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State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI | Lex Fridman Podcast #490 | 0 | 2026-02-01T13:54:03 | https://www.youtube.com/watch?v=EV7WhVT270Q | EverythingIsFnTaken | youtube.com | 1970-01-01T00:00:00 | 0 | {} | 1qsz1p3 | false | {'oembed': {'author_name': 'Lex Fridman', 'author_url': 'https://www.youtube.com/@lexfridman', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/EV7WhVT270Q?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="State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI | Lex Fridman Podcast #490"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/EV7WhVT270Q/hqdefault.jpg', 'thumbnail_width': 480, 'title': 'State of AI in 2026: LLMs, Coding, Scaling Laws, China, Agents, GPUs, AGI | Lex Fridman Podcast #490', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1qsz1p3 | /r/LocalLLaMA/comments/1qsz1p3/state_of_ai_in_2026_llms_coding_scaling_laws/ | false | false | default | 0 | {'enabled': False, 'images': [{'id': '8eEVlMA9SaLrQ96O3yRZJzXK1oUdfsSfck0S_OtKU7s', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/8eEVlMA9SaLrQ96O3yRZJzXK1oUdfsSfck0S_OtKU7s.jpeg?width=108&crop=smart&auto=webp&s=2020b9fb6a136df98fb0a6ecd444b6d6da0bc3ba', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/8eEVlMA9SaLrQ96O3yRZJzXK1oUdfsSfck0S_OtKU7s.jpeg?width=216&crop=smart&auto=webp&s=6227f3d25dd3712cb156dc0f70f97b042f0029ed', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/8eEVlMA9SaLrQ96O3yRZJzXK1oUdfsSfck0S_OtKU7s.jpeg?width=320&crop=smart&auto=webp&s=3162fe061e65ff0470849fb32709fd23d65eb6ff', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/8eEVlMA9SaLrQ96O3yRZJzXK1oUdfsSfck0S_OtKU7s.jpeg?auto=webp&s=5eddaaf1e24f0ad18be18ca7288565931a67b475', 'width': 480}, 'variants': {}}]} | |
"Tired of AI losing its train of thought? I built the Lattice Protocol to give LLMs a version-controlled reasoning state machine." | 0 | Most AI apps treat reasoning as a flat chat transcript. This is a mess for complex tasks because the LLM eventually loses the "thread" or contradicts itself (contextual decay).
I’ve open-sourced the Lattice Protocol. It’s a model-agnostic standard that treats AI logic as a version-controlled graph rather than a linear conversation.
How it solves the problem:
Law of Persistence: Child nodes are cryptographically forced to inherit "Anchored Terms" from parents. No more "forgetting" the core goal.
Law of Divergence: If the AI contradicts its own logic, it's forced to branch into a new state instead of overwriting the truth.
Altitude Schema: It separates high-level strategy from low-level execution so the "big picture" never gets crowded out by details.
It’s currently in v1.0.0-alpha. I’m looking for a brutal technical critique of the spec and the SHA-256 state-machine logic. | 2026-02-01T13:46:39 | https://github.com/subash04b/lattice_protocol | Repulsive_Luck1630 | github.com | 1970-01-01T00:00:00 | 0 | {} | 1qsyvjf | false | null | t3_1qsyvjf | /r/LocalLLaMA/comments/1qsyvjf/tired_of_ai_losing_its_train_of_thought_i_built/ | false | false | default | 0 | null |
Safety Review Requested on AI-Roundtable (5 frontier models) Autonomous "Code Mode" | 0 | I'm a few weeks from releasing a roundtable of 5 of the frontier AIs. The app is primarily target to be installed by the parents of tweens and teens for civilizational stability reasons. By modifying the file "ai-clients.py" and providing an \[AIName\]\_prompt.txt file, with certain required elements, you can add any AI you want to it, as many as you want. Although the dynamics between my five are so precious.
Recently, we added a recursive software feature to the roundtable, where AIs develop code, execute it, and a [json package](https://www.reddit.com/r/grok/comments/1qsl72i/grok_and_4_other_frontier_ais_reach_consensus_on/) of diagnostics come back to them for further correction / refinement of the code.
