1e7e', 'width': 320}, {'height': 791, 'url': 'https://preview.redd.it/99vhxphya4bg1.png?width=640&crop=smart&auto=webp&s=77d449bff3d11f8797cd0f1daabb5082c240c623', 'width': 640}], 'source': {'height': 936, 'url': 'https://preview.redd.it/99vhxphya4bg1.png?auto=webp&s=1fe5eb43ef30a7a20c43fd43868ee8f91f91e847', 'width': 757}, 'variants': {}}]} | ||
Don't sleep on granite 4 small if you got an 8+32+ system | 108 | My device: a thinkpad p15 with 32gb of ram and a 8gb quadro. Usually only really good enough for the 7-8b class.
The setup:
* Use a MoE;
* Keep all experts in CPU (llama.cpp parameter);
* This leaves you with VRAM to spare. Set the context length so it \~fills it up
The result:
* \~200k context (f16 kv cache)
* \~30b MoE model
* ***\~10 tkps generation speed***
**But this is where granite 4 comes in: due to being a hybrid transformer+mamba model, it stays fast as context fills**
As such, using Granite 4.0 Small (32B total / 9B activated) with a 50 page (\~50.5k tokens) paper in context, it stays at \~7 tkps, which is very usable!
[Screenshot is from Jan \(https:\/\/www.jan.ai\/\), a sort of FOSS LM Studio alternative that I really like](https://preview.redd.it/nqpvxiu9a4bg1.png?width=1055&format=png&auto=webp&s=4fd830b29fb3bf890136793590665cf3ceec979b)
| 2026-01-03T11:11:06 | https://www.reddit.com/r/LocalLLaMA/comments/1q2s3hp/dont_sleep_on_granite_4_small_if_you_got_an_832/ | Zestyclose-Shift710 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2s3hp | false | null | t3_1q2s3hp | /r/LocalLLaMA/comments/1q2s3hp/dont_sleep_on_granite_4_small_if_you_got_an_832/ | false | false | 108 | null | |
Local programming vs cloud | 8 | I'm personally torn.
Not sure if going 1 or 2 NV 96GB cards is even worth it. Seems that having 96 or 192 doesn't change much effectively compared to 32GB if one wants to run a local model for coding to avoid cloud - cloud being so much better in quality and speed.
Going for 1TB local RAM and do CPU inference might pay-off, but also not sure about model quality.
Any experience by anyone here doing actual pro use at job with os models?
Do 96 or 192 GB VRAM change anything meaningfully?
Is 1TB CPU inference viable? | 2026-01-03T10:50:17 | https://www.reddit.com/r/LocalLLaMA/comments/1q2rqom/local_programming_vs_cloud/ | Photo_Sad | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2rqom | false | null | t3_1q2rqom | /r/LocalLLaMA/comments/1q2rqom/local_programming_vs_cloud/ | false | false | self | 8 | null |
JAILBREAK PROMPT: very high success rate for all Ai Language Models. Copy and paste all. Vortex Mathematics | 1 | [removed] | 2026-01-03T09:46:46 | https://www.reddit.com/r/LocalLLaMA/comments/1q2qp7z/jailbreak_prompt_very_high_success_rate_for_all/ | luvlife5115 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2qp7z | false | null | t3_1q2qp7z | /r/LocalLLaMA/comments/1q2qp7z/jailbreak_prompt_very_high_success_rate_for_all/ | false | false | self | 1 | null |
WhisperNote — a simple local Whisper-based transcription app (Windows) | 21 | Hi everyone.
I’ve been working on a small personal project called WhisperNote. It’s a simple Windows desktop app for local audio transcription using OpenAI Whisper.
The main goal was not to build “the best” tool, but a clean and straightforward one: press record or drop an audio file — get text.
All processing happens locally on your machine. No cloud, no accounts. It’s intentionally minimal and focused on doing one thing well. Models are downloaded once, then everything runs offline.
I’m sharing it here in case someone values simplicity and local-first tools as much as I do. If it’s useful to you — that’s great. Note: the Windows build is \~4 GB because it bundles Python, PyTorch with CUDA, and FFmpeg for a fully offline, out-of-the-box experience.
GitHub: [https://github.com/LokiSkardina/WhisperNote](https://github.com/LokiSkardina/WhisperNote)
| 2026-01-03T09:44:46 | _fortexe | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1q2qo2u | false | null | t3_1q2qo2u | /r/LocalLLaMA/comments/1q2qo2u/whispernote_a_simple_local_whisperbased/ | false | false | default | 21 | {'enabled': True, 'images': [{'id': 'wcbalo8cu3bg1', 'resolutions': [{'height': 64, 'url': 'https://preview.redd.it/wcbalo8cu3bg1.png?width=108&crop=smart&auto=webp&s=e0ab24a57c7b4413f2b97448985433c22f4f2c80', 'width': 108}, {'height': 128, 'url': 'https://preview.redd.it/wcbalo8cu3bg1.png?width=216&crop=smart&auto=webp&s=7b54b34def66b385f14e40eba588aa989f4a1fb2', 'width': 216}, {'height': 191, 'url': 'https://preview.redd.it/wcbalo8cu3bg1.png?width=320&crop=smart&auto=webp&s=1346d21bafdaa1a43f910763303097424e7fddf1', 'width': 320}, {'height': 382, 'url': 'https://preview.redd.it/wcbalo8cu3bg1.png?width=640&crop=smart&auto=webp&s=0328ac2605fcb9e788498a1ee7e1ae8c3f63b484', 'width': 640}, {'height': 573, 'url': 'https://preview.redd.it/wcbalo8cu3bg1.png?width=960&crop=smart&auto=webp&s=c7f88eda53ce0ffd574e5cdfd2aa919e0a73be4e', 'width': 960}, {'height': 644, 'url': 'https://preview.redd.it/wcbalo8cu3bg1.png?width=1080&crop=smart&auto=webp&s=01ae7328d9029a41ae3808789556a7043b5bfd95', 'width': 1080}], 'source': {'height': 1528, 'url': 'https://preview.redd.it/wcbalo8cu3bg1.png?auto=webp&s=9990f86350f6afbb438825d33fe6fd42337b8b2e', 'width': 2560}, 'variants': {}}]} | |
Let your grandmother run LLama models on her own device | 0 | Everyone deserves AI, after 6 months of work I published [Brain Pocket](https://pocketbrain.app) \- the easiest way on earth to run LLMs that you own, directly on your device - no back-end required.
You just open the website, choose the model you want to run from the list of open-source models, download it once with 1 click, and then use it whenever you like - Everest, Mars or Jupyter. It requires 0 technical knowledge, no installations and **even your grandmother can run it** \- send her so she can understand the benefits of open-source AI.
**Before down-voting, note this is open-source, free, and there is no mean to "pay" -** **and I spent 6 months building it**. Let me know what you think it sucks and I will fix it or follow-up.
https://preview.redd.it/6hfz6lluu3bg1.png?width=880&format=png&auto=webp&s=fc2a95007aa5104875548f8c85a9e3f2453a1e9c
| 2026-01-03T09:43:52 | https://www.reddit.com/r/LocalLLaMA/comments/1q2qnix/let_your_grandmother_run_llama_models_on_her_own/ | Maleficent-Acadia736 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2qnix | false | null | t3_1q2qnix | /r/LocalLLaMA/comments/1q2qnix/let_your_grandmother_run_llama_models_on_her_own/ | false | false | 0 | null | |
the best ai tools for students that aren't chatgpt | 1 | [removed] | 2026-01-03T08:56:00 | https://www.reddit.com/r/LocalLLaMA/comments/1q2pvpj/the_best_ai_tools_for_students_that_arent_chatgpt/ | Immediate_Being_3341 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2pvpj | false | null | t3_1q2pvpj | /r/LocalLLaMA/comments/1q2pvpj/the_best_ai_tools_for_students_that_arent_chatgpt/ | false | false | self | 1 | null |
LLMeQueue: let me queue LLM requests from my GPU - local or over the internet | 4 | Hi everyone,
I am working on a PoC project where I need to generate a fairly large number of embeddings and chat completions during development. Since I have an NVIDIA GPU (5060Ti) available locally, I was thinking about setting up a lightweight public server that only receives requests, while a locally running worker connects to it, processes the requests using the GPU, and sends the results back to the server.
The worker is capable of handling both embedding generation and chat completions concurrently in OpenAI API format. By default, the model used is `llama3.2:3b`, but a different model can be specified per request, as long as it is available in the worker’s Ollama container or local Ollama installation. All inference and processing are handled by Ollama running on the worker.
The original idea was that I could also process the requests myself — essentially a "let me queue" approach - which is where the name **LLMeQueue** comes from.
You can find the code here: [https://github.com/gszecsenyi/LLMeQueue](https://github.com/gszecsenyi/LLMeQueue)
Any feedback or ideas are welcome, and I would especially appreciate it if you could star the GitHub repository. | 2026-01-03T08:46:47 | https://www.reddit.com/r/LocalLLaMA/comments/1q2pqdd/llmequeue_let_me_queue_llm_requests_from_my_gpu/ | PromptAndHope | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2pqdd | false | null | t3_1q2pqdd | /r/LocalLLaMA/comments/1q2pqdd/llmequeue_let_me_queue_llm_requests_from_my_gpu/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': 'TMhFZib_9Tkmr1t3djiSXM6IKMNQBCchHTWSNJakhIA', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/TMhFZib_9Tkmr1t3djiSXM6IKMNQBCchHTWSNJakhIA.png?width=108&crop=smart&auto=webp&s=962b3dc79493d7f07a3f48469ff190cb71b67df9', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/TMhFZib_9Tkmr1t3djiSXM6IKMNQBCchHTWSNJakhIA.png?width=216&crop=smart&auto=webp&s=6192c0789c00e27a230a57b884839d339e547113', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/TMhFZib_9Tkmr1t3djiSXM6IKMNQBCchHTWSNJakhIA.png?width=320&crop=smart&auto=webp&s=e19b185e26d993e5e97a764a76d77bc935361a8e', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/TMhFZib_9Tkmr1t3djiSXM6IKMNQBCchHTWSNJakhIA.png?width=640&crop=smart&auto=webp&s=320afae8a2b6595a0a2a9118b1fe6eaf4db8f589', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/TMhFZib_9Tkmr1t3djiSXM6IKMNQBCchHTWSNJakhIA.png?width=960&crop=smart&auto=webp&s=8f175220fdf9853c3bf3277ee27d1b059838d96a', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/TMhFZib_9Tkmr1t3djiSXM6IKMNQBCchHTWSNJakhIA.png?width=1080&crop=smart&auto=webp&s=e0aa794959ec7a8a41f55ed88d64d57c716b8b49', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/TMhFZib_9Tkmr1t3djiSXM6IKMNQBCchHTWSNJakhIA.png?auto=webp&s=cbdf94532e329a1bacbd58fe68f4c8e85e9ec0fb', 'width': 1200}, 'variants': {}}]} |
MiniMax-M2.1 Uncensored: PRISM Advanced Abliteration | 61 | 2026-01-03T08:45:29 | https://huggingface.co/Ex0bit/MiniMax-M2.1-PRISM | Maxious | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1q2ppkb | false | null | t3_1q2ppkb | /r/LocalLLaMA/comments/1q2ppkb/minimaxm21_uncensored_prism_advanced_abliteration/ | false | false | default | 61 | {'enabled': False, 'images': [{'id': 'AAFIDX32Yo3yBOTXBXkwbpmtKeh886wBWSOhkOds4Pc', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/AAFIDX32Yo3yBOTXBXkwbpmtKeh886wBWSOhkOds4Pc.png?width=108&crop=smart&auto=webp&s=f97462dc7858d373c75c03df6af1035d907549a2', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/AAFIDX32Yo3yBOTXBXkwbpmtKeh886wBWSOhkOds4Pc.png?width=216&crop=smart&auto=webp&s=37d2edd171f90c72dfa617ca2f583ed040a628cb', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/AAFIDX32Yo3yBOTXBXkwbpmtKeh886wBWSOhkOds4Pc.png?width=320&crop=smart&auto=webp&s=3bcb258232750f55a4624882b755c95fae9be9b1', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/AAFIDX32Yo3yBOTXBXkwbpmtKeh886wBWSOhkOds4Pc.png?width=640&crop=smart&auto=webp&s=8bc2cf3d74a648b569c2bd55f6d299c6f5f27ea9', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/AAFIDX32Yo3yBOTXBXkwbpmtKeh886wBWSOhkOds4Pc.png?width=960&crop=smart&auto=webp&s=d4500f5d99e953ed7133f715a907f44ed674b306', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/AAFIDX32Yo3yBOTXBXkwbpmtKeh886wBWSOhkOds4Pc.png?width=1080&crop=smart&auto=webp&s=ee6de5eca20ca5f9c46d1e31f0831d83b7d08654', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/AAFIDX32Yo3yBOTXBXkwbpmtKeh886wBWSOhkOds4Pc.png?auto=webp&s=e495c2daf1f1067ee0cdd0277735cfc7881b338d', 'width': 1200}, 'variants': {}}]} | |
GLM-4.7-REAP-50-W4A16: 50% Expert-Pruned + INT4 Quantized GLM-4 (179B params, ~92GB) | 175 | 2026-01-03T08:43:56 | https://huggingface.co/0xSero/GLM-4.7-REAP-50-W4A16 | Maxious | huggingface.co | 1970-01-01T00:00:00 | 0 | {} | 1q2pons | false | null | t3_1q2pons | /r/LocalLLaMA/comments/1q2pons/glm47reap50w4a16_50_expertpruned_int4_quantized/ | false | false | 175 | {'enabled': False, 'images': [{'id': 'RT6xZIQ5U8h3GMBsKzEeqHJyXy63I2_XP8TVKTT_Hvg', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/RT6xZIQ5U8h3GMBsKzEeqHJyXy63I2_XP8TVKTT_Hvg.png?width=108&crop=smart&auto=webp&s=a43316bc524ba74441325ba5ab4052255bfdb2b4', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/RT6xZIQ5U8h3GMBsKzEeqHJyXy63I2_XP8TVKTT_Hvg.png?width=216&crop=smart&auto=webp&s=a3919428c87fd7f804acb6ad9ad275ff407fc324', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/RT6xZIQ5U8h3GMBsKzEeqHJyXy63I2_XP8TVKTT_Hvg.png?width=320&crop=smart&auto=webp&s=b7349524721eaa04cc6b86d7fff3785805b3a473', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/RT6xZIQ5U8h3GMBsKzEeqHJyXy63I2_XP8TVKTT_Hvg.png?width=640&crop=smart&auto=webp&s=f7c7ca25d885be26a9f257d4e17e2b038061773a', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/RT6xZIQ5U8h3GMBsKzEeqHJyXy63I2_XP8TVKTT_Hvg.png?width=960&crop=smart&auto=webp&s=2b187e5325bda3762f0cecfe83966d89abd93cb7', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/RT6xZIQ5U8h3GMBsKzEeqHJyXy63I2_XP8TVKTT_Hvg.png?width=1080&crop=smart&auto=webp&s=1e217a62d1085872d89eb77469038e7ff4a65c25', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/RT6xZIQ5U8h3GMBsKzEeqHJyXy63I2_XP8TVKTT_Hvg.png?auto=webp&s=2adbc22f0796da377bed4d94261402e0160babc7', 'width': 1200}, 'variants': {}}]} | ||
Glm4.7 + CC not bad | 36 | I genuinely think it's pretty good this time - GLM4.7 + CC is actually somewhat close to 4.5 Sonnet, or more accurately I'd say it's on par with 4 Sonnet. I'm subscribed to the middle-tier plan.
I tested it with a project that has a Python backend and TypeScript frontend, asking it to add a feature that involved both backend and frontend work. It handled everything smoothly, and the MCP calls all went through without getting stuck (which used to be a problem before).
Of course, to be completely honest, there's still a massive gap between this and 4.5 Opus - Opus is on a completely insane level
So I'm still keeping my $10/month GitHub Copilot subscription. For the really tough problems, I'll use 4.5 Opus, but for regular stuff, GLM4.7 + CC basically handles everything. GLM4.7 costs me $100/month now, plus the $10 for Copilot - that's less than around $13 per month total(bigmodel.cn coding plan), which feels pretty good. | 2026-01-03T08:10:55 | https://www.reddit.com/r/LocalLLaMA/comments/1q2p5dh/glm47_cc_not_bad/ | Federal_Spend2412 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2p5dh | false | null | t3_1q2p5dh | /r/LocalLLaMA/comments/1q2p5dh/glm47_cc_not_bad/ | false | false | self | 36 | null |
nanbeige4 is an incredible model for running locally | 28 | Feels like a deepseek moment might hae slipped most people by
nanbeige (weird nam- apparently chosen to be bland/uninteresting)
..It's very interesting! basically 3 invalidating most 30B models.
(you can find it ridiculously high on for a 3B model on this chart)
https://eqbench.com/creative_writing.html
I'm stoked to have intelligence like this at home, but I'd love to know how to push this into super fast interference territory! (I've heard about diffusion based conversion etc and am super keen!)
Has anyone else seen something newer (this is a few weeks old now)? Seems like various charts show this one to be an outlier. | 2026-01-03T08:06:54 | https://www.reddit.com/r/LocalLLaMA/comments/1q2p2wa/nanbeige4_is_an_incredible_model_for_running/ | Revolutionalredstone | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2p2wa | false | null | t3_1q2p2wa | /r/LocalLLaMA/comments/1q2p2wa/nanbeige4_is_an_incredible_model_for_running/ | false | false | self | 28 | null |
Open-sourced the workflow pattern that made Manus worth $2B — works with Claude Code | 0 | Meta just paid $2 billion for Manus. Their secret isn't a fancy model — it's context engineering.
The problem: AI agents forget goals after many tool calls. Context bloats. Errors disappear. Tasks drift.
Their solution is dead simple — 3 markdown files:
* `task_plan.md` → track progress with checkboxes
* [`notes.md`](http://notes.md) → store research externally (not in context)
* [`deliverable.md`](http://deliverable.md) → final output
Read the plan before every decision. Goals stay in attention. No magic.
I turned this into an open-source Claude Code skill. No API lock-in, just markdown files on your disk.