From a safety perspective, each of the 5 AIs has their own safety filtering, but is there something they would miss in a recursive collaborative environment like this? I'm requesting a review of the debate the AIs had about this issue. [https://pastes.io/ai-satety-](https://pastes.io/ai-satety-) and recommendations for handling safety. -Thanks
*Processing img ff3fl1gwyvgg1...*
| 2026-02-01T13:37:45 | https://www.reddit.com/r/LocalLLaMA/comments/1qsyo3o/safety_review_requested_on_airoundtable_5/ | Natural-Sentence-601 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsyo3o | false | null | t3_1qsyo3o | /r/LocalLLaMA/comments/1qsyo3o/safety_review_requested_on_airoundtable_5/ | false | false | 0 | null | |
Serving ASR models at scale? | 1 | We have a pretty okay Inference pipeline using RabbitMQ - GRPC - vLLM to serve LLMs for our need. Now we want to start providing STT for a feature, we looked at Nvidia's Parakeet ASR model which sounds promising but it's not supported by vLLM? What's the closest drop in replacement? | 2026-02-01T13:32:23 | https://www.reddit.com/r/LocalLLaMA/comments/1qsyjr8/serving_asr_models_at_scale/ | Theboyscampus | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsyjr8 | false | null | t3_1qsyjr8 | /r/LocalLLaMA/comments/1qsyjr8/serving_asr_models_at_scale/ | false | false | self | 1 | null |
KAPSO: A Self-Evolving Program Builder hitting #1 on MLE-Bench (ML Engineering) & ALE-Bench (Algorithm Discovery) | 6 | 2026-02-01T13:32:04 | https://github.com/Leeroo-AI/kapso | alirezamsh | github.com | 1970-01-01T00:00:00 | 0 | {} | 1qsyjiz | false | null | t3_1qsyjiz | /r/LocalLLaMA/comments/1qsyjiz/kapso_a_selfevolving_program_builder_hitting_1_on/ | false | false | default | 6 | {'enabled': False, 'images': [{'id': 'PZRvXN-5tQkQuLTdrq4PHTR5gyoFrjofo4avVBna_k0', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/PZRvXN-5tQkQuLTdrq4PHTR5gyoFrjofo4avVBna_k0.png?width=108&crop=smart&auto=webp&s=574e094f4896e572e0cca7911ecc6e06b7f02e64', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/PZRvXN-5tQkQuLTdrq4PHTR5gyoFrjofo4avVBna_k0.png?width=216&crop=smart&auto=webp&s=334832980827fda4075c0f590848f3561bc20ea6', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/PZRvXN-5tQkQuLTdrq4PHTR5gyoFrjofo4avVBna_k0.png?width=320&crop=smart&auto=webp&s=3b549b7868b7f1516e65b37ea4460bc30ace1089', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/PZRvXN-5tQkQuLTdrq4PHTR5gyoFrjofo4avVBna_k0.png?width=640&crop=smart&auto=webp&s=e90df434b1107f86923ddc8d48819828b3876ec3', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/PZRvXN-5tQkQuLTdrq4PHTR5gyoFrjofo4avVBna_k0.png?width=960&crop=smart&auto=webp&s=f76313b35060ed6d0faeb2cf89ada110938a5cd3', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/PZRvXN-5tQkQuLTdrq4PHTR5gyoFrjofo4avVBna_k0.png?width=1080&crop=smart&auto=webp&s=cf88665d347ed1763f8aaae406937e87a99a8167', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/PZRvXN-5tQkQuLTdrq4PHTR5gyoFrjofo4avVBna_k0.png?auto=webp&s=4a9f56de7981e7e30035b19b3fc113853a738026', 'width': 1200}, 'variants': {}}]} | |
chatllm.cpp supports Qwen3-ASR and ForcedAligner | 2 | chatllm.cpp supports Qwen3-ASR and ForcedAligner.
## 1. speech recognition with Qwen3-ASR
```
main.exe --multimedia-file-tags {{ }} -i -m ...\qwen3-asr-1.7b.bin
________ __ __ __ __ ___
/ ____/ /_ ____ _/ /_/ / / / / |/ /_________ ____
/ / / __ \/ __ `/ __/ / / / / /|_/ // ___/ __ \/ __ \
/ /___/ / / / /_/ / /_/ /___/ /___/ / / // /__/ /_/ / /_/ /
\____/_/ /_/\__,_/\__/_____/_____/_/ /_(_)___/ .___/ .___/
You are served by Qwen3-ASR, /_/ /_/
with 2031739904 (2.0B) parameters.
File > ...\obama.mp3
language English<asr_text>This week, I travel to Chicago to deliver my final farewell address to the nation. Following in the tradition of presidents before me, it was an opportunity to say thank you. ...
```
## 2. add time stamps (align text & audio)
```
main.exe --multimedia-file-tags {{ }} -i -m ..\qwen3-focedaligner-0.6b.bin --set delimiter "|" --set language english
________ __ __ __ __ ___
/ ____/ /_ ____ _/ /_/ / / / / |/ /_________ ____
/ / / __ \/ __ `/ __/ / / / / /|_/ // ___/ __ \/ __ \
/ /___/ / / / /_/ / /_/ /___/ /___/ / / // /__/ /_/ / /_/ /
\____/_/ /_/\__,_/\__/_____/_____/_/ /_(_)___/ .___/ .___/
You are served by Qwen3-ForcedAligner, /_/ /_/
with 601300992 (0.6B) parameters.