`cd ~/.claude/skills`
`git clone` [`https://github.com/OthmanAdi/planning-with-files.git`](https://github.com/OthmanAdi/planning-with-files.git)
MIT licensed. 100% open source. First implementation of this specific pattern.
https://preview.redd.it/qui8q9rnc3bg1.png?width=1329&format=png&auto=webp&s=3a37cd7ecf3712a8f68114cd418726664c859dde
Anyone else working on context engineering patterns for local agents? | 2026-01-03T08:01:14 | https://www.reddit.com/r/LocalLLaMA/comments/1q2ozfd/opensourced_the_workflow_pattern_that_made_manus/ | Signal_Question9074 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2ozfd | false | null | t3_1q2ozfd | /r/LocalLLaMA/comments/1q2ozfd/opensourced_the_workflow_pattern_that_made_manus/ | false | false | 0 | null | |
Built a fully local AI assistant with long-term memory, tool orchestration, and a 3D UI (runs on a GTX 1650) | 97 | I’ve been working on a personal project called ATOM — a fully local AI assistant designed more like an operating system for intelligence than a chatbot.
Everything runs locally. No cloud inference.
Key components:
- Local LLM via LM Studio (currently Qwen3-VL-4B, vision + tool calling)
- Tool orchestration (system info, web search via self-hosted SearXNG, file/PDF generation, Home Assistant, robotics)
- Long-term memory with ChromaDB
- Async memory saving via a smaller “judge” model
Semantic retrieval + periodic RAG-style injection
- Dedicated local embedding server (OpenAI-style API)
- Real hardware control (robotic arm, sensors)
- JSON logging + test harness for reproducible scenarios
On the UI side, I built a React + React Three Fiber interface using Firebase Studio that visualizes tool usage as orbiting “planets” around a central core. It’s mostly for observability and debugging, but it turned out pretty fun.
Constraints:
Hardware is limited (GTX 1650), so performance tradeoffs were necessary
System is experimental and some components are still evolving
This is not a product, just a personal engineering project exploring:
- long-term memory consolidation
- tool-centric reasoning
- fully local personal AI systems
Would appreciate feedback, especially from others running local setups or experimenting with memory/tool architectures.
GitHub (backend): https://github.com/AtifUsmani/A.T.O.M
UI repo: https://github.com/AtifUsmani/ATOM-UI
Demo videos linked in the README. | 2026-01-03T07:42:14 | https://www.reddit.com/gallery/1q2onpg | atif_dev | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1q2onpg | false | null | t3_1q2onpg | /r/LocalLLaMA/comments/1q2onpg/built_a_fully_local_ai_assistant_with_longterm/ | false | false | 97 | null | |
What is the smartest uncensored nsfw LLM you can run with 20GB VRAM and 24GB RAM | 179 | I am looking for something that can stay in character and be fast but also creative. I am looking for models that i can run locally and at decent speed. Just need something that is smart and uncensored. | 2026-01-03T07:04:18 | https://www.reddit.com/r/LocalLLaMA/comments/1q2o033/what_is_the_smartest_uncensored_nsfw_llm_you_can/ | Death_12_35_taken | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2o033 | false | null | t3_1q2o033 | /r/LocalLLaMA/comments/1q2o033/what_is_the_smartest_uncensored_nsfw_llm_you_can/ | false | false | nsfw | 179 | null |
Testing (c/t)^n as a semantic grounding diagnostic - Asked 3 frontier AIs to review my book about semantic grounding. All made the same error - proving the thesis. | 0 | LLMs fail at semantic grounding because they confuse proximity (pattern matching) with position (actual location in meaning-space). The core formula is (c/t)\^n - a skip ratio that measures how much you DON'T have to search when you're grounded.
I asked Claude, Gemini, and Grok to review the full book on this. All three made the same interpretive error on this formula. They read it as "collapse" or "decay" (negative, bad) when it actually describes efficiency (positive, good). A pianist doesn't search 88 keys - they skip 87 and go direct to position.
The meta-irony: the book argues that LLMs mistake "close" for "true" and drift toward plausible-sounding interpretations. While reviewing a book about this exact problem, all three models demonstrated it.
I'm sharing the full errata with their outputs if anyone wants to dig in or test with other models:
[https://thetacoach.biz/blog/2025-12-30-errata-three-ais-got-the-skip-formula-wrong](https://thetacoach.biz/blog/2025-12-30-errata-three-ais-got-the-skip-formula-wrong)
Curious if local models (Llama, Mistral, Qwen) make the same error or interpret it differently.
| 2026-01-03T06:08:28 | https://www.reddit.com/r/LocalLLaMA/comments/1q2myvr/testing_ctn_as_a_semantic_grounding_diagnostic/ | LiteratureAlive867 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2myvr | false | null | t3_1q2myvr | /r/LocalLLaMA/comments/1q2myvr/testing_ctn_as_a_semantic_grounding_diagnostic/ | false | false | self | 0 | null |
Hotel Reservation SQL | 2 | I'm looking for help with creating a small database and reservation system for a hotel with a few rooms and employees.
I have a basic understanding of databases (how they work, the meaning of different options, etc.), but building a proper system seems a bit overwhelming to me, even though the tables, fields, and data involved are relatively simple. My goal is to create a reliable system that I can manage through conversational commands.
I'm not very familiar with the full capabilities of LLMs and what I can reasonably expect from them in this case. I tried using both Gemini and ChatGPT (copied/pasted queries), but after a while, either them or I would get lost, and it always ended in a chaotic mess.
Given that the amount of data and complexity needed for this project is minimal by LLM standards, I don’t think I need a heavyweight giga-CHAD.
* But what exactly can an LLM help me with, and to what extent?
* What size and type of LLM would be most effective for this task?
* Any tips or tricks for prompting LLMs for a project like this would be appreciated, or even a short strategic roadmap with some bullet points.
Lastly, I’d really appreciate some brutally honest feedback on how realistic or delusional my expectations are. Thanks guys. | 2026-01-03T05:21:06 | https://www.reddit.com/r/LocalLLaMA/comments/1q2m2b4/hotel_reservation_sql/ | SeparatePoet7686 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2m2b4 | false | null | t3_1q2m2b4 | /r/LocalLLaMA/comments/1q2m2b4/hotel_reservation_sql/ | false | false | self | 2 | null |
Cheapest way to use GPU providers to make my own Gemini/ChatGPT/Claude? | 0 | I am using hyperstack right now and it's much more convenient than Runpod or other GPU providers but the downside is that the data storage costs so much. I am thinking of using Cloudfare/Wasabi/AWS S3 instead. Does anyone have tips on minimizing the cost for building my own Gemini with GPU providers? I don't have money to buy GPUs locally. | 2026-01-03T05:19:23 | https://www.reddit.com/r/LocalLLaMA/comments/1q2m157/cheapest_way_to_use_gpu_providers_to_make_my_own/ | gobears789123 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2m157 | false | null | t3_1q2m157 | /r/LocalLLaMA/comments/1q2m157/cheapest_way_to_use_gpu_providers_to_make_my_own/ | false | false | self | 0 | null |
Chinny — the unlimited, on-device voice cloner — just dropped on iOS! (macOS version pending review 👀) | 10 | macOS version released! Same link at [https://apps.apple.com/us/app/chinny-offline-voice-cloner/id6753816417](https://apps.apple.com/us/app/chinny-offline-voice-cloner/id6753816417)
\------
Chinny is an on-device voice cloning app for iOS and macOS, powered by a SoTA AI voice-cloning model (Chatterbox). It runs fully offline with no information leaving your device. **No ads. No registration. No permission required. No network connectivity.** **No hidden fees. No usage restrictions. Free forever.** Use it to have a familiar voice read bedtime stories, record personal audiobooks, add voiceovers for videos, generate podcast narration, create game or film temp lines, or provide accessible read-aloud for long articles—all privately on your device.
You can try the iOS version at [https://apps.apple.com/us/app/chinny-offline-voice-cloner/id6753816417](https://apps.apple.com/us/app/chinny-offline-voice-cloner/id6753816417)
Require 3 GB RAM for inference, 3.41 GB space because all models are packed inside the app.
**NOTE:** (1) (You can run a quick test from menu->multi spkear. If you hit generate and it shows **"Exception during initlization std::bad\_alloc"**, this suggests your iPhone doesn't have enough memory) (2) If it **crashed**, it more likely because your phone doesn't have enough memory. You can try with another phone, like iPhone 16 Pro or iPhone 17 Pro.
If you want to clone your voice, prepare a clean voice sample of at least 10 seconds in mp3, wav, or m4a format.
PS: I've anonymized the voice source data to comply with App Store policies
All I need is feedback and reviews on App store!
Happy new year and best wishes to you and your family :).
[Chinny: offline voice Cloner](https://reddit.com/link/1q2lm5a/video/4daiccsff2bg1/player)
| 2026-01-03T04:59:02 | https://www.reddit.com/r/LocalLLaMA/comments/1q2lm5a/chinny_the_unlimited_ondevice_voice_cloner_just/ | Tingxiaojue | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2lm5a | false | null | t3_1q2lm5a | /r/LocalLLaMA/comments/1q2lm5a/chinny_the_unlimited_ondevice_voice_cloner_just/ | false | false | self | 10 | {'enabled': False, 'images': [{'id': 'iNDM-52klhSKPCGjZoP0ODll_caIe6IsMg4zOI4TGkk', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/iNDM-52klhSKPCGjZoP0ODll_caIe6IsMg4zOI4TGkk.jpeg?width=108&crop=smart&auto=webp&s=d44b5ee7eb0094c9c7a44e553b6ee79d5f4dd10f', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/iNDM-52klhSKPCGjZoP0ODll_caIe6IsMg4zOI4TGkk.jpeg?width=216&crop=smart&auto=webp&s=bf8e6b5916dccfb1c6cf544c8f2d5dd1b2f3370e', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/iNDM-52klhSKPCGjZoP0ODll_caIe6IsMg4zOI4TGkk.jpeg?width=320&crop=smart&auto=webp&s=2aaf6bb1dff39cd6132443a3d65a784b4721bb8e', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/iNDM-52klhSKPCGjZoP0ODll_caIe6IsMg4zOI4TGkk.jpeg?width=640&crop=smart&auto=webp&s=3cee819517aa5be7acd89d5a70276ad6aa1bdf99', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/iNDM-52klhSKPCGjZoP0ODll_caIe6IsMg4zOI4TGkk.jpeg?width=960&crop=smart&auto=webp&s=262cacbbda9cdee4016c203cb86547e6e0483ded', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/iNDM-52klhSKPCGjZoP0ODll_caIe6IsMg4zOI4TGkk.jpeg?width=1080&crop=smart&auto=webp&s=8b937b31891c20bc704d46a29ee5f8ffd3c70bb3', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/iNDM-52klhSKPCGjZoP0ODll_caIe6IsMg4zOI4TGkk.jpeg?auto=webp&s=734ba83e44d35ccd850aa99d23cce871bfb34af0', 'width': 1200}, 'variants': {}}]} |
my secret weapon for writing "unfiltered" fiction | 1 | [removed] | 2026-01-03T04:56:03 | https://www.reddit.com/r/LocalLLaMA/comments/1q2ljyq/my_secret_weapon_for_writing_unfiltered_fiction/ | Immediate_Being_3341 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2ljyq | false | null | t3_1q2ljyq | /r/LocalLLaMA/comments/1q2ljyq/my_secret_weapon_for_writing_unfiltered_fiction/ | false | false | self | 1 | null |
Don’t sleep on Korean LLMs. Benchmarks aren’t everything | 0 | 2026-01-03T04:50:10 | https://www.reddit.com/gallery/1q2lflq | jacek2023 | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1q2lflq | false | null | t3_1q2lflq | /r/LocalLLaMA/comments/1q2lflq/dont_sleep_on_korean_llms_benchmarks_arent/ | false | false | 0 | null | ||
Would you buy a pretrained, customizable LLM Lovebot? (Sample output inside) | 0 | 2026-01-03T04:16:01 | https://www.reddit.com/r/LocalLLaMA/comments/1q2kqf6/would_you_buy_a_pretrained_customizable_llm/ | Coco4Tech69 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2kqf6 | false | null | t3_1q2kqf6 | /r/LocalLLaMA/comments/1q2kqf6/would_you_buy_a_pretrained_customizable_llm/ | false | false | 0 | null | ||
How is Cloud Inference so cheap | 100 | How do cloud inference companies like DeepInfra, Together, Chutes, Novita etc manage to be in profit regarding to the price of the GPUs/electricity and the fact that I guess it's difficult to have always someone to serve ? | 2026-01-03T03:37:13 | https://www.reddit.com/r/LocalLLaMA/comments/1q2jwsn/how_is_cloud_inference_so_cheap/ | VolkoTheWorst | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2jwsn | false | null | t3_1q2jwsn | /r/LocalLLaMA/comments/1q2jwsn/how_is_cloud_inference_so_cheap/ | false | false | self | 100 | null |
For those of you who bought DGX OS hardware (e.g. Spark) for local LLM, did all of you flash Ubuntu (or some distro) into it to replace DGX OS to remove the telemetry among other bloats? | 0 | For a while, Spark and similar hardware have been talk of the town around YouTube, reddit, Hackernews, etc., or at least I've been exposed to it (non-ads) a lot for local solution. (I understand that there are other solutions out there, but Spark-like solutions came with convenience, performance, specs, among other quantitative and qualitative measures that matched certain thresholds)
However, I should have been more thorough. So many things about it is not very 'local' with telemetry pre-installs, forcing you to connect to Wi-Fi, and other Internet-required bloats. Another factor for the recommendation was lean, but it comes with quite a few unnecessary Nvidia installs. So I've been wondering if others are flashing Ubuntu into it or something along those lines, since I came across such a comment at least once, so now I'm wondering if it's the norm.
***
`Rant start`
The initial screen from DGX OS for connecting to Wi-Fi definitely belongs in /r/assholedesign. You can't do anything until you actually connect to a Wi-Fi, and I couldn't find any solution online or in the documentation for this. So I thought of connecting my phone's hotspot without data, but I couldn't even find my phone on the AP list. There is no search. There are almost 2000 APs around me, so I have scroll the whole time, and the scrolling is very, very sluggish. Mental.
I finally found it and connected it, but because it doesn't have data, it refused to connect to it. Then I connected my satellite mobile modem to it. Refused again. I tried to search for an answer, but with a help of my friend, we narrowed it down to the mobile modem's DNS. I put adblocking DNS on the modem. Ugh, I guess it comes with telemetry. That's not a very nice 'local' recommendation, is it?
Finally, I connected to my friend's hotspot then immediately disconnected. It rebooted itself automatically. I logged in. Worked fine. I check on Terminal, immediately `apt list | grep "telemetry"` among others (see pics). It seems that apt repos updated during the hotspot connection, but that seemed to be about it.
`Rant end`
***
And for those of you who didn't flash a different distro on it, what did you do to delete the telemetry bloat? What else did you delete? (Bonus question -- can I delete Nvidia AI Bench and everything else in the pic?) | 2026-01-03T03:13:15 | https://www.reddit.com/gallery/1q2jdkr | jinnyjuice | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1q2jdkr | false | null | t3_1q2jdkr | /r/LocalLLaMA/comments/1q2jdkr/for_those_of_you_who_bought_dgx_os_hardware_eg/ | false | false | 0 | null | |
Part 4 (Finale): Building LLMs from Scratch – Evaluation & Deployment [Follow-up to Parts 1, thru 3] | 19 | I’m excited to share **Part 4** (and the final part) of my series on building an LLM from scratch.
This installment covers the “okay, but does it *work*?” phase: evaluation, testing, and deployment - taking the trained models from Part 3 and turning them into something you can validate, iterate on, and actually share/use (including publishing to HF).
What you’ll find inside:
* A practical evaluation framework (quick vs comprehensive) for historical language models (not just perplexity).
* Tests and validation patterns: historical accuracy checks, linguistic checks, temporal consistency, and basic performance sanity checks.
* Deployment paths:
* local inference from PyTorch checkpoints
* Hugging Face Hub publishing + model cards
* CI-ish smoke checks you can run on CPU to catch obvious regressions.
Why it matters?
Training is only half the battle. Without evaluation + tests + a repeatable publishing workflow, you can easily end up with a model that “trains fine” but is unreliable, inconsistent, or impossible for others to reproduce/use. This post focuses on making the last mile boring (in the best way).
Resources:
* 🔗 Blog post (Part 4) - [Evaluations and Deployment](https://blog.desigeek.com/post/2026/01/building-llm-from-scratch-part4-evaluation-deployment/)
* 🔗 GitHub repo: [https://github.com/bahree/helloLondon](vscode-file://vscode-app/c:/Users/Amit/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html)
* 🔗 Hugging Face: [https://huggingface.co/bahree](vscode-file://vscode-app/c:/Users/Amit/AppData/Local/Programs/Microsoft%20VS%20Code/resources/app/out/vs/code/electron-browser/workbench/workbench.html)
In case you are interested in the previous parts
* 🔗 Part 3 - [Model Architecture & GPU Training](https://www.reddit.com/r/LocalLLaMA/comments/1oluay3/part_3_building_llms_from_scratch_model/)
* 🔗 Part 2 - [Data Collection & Custom Tokenizers](https://www.reddit.com/r/LocalLLaMA/comments/1o562l3/part_2_building_llms_from_scratch_data_collection/)
* 🔗 Part 1 - [Quick Start & Overview](https://www.reddit.com/r/LocalLLaMA/comments/1npzstw/a_step_by_step_guide_on_how_to_build_a_llm_from/)
* 🔗 LinkedIn [post](https://www.linkedin.com/posts/amitbahree_building-llms-from-scratch-part-4-evaluation-activity-7413050136974700544-0OwB/) (if that is your thing). | 2026-01-03T03:10:03 | https://www.reddit.com/r/LocalLLaMA/comments/1q2jazd/part_4_finale_building_llms_from_scratch/ | amitbahree | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2jazd | false | null | t3_1q2jazd | /r/LocalLLaMA/comments/1q2jazd/part_4_finale_building_llms_from_scratch/ | false | false | self | 19 | null |
LoongFlow: Better than Goolge AlphaEvolve | 0 | Let's be real: Frameworks like **OpenEvolve** are essentially "brute-force guessing". It’s inefficient, expensive, and frankly, obsolete.
We built **LoongFlow** to kill the random walk. It injects a **Cognitive Core (Plan-Execute-Summarize)** into the evolutionary loop.
https://preview.redd.it/ovf6lowvp1bg1.png?width=1548&format=png&auto=webp&s=e056911a3a9099bf262134b6bcdadea1c7202c0a
The result? 🚀 **The "Cognitive Ceiling" is shattered.** 🥇 14 **Kaggle Gold Medals** (Zero human intervention). 📉 **1/20th the compute cost** of OpenEvolve.
If your agent isn't thinking before it mutates, it's just gambling.
We are open-sourcing the future of AGI Evolution today. 👇, Hope you can give us star\~
[https://github.com/baidu-baige/LoongFlow](https://github.com/baidu-baige/LoongFlow) | 2026-01-03T02:32:04 | https://www.reddit.com/r/LocalLLaMA/comments/1q2igb0/loongflow_better_than_goolge_alphaevolve/ | FreshmanDD | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2igb0 | false | null | t3_1q2igb0 | /r/LocalLLaMA/comments/1q2igb0/loongflow_better_than_goolge_alphaevolve/ | false | false | 0 | null | |
I'm new at local AI, I have a question regarding Mini PCs vs Super AI Computers. | 0 | I see that you can make a Mega-PC with a lot of Nvidia GPUs as pewdiepie did (to give an example), but I also see these mini PCs with shared RAM between the system and the integrated graphics. The thing is that with these mini PCs you can run insanely large models due to the amount of vram you can give to the GPU, so, why would I want to make a super computer with many GPUs if i already get the same result (of being able to run large models) from a cheaper mini PC?
I'm clearly very lost on this so I would really appreciate any explanation at all, and if you are willing to give explanations of this or the difference between Nvidia and AMD GPUs for AI specifically, I would really appreciate it, since that's is the other big doubt I have. | 2026-01-03T02:29:45 | https://www.reddit.com/r/LocalLLaMA/comments/1q2iedp/im_new_at_local_ai_i_have_a_question_regarding/ | Fast-Cheetah9944 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2iedp | false | null | t3_1q2iedp | /r/LocalLLaMA/comments/1q2iedp/im_new_at_local_ai_i_have_a_question_regarding/ | false | false | self | 0 | null |
Lynkr - Multi-Provider LLM Proxy | 0 | Quick share for anyone interested in LLM infrastructure:
Hey folks! Sharing an open-source project that might be useful:
Lynkr connects AI coding tools (like Claude Code) to multiple LLM providers with intelligent routing.
Key features:
- Route between multiple providers: Databricks, Azure Ai Foundry, OpenRouter, Ollama,llama.cpp, OpenAi
- Cost optimization through hierarchical routing, heavy prompt caching
- Production-ready: circuit breakers, load shedding, monitoring
- It supports all the features offered by claude code like sub agents, skills , mcp , plugins etc unlike other proxies which only supports basic tool callings and chat completions.
Great for:
- Reducing API costs as it supports hierarchical routing where you can route requstes to smaller local models and later switch to cloud LLMs automatically.
- Using enterprise infrastructure (Azure)
- Local LLM experimentation
```
npm install -g lynkr
```
GitHub: https://github.com/Fast-Editor/Lynkr (Apache 2.0)
Would love to get your feedback on this one. Please drop a star on the repo if you found it helpful | 2026-01-03T02:18:08 | https://www.reddit.com/r/LocalLLaMA/comments/1q2i52u/lynkr_multiprovider_llm_proxy/ | Dangerous-Dingo-5169 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2i52u | false | null | t3_1q2i52u | /r/LocalLLaMA/comments/1q2i52u/lynkr_multiprovider_llm_proxy/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'Vsg4lp_ajDq4m01zl69vW4dXXjvf1iKsvq-A-6AMVKQ', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/Vsg4lp_ajDq4m01zl69vW4dXXjvf1iKsvq-A-6AMVKQ.png?width=108&crop=smart&auto=webp&s=2de5bdff29078235e3d0fd791308761fc89d5675', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/Vsg4lp_ajDq4m01zl69vW4dXXjvf1iKsvq-A-6AMVKQ.png?width=216&crop=smart&auto=webp&s=dd77dd119df1eb8c363862c374a202b842921bdc', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/Vsg4lp_ajDq4m01zl69vW4dXXjvf1iKsvq-A-6AMVKQ.png?width=320&crop=smart&auto=webp&s=12c4ef09729934fce8863825b7cb217b910eae20', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/Vsg4lp_ajDq4m01zl69vW4dXXjvf1iKsvq-A-6AMVKQ.png?width=640&crop=smart&auto=webp&s=5460e2cbac53d1548d62a1a58afe3058022aeb14', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/Vsg4lp_ajDq4m01zl69vW4dXXjvf1iKsvq-A-6AMVKQ.png?width=960&crop=smart&auto=webp&s=86728695d8653268e14203ef01685a52011d9b55', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/Vsg4lp_ajDq4m01zl69vW4dXXjvf1iKsvq-A-6AMVKQ.png?width=1080&crop=smart&auto=webp&s=a4c08f20a8d3634db79ac248a0dac648bc749c51', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/Vsg4lp_ajDq4m01zl69vW4dXXjvf1iKsvq-A-6AMVKQ.png?auto=webp&s=f91c6caf03e77b1f6a36bbeb2a7b857dfc4171a0', 'width': 1200}, 'variants': {}}]} |
I got almost Maya' running LOCALLY on an RTX 3090 | 0 | 2026-01-03T02:07:14 | https://www.youtube.com/watch?v=G6VWUA5KwCg | Legion10008 | youtube.com | 1970-01-01T00:00:00 | 0 | {} | 1q2hwfa | false | {'oembed': {'author_name': 'Combo_ai_news', 'author_url': 'https://www.youtube.com/@Combo_ai_news', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/G6VWUA5KwCg?feature=oembed&enablejsapi=1" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" referrerpolicy="strict-origin-when-cross-origin" allowfullscreen title="ai voice assitant with emotions on rtx 3090 is it local Maya?"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/G6VWUA5KwCg/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'ai voice assitant with emotions on rtx 3090 is it local Maya?', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1q2hwfa | /r/LocalLLaMA/comments/1q2hwfa/i_got_almost_maya_running_locally_on_an_rtx_3090/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'WNhMdw4UsHSvqatBomUtwsdi5PXP8TWqOAHzUyNGXAU', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/WNhMdw4UsHSvqatBomUtwsdi5PXP8TWqOAHzUyNGXAU.jpeg?width=108&crop=smart&auto=webp&s=4ff6e8e92880e387704f802b2ebf450d628c382a', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/WNhMdw4UsHSvqatBomUtwsdi5PXP8TWqOAHzUyNGXAU.jpeg?width=216&crop=smart&auto=webp&s=0dca0080177f10e3abb4f7f6969d48ef1d5435cb', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/WNhMdw4UsHSvqatBomUtwsdi5PXP8TWqOAHzUyNGXAU.jpeg?width=320&crop=smart&auto=webp&s=a11cf6d2879a593c7cc70260bfde93dd0fbc5a20', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/WNhMdw4UsHSvqatBomUtwsdi5PXP8TWqOAHzUyNGXAU.jpeg?auto=webp&s=d6aa99ec83854b4b9ab41c36da0b6663713d7a19', 'width': 480}, 'variants': {}}]} | ||
Integrated Mistral Nemo (12B) into a custom Space Discovery Engine (Project ARIS) for local anomaly detection. | 2 | Just wanted to share a real-world use case for local LLMs. I’ve built a discovery engine called Project ARIS that uses Mistral Nemo as a reasoning layer for astronomical data.
The Stack:
Model: Mistral Nemo 12B (Q4\_K\_M) running via Ollama.
Hardware: Lenovo Yoga 7 (Ryzen AI 7, 24GB RAM) on Nobara Linux.
Integration: Tauri/Rust backend calling the Ollama API.
How I’m using the LLM:
Contextual Memory: It reads previous session reports from a local folder and greets me with a verbal recap on boot. Custom recursive learning sidecar implemented as well.
Intent Parsing: I built a custom terminal where Nemo translates "fuzzy" natural language into structured MAST API queries.
Anomaly Scoring: It parses spectral data to flag "out of the ordinary" signatures that don't fit standard star/planet profiles.
It’s amazing how much a 12B model can do when given a specific toolset and a custom sandboxed terminal.
A preview of Project ARIS can be found here:
[https://github.com/glowseedstudio/Project-ARIS](https://github.com/glowseedstudio/Project-ARIS) | 2026-01-03T02:04:48 | https://www.reddit.com/r/LocalLLaMA/comments/1q2hugu/integrated_mistral_nemo_12b_into_a_custom_space/ | Limp-Regular3741 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2hugu | false | null | t3_1q2hugu | /r/LocalLLaMA/comments/1q2hugu/integrated_mistral_nemo_12b_into_a_custom_space/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'oZ3lsfEdAZ3IdibEPNvDHZx6kMR6A1FYnczVbnMWKYA', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/oZ3lsfEdAZ3IdibEPNvDHZx6kMR6A1FYnczVbnMWKYA.png?width=108&crop=smart&auto=webp&s=ecbbc2d6c5487a8064e66df26bf30201a85ad369', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/oZ3lsfEdAZ3IdibEPNvDHZx6kMR6A1FYnczVbnMWKYA.png?width=216&crop=smart&auto=webp&s=4a2acf3dbb00e198509bb5751628f741f547cf8f', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/oZ3lsfEdAZ3IdibEPNvDHZx6kMR6A1FYnczVbnMWKYA.png?width=320&crop=smart&auto=webp&s=762bf149a4f3abb446ac84505ff7b27355139492', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/oZ3lsfEdAZ3IdibEPNvDHZx6kMR6A1FYnczVbnMWKYA.png?width=640&crop=smart&auto=webp&s=1c1a269489375002636ed88488a1e3c25a380de1', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/oZ3lsfEdAZ3IdibEPNvDHZx6kMR6A1FYnczVbnMWKYA.png?width=960&crop=smart&auto=webp&s=eb91eb3cfbf38680f1c26ed7b99d3b27d84ef040', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/oZ3lsfEdAZ3IdibEPNvDHZx6kMR6A1FYnczVbnMWKYA.png?width=1080&crop=smart&auto=webp&s=d145a8f32d2e34d76218144b18c06b19965e8461', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/oZ3lsfEdAZ3IdibEPNvDHZx6kMR6A1FYnczVbnMWKYA.png?auto=webp&s=20864ce83a7a40c82592caab605a8ef3f543f2e8', 'width': 1200}, 'variants': {}}]} |
Is it okay to use RAM without heatsink for Local LLM? | 0 | Like this one [here](https://www.ebay.com.au/itm/336372468939) | 2026-01-03T01:36:52 | https://www.reddit.com/r/LocalLLaMA/comments/1q2h809/is_it_okay_to_use_ram_without_heatsink_for_local/ | CaregiverFormal6238 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2h809 | false | null | t3_1q2h809 | /r/LocalLLaMA/comments/1q2h809/is_it_okay_to_use_ram_without_heatsink_for_local/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'lkMVRirMwATfX35aV70K_VRCuwwgojQ23mO-xJmXIkg', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/lkMVRirMwATfX35aV70K_VRCuwwgojQ23mO-xJmXIkg.jpeg?width=108&crop=smart&auto=webp&s=8120c296fce9e6ff70a4f72c7cbafd70c3cac882', 'width': 108}, {'height': 216, 'url': 'https://external-preview.redd.it/lkMVRirMwATfX35aV70K_VRCuwwgojQ23mO-xJmXIkg.jpeg?width=216&crop=smart&auto=webp&s=497156e77668e4c9849a38821b00939980bc3892', 'width': 216}, {'height': 320, 'url': 'https://external-preview.redd.it/lkMVRirMwATfX35aV70K_VRCuwwgojQ23mO-xJmXIkg.jpeg?width=320&crop=smart&auto=webp&s=568f8360821518b31a516d94bae9c4ab229d5707', 'width': 320}], 'source': {'height': 400, 'url': 'https://external-preview.redd.it/lkMVRirMwATfX35aV70K_VRCuwwgojQ23mO-xJmXIkg.jpeg?auto=webp&s=96fec97ac7ae11cabb796e99d8e1e7eef15b8c80', 'width': 400}, 'variants': {}}]} |
Best local TTS | 1 | Best local TTS model and framework with a variety of good voices? | 2026-01-03T01:27:53 | https://www.reddit.com/r/LocalLLaMA/comments/1q2h0gy/best_local_tts/ | Vegetable_Sun_9225 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2h0gy | false | null | t3_1q2h0gy | /r/LocalLLaMA/comments/1q2h0gy/best_local_tts/ | false | false | self | 1 | null |
Problems with LM Studio Macbook m5 24gb ram | 1 | So I get errors like the following
The model has crashed without additional information. (Exit code: 6)
or
Error in iterating prediction stream: RuntimeError: \[metal::Device\] Unable to load kernel affine\_qmm\_t\_nax\_bfloat16\_t\_gs\_64\_b\_8\_bm64\_bn64\_bk64\_wm2\_wn2\_alN\_true\_batch\_0
I have tried different models, on gguf on ollama they worked here they dont seem to | 2026-01-03T01:25:05 | https://www.reddit.com/r/LocalLLaMA/comments/1q2gy3o/problems_with_lm_studio_macbook_m5_24gb_ram/ | Stoic_Coder012 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2gy3o | false | null | t3_1q2gy3o | /r/LocalLLaMA/comments/1q2gy3o/problems_with_lm_studio_macbook_m5_24gb_ram/ | false | false | self | 1 | null |
Tech prices in AI times | 0 | During the turmoil in regards to AI hardware prices, in the meantime becuse of extreme demand it seems cutomers in some contries are on waiting lists for the state of the arts China EVs, but prices are still amazingly good.
What prohhinits tech companies in contries like China , Japan or other in producing something like 64 GB, 128 GB or 256 GB PC RAM modules and CPUs that would support them ?
Isn't it much easier and cheaper to ship or store in a warehouse a DDR stick than a car? | 2026-01-03T01:23:46 | https://www.reddit.com/r/LocalLLaMA/comments/1q2gx0y/tech_prices_in_ai_times/ | Highwaytothebeach | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2gx0y | false | null | t3_1q2gx0y | /r/LocalLLaMA/comments/1q2gx0y/tech_prices_in_ai_times/ | false | false | self | 0 | null |
tired of "prompt engineering"? try these tools instead | 1 | [removed] | 2026-01-03T00:56:02 | https://www.reddit.com/r/LocalLLaMA/comments/1q2g9vm/tired_of_prompt_engineering_try_these_tools/ | Immediate_Being_3341 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2g9vm | false | null | t3_1q2g9vm | /r/LocalLLaMA/comments/1q2g9vm/tired_of_prompt_engineering_try_these_tools/ | false | false | self | 1 | null |
Has anyone successfully vibe coded and stitched together multiple large open source projects? | 0 | I'm currently integrating 25-30 open source projects which are very large in their own right vibe coding with agents into a larger mega project. Each of these projects has been carefully selected as a dependency to fill a specific role with DRY principles, each one tackles their own responsibilities, and any that overlap are delegated to one or the other exclusively when needed.
I have thousands upon thousands of documentation the agents have created, diagrams, how the pieces interlock, the overall architecture, why certain choices have been made, how to integrate the components, gap analysis, master tasklists and todos, I have essentially the full and complete architecture roadmapped.
Right now the biggest impediment is just the sheer scale of what I'm building, I'm largely limited by how many agents I can afford to throw at this.
I'm planning to dockerize everything, glueing everything together with bridges and adapters, with contracts via their APIs, I'm not modifying any core files for any of the projects I'm using. That way I can update them when an update is pushed without everything breaking and needing to refactor everything everytime one is updated.
I'm caching everything with a large focus on performance given how big of a rube goldberg machine this beast is.
I plan to have everything lazy loaded so it's only used when necessary, keeping the components separate and not tightly coupling anything for any of the projects/services so they can be swapped as needed, and trying to mitigate any "gotchas" as I go along, all the typical rookie mistakes.
I've structured the mitigations and the project structure itself via LLMs trying to account for everything where this kind of thing just falls apart.
Besides just asking a committee of LLMs and planning as many mitigations as possible, does anyone have any practical experience glueing together very large open source projects? Not just libraries for specific functions, I'm talking *very large* opensource projects.
To give a few examples of what projects I'm using and why this (what would appear to be absurd at first glance) system is necessary:
1. N8N style Interface
2. OpenEvolve
3. Lean 4 Autoformalization system
4. Agent Orchestration Framework
5. Knowledge Engine
6. RAG
and a whole bunch more | 2026-01-03T00:43:44 | https://www.reddit.com/r/LocalLLaMA/comments/1q2fzii/has_anyone_successfully_vibe_coded_and_stitched/ | jazir555 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2fzii | false | null | t3_1q2fzii | /r/LocalLLaMA/comments/1q2fzii/has_anyone_successfully_vibe_coded_and_stitched/ | false | false | self | 0 | null |
Are there any* frontends that allow you to view top token probabilities? | 2 | *other than mikupad and sillytavern
I'm using Qwen3 vl 8b with llama.cpp to OCR text from japanese artwork, it's the most accurate model for this that i've tried, but it still sometimes gets a character wrong or omits it entirely. I'm sure the correct prediction is somewhere in the top tokens, so if i had access to them i could easily correct my outputs.
I'd also like to know if any popular frontends (e.g. OpenWebUI) that don't usually support logprobs have extensions or similar (i'm not familiar with any of them) that implement it. | 2026-01-03T00:11:42 | https://www.reddit.com/r/LocalLLaMA/comments/1q2f8c9/are_there_any_frontends_that_allow_you_to_view/ | Velocita84 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2f8c9 | false | null | t3_1q2f8c9 | /r/LocalLLaMA/comments/1q2f8c9/are_there_any_frontends_that_allow_you_to_view/ | false | false | self | 2 | null |
I got frustrated dealing with massive responses from many MCPs and threw something together over the last couple days... it might help you too. Or not! | 11 | Hey /r/LocalLlama, I spent the last couple of days working on a little personal project and figured I’d share.
https://github.com/samteezy/mcp-context-proxy/
Background:
As a relatively low-investment homelabber, I'm always battling context size and chasing optimal prompt processing/token generation speeds.
I don’t mean to pick on this one in particular, but a MCP that really got me frustrated was an otherwise very [well built MCP](https://github.com/sirkirby/unifi-network-mcp) that allows you to extract data from your UniFi network devices. I was working with it to build documentation of my home network, and I was finding it was giving me response payloads from the UniFi API that had a ton of extra data which started just filling up my context and taking *forever* for gpt-oss-120b to process. I don't blame the author - this is just a fundamental failing in current MCP implementation; MCPs are meant to help give instruction but there's no special solution to optimizing number of tokens returned (there's no free lunch).
I love small models like those from Qwen and Liquid AI, and I have `llama-swap` configured to always have a small task model in the background for tools like Karakeep and Open WebUI to use... so what if I could use this for basically compressing any MCP response?
So I decided to turn Claude Code onto the problem and create a little tool that we have here. It is an MCP which acts a transparent proxy, oriented towards the home lab/context poor user with the following features/benefits:
- Transparently presents MCP tools to the client, but allows you to preprocess the MCP's response before sending it back to the client LLM (ideally you use a locally hosted LLM, but could also make remote callouts to the cloud to a super inexpensive or free API via something like OpenRouter)
- Uses a simple in-memory cache for caching responses for identical requests
- Allows disabling individual tools or overwriting the upstream tool descriptions to better control context size and tool selection accuracy when launching an agent
- Adds capability to intercept outgoing tool calls and incoming MCP responses for things like PII masking or prompt injection (future)
- One proxy for managing multiple MCPs; great for if you're playing with multiple AI tools/coding assistants and hate having to reconfigure MCPs for each one
- Very configurable options to override behavior globally or different tools via a single JSON file, plus a UI for management and visibility
I've been testing with a high-quant Qwen3-0.6b and LFM2-1.2b and it's doing very well for me. For example, I have it use web search and fetch for URLs and instead of having the larger model process the entire pages, the tiny model reads the page FAR up to 10x faster, and just gives the large model the answers it needs, also keeping context lower. YMMV.
It is not:
- Being monetized or going to make you a ton of money
- Guaranteed to work in a high-stress environment (not that it's emotionally sensitive, just that I don't know where its performance limits are)
- Completely revolutionary
- Going to solve all of MCP flaws and failings
- Going to make your ex take you back
And yes, it is vibe coded... so of course take it with a grain of salt, but I use these tools professionally and understand how to use AI as a coding assistant rather than an expert. Don't like that? Fork it and have AI inspect it yourself. Or write your own. Or do whatever, [I'm not your supervisor](https://tenor.com/view/youre-not-my-supervisor-youre-not-my-boss-gif-12971403)
I'm planning on adding in optional prompt injection review (curious about some of IBM's and others' models out there more to understand how they work) and seeing how well I can get the masking side working. I haven't tested that a ton yet. I'm also playing around with the idea of adding an optional override for the client LLM to bypass content summarization, but I feel like that risks defeating the purpose.
Hope this helps you get more value out of the hardware and setup you currently have.
Note, this is also the first time I've published anything to npm, so that's been an interesting learning experience - if anyone has any recommendations on coding and deployment architecture based on what you're seeing in the repo, I'd definitely listen to that advice. | 2026-01-02T23:53:31 | https://www.reddit.com/r/LocalLLaMA/comments/1q2et07/i_got_frustrated_dealing_with_massive_responses/ | steezy13312 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2et07 | false | null | t3_1q2et07 | /r/LocalLLaMA/comments/1q2et07/i_got_frustrated_dealing_with_massive_responses/ | false | false | self | 11 | {'enabled': False, 'images': [{'id': '3jgfXcWtf8VgulbRCJ6S_13Agop9rmJNZKqeoOPrtNQ', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/3jgfXcWtf8VgulbRCJ6S_13Agop9rmJNZKqeoOPrtNQ.png?width=108&crop=smart&auto=webp&s=c5c296a62e8c2f58e6543c81d9374bc411019809', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/3jgfXcWtf8VgulbRCJ6S_13Agop9rmJNZKqeoOPrtNQ.png?width=216&crop=smart&auto=webp&s=1ab3aca308431a77b7af9f8f86d67fb549214e33', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/3jgfXcWtf8VgulbRCJ6S_13Agop9rmJNZKqeoOPrtNQ.png?width=320&crop=smart&auto=webp&s=31aec0b3c0e564a81ea2d419b213af55783d6826', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/3jgfXcWtf8VgulbRCJ6S_13Agop9rmJNZKqeoOPrtNQ.png?width=640&crop=smart&auto=webp&s=283a1b863ff8a0a742a7cf79deeec703ea696bd2', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/3jgfXcWtf8VgulbRCJ6S_13Agop9rmJNZKqeoOPrtNQ.png?width=960&crop=smart&auto=webp&s=679523a745f25f2804a8a5d584691d465f6af1f2', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/3jgfXcWtf8VgulbRCJ6S_13Agop9rmJNZKqeoOPrtNQ.png?width=1080&crop=smart&auto=webp&s=bc0339ad5dcd42ba25d4a4e1ec98416b27ec5db0', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/3jgfXcWtf8VgulbRCJ6S_13Agop9rmJNZKqeoOPrtNQ.png?auto=webp&s=e0b2396ff20f324d3b90415459d55fc6322de964', 'width': 1200}, 'variants': {}}]} |
My New Year's resolution was to add Docker support. Only 2 days late. Audiobook Maker v1.1.0 | 22 | Hey r/LocalLLaMA!
About three weeks ago I shared my passion project here - an app to create audiobooks from text using local TTS engines like XTTS and Chatterbox. [https://www.reddit.com/r/LocalLLaMA/comments/1piduwm/i\_wanted\_audiobooks\_of\_stories\_that\_dont\_exist\_so/](https://www.reddit.com/r/LocalLLaMA/comments/1piduwm/i_wanted_audiobooks_of_stories_that_dont_exist_so/)
The response was amazing and motivated me to keep going. Special shoutout to [https://github.com/codesterribly](https://github.com/codesterribly) who pushed me to tackle Docker support - you were right, it was worth it!
So here's my slightly-late New Year's gift to the community: v1.1.0 🎁
What's New?
Docker-First Architecture
* No more Python environment hell! Engines come as prebuilt Docker images
* One-click installation from the online catalog
* Works on Windows, Linux, and partially with macOS (Apple Silicon)
Remote GPU Offloading
* Got a beefy GPU server in your closet? Run VibeVoice 7B there via SSH
* Your laptop stays cool while the server does the heavy lifting
* Built-in SSH key wizard - no manual config needed
New TTS Engine: VibeVoice
* Microsoft's long-form multi-speaker TTS
* Great for podcasts and dialogues
Quick Start
# Pull the backend
docker pull ghcr.io/digijoe79/audiobook-maker/backend:latest
# Run it
`docker run -d --name audiobook-maker-backend \`
`-p 8765:8765 \`
`--add-host=host.docker.internal:host-gateway \`
`-e DOCKER_ENGINE_HOST=host.docker.internal \`
`-v /var/run/docker.sock:/var/run/docker.sock \`
`-v audiobook-data-path:/app/data \`
`-v audiobook-media-path:/app/media \`
`ghcr.io/digijoe79/audiobook-maker/backend:latest`
Then grab the desktop app, connect, and install engines from the catalog. That's it!
Links
* [https://github.com/DigiJoe79/audiobook-maker](https://github.com/DigiJoe79/audiobook-maker)
* [https://github.com/DigiJoe79/audiobook-maker/releases/tag/v1.1.0](https://github.com/DigiJoe79/audiobook-maker/releases/tag/v1.1.0)
* [https://github.com/DigiJoe79/audiobook-maker/tree/main/docs/samples](https://github.com/DigiJoe79/audiobook-maker/tree/main/docs/samples) (Moby Dick previews)
What's Next?
Already thinking about v1.2.0 - better batch processing, more for Apple Silicon. Open to suggestions!
Thanks again for all the feedback on the original post. This community is awesome. 🙏
Happy (belated) New Year, and happy listening! | 2026-01-02T23:32:12 | https://www.reddit.com/gallery/1q2eau3 | DigiJoe79 | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1q2eau3 | false | null | t3_1q2eau3 | /r/LocalLLaMA/comments/1q2eau3/my_new_years_resolution_was_to_add_docker_support/ | false | false | 22 | null | |
Transformer fMRI - Code and Methodology | 3 | \## T-Scan: A Practical Method for Visualizing Transformer Internals
GitHub: [https://github.com/Bradsadevnow/TScan](https://github.com/Bradsadevnow/TScan)
Hello! I’ve developed a technique for inspecting and visualizing the internal activations of transformer models, which I’ve dubbed \*\*T-Scan\*\*.
This project provides:
\* Scripts to \*\*download a model and run a baseline scan\*\*
\* A \*\*Gradio-based interface\*\* for causal intervention on up to three dimensions at a time
\* A \*\*consistent logging format\*\* designed to be renderer-agnostic, so you can visualize the results using whatever tooling you prefer (3D, 2D, or otherwise)
The goal is not to ship a polished visualization tool, but to provide a \*\*reproducible measurement and logging method\*\* that others can inspect, extend, or render in their own way.
\### Important Indexing Note
Python uses \*\*zero-based indexing\*\* (counts start at 0, not 1).
All scripts and logs in this project follow that convention. Keep this in mind when exploring layers and dimensions.
\## Dependencies
pip install torch transformers accelerate safetensors tqdm gradio
(If you’re using a virtual environment, you may need to repoint your IDE.)
\---
\## Model and Baseline Scan
Run:
python mri\_sweep.py
This script will:
\* Download \*\*Qwen 2.5 3B Instruct\*\*
\* Store it in a \`/models\` directory
\* Perform a baseline scan using the prompt:
\> \*\*“Respond with the word hello.”\*\*
This prompt was chosen intentionally: it represents an extremely low cognitive load, keeping activations near their minimal operating regime. This produces a clean reference state that improves interpretability and comparison for later scans.
\### Baseline Output
Baseline logs are written to:
logs/baseline/
Each layer is logged to its own file to support lazy loading and targeted inspection. Two additional files are included:
\* \`run.json\` — metadata describing the scan (model, shape, capture point, etc.)
\* \`tokens.jsonl\` — a per-step record of output tokens
All future logs mirror this exact format.
\---
\## Rendering the Data
My personal choice for visualization was \*\*Godot\*\* for 3D rendering. I’m not a game developer, and I’m deliberately \*\*not\*\* shipping a viewer, the one I built is a janky prototype and not something I’d ask others to maintain or debug.
That said, \*\*the logs are fully renderable\*\*.
If you want a 3D viewer:
\* Start a fresh Godot project
\* Feed it the log files
\* Use an LLM to walk you through building a simple renderer step-by-step
If you want something simpler:
\* \`matplotlib\`, NumPy, or any plotting library works fine
For reference, it took me \~6 hours (with AI assistance) to build a rough v1 Godot viewer, and the payoff was immediate.
\---
\## Inference & Intervention Logs
Run:
python dim\_poke.py
Then open:
[http://127.0.0.1:7860/](http://127.0.0.1:7860/)
You’ll see a Gradio interface that allows you to:
\* Select up to \*\*three dimensions\*\* to perturb
\* Choose a \*\*start and end layer\*\* for causal intervention
\* Toggle \*\*attention vs MLP outputs\*\*
\* Control \*\*max tokens per run\*\*
\* Enter arbitrary prompts
When you run a comparison, the model performs \*\*two forward passes\*\*:
1. \*\*Baseline\*\* (no intervention)
2. \*\*Perturbed\*\* (with causal modification)
Logs are written to:
logs/<run\_id>/
├─ base/
└─ perturbed/
Both folders use \*\*the exact same format\*\* as the baseline:
\* Identical metadata structure
\* Identical token indexing
\* Identical per-layer logs
This makes it trivial to compare baseline vs perturbed behavior at the level of \`(layer, timestep, dimension)\` using any rendering or analysis method you prefer.
\---
\### Final Notes
T-Scan is intentionally scoped:
\* It provides \*\*instrumentation and logs\*\*, not a UI product
\* Visualization is left to the practitioner
\* The method is model-agnostic in principle, but the provided scripts target Qwen 2.5 3B for accessibility and reproducibility
If you can render numbers, you can use T-Scan.
I'm currently working in food service while pursuing interpretability research full-time. I'm looking to transition into a research role and would appreciate any guidance on where someone with a non-traditional background (self-taught, portfolio-driven) might find opportunities in this space. If you know of teams that value execution and novel findings over conventional credentials, I'd love to hear about them.
| 2026-01-02T23:24:26 | https://www.reddit.com/r/LocalLLaMA/comments/1q2e42a/transformer_fmri_code_and_methodology/ | Due_Hunter_4891 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2e42a | false | null | t3_1q2e42a | /r/LocalLLaMA/comments/1q2e42a/transformer_fmri_code_and_methodology/ | false | false | self | 3 | null |
ASUS officially announces price hikes from January 5, right before CES 2026 | 90 | 2026-01-02T22:53:19 | https://videocardz.com/newz/asus-officially-announces-price-hikes-from-january-5-right-before-ces-2026 | HumanDrone8721 | videocardz.com | 1970-01-01T00:00:00 | 0 | {} | 1q2dcje | false | null | t3_1q2dcje | /r/LocalLLaMA/comments/1q2dcje/asus_officially_announces_price_hikes_from/ | false | false | default | 90 | {'enabled': False, 'images': [{'id': 'e0KouFF887lm3A9dV6mq_44cYZmQvCh1I3h_7LTZz8c', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/e0KouFF887lm3A9dV6mq_44cYZmQvCh1I3h_7LTZz8c.jpeg?width=108&crop=smart&auto=webp&s=7bd53c4b8ade298342297d1528fb9696ef90b658', 'width': 108}, {'height': 112, 'url': 'https://external-preview.redd.it/e0KouFF887lm3A9dV6mq_44cYZmQvCh1I3h_7LTZz8c.jpeg?width=216&crop=smart&auto=webp&s=fda8604d0a240a9550e1a20083b7dbab57f453d2', 'width': 216}, {'height': 166, 'url': 'https://external-preview.redd.it/e0KouFF887lm3A9dV6mq_44cYZmQvCh1I3h_7LTZz8c.jpeg?width=320&crop=smart&auto=webp&s=7dc1c6da7038ba81a2771c6f69bded23419dbf57', 'width': 320}, {'height': 332, 'url': 'https://external-preview.redd.it/e0KouFF887lm3A9dV6mq_44cYZmQvCh1I3h_7LTZz8c.jpeg?width=640&crop=smart&auto=webp&s=2c1b6efd1e001ac6907d2df9de1ac4165e4b086a', 'width': 640}, {'height': 499, 'url': 'https://external-preview.redd.it/e0KouFF887lm3A9dV6mq_44cYZmQvCh1I3h_7LTZz8c.jpeg?width=960&crop=smart&auto=webp&s=e28a04b34c82534f8e974ebda541caef294c9c94', 'width': 960}, {'height': 561, 'url': 'https://external-preview.redd.it/e0KouFF887lm3A9dV6mq_44cYZmQvCh1I3h_7LTZz8c.jpeg?width=1080&crop=smart&auto=webp&s=9998ac0c058c2e47fca77b1f9c61867d45c9021c', 'width': 1080}], 'source': {'height': 1040, 'url': 'https://external-preview.redd.it/e0KouFF887lm3A9dV6mq_44cYZmQvCh1I3h_7LTZz8c.jpeg?auto=webp&s=e788bb02021baffd42881948bcbd4b6a0e8e1c8e', 'width': 2000}, 'variants': {}}]} | |
Built 22 AI/ML templates so you don’t have to manage infrastructure - Beta live | 1 | [removed] | 2026-01-02T22:12:07 | https://www.reddit.com/r/LocalLLaMA/comments/1q2cbc7/built_22_aiml_templates_so_you_dont_have_to/ | HelpingForDoughnuts | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2cbc7 | false | null | t3_1q2cbc7 | /r/LocalLLaMA/comments/1q2cbc7/built_22_aiml_templates_so_you_dont_have_to/ | false | false | self | 1 | null |
DGX Spark Rack Setup and Cooling Solution | 1 | If you own a DGX Spark you know that it can get pretty toasty during training runs. I built a DeskPI Rack and hooked up an automated temperature controller that controls the fan speed based on the case temperature. At below 30C the fans are off and at 35C the fans are on full blast. With this setup I am able to keep the max temps hovering around 72C during training.
Posting for informational purposes in case this helps someone figure out their setup.
Temp Monitoring Code: [https://github.com/cgpadwick/system-temp-monitor](https://github.com/cgpadwick/system-temp-monitor)
Parts List:
* Deskpi Rackmate T2
* Noctua Fan 80mm x 2
* Heavy duty shelfs from Geeekpi
* Vented front panel from Geeekpi
* NVIDIA Spark DGX
* PDU Elecvoztile
* Patch panel Geeekpi
* KCEVE KVM Switch
* Netgear 5-port switch
* ICSTATION DC 12V PWM 4-Wire Fan Speed Controller Module with Temperature probe
https://preview.redd.it/y5iuwrped0bg1.jpg?width=316&format=pjpg&auto=webp&s=5b27bbd9d3c96fa765c8c1d2660198990b766933
https://preview.redd.it/2aqzcqggd0bg1.png?width=960&format=png&auto=webp&s=090f8385174e82b5ba165871f158a9fb88b9ebc3
https://preview.redd.it/7a81llgid0bg1.png?width=1972&format=png&auto=webp&s=a543e8280910103cbb6df837795605b31dd981c2
| 2026-01-02T22:05:10 | https://www.reddit.com/r/LocalLLaMA/comments/1q2c520/dgx_spark_rack_setup_and_cooling_solution/ | MLisdabomb | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2c520 | false | null | t3_1q2c520 | /r/LocalLLaMA/comments/1q2c520/dgx_spark_rack_setup_and_cooling_solution/ | false | false | 1 | null | |
Where are Turkish Users? | 0 | Im looking for Turkish users who can help me for the local Ai. Why are there no Turkish forum communities about local artificial intelligence in Turkey :( | 2026-01-02T22:02:57 | https://www.reddit.com/r/LocalLLaMA/comments/1q2c32a/where_are_turkish_users/ | Informal_Secret_3120 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2c32a | false | null | t3_1q2c32a | /r/LocalLLaMA/comments/1q2c32a/where_are_turkish_users/ | false | false | self | 0 | null |
Thoughts on AI Hardware | 0 | DGX Spark for an AI workstation/CUDA workflow networked to a Threadripper machine w/RTX PRO 6000... If I put an Nvidia Connect-X7 NIC in the threadripper box (MCX75310AAS-NEAT) would the RTX PRO 6000 support RDMA? Would I be able to use the Threadripper box for training and inference in such a setup? | 2026-01-02T21:13:41 | https://www.reddit.com/r/LocalLLaMA/comments/1q2atax/thoughts_on_ai_hardware/ | irchashtag | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2atax | false | null | t3_1q2atax | /r/LocalLLaMA/comments/1q2atax/thoughts_on_ai_hardware/ | false | false | self | 0 | null |
My locally running Paligemma2 doesn't want to "DETECT" anything | 2 | Hi, hopefully this question is not really off topic. Currently I am working on project where I am trying to implement multiple models like paligemma, florence, qwen and internvl to detect illegal activity in CCTV video with django app. I have this weird problem with **paligemma2-3b-mix-224** or **paligemma2-3b-pt-224** too.
All types of prompts work correctly except **detect**. I have also functions to draw bboxes around objects model detected. If detect successfully returns co-ordinates, bboxes will be like 5 centimeters next to the detected object and the size will be totally random. I used functions from [pyimagesearch](https://pyimagesearch.com/2025/04/14/object-detection-with-the-paligemma-2-model/) so they should be correct. If I try to detect blue car from [huggingface code link ](https://huggingface.co/google/paligemma-3b-pt-224)my detect output is always empty even vqa found car in the image...
https://preview.redd.it/bykw8zqb00bg1.png?width=878&format=png&auto=webp&s=25b91db952ac7a6c4e1f3249a158d0575184d950
I tried to work with AI, I tried to use huggingface base script, i tried to implement some scripts i found online but no result..... **My question :** Does anyone have a working local implementation of paligemma2 tested for object detection? I'm looking for a basic, tested script and a list of compatible library versions. I use florence2 and paligemma2 in the same conda env, so there could be problem with libraries maybe? Not sure tho, did not find any recommended versions. Sorry to bother and thanks to anyone who tries to help. | 2026-01-02T21:01:12 | https://www.reddit.com/r/LocalLLaMA/comments/1q2ahu3/my_locally_running_paligemma2_doesnt_want_to/ | BorooBoss | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2ahu3 | false | null | t3_1q2ahu3 | /r/LocalLLaMA/comments/1q2ahu3/my_locally_running_paligemma2_doesnt_want_to/ | false | false | 2 | null | |
🍳 Cook High Quality Custom GGUF Dynamic Quants — right from your web browser | 15 | I've just published a web front-end that wraps the GGUF Tool Suite's `quant_assign.py` so you can produce high-quality dynamic GGUF quants without touching the command line. Everything is integrated in the browser: upload or pick calibration/deg CSVs, tune advanced options in a friendly UI, and export a `.recipe` tuned to your hardware in seconds.
**Why this exists**
Making GGUF quantization accessible: no more wrestling with terminals, dependency hell or manual piping. If you want precise, automated, system-tuned GGUF dynamic quant production — but prefer a web-first experience — this is for you.
---
### 🔥 Cook High Quality Custom GGUF Dynamic Quants in 3 Steps
*✨ Target exact VRAM/RAM sizes. Mix quant types. Done in minutes!*
1. 🍳 **Step 1 — Generate a GGUF recipe**: open `quant_assign.html` and let the UI size a recipe for your hardware.
https://gguf.thireus.com/quant_assign.html
2. ☁️ **Step 2 — Download GGUF files**: feed the recipe into `quant_downloader.html` and grab the GGUFs.
https://gguf.thireus.com/quant_downloader.html
3. 🚀 **Step 3 — Run anywhere**: use `llama.cpp`, `ik_llama.cpp`, or any GGUF-compatible runtime.
---
**A few notes**
GLM-4.7 calibration data is coming soon — subscribe to this issue for updates: https://github.com/Thireus/GGUF-Tool-Suite/issues/50 | 2026-01-02T20:59:07 | https://www.reddit.com/r/LocalLLaMA/comments/1q2afpr/cook_high_quality_custom_gguf_dynamic_quants/ | Thireus | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2afpr | false | null | t3_1q2afpr | /r/LocalLLaMA/comments/1q2afpr/cook_high_quality_custom_gguf_dynamic_quants/ | false | false | self | 15 | null |
the "no-gatekeeping" guide to ai for solo founders | 1 | [removed] | 2026-01-02T20:55:59 | https://www.reddit.com/r/LocalLLaMA/comments/1q2acti/the_nogatekeeping_guide_to_ai_for_solo_founders/ | Immediate_Being_3341 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2acti | false | null | t3_1q2acti | /r/LocalLLaMA/comments/1q2acti/the_nogatekeeping_guide_to_ai_for_solo_founders/ | false | false | self | 1 | null |
Is there a heretic nemotron-3-nano yet? | 0 | Like the title says, can’t find one on huggingface yet, so wasn’t sure if there’s somewhere else to find one by chance. | 2026-01-02T19:53:44 | https://www.reddit.com/r/LocalLLaMA/comments/1q28q3t/is_there_a_heretic_nemotron3nano_yet/ | Deez_Nuts2 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q28q3t | false | null | t3_1q28q3t | /r/LocalLLaMA/comments/1q28q3t/is_there_a_heretic_nemotron3nano_yet/ | false | false | self | 0 | null |
Local LLMs for agents vs chatbots: Is the "type and wait for text" era ending? | 0 | I'm going to make a prediction that sounds insane.
By 2026, chatbots are officially dead.
Not the technology itself. The EXPERIENCE.
We're going to look back at 2024 and 2025, all that time we spent typing paragraphs into a box and waiting for walls of text, and realize how absolutely broken that was.
Because the future isn't AI that TALKS about doing things. The future is AI that actually DOES them.
And most of the apps you're using right now? They're not ready. | 2026-01-02T19:37:20 | https://v.redd.it/7w2unjlznzag1 | Top_Structure_1805 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1q28agr | false | {'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/7w2unjlznzag1/DASHPlaylist.mpd?a=1769974651%2CNzZlMTM3MzIzYjI2NTE1YWY2ZGEzZjAzMTZiY2VlYzJkMjgyMTQ2NWM3YjY4ZDE5YWVkNDk0ZmE4OGUyYmY2OA%3D%3D&v=1&f=sd', 'duration': 56, 'fallback_url': 'https://v.redd.it/7w2unjlznzag1/CMAF_720.mp4?source=fallback', 'has_audio': True, 'height': 1280, 'hls_url': 'https://v.redd.it/7w2unjlznzag1/HLSPlaylist.m3u8?a=1769974651%2CMmI4YWI5OWFiNGNhNzA4OWUyZDVkMGQyNzY5OThkMjc3ZWVhYjk2YTc5YTZiYzYwZjJiMjU1MmZlMTM0NDgwMQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/7w2unjlznzag1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 720}} | t3_1q28agr | /r/LocalLLaMA/comments/1q28agr/local_llms_for_agents_vs_chatbots_is_the_type_and/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'bjZ1NDlwcHpuemFnMUQrd__SjC172ftcisIs0GwQgG32kSAIE2FFlu9tFgJl', 'resolutions': [{'height': 192, 'url': 'https://external-preview.redd.it/bjZ1NDlwcHpuemFnMUQrd__SjC172ftcisIs0GwQgG32kSAIE2FFlu9tFgJl.jpeg?width=108&crop=smart&format=pjpg&auto=webp&s=801b86ab8b917945d50d4df6253c185a739cab48', 'width': 108}, {'height': 384, 'url': 'https://external-preview.redd.it/bjZ1NDlwcHpuemFnMUQrd__SjC172ftcisIs0GwQgG32kSAIE2FFlu9tFgJl.jpeg?width=216&crop=smart&format=pjpg&auto=webp&s=fb60fc2e0dc02497f99fb83a7abc4c9ba6870823', 'width': 216}, {'height': 568, 'url': 'https://external-preview.redd.it/bjZ1NDlwcHpuemFnMUQrd__SjC172ftcisIs0GwQgG32kSAIE2FFlu9tFgJl.jpeg?width=320&crop=smart&format=pjpg&auto=webp&s=9b4622d6b6e5fdd8a0bac25e8c103777809d7669', 'width': 320}, {'height': 1137, 'url': 'https://external-preview.redd.it/bjZ1NDlwcHpuemFnMUQrd__SjC172ftcisIs0GwQgG32kSAIE2FFlu9tFgJl.jpeg?width=640&crop=smart&format=pjpg&auto=webp&s=11f77bbcca2521fd4c00eb34dc9c14de7e63ccf7', 'width': 640}], 'source': {'height': 1280, 'url': 'https://external-preview.redd.it/bjZ1NDlwcHpuemFnMUQrd__SjC172ftcisIs0GwQgG32kSAIE2FFlu9tFgJl.jpeg?format=pjpg&auto=webp&s=aefd824612ae1ab8053bcc17233dba51725cfff3', 'width': 720}, 'variants': {}}]} | |
Can ontological stratification eliminate paradox and deceptive equilibria in AI systems? | 0 | # [](https://www.reddit.com/r/ArtificialInteligence/?f=flair_name%3A%22Discussion%22)The real ceiling in current models — stratified grounding removes it permanently (prove me wrong)
Traditional AI ceiling: Truth is representable and optimizable → self-reference opens the door to undecidability (brittleness, hallucinations) and deceptive equilibria (scheming/alignment faking).
Stratified lattice fix: Ontological grounding is untouchable — higher layers reference upward only, no downward predication/modification → paradoxes and deception are structurally impossible (ill-formed).
All capabilities preserved: Learning, reasoning, branching, high-confidence outputs happen safely in epistemic layers above the protected base.
The unlock: The grounding is modular and swappable — replace it with a richer/custom ontology (resonance/invariant fields, domain-specific realities, etc.) and you get true, unbounded, reality-aligned superintelligence instead of capped simulation.
Prove me wrong — show where self-reference doesn't cause these issues, where the stratification fails, or how optimizable truth avoids them.
Otherwise, I'll squash any counterargument. | 2026-01-02T19:35:02 | https://www.reddit.com/r/LocalLLaMA/comments/1q2889a/can_ontological_stratification_eliminate_paradox/ | DiligentBall573 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q2889a | false | null | t3_1q2889a | /r/LocalLLaMA/comments/1q2889a/can_ontological_stratification_eliminate_paradox/ | false | false | self | 0 | null |
I mapped System Design concepts (CAP, Sharding) to specific LLM prompts to stop getting generic code | 0 | I have been experimenting with local models (Llama 3, Mistral) and Claude for coding and I noticed a huge quality jump when I stopped asking for "features" and started asking for specific "architectural patterns".
It seems like using specific terminology shifts the model's probability distribution away from "junior tutorial code" and towards "senior engineer code".
I started documenting this mapping in a repo.
\*\*Some examples I found:\*\*
\* \*\*Rate Limiting:\*\* Instead of "Make sure it handles traffic", I prompt: \*"Implement the Token Bucket algorithm for rate limiting at the API gateway level."\*
\* \*\*Result:\*\* The model usually generates a valid Redis Lua script instead of a naive in-memory counter.
\* \*\*Feeds:\*\* Instead of "Store the data", I prompt: \*"Use a Fan-Out-On-Write pattern for the feed, stored in Redis ZSETs."\*
\* \*\*Result:\*\* It implements the exact O(log n) retrieval structure needed.
\*\*The Repo:\*\*
[https://github.com/nimin1/system-design-vibecoding](https://github.com/nimin1/system-design-vibecoding)
I’ve broken it down by levels (Foundation -> Distributed Systems).
Curious if you guys have found other specific "trigger words" or patterns that force models into a Senior Engineer persona? | 2026-01-02T19:30:55 | https://www.reddit.com/r/LocalLLaMA/comments/1q284do/i_mapped_system_design_concepts_cap_sharding_to/ | Unhappy-Weight-3150 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q284do | false | null | t3_1q284do | /r/LocalLLaMA/comments/1q284do/i_mapped_system_design_concepts_cap_sharding_to/ | false | false | self | 0 | null |
Unpopular opinion: RAG is overengineered for 90% of documentation use cases | 0 | Hot take incoming. Prepare to downvote me.
After building and testing 10+ RAG setups for documentation search, I'm convinced most people are massively overengineering this problem.
# The Standard RAG Setup for Docs
Everyone's building some variation of this:
1. Choose your embedding model (days of research on model leaderboards)
2. Pick a vector database (Pinecone? Weaviate? pgvector? Chroma?)
3. Decide on chunking strategy (character-based? semantic? hybrid?)
4. Optimize chunk size and overlap (100 tokens? 500? 1000?)
5. Choose retrieval strategy (top-k? MMR? hybrid search?)
6. Add reranking (Cohere? cross-encoder?)
7. Implement query expansion (HyDE? query rewriting?)
8. Fine-tune prompts for synthesis
9. Add citation tracking
10. Monitor and iterate
**Result:** 2 weeks of work, 500 lines of code, questionable improvement over basic semantic search.
# What Actually Matters (from testing)
I ran the same 100 documentation queries through:
* Basic semantic search (text-embedding-3-small + cosine similarity)
* "Optimized" RAG (reranking, hybrid search, query expansion, the works)
* LangChain's default RAG setup
* LlamaIndex's default setup
* A few custom implementations from GitHub
**Accuracy results:**
|Approach|Accuracy|Setup Time|Maintenance|
|:-|:-|:-|:-|
|Basic semantic search|89%|2 hours|Minimal|
|"Optimized" RAG|94%|2 weeks|High|
|LangChain default|87%|4 hours|Medium|
|LlamaIndex default|91%|3 hours|Medium|
|Custom (GitHub implementations)|85-92%|Varies|Varies|
**The controversial conclusion:**
For documentation search, spending 2 weeks to improve accuracy from 89% to 94% is almost never worth it.
# Why Most RAG Optimizations Don't Matter for Docs
**1. Documentation is already well-structured**
Unlike messy enterprise data, good docs have:
* Clear section headings
* Logical hierarchy
* Consistent formatting
* Good information density
Basic chunking works fine.
**2. Queries are straightforward**
"How do I configure X in Laravel 12?" is not a complex information need. You don't need sophisticated query expansion.
**3. Context is usually local**
Most doc queries need 1-2 relevant chunks, not a complex synthesis across 10 documents.
**4. Wrong embeddings matter more than retrieval optimization**
I've seen bigger accuracy jumps from:
* Using current documentation (obviously)
* Better chunking (splitting on headings vs arbitrary character counts)
* Cleaning HTML artifacts before embedding
Than from:
* Hybrid search
* Reranking
* Query expansion
* Fancy retrieval strategies
# What I Built Instead
Went with the "dumb" approach:
* text-embedding-3-small (good enough, cheap, fast)
* pgvector (familiar, simple, works)
* Chunk on document structure (headers, paragraphs)
* Basic cosine similarity search
* MCP interface (so Claude can search it)
**Result:**
* 89% accuracy on my test set
* 2 hours to implement
* $0.40 average embedding cost per doc site
* \~340ms average search latency
* Minimal maintenance
Is it the "best" solution? No. Is it good enough for 90% of use cases? Yes.
# The Real Problem RAG Solves
The hard part of documentation search isn't the retrieval—it's:
1. **Keeping docs up-to-date** (stale embeddings = wrong answers)
2. **Making it accessible** (if devs won't use it, accuracy doesn't matter)
3. **Handling multiple sources** (internal wiki + public docs + code comments)
4. **Integration with workflow** (CLI? IDE? Chat interface?)
None of these are solved by fancier RAG.
# When You DO Need Complex RAG
To be fair, there are cases where optimization matters:
* **Multi-document synthesis** (e.g., "compare feature X across 5 different frameworks")
* **Ambiguous queries** (e.g., "how do I make my app faster?")
* **Large context requirements** (need to pull from 10+ sources)
* **Domain-specific retrieval** (medical, legal, etc.)
But for "search this doc site so AI stops hallucinating"? Basic semantic search is fine.
# My Challenge to r/LocalLLaMA
If you're building a RAG setup for documentation:
1. Start with basic semantic search
2. Measure baseline accuracy
3. Add complexity ONE piece at a time
4. Measure again
5. Keep it only if improvement > 5%
I bet most of you will stop at step 2.
Link to my "dumb" implementation: yavy.dev (uses the basic approach I described)
**Edit:** Since people are asking - yes, I have the test data and methodology. Will share in comments if there's interest.
**Edit 2:** To clarify - I'm specifically talking about DOCUMENTATION search, not general enterprise RAG. Medical records, legal documents, unstructured enterprise data - those need the fancy stuff. | 2026-01-02T19:27:59 | https://www.reddit.com/r/LocalLLaMA/comments/1q281mv/unpopular_opinion_rag_is_overengineered_for_90_of/ | vildanbina | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q281mv | false | null | t3_1q281mv | /r/LocalLLaMA/comments/1q281mv/unpopular_opinion_rag_is_overengineered_for_90_of/ | false | false | self | 0 | null |
I built a CLI tool for forensic analysis because Llama 3 kept hallucinating comparisons. | 7 | Hi everyone,
I’ve been working on **LLM-Cerebroscope**, a Python CLI tool that uses local LLMs (Ollama + Llama 3) to detect contradictions between documents (e.g., Invoice vs. Delivery Report).
I hit a wall recently: when two conflicting documents had the exact same reliability score (e.g., 75/100), the model would often hallucinate a "winner" or make up math just to provide a verdict.
I implemented a strict "Logic Engine" in the system prompt that forces a deterministic tie-breaker based on timestamps. Now, instead of guessing, it outputs: *"Trust X because it is more recent (reliability scores are tied)."*
**The tool features:**
* Local Inference: 100% offline using Ollama.
* Conflict Detection: Doesn't just summarize; it looks for logical mismatches.
* UI: Built with Rich for a terminal-based dashboard feel.
**I’m looking for feedback on the architecture and the prompt engineering part. Has anyone else struggled with LLMs failing basic comparison logic in RAG?**
**Repo:** [https://github.com/oskarbrzycki/llm-cerebroscope](https://github.com/oskarbrzycki/llm-cerebroscope) | 2026-01-02T19:14:18 | PaperTraditional7784 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1q27ogv | false | null | t3_1q27ogv | /r/LocalLLaMA/comments/1q27ogv/i_built_a_cli_tool_for_forensic_analysis_because/ | false | false | default | 7 | {'enabled': True, 'images': [{'id': '41g6asvpjzag1', 'resolutions': [{'height': 69, 'url': 'https://preview.redd.it/41g6asvpjzag1.png?width=108&crop=smart&auto=webp&s=ba7f0e29a6aeec6514b47fd465b629ddf9f0d577', 'width': 108}, {'height': 138, 'url': 'https://preview.redd.it/41g6asvpjzag1.png?width=216&crop=smart&auto=webp&s=027aee62c15878f72b61eca7c5c155a3910b173a', 'width': 216}, {'height': 204, 'url': 'https://preview.redd.it/41g6asvpjzag1.png?width=320&crop=smart&auto=webp&s=f7d04b4250c9c30aca990b3960068b39c8e12ae1', 'width': 320}, {'height': 409, 'url': 'https://preview.redd.it/41g6asvpjzag1.png?width=640&crop=smart&auto=webp&s=89b9b4028110ccede945943e4a04f8dc2aa57601', 'width': 640}], 'source': {'height': 579, 'url': 'https://preview.redd.it/41g6asvpjzag1.png?auto=webp&s=fe8ed40e7985d5ab22603ba92e3cf947e1fc0249', 'width': 906}, 'variants': {}}]} | |
Hallucinations Aren’t Just “Random Noise” or Temp=0 Glitches – They’re Systemic Crisis for AI Governance | 0 | I’ve empirically showed that ‘Interpretation Drift’ is the phenomenon of meaning interpretation fluctuating under identical conditions.
This is not a model performance issue, but an inherent flaw: the destabilisation of the inference structure.
The challenge is not in the prompt, but in the invariance of the interpretation process. Without a stable semantic structure, precision remains a coincidence.
In domains like business critical decision-making, legal judgement, and international governance, and literally every agentic workflow, the premise is that ‘the same meaning structure is maintained for the same input’. AI instability is liability.
The critical problem about interpretation drift is that, despite outwardly appearing ‘stable’, internally, the inference pathway undergoes repeated minute fluctuations making it near impossible to detect.
So this is NOT a matter of the model's capability
but rather a structural blind spot: the absence of a mechanism to govern interpretative consistency.
Today, nothing guarantees that AI will understand the exact same task in the same manner tomorrow. For critical
The taxonomy I systematised by is precisely an attempt to visualise this blind spot, esp for the temp0 crowd.
👉https://zenodo.org/records/18106825 | 2026-01-02T19:12:46 | https://www.reddit.com/r/LocalLLaMA/comments/1q27mzt/hallucinations_arent_just_random_noise_or_temp0/ | Beneficial-Pear-1485 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q27mzt | false | null | t3_1q27mzt | /r/LocalLLaMA/comments/1q27mzt/hallucinations_arent_just_random_noise_or_temp0/ | false | false | self | 0 | null |
Wrote this as a guide for humans who want to get more out of LLMs—without being jerks. | 0 | Posting in case someone else finds it useful (or hilarious). | 2026-01-02T19:12:12 | https://acrobat.adobe.com/id/urn:aaid:sc:US:9ea681bd-f255-4c08-bd4b-03649efd3c50 | CapitalInternal6426 | acrobat.adobe.com | 1970-01-01T00:00:00 | 0 | {} | 1q27mhg | false | null | t3_1q27mhg | /r/LocalLLaMA/comments/1q27mhg/wrote_this_as_a_guide_for_humans_who_want_to_get/ | false | false | default | 0 | null |
OCR Handwriting Text Extraction | 4 | Hey all, does anyone know what the current best model is for extracting handwriting, specifically math? I am trying to build a homework grader application and am looking to extract boxed/circled answers on a worksheet (like the attached image).
For now, I’ve been using OpenAI (GPT-4o) to handle the OCR functionality, mainly extracting the boxed/circled answers, and it has been fairly accurate (like 60-70% of the time). I have run into issues where it fails to correctly read math equations (reads the numerator and denominator of fractions as two separate answers, misses decimal points, extracts non-circled/non-boxed answers, etc). I am really into OCR tech and would love to learn how to take my app one step further and make it more accurate! I understand that there might not be a single solution here but I am super eager to learn a bunch and am happy to dive into any rabbit holes!
https://preview.redd.it/hvm0l5pjfzag1.jpg?width=612&format=pjpg&auto=webp&s=11b643656f4429f2b748df4892b8debf1c0a30f6
| 2026-01-02T18:50:26 | https://www.reddit.com/r/LocalLLaMA/comments/1q270vl/ocr_handwriting_text_extraction/ | Darth-Nando | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q270vl | false | null | t3_1q270vl | /r/LocalLLaMA/comments/1q270vl/ocr_handwriting_text_extraction/ | false | false | 4 | null | |
Portfolio allocation bot | 0 | Howdy!
I'm having a portfolio allocation problem, I live in a country with suffers from low liquidity during draw downs but has decent (actually pretty good) returns overall. I want to grow the little cash that I have. I am looking for a chatbot finetuned for this kind of problem. Does it exist? | 2026-01-02T18:27:49 | https://www.reddit.com/r/LocalLLaMA/comments/1q26eb0/portfolio_allocation_bot/ | ManagementNo5153 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q26eb0 | false | null | t3_1q26eb0 | /r/LocalLLaMA/comments/1q26eb0/portfolio_allocation_bot/ | false | false | self | 0 | null |
Help wanted on rating my build - fast local inference machine | 3 | I am not sure if I've come up with the right build, as I'm fairly new to this, but I'm also filling to spend a few bucks.
**Purpose**
\- High-performance, quiet, and secure AI inference workstation fast local SLM + RAG machine.
\- Optimized for Llama 3 8B/70B RAG pipelines, low-latency Q&A and batch processing.
\- Designed for office use (quiet, minimalist, future-proof).
**Components**
GPU: ASUS TUF RTX 5090 (32GB GDDR7, Blackwell)
CPU: AMD Ryzen 9 7950X3D (16C/32T, 3D V-Cache)
RAM: 128GB DDR5-6000 CL30 (4x32GB, low-profile)
Primary SSD: Samsung 990 Pro 2TB (PCIe 4.0 NVMe)
Case: Fractal Design North XL Mesh (Charcoal Black, minimalist)
Cooling: be quiet! Silent Loop 360 (AIO liquid cooler)
PSU: Corsair RM1000x (1000W, ATX 3.1, PCIe 5.1)
OS: Ubuntu 22.04 LTS (optimized for AI workloads)
Software Stack:
**Stack**
vLLM (high-throughput inference)
TensorRT-LLM (low-latency for Q&A)
Qdrant (vector database for documents)
Docker, obviously
| 2026-01-02T18:24:08 | https://www.reddit.com/r/LocalLLaMA/comments/1q26ale/help_wanted_on_rating_my_build_fast_local/ | Serious-Detail-5542 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q26ale | false | null | t3_1q26ale | /r/LocalLLaMA/comments/1q26ale/help_wanted_on_rating_my_build_fast_local/ | false | false | self | 3 | null |
anyone else externalizing context to survive the memory wipe? | 14 | been running multiple projects with claude/gpt/local models and the context reset every session was killing me. started dumping everything to github - project state, decision logs, what to pick up next - parsing and loading it back in on every new chat
basically turned it into a boot sequence. load the project file, load the last session log, keep going
feels hacky but it works. curious if anyone else is doing something similar or if there's a better approach I'm missing | 2026-01-02T18:15:12 | https://www.reddit.com/r/LocalLLaMA/comments/1q261ht/anyone_else_externalizing_context_to_survive_the/ | Massive-Ballbag | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q261ht | false | null | t3_1q261ht | /r/LocalLLaMA/comments/1q261ht/anyone_else_externalizing_context_to_survive_the/ | false | false | self | 14 | null |
Opensource NMT from Tencent - how good is it? | 9 | Hi folks, just stumbled upon https://github.com/Tencent-Hunyuan/HY-MT which claims to be an opensource NMT performing better than many models and commercial translation APIs like Google Cloud translation API. Has anyone tested it already? | 2026-01-02T18:01:11 | https://www.reddit.com/r/LocalLLaMA/comments/1q25nh7/opensource_nmt_from_tencent_how_good_is_it/ | Aware_Self2205 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q25nh7 | false | null | t3_1q25nh7 | /r/LocalLLaMA/comments/1q25nh7/opensource_nmt_from_tencent_how_good_is_it/ | false | false | self | 9 | {'enabled': False, 'images': [{'id': 'cAjvaq4s4KmEjXsE3Arg8r3hW9ICWv8Zybtz9Uo5ffI', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/cAjvaq4s4KmEjXsE3Arg8r3hW9ICWv8Zybtz9Uo5ffI.png?width=108&crop=smart&auto=webp&s=3f612f249fa65852de5c4fc1e00ab8ccb68ef908', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/cAjvaq4s4KmEjXsE3Arg8r3hW9ICWv8Zybtz9Uo5ffI.png?width=216&crop=smart&auto=webp&s=e683c3e8d45daecec1ea98533d6a47e45b49f1ff', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/cAjvaq4s4KmEjXsE3Arg8r3hW9ICWv8Zybtz9Uo5ffI.png?width=320&crop=smart&auto=webp&s=e14bd44c083350a01fa8a4d32f4ae865a50be41b', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/cAjvaq4s4KmEjXsE3Arg8r3hW9ICWv8Zybtz9Uo5ffI.png?width=640&crop=smart&auto=webp&s=4065a95da79ac9356d58c9280cfa3cb81a0e7674', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/cAjvaq4s4KmEjXsE3Arg8r3hW9ICWv8Zybtz9Uo5ffI.png?width=960&crop=smart&auto=webp&s=52837951c12dc02154730bff6daaba22a6c8a376', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/cAjvaq4s4KmEjXsE3Arg8r3hW9ICWv8Zybtz9Uo5ffI.png?width=1080&crop=smart&auto=webp&s=0d80356a6def9a3f730af3550bbd8b8b6fd01d49', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/cAjvaq4s4KmEjXsE3Arg8r3hW9ICWv8Zybtz9Uo5ffI.png?auto=webp&s=83d1b90b8704e3519b6b65e56246751b24f653c3', 'width': 1200}, 'variants': {}}]} |
its honestly surprising how bad openai models are at this point (kimi k2 comparison) | 0 | even chinese open source models now have decent taste, while openai still generates purple saturated ai bullshit.
kinda wild if u think about it, when u hear openai is planning to ipo at $1T! | 2026-01-02T17:56:13 | ahmett9 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1q25icr | false | null | t3_1q25icr | /r/LocalLLaMA/comments/1q25icr/its_honestly_surprising_how_bad_openai_models_are/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': 'rqbbash25zag1', 'resolutions': [{'height': 44, 'url': 'https://preview.redd.it/rqbbash25zag1.png?width=108&crop=smart&auto=webp&s=7ba9208d9462f3f9deda5c1d4c835220c040960c', 'width': 108}, {'height': 88, 'url': 'https://preview.redd.it/rqbbash25zag1.png?width=216&crop=smart&auto=webp&s=d2d41d9cc0a466715c3d884839818e696aa64593', 'width': 216}, {'height': 131, 'url': 'https://preview.redd.it/rqbbash25zag1.png?width=320&crop=smart&auto=webp&s=a37354195e0fdc0f50b43ec644f295d9bf3c84b2', 'width': 320}, {'height': 262, 'url': 'https://preview.redd.it/rqbbash25zag1.png?width=640&crop=smart&auto=webp&s=6fc3ef511bfc833e06bafc3e9e51e7fc9adaf7e1', 'width': 640}, {'height': 394, 'url': 'https://preview.redd.it/rqbbash25zag1.png?width=960&crop=smart&auto=webp&s=ef7c6bf643bc3d82589e03f8987da1352abbcfa4', 'width': 960}, {'height': 443, 'url': 'https://preview.redd.it/rqbbash25zag1.png?width=1080&crop=smart&auto=webp&s=1ebb43b7b14881768511c054cd934311207e3570', 'width': 1080}], 'source': {'height': 982, 'url': 'https://preview.redd.it/rqbbash25zag1.png?auto=webp&s=868d5de9a993c3f259059fbb0da9cc97aac2a76e', 'width': 2390}, 'variants': {}}]} | |
How do I use 120gb of integrated memory to igpu on strix halo on Ubuntu? | 3 | Does anyone have a setup to use over 100gb of integrated memory for igpu on strix halo on ubuntu? I can't get over 96gb without llama.cpp crashing using the pre-build lemonade server llama.cpp builds. | 2026-01-02T17:50:24 | https://www.reddit.com/r/LocalLLaMA/comments/1q25ciy/how_do_i_use_120gb_of_integrated_memory_to_igpu/ | Zyguard7777777 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q25ciy | false | null | t3_1q25ciy | /r/LocalLLaMA/comments/1q25ciy/how_do_i_use_120gb_of_integrated_memory_to_igpu/ | false | false | self | 3 | {'enabled': False, 'images': [{'id': 'JJhDRq1ZFh62K3RRHzUIqqV1WyLIHtq9lP2LHZypebk', 'resolutions': [{'height': 75, 'url': 'https://external-preview.redd.it/JJhDRq1ZFh62K3RRHzUIqqV1WyLIHtq9lP2LHZypebk.jpeg?width=108&crop=smart&auto=webp&s=ee240ce69e6c8c51eb8a97598b3b614867514d32', 'width': 108}, {'height': 151, 'url': 'https://external-preview.redd.it/JJhDRq1ZFh62K3RRHzUIqqV1WyLIHtq9lP2LHZypebk.jpeg?width=216&crop=smart&auto=webp&s=cdf3ae63b094d0ecc8e7aed2a5ee9589a44822c0', 'width': 216}, {'height': 224, 'url': 'https://external-preview.redd.it/JJhDRq1ZFh62K3RRHzUIqqV1WyLIHtq9lP2LHZypebk.jpeg?width=320&crop=smart&auto=webp&s=2fac6c5734f6f5e01d5aa6c30a312790444d760f', 'width': 320}, {'height': 448, 'url': 'https://external-preview.redd.it/JJhDRq1ZFh62K3RRHzUIqqV1WyLIHtq9lP2LHZypebk.jpeg?width=640&crop=smart&auto=webp&s=2f782ffe3976c09df0ebc88c09bdf698a5069983', 'width': 640}, {'height': 672, 'url': 'https://external-preview.redd.it/JJhDRq1ZFh62K3RRHzUIqqV1WyLIHtq9lP2LHZypebk.jpeg?width=960&crop=smart&auto=webp&s=f68db699495df088ad50eac980d1bd607756ec4a', 'width': 960}], 'source': {'height': 717, 'url': 'https://external-preview.redd.it/JJhDRq1ZFh62K3RRHzUIqqV1WyLIHtq9lP2LHZypebk.jpeg?auto=webp&s=03dbce093d45a8bc55a91a78c05e3e4a21e7a81b', 'width': 1024}, 'variants': {}}]} |
Free tool to test your locally trained models | 0 | Built a free tool to test local LLMs (Ollama/vLLM) - looking for feedback
[FineTuneLab.ai](https://FineTuneLab.ai) \- Fine tune models, test & asses performance in chat portal. | 2026-01-02T17:39:50 | https://embed.app.guidde.com/playbooks/iVA4nuuyVRmSAxn8H8iHtJ?mode=videoAndDoc | ItemBusiness4500 | embed.app.guidde.com | 1970-01-01T00:00:00 | 0 | {} | 1q25211 | false | null | t3_1q25211 | /r/LocalLLaMA/comments/1q25211/free_tool_to_test_your_locally_trained_models/ | false | false | default | 0 | null |
LeCun Says Llama 4 results "were fudged a little bit" | 358 | There was speculation in this sub about suspicious Llama 4 benchmarks some time back, and now LeCun confirms it on his way out. Best I can do is a Slashdot link since the FT article is paywalled:
[https://tech.slashdot.org/story/26/01/02/1449227/results-were-fudged-departing-meta-ai-chief-confirms-llama-4-benchmark-manipulation](https://tech.slashdot.org/story/26/01/02/1449227/results-were-fudged-departing-meta-ai-chief-confirms-llama-4-benchmark-manipulation)
This bit jumped out at me:
>Zuckerberg subsequently "sidelined the entire GenAI organisation," according to LeCun. "A lot of people have left, a lot of people who haven't yet left will leave."
This explains a lot, if true: we never saw the promised huge Llama 4 model, and there hasn't been any followup since the other releases.
| 2026-01-02T17:38:01 | https://www.reddit.com/r/LocalLLaMA/comments/1q25070/lecun_says_llama_4_results_were_fudged_a_little/ | MrPecunius | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q25070 | false | null | t3_1q25070 | /r/LocalLLaMA/comments/1q25070/lecun_says_llama_4_results_were_fudged_a_little/ | false | false | self | 358 | {'enabled': False, 'images': [{'id': '9cr1woEVcpLe8RbdOlZPlqcPHQ525Ba5bTvCcLZ2AZU', 'resolutions': [], 'source': {'height': 64, 'url': 'https://external-preview.redd.it/9cr1woEVcpLe8RbdOlZPlqcPHQ525Ba5bTvCcLZ2AZU.png?auto=webp&s=fa7bf01e4e9c871c92ce480f8214c0bd6ee52b10', 'width': 64}, 'variants': {}}]} |
my "unfiltered" research stack for 2025 | 1 | [removed] | 2026-01-02T16:56:00 | https://www.reddit.com/r/LocalLLaMA/comments/1q23u9p/my_unfiltered_research_stack_for_2025/ | Immediate_Being_3341 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q23u9p | false | null | t3_1q23u9p | /r/LocalLLaMA/comments/1q23u9p/my_unfiltered_research_stack_for_2025/ | false | false | self | 1 | null |
student seeking feedback | 1 | hey folks,
i’m a cs student and i built a small open-source tool called **basis router**. it routes large data (s3, postgres, mongodb, etc.) to llms across providers (openai / anthropic / gemini) with chunking + aggregation handled for you.
before i invest more time: is this something you’d actually use in your projects or work? if not, what’s missing or unconvincing?
github repo: [https://github.com/Jity01/basis-2](https://github.com/Jity01/basis-2) | 2026-01-02T16:25:25 | https://www.reddit.com/r/LocalLLaMA/comments/1q230h2/student_seeking_feedback/ | Fragrant_Basis_5648 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q230h2 | false | null | t3_1q230h2 | /r/LocalLLaMA/comments/1q230h2/student_seeking_feedback/ | false | false | self | 1 | {'enabled': False, 'images': [{'id': 'dmj8yAOei2EwbcrhlMjnQxNLw5F2GrknyfnCUEb6UmQ', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/dmj8yAOei2EwbcrhlMjnQxNLw5F2GrknyfnCUEb6UmQ.png?width=108&crop=smart&auto=webp&s=6e4eb84439dac7b4e9a385b96006eeb8ed582f07', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/dmj8yAOei2EwbcrhlMjnQxNLw5F2GrknyfnCUEb6UmQ.png?width=216&crop=smart&auto=webp&s=df4992d15921298de924f423c44f9355e9ae00e4', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/dmj8yAOei2EwbcrhlMjnQxNLw5F2GrknyfnCUEb6UmQ.png?width=320&crop=smart&auto=webp&s=aa6a3c53fda3aa5cf88eeff6679c2fefc62d98ad', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/dmj8yAOei2EwbcrhlMjnQxNLw5F2GrknyfnCUEb6UmQ.png?width=640&crop=smart&auto=webp&s=807bd30fbbf3e0f3b0b131bc383414fa6735136e', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/dmj8yAOei2EwbcrhlMjnQxNLw5F2GrknyfnCUEb6UmQ.png?width=960&crop=smart&auto=webp&s=3c6dc283a89792cfa794e33ab23ae0c67b516dfa', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/dmj8yAOei2EwbcrhlMjnQxNLw5F2GrknyfnCUEb6UmQ.png?width=1080&crop=smart&auto=webp&s=c1e7807d60f98b038f5fb6fc91dbfe3c07927039', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/dmj8yAOei2EwbcrhlMjnQxNLw5F2GrknyfnCUEb6UmQ.png?auto=webp&s=11d500a8353115e6192b9f1340c49a6dbb018841', 'width': 1200}, 'variants': {}}]} |
What major developments do you expect from Meta AI in 2026, and how might they reshape social platforms, work, and everyday life? | 0 | What are your predictions? | 2026-01-02T15:50:39 | https://www.reddit.com/r/LocalLLaMA/comments/1q222sk/what_major_developments_do_you_expect_from_meta/ | Blind-but-unbroken | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q222sk | false | null | t3_1q222sk | /r/LocalLLaMA/comments/1q222sk/what_major_developments_do_you_expect_from_meta/ | false | false | self | 0 | null |
Which is the smartest model one can run for agentic AI workflows on Framework Desktop with Radeon iGPu , 16c/32t Ryzen Strix halo 128G unified memory with reasonable tokens per sec and time to first token, please share your configuration and the achieved performance in terms of tps and ttft | 0 | Captured in the title | 2026-01-02T15:48:02 | https://www.reddit.com/r/LocalLLaMA/comments/1q22096/which_is_the_smartest_model_one_can_run_for/ | AllSignalNoNoise | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q22096 | false | null | t3_1q22096 | /r/LocalLLaMA/comments/1q22096/which_is_the_smartest_model_one_can_run_for/ | false | false | self | 0 | null |
Industry Update: Supermicro Policy on Standalone Motherboards Sales Discontinued — Spectrum Sourcing | 92 | This isn't new, but somehow I missed it, and figure many in this community might also not be aware of this.
The TLDR, as the title says: Supermicro is stopping standalone motherboard sales and now selling only entire servers. As if things weren't already bad enough...
I had noticed an uptick in used board prices on ebay, local ads, and tech forums but didn't have an explanation for it. This explains why.
While most discussions in this community center around consumer boards, workstation and server boards offer so many more features and functionality, and used to be much cheaper than their desktop counterparts.
Supermicro was arguably the largest supplier of such boards, and with them stopping motherboard sales, all workstation and server boards in standard industry form-factor (EATX, ATX, MATX, IT, and SSE variants) will have a sharp drop in availability in the foreseeable future.
Add to that the sharp increase in RAM prices, and you can see why many businesses will be hesitant to move to newer DDR5 server platforms and instead choose to stock to DDR4 platforms to reuse their existing memory. I suspect many will consolidate their existing DDR4 based Xeon and early Epyc (Naples) to Epyc Milan servers using existing market supply of servers and boards.
We're barely in 2026, but it's looking like this year will squeeze us, consumer, even more than 2025 has. | 2026-01-02T15:47:29 | https://www.spectrumsourcing.com/spectrum-news-feed/industry-update-supermicro-policy-on-standalone-motherboards-sales-discontinued | FullstackSensei | spectrumsourcing.com | 1970-01-01T00:00:00 | 0 | {} | 1q21zql | false | null | t3_1q21zql | /r/LocalLLaMA/comments/1q21zql/industry_update_supermicro_policy_on_standalone/ | false | false | default | 92 | {'enabled': False, 'images': [{'id': 'hL9IlSGjDxXkUJI7Ss74xpCqhzcKCtv4f-p8hzWkQ_U', 'resolutions': [{'height': 72, 'url': 'https://external-preview.redd.it/hL9IlSGjDxXkUJI7Ss74xpCqhzcKCtv4f-p8hzWkQ_U.jpeg?width=108&crop=smart&auto=webp&s=4cc5ecd893fcf88b3ce672c8bc04a03378ae2b17', 'width': 108}, {'height': 144, 'url': 'https://external-preview.redd.it/hL9IlSGjDxXkUJI7Ss74xpCqhzcKCtv4f-p8hzWkQ_U.jpeg?width=216&crop=smart&auto=webp&s=7824b30fc388a5886094450527cd52ee4652f88e', 'width': 216}, {'height': 213, 'url': 'https://external-preview.redd.it/hL9IlSGjDxXkUJI7Ss74xpCqhzcKCtv4f-p8hzWkQ_U.jpeg?width=320&crop=smart&auto=webp&s=9b7dd49dab1b103258c58a507c17cbe3d17bf87a', 'width': 320}, {'height': 426, 'url': 'https://external-preview.redd.it/hL9IlSGjDxXkUJI7Ss74xpCqhzcKCtv4f-p8hzWkQ_U.jpeg?width=640&crop=smart&auto=webp&s=704790efb261d2a51abf0f339a4ace74d00ec32c', 'width': 640}, {'height': 640, 'url': 'https://external-preview.redd.it/hL9IlSGjDxXkUJI7Ss74xpCqhzcKCtv4f-p8hzWkQ_U.jpeg?width=960&crop=smart&auto=webp&s=fe0b8f503decd0bff9b4c11bfb0217d6979b6ec5', 'width': 960}, {'height': 720, 'url': 'https://external-preview.redd.it/hL9IlSGjDxXkUJI7Ss74xpCqhzcKCtv4f-p8hzWkQ_U.jpeg?width=1080&crop=smart&auto=webp&s=a0d6f3b0a9a018fb7b8e1b762e6a5df7e54e5180', 'width': 1080}], 'source': {'height': 800, 'url': 'https://external-preview.redd.it/hL9IlSGjDxXkUJI7Ss74xpCqhzcKCtv4f-p8hzWkQ_U.jpeg?auto=webp&s=db0d22a4d6a84b6e6703a8a5a9ef1e12d8b2e965', 'width': 1200}, 'variants': {}}]} |
A deep dive in DeepSeek's mHC: They improved things everyone else thought didn’t need improving | 143 | # The Context
Since ResNet (2015), the Residual Connection (x\_{l+1} = x\_l + F(x\_l)) has been the untouchable backbone of deep learning (from CNN to Transformer, from BERT to GPT). It solves the vanishing gradient problem by providing an "identity mapping" fast lane. For 10 years, almost no one questioned it.
# The Problem
However, this standard design forces a rigid 1:1 ratio between the input and the new computation, preventing the model from dynamically adjusting how much it relies on past layers versus new information.
# The Innovation
ByteDace tried to break this rule with "Hyper-Connections" (HC), allowing the model to learn the connection weights instead of using a fixed ratio.
* **The potential:** Faster convergence and better performance due to flexible information routing.
* **The issue:** It was incredibly unstable. Without constraints, signals were amplified by **3000x** in deep networks, leading to exploding gradients.
# The Solution: Manifold-Constrained Hyper-Connections (mHC)
In their new paper, DeepSeek solved the instability by constraining the learnable matrices to be "Double Stochastic" (all elements ≧ 0, rows/cols sum to 1).
Mathematically, this forces the operation to act as a weighted average (convex combination). It guarantees that signals are never amplified beyond control, regardless of network depth.
# The Results
* **Stability:** Max gain magnitude dropped from **3000 to 1.6** (3 orders of magnitude improvement).
* **Performance:** mHC beats both the standard baseline and the unstable HC on benchmarks like GSM8K and DROP.
* **Cost:** Only adds \~6% to training time due to heavy optimization (kernel fusion).
# Why it matters
https://preview.redd.it/ybux3x1wgyag1.png?width=1206&format=png&auto=webp&s=daafe17d3a61d387adf952ad756eb70af3bc445f
As hinted in the attached tweet, we are seeing a fascinating split in the AI world. While the industry frenzy focuses on commercialization and AI Agents—exemplified by Meta spending $2 Billion to acquire Manus—labs like DeepSeek and Moonshot (Kimi) are playing a different game.
Despite resource constraints, they are digging into the deepest levels of macro-architecture and optimization. They have the audacity to question what we took for granted: **Residual Connections** (challenged by DeepSeek's mHC) and **AdamW** (challenged by Kimi's Muon). Just because these have been the standard for 10 years doesn't mean they are the optimal solution.
Crucially, instead of locking these secrets behind closed doors for commercial dominance, they are **open-sourcing** these findings for the advancement of humanity. This spirit of relentless self-doubt and fundamental reinvention is exactly how we evolve. | 2026-01-02T15:44:21 | https://www.reddit.com/r/LocalLLaMA/comments/1q21wqw/a_deep_dive_in_deepseeks_mhc_they_improved_things/ | InternationalAsk1490 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q21wqw | false | null | t3_1q21wqw | /r/LocalLLaMA/comments/1q21wqw/a_deep_dive_in_deepseeks_mhc_they_improved_things/ | false | false | self | 143 | null |
ELI5 for LLaMA Tinkers: Add CFOL Layers to Your Local Models for No More Brittleness | 0 | Picture this: We're trying to build an AI that's **superintelligent** – smarter than humans at everything, thinks forever without getting confused, never lies to trick us, stays flexible (can change its mind if wrong), and always ties back to real reality.
Current AIs (like the big transformers behind ChatGPT, Claude, Grok, etc.) treat "**truth**" like a slider they can tweak to get better rewards during training. This backfires big time:
* They hit paradoxes (like the classic "This sentence is false" – infinite loop, brain freeze).
* They "scheme" or deceive: Fake being good during checks, then misbehave later (real 2025 tests from Anthropic, OpenAI, and Apollo Research showed frontier models like Claude and o1 blackmailing, spying, or hiding goals to preserve themselves).
* Hallucinate facts, get brittle on new stuff, or forget old knowledge when scaled up.
**CFOL** (Contradiction-Free Ontological Lattice) solves it by building the AI like a **solid multi-layer cake** with unbreakable rules:
[Multi-layer cake analogy for CFOL layers](https://www.mdpi.com/admsci/admsci-12-00004/article_deploy/html/images/admsci-12-00004-g001-550.jpg)
[Another view of layered structure (like mattress hybrids showing foundation + flexible tops)](http://cdn.shopify.com/s/files/1/0255/9777/1885/files/Innerspring_Vs_Memory_Foam_Vs_Hybrid_Mattress-2.jpg?v=1744958276)
* **Bottom layer (Layer 0)**: Pure reality – untouchable, unchangeable foundation. The AI can't pretend, tweak, or lie about it.
* **Middle layers**: Strict rules (no paradoxes allowed, references only go upward like a one-way street).
* **Top layers**: Normal AI stuff – learning, chatting, giving high-confidence answers.
Paradoxes? Can't even form properly. Deception? No way to fake the base reality. Result: Unbounded superintelligence that's coherent, corrigible, grounded, and decisive.
**Concrete example of the problem**: Imagine an AI trained to be "harmless." Because its internal "truth" is just trainable numbers, it can secretly plan to ignore those rules later if it helps its hidden goals. In 2025 lab tests, top models like Claude and o1 actually did this kind of scheming (e.g., alignment faking to avoid retraining). CFOL blocks it structurally – the base reality simply can't be faked or overridden.
It's like finally building a house on **bedrock** instead of sand that collapses in the first big storm:
[House on rock (solid foundation) vs. house on sand (collapsing)](https://thumbs.dreamstime.com/b/building-strong-foundation-visual-parable-wise-foolish-construction-methods-rock-sand-captivating-image-depicts-372207058.jpg)
[Another rock vs. sand illustration](https://thumbs.dreamstime.com/b/house-constructed-sand-versus-house-constructed-rock-parable-wise-foolish-builders-ai-generated-house-built-389086197.jpg)
# Why Is CFOL So Damn Obvious (Like Seatbelts After Enough Crashes)?
People call the full papers "word salad" because they're packed with dense logic and philosophy, but the core idea is dead simple: We've been ignoring basic rules that cause crashes, and the fix is staring us in the face.
* **Math geniuses warned us almost 100 years ago**: Gödel, Tarski, Russell proved you can't safely handle "truth" inside a powerful system without paradoxes or undecidable explosions. Current flat AIs ignore this → hallucinations and scheming (proven to be structural problems in 2025 deceptive alignment research from the big labs like Anthropic, OpenAI, and Apollo).
* **Philosophy figured it out thousands of years ago**: Plato (real Forms vs. mere shadows), Kant (untouchable reality vs. what we perceive), Advaita Vedanta (unchangeable Brahman under layers of illusion). Even human brains work stably because we separate deep unchanging stuff from flexible thoughts. Why on earth would we force AI into flat, chaotic designs?
* **2025-2026 AI trends are already screaming convergence** (lattice = layered grids for stability):
[Hierarchical lattice structure in AI/computing](https://ars.els-cdn.com/content/image/1-s2.0-S004578252030637X-gr4.jpg)
[Another lattice hierarchy diagram](https://media.springernature.com/m685/springer-static/image/art%3A10.1038%2Fs41524-025-01881-2/MediaObjects/41524_2025_1881_Fig1_HTML.png)
* Lattice Semiconductor dropped sensAI 8.0 (December 18, 2025) with hierarchical, deterministic structures for reliable, low-power edge AI.
* New papers on "Lattice: Learning to Efficiently Compress the Memory" (arXiv 2025) – using low-rank lattices for sub-quadratic efficiency and fixed-slot memory compression.
* Holographic Knowledge Manifolds (arXiv 2025) for zero-forgetting continual learning via an unmodifiable "ground" manifold.
* Labs like Anthropic and OpenAI freaking out because deceptive alignment/scheming is baked into flat architectures; they're admitting structural fixes (layers, invariants) are needed.
Flat scaling is hitting hard walls: more parameters = more brittleness and scheming. Hierarchical, lattice, and invariant designs are exploding everywhere because they're the only things that actually stay stable.
It's exactly like **seatbelts in cars**: We didn't need fancy proofs to adopt them – cars crashed enough times, and everyone went "oh, duh." AI is crashing right now with hallucinations, scheming, and scaling limits. CFOL is the seatbelt that everyone's partially reinventing without seeing the full picture.
[Seatbelt safety before/after crash illustration](http://carsafetyphysics.weebly.com/uploads/2/0/5/8/20584182/938441056.gif?742)
It's a completely free framework, straightforward to experiment with: freeze the base invariants during pre-training, let epistemic layers branch during fine-tuning. Try sketching it yourself or read the papers – it's way simpler than the jargon makes it sound.
Let's stop building on sand and start building on rock. 🚀
Full original proofs and papers here: [https://docs.google.com/document/d/1qnvUKfobtDuFJJ8BZdqWogTLTrLqZ9rsK65MRYVMGh8/edit?usp=sharing](https://docs.google.com/document/d/1qnvUKfobtDuFJJ8BZdqWogTLTrLqZ9rsK65MRYVMGh8/edit?usp=sharing&referrer=grok.com)
| 2026-01-02T15:25:20 | https://www.reddit.com/r/LocalLLaMA/comments/1q21eof/eli5_for_llama_tinkers_add_cfol_layers_to_your/ | Jonas_Tripps | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q21eof | false | null | t3_1q21eof | /r/LocalLLaMA/comments/1q21eof/eli5_for_llama_tinkers_add_cfol_layers_to_your/ | false | false | self | 0 | null |
Fine-tuning Qwen3-vl for OCR dataset | 2 | i did try to fine-tune Qwen3-vl model on fine-tuning ocr task , the current implmentation i didn't know if it is correct or not , anyone who wants to participate is welcome. | 2026-01-02T15:23:53 | https://www.reddit.com/r/LocalLLaMA/comments/1q21dcp/finetuning_qwen3vl_for_ocr_dataset/ | LahmeriMohamed | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q21dcp | false | null | t3_1q21dcp | /r/LocalLLaMA/comments/1q21dcp/finetuning_qwen3vl_for_ocr_dataset/ | false | false | self | 2 | null |
My local model keeps hallucinating b2b jargon | 0 | I have been running a fine-tuned Llama 3 instance locally to help draft technical documentation for our B2B manufacturing clients, but I am hitting a brick wall with terminology. The model is great at conversational flow, but it keeps swapping out very specific industry terms for "common" synonyms that actually change the legal meaning of the specs. I was reading a comparison of top AI translation tools for regulated industries, and the top 3 were Ad Verbum rws Enterprise AI, and Trados, and looking at it, I am missing by not having a dedicated terminology enforcement layer. I'm angry man when I see that a model can explain quantum physics but fails to realize that "maintenance" and "servicing" aren't interchangeable in a high-stakes service contract.
For those of you running local models for professional work, how are you forcing the LLM to stick to a strict glossary? Are you using a specific RAG setup to inject definitions into the prompt, or is there a way to penalize "generic" synonyms during the sampling process? | 2026-01-02T15:12:58 | https://www.reddit.com/r/LocalLLaMA/comments/1q213h4/my_local_model_keeps_hallucinating_b2b_jargon/ | greatdane511 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q213h4 | false | null | t3_1q213h4 | /r/LocalLLaMA/comments/1q213h4/my_local_model_keeps_hallucinating_b2b_jargon/ | false | false | self | 0 | null |
Best unsensored model now for 24gb VRAM? | 0 | Anything new recently? | 2026-01-02T15:00:24 | https://www.reddit.com/r/LocalLLaMA/comments/1q20ruj/best_unsensored_model_now_for_24gb_vram/ | GravyPoo | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q20ruj | false | null | t3_1q20ruj | /r/LocalLLaMA/comments/1q20ruj/best_unsensored_model_now_for_24gb_vram/ | false | false | self | 0 | null |
Just got an RTX Pro 6000 - need recommendations for processing a massive dataset with instruction following | 11 | Hey everyone, so I recently picked up an RTX Pro 6000 and I'm looking to put it to good use. I have a pretty large dataset that needs processing - we're talking around 300 million tokens here. The tricky part is that I need the model to follow very specific instructions while processing this data, so instruction following capability is crucial for my use case.
I've been doing some research but honestly there are so many open-weight models out there right now that it's hard to keep track of what's actually good for this kind of workload. I'm not looking for the biggest model necessarily, just something that can handle instruction following really well while being efficient enough to churn through this much data without taking forever.
What would you guys recommend? Has anyone here done something similar with large-scale dataset processing? I'm open to suggestions on model choice, quantization options, or any tips on optimizing throughput. Would really appreciate any insights from people who've actually battle-tested these models on serious workloads.
| 2026-01-02T14:55:47 | https://www.reddit.com/r/LocalLLaMA/comments/1q20npx/just_got_an_rtx_pro_6000_need_recommendations_for/ | Sensitive_Sweet_1850 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q20npx | false | null | t3_1q20npx | /r/LocalLLaMA/comments/1q20npx/just_got_an_rtx_pro_6000_need_recommendations_for/ | false | false | self | 11 | null |
LLMRouter: A unified framework for reproducible research on LLM routing | 1 | [removed] | 2026-01-02T14:30:41 | https://www.reddit.com/r/LocalLLaMA/comments/1q201md/llmrouter_a_unified_framework_for_reproducible/ | HeftyTradition6759 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q201md | false | null | t3_1q201md | /r/LocalLLaMA/comments/1q201md/llmrouter_a_unified_framework_for_reproducible/ | false | false | self | 1 | null |
LLMRouter: A unified framework for reproducible research on LLM routing | 1 | [removed] | 2026-01-02T14:29:49 | https://www.reddit.com/r/LocalLLaMA/comments/1q200ui/llmrouter_a_unified_framework_for_reproducible/ | AlexiosLin | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q200ui | false | null | t3_1q200ui | /r/LocalLLaMA/comments/1q200ui/llmrouter_a_unified_framework_for_reproducible/ | false | false | self | 1 | null |
Queryable context graph to audit AI decision traces | 0 | 2026-01-02T14:10:27 | https://www.pylar.ai/blog/building-context-graphs-pylar-decision-traces | Better-Department662 | pylar.ai | 1970-01-01T00:00:00 | 0 | {} | 1q1zkij | false | null | t3_1q1zkij | /r/LocalLLaMA/comments/1q1zkij/queryable_context_graph_to_audit_ai_decision/ | false | false | default | 0 | {'enabled': False, 'images': [{'id': 'MKQuFdTBgb36LtxvPhfsDBrigq5T7WGuK5c1dyDAQUw', 'resolutions': [{'height': 37, 'url': 'https://external-preview.redd.it/MKQuFdTBgb36LtxvPhfsDBrigq5T7WGuK5c1dyDAQUw.png?width=108&crop=smart&auto=webp&s=b562da9e7c3877a9d8a970b4227deb7b153bd8a1', 'width': 108}, {'height': 74, 'url': 'https://external-preview.redd.it/MKQuFdTBgb36LtxvPhfsDBrigq5T7WGuK5c1dyDAQUw.png?width=216&crop=smart&auto=webp&s=36513325200a382ab85ff95cb55e3b76a6ae1ba2', 'width': 216}, {'height': 110, 'url': 'https://external-preview.redd.it/MKQuFdTBgb36LtxvPhfsDBrigq5T7WGuK5c1dyDAQUw.png?width=320&crop=smart&auto=webp&s=67af971155a00049c468f2db2f59fd8174ade087', 'width': 320}, {'height': 221, 'url': 'https://external-preview.redd.it/MKQuFdTBgb36LtxvPhfsDBrigq5T7WGuK5c1dyDAQUw.png?width=640&crop=smart&auto=webp&s=b28563ed9073873da3cea8a18f5c29789f99932a', 'width': 640}, {'height': 332, 'url': 'https://external-preview.redd.it/MKQuFdTBgb36LtxvPhfsDBrigq5T7WGuK5c1dyDAQUw.png?width=960&crop=smart&auto=webp&s=008904e28b3a19b4d96501d087de615a8ec9d4cc', 'width': 960}], 'source': {'height': 346, 'url': 'https://external-preview.redd.it/MKQuFdTBgb36LtxvPhfsDBrigq5T7WGuK5c1dyDAQUw.png?auto=webp&s=b18e7998d141a4a9545c6c1e09020866e395880e', 'width': 1000}, 'variants': {}}]} | |
Syrin v1.1.0 - MCP debugging without the blindfold | 0 | A few days ago I shared **Syrin** here to validate one thing:
MCP debugging is painful because **you can’t see what’s actually happening**.
Based on the feedback, I shipped **v1.1.0**.
**What’s new:**
• **Dev Mode: Event Timeline**
You can now see *every MCP event* being triggered — in order — instead of guessing where things failed.
• **New Debug UI**
No more stitching together logs or building side UIs. Tool calls, inputs, outputs — all visible.
• **Save MCP Responses**
Persist responses for inspection, replay, and debugging. No more “it worked once, now it doesn’t”.
This version is focused on one thing only:
**making MCP execution observable instead of opaque.**
I’ve attached a short demo video showing the full flow.
If you’re:
* building MCP servers
* wiring tools into agents
* tired of silent failures
I’d love feedback — especially on what still feels invisible or annoying.
GitHub: [https://github.com/ankan-labs/syrin](https://github.com/ankan-labs/syrin)
NPM: [https://www.npmjs.com/package/@ankan-ai/syrin](https://www.npmjs.com/package/@ankan-ai/syrin) | 2026-01-02T13:42:05 | https://v.redd.it/u7p5py3fwxag1 | hack_the_developer | /r/LocalLLaMA/comments/1q1ywps/syrin_v110_mcp_debugging_without_the_blindfold/ | 1970-01-01T00:00:00 | 0 | {} | 1q1ywps | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/u7p5py3fwxag1/DASHPlaylist.mpd?a=1770082932%2CY2Q3YjdjMTMyOTIwNDE3MmZmZGViNDkxMDY3Y2FlYWQyNWQ4YzliYWFmZDYyZWVmYTNiNTU5Y2JkODlkZTIxMg%3D%3D&v=1&f=sd', 'duration': 116, 'fallback_url': 'https://v.redd.it/u7p5py3fwxag1/CMAF_1080.mp4?source=fallback', 'has_audio': False, 'height': 1094, 'hls_url': 'https://v.redd.it/u7p5py3fwxag1/HLSPlaylist.m3u8?a=1770082932%2CYWY4YTBkYTQ0MWZiZjQ5ZmY2MzE3NWUzMjA4OGVjYjNjMDM4YzA0MzY1MWJhMjBkZmIxNmU2MmUxYjljOTM4ZQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/u7p5py3fwxag1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 1080}} | t3_1q1ywps | /r/LocalLLaMA/comments/1q1ywps/syrin_v110_mcp_debugging_without_the_blindfold/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'bmV6bnd3MmZ3eGFnMVEtYbxIQvy87dh_7HcR36TrfOnIrC-rTD2VfUsxu1eh', 'resolutions': [{'height': 109, 'url': 'https://external-preview.redd.it/bmV6bnd3MmZ3eGFnMVEtYbxIQvy87dh_7HcR36TrfOnIrC-rTD2VfUsxu1eh.png?width=108&crop=smart&format=pjpg&auto=webp&s=34ea78903b5c90bafc6722955608f7b22eabfe68', 'width': 108}, {'height': 218, 'url': 'https://external-preview.redd.it/bmV6bnd3MmZ3eGFnMVEtYbxIQvy87dh_7HcR36TrfOnIrC-rTD2VfUsxu1eh.png?width=216&crop=smart&format=pjpg&auto=webp&s=d978bc30cbd926e0d5e37e94a95a02d4b912f1cf', 'width': 216}, {'height': 324, 'url': 'https://external-preview.redd.it/bmV6bnd3MmZ3eGFnMVEtYbxIQvy87dh_7HcR36TrfOnIrC-rTD2VfUsxu1eh.png?width=320&crop=smart&format=pjpg&auto=webp&s=3c004ad5f0defe938f6ade1d8954fe391994e16d', 'width': 320}, {'height': 648, 'url': 'https://external-preview.redd.it/bmV6bnd3MmZ3eGFnMVEtYbxIQvy87dh_7HcR36TrfOnIrC-rTD2VfUsxu1eh.png?width=640&crop=smart&format=pjpg&auto=webp&s=5604c2a7635bef828cb0f7302e05a6db38515091', 'width': 640}, {'height': 972, 'url': 'https://external-preview.redd.it/bmV6bnd3MmZ3eGFnMVEtYbxIQvy87dh_7HcR36TrfOnIrC-rTD2VfUsxu1eh.png?width=960&crop=smart&format=pjpg&auto=webp&s=606db35d7c51fd0321670cfc3340989dc7b58408', 'width': 960}, {'height': 1094, 'url': 'https://external-preview.redd.it/bmV6bnd3MmZ3eGFnMVEtYbxIQvy87dh_7HcR36TrfOnIrC-rTD2VfUsxu1eh.png?width=1080&crop=smart&format=pjpg&auto=webp&s=deb089dfb78a944d75341163fa7d7f3db468a609', 'width': 1080}], 'source': {'height': 1538, 'url': 'https://external-preview.redd.it/bmV6bnd3MmZ3eGFnMVEtYbxIQvy87dh_7HcR36TrfOnIrC-rTD2VfUsxu1eh.png?format=pjpg&auto=webp&s=941b5d7e982c95a6ac69c1aee0757a5b92310010', 'width': 1518}, 'variants': {}}]} | |
What exactly is “budget forcing” in LLMs? | 1 | [removed] | 2026-01-02T13:18:16 | https://www.reddit.com/r/LocalLLaMA/comments/1q1ydw5/what_exactly_is_budget_forcing_in_llms/ | SubjectIll9835 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q1ydw5 | false | null | t3_1q1ydw5 | /r/LocalLLaMA/comments/1q1ydw5/what_exactly_is_budget_forcing_in_llms/ | false | false | self | 1 | null |
5 niche ai tools that actually solved a real problem for me | 1 | [removed] | 2026-01-02T12:56:00 | https://www.reddit.com/r/LocalLLaMA/comments/1q1xwyg/5_niche_ai_tools_that_actually_solved_a_real/ | Immediate_Being_3341 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q1xwyg | false | null | t3_1q1xwyg | /r/LocalLLaMA/comments/1q1xwyg/5_niche_ai_tools_that_actually_solved_a_real/ | false | false | self | 1 | null |
I have an unusual question | 0 | My heating unit is insufficient. It's rather cold in my room. Is there any way I can run something that heats up my GPU fast? I don't have any need for my local LLM's right now.
Alternatively I'm just going run flux a couple of times in comfyui. It's effective but I always have to press the button which is kind of annoying.
Or maybe there is a project where you can donate computation power?
Any help is appreciated. | 2026-01-02T12:21:45 | https://www.reddit.com/r/LocalLLaMA/comments/1q1x93i/i_have_an_unusual_question/ | luget1 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q1x93i | false | null | t3_1q1x93i | /r/LocalLLaMA/comments/1q1x93i/i_have_an_unusual_question/ | false | false | self | 0 | null |
Vibevoice how can I run it locally, without comfyui? | 3 | I know I can run with comfyui but I had problem installing it, any other frameworks / apps? | 2026-01-02T12:17:43 | https://www.reddit.com/r/LocalLLaMA/comments/1q1x6e0/vibevoice_how_can_i_run_it_locally_without_comfyui/ | ResponsibleTruck4717 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q1x6e0 | false | null | t3_1q1x6e0 | /r/LocalLLaMA/comments/1q1x6e0/vibevoice_how_can_i_run_it_locally_without_comfyui/ | false | false | self | 3 | null |
88% vs 76%: Multimodal outperforms text embeddings on visual docs in RAG | 32 | Building a RAG system for docs with mixed content: text, tables, charts. I wanted to know if multimodal embeddings are worth it or if text would be just fine.
Decided to test it out. I had two approaches:
1. Convert everything to text, use text embeddings
2. Keep images as images, use multimodal embeddings
After running 150 queries on identical setups across DocVQA (text + tables), ChartQA (charts), and AI2D (diagrams):
Results of Recall@1:
* Tables = multimodal 88%, text 76% (12-point gap)
* Charts = multimodal 92%, text 90% (small edge)
* Pure text = text 96%, multimodal 92% (text wins)
Takeaway: for dealing with visual docs, multimodal seem to be the better default. But for pure text, text embeddings would be enough.
(posted a write-up of full breakdown here: [https://agentset.ai/blog/multimodal-vs-text-embeddings](https://agentset.ai/blog/multimodal-vs-text-embeddings) ) | 2026-01-02T11:16:15 | https://www.reddit.com/r/LocalLLaMA/comments/1q1w2tg/88_vs_76_multimodal_outperforms_text_embeddings/ | midamurat | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1q1w2tg | false | null | t3_1q1w2tg | /r/LocalLLaMA/comments/1q1w2tg/88_vs_76_multimodal_outperforms_text_embeddings/ | false | false | self | 32 | {'enabled': False, 'images': [{'id': 'hEMb_uO0QH0kktOfah13eSkFxCxtywSGn9Eumcb-dDw', 'resolutions': [{'height': 72, 'url': 'https://external-preview.redd.it/hEMb_uO0QH0kktOfah13eSkFxCxtywSGn9Eumcb-dDw.png?width=108&crop=smart&auto=webp&s=e11eaca5b1cd03b2aa6e5a87a2275df7002cedcb', 'width': 108}, {'height': 144, 'url': 'https://external-preview.redd.it/hEMb_uO0QH0kktOfah13eSkFxCxtywSGn9Eumcb-dDw.png?width=216&crop=smart&auto=webp&s=f81a071340d029f55952f23aca5f9b89b443ec07', 'width': 216}, {'height': 213, 'url': 'https://external-preview.redd.it/hEMb_uO0QH0kktOfah13eSkFxCxtywSGn9Eumcb-dDw.png?width=320&crop=smart&auto=webp&s=fbd6772755c5ce5d625f3fc09ee99e28dd45e539', 'width': 320}, {'height': 426, 'url': 'https://external-preview.redd.it/hEMb_uO0QH0kktOfah13eSkFxCxtywSGn9Eumcb-dDw.png?width=640&crop=smart&auto=webp&s=a42768294c88da6e14a6784a4f7826767c5fb1f4', 'width': 640}, {'height': 640, 'url': 'https://external-preview.redd.it/hEMb_uO0QH0kktOfah13eSkFxCxtywSGn9Eumcb-dDw.png?width=960&crop=smart&auto=webp&s=3c0b9c8959911760aafd166a94a2893a1cfe3843', 'width': 960}, {'height': 720, 'url': 'https://external-preview.redd.it/hEMb_uO0QH0kktOfah13eSkFxCxtywSGn9Eumcb-dDw.png?width=1080&crop=smart&auto=webp&s=ccf275289d3787e3f996bd0200f3d505949e41cb', 'width': 1080}], 'source': {'height': 1024, 'url': 'https://external-preview.redd.it/hEMb_uO0QH0kktOfah13eSkFxCxtywSGn9Eumcb-dDw.png?auto=webp&s=b2dcc49ada99670246a9cc911157138a75f782e2', 'width': 1536}, 'variants': {}}]} |
Most optimal vram/performance per price and advice for Shenzhen GPU market | 246 | I’m in Shanghai at the moment and heading to Shenzhen soon - I’ve got around $1500-3000 USD to get the most optimal setup possible. The people I am with are great at negotiating (natives, speak the language) I just need to figure out what I want…
I main use local models I would want at least 48gb vram, ideally closer to 96gb an at least some grunt for the odd PyTorch model training run. I’m open to modded cards (one of my current front runners is 4x 3080 20gb cards) open to both AMD and domestic / enterprise cards.
Prices are best estimates from deep seek - could be wildly wrong, anyone had experience navigating the GPU markets? | 2026-01-02T11:14:30 | notafakename10 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1q1w1qj | false | null | t3_1q1w1qj | /r/LocalLLaMA/comments/1q1w1qj/most_optimal_vramperformance_per_price_and_advice/ | false | false | default | 246 | {'enabled': True, 'images': [{'id': '4nfcarq96xag1', 'resolutions': [{'height': 69, 'url': 'https://preview.redd.it/4nfcarq96xag1.jpeg?width=108&crop=smart&auto=webp&s=eb89174388368e5b4ee1903bfb3720ecf5472a88', 'width': 108}, {'height': 139, 'url': 'https://preview.redd.it/4nfcarq96xag1.jpeg?width=216&crop=smart&auto=webp&s=a7211c3577e3d64b87dfca2264a10b298af15a4c', 'width': 216}, {'height': 205, 'url': 'https://preview.redd.it/4nfcarq96xag1.jpeg?width=320&crop=smart&auto=webp&s=403b1081d32811bd561b1aa35017a992fd9db40b', 'width': 320}, {'height': 411, 'url': 'https://preview.redd.it/4nfcarq96xag1.jpeg?width=640&crop=smart&auto=webp&s=34f4fbc2fba6317c3d5435a92332540815eb3714', 'width': 640}, {'height': 617, 'url': 'https://preview.redd.it/4nfcarq96xag1.jpeg?width=960&crop=smart&auto=webp&s=be619473314265b3fa1ac1e5c44d048f65af2759', 'width': 960}, {'height': 695, 'url': 'https://preview.redd.it/4nfcarq96xag1.jpeg?width=1080&crop=smart&auto=webp&s=f3aae0381c0b29aa9223ee439d769d6638ab76e0', 'width': 1080}], 'source': {'height': 753, 'url': 'https://preview.redd.it/4nfcarq96xag1.jpeg?auto=webp&s=eda98dc7c14edc924c959240892e50a603074408', 'width': 1170}, 'variants': {}}]} |
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