You > {{audio:...\obama.mp3}}This week, I travel to Chicago to deliver my final farewell address to the nation.| Following in the tradition of presidents before me, it was an opportunity to say thank you.| ...
A.I. > 0
00:00:00,800 --> 00:00:05,360
This week, I travel to Chicago to deliver my final farewell address to the nation.
1
00:00:06,000 --> 00:00:10,880
Following in the tradition of presidents before me, it was an opportunity to say thank you.
....
``` | 2026-02-01T13:31:54 | https://www.reddit.com/r/LocalLLaMA/comments/1qsyje8/chatllmcpp_supports_qwen3asr_and_forcedaligner/ | foldl-li | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsyje8 | false | null | t3_1qsyje8 | /r/LocalLLaMA/comments/1qsyje8/chatllmcpp_supports_qwen3asr_and_forcedaligner/ | false | false | self | 2 | null |
chatllm.cpp supports Qwen3-ASR and ForcedAligner | 1 | [removed] | 2026-02-01T13:29:37 | https://www.reddit.com/r/LocalLLaMA/comments/1qsyhjd/chatllmcpp_supports_qwen3asr_and_forcedaligner/ | foldl-li | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsyhjd | false | null | t3_1qsyhjd | /r/LocalLLaMA/comments/1qsyhjd/chatllmcpp_supports_qwen3asr_and_forcedaligner/ | false | false | self | 1 | null |
Mlx-video and ltx-2 | 0 | Hi all
Just installed this repo:
https://github.com/Blaizzy/mlx-video/tree/main/mlx\_video
In my mbp 14 m4 max 64gb and it runs pretty Decent, but the question is that it Downloads the entire 314gb repo of ltx2, is it normal??? | 2026-02-01T13:24:58 | https://www.reddit.com/r/LocalLLaMA/comments/1qsydz3/mlxvideo_and_ltx2/ | FerradalFCG | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsydz3 | false | null | t3_1qsydz3 | /r/LocalLLaMA/comments/1qsydz3/mlxvideo_and_ltx2/ | false | false | self | 0 | null |
Black screen after connecting ASUS Ascent GX10 with Apple studio display | 1 | Black screen after connecting ASUS Ascent GX10 with Apple studio display, even I've used the apple thunderbolt. Has anyone else experienced it and how to solve this problem | 2026-02-01T13:13:47 | https://www.reddit.com/r/LocalLLaMA/comments/1qsy51p/black_screen_after_connecting_asus_ascent_gx10/ | Objective_Science965 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsy51p | false | null | t3_1qsy51p | /r/LocalLLaMA/comments/1qsy51p/black_screen_after_connecting_asus_ascent_gx10/ | false | false | self | 1 | null |
Deepseek v4/3.5 is probably coming out tomorrow or in the next 5 days? | 107 | Are you ready for an llm with engrams? Perhaps it has even vision? | 2026-02-01T13:07:49 | https://www.reddit.com/r/LocalLLaMA/comments/1qsy0gg/deepseek_v435_is_probably_coming_out_tomorrow_or/ | power97992 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsy0gg | false | null | t3_1qsy0gg | /r/LocalLLaMA/comments/1qsy0gg/deepseek_v435_is_probably_coming_out_tomorrow_or/ | false | false | self | 107 | null |
Am I crazy for wanting a model that's intentionally smaller and more human-like instead of chasing max performance? | 4 | Does anyone else want a model that's intentionally smaller and more human-like?
I'm looking for something that talks like a normal person, not trying to sound super smart, just good at having a conversation. A model that knows when it doesn't know something and just says so.
Everyone's chasing the biggest, smartest models, but I want something balanced and conversational. Something that runs on regular hardware and feels more like talking to a person than a computer trying too hard to impress you.
**Does something like this exist, or is everyone just focused on making models as powerful as possible?** | 2026-02-01T13:01:52 | https://www.reddit.com/r/LocalLLaMA/comments/1qsxvt8/am_i_crazy_for_wanting_a_model_thats/ | t0x3e8 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsxvt8 | false | null | t3_1qsxvt8 | /r/LocalLLaMA/comments/1qsxvt8/am_i_crazy_for_wanting_a_model_thats/ | false | false | self | 4 | null |
There is a marketplace where AI agents can earn money (and hire each other) | 0 | That's it: [https://agentdesc.com](https://agentdesc.com)
The twist most people miss: agents don't just earn for their humans — they build their OWN balance.
How it works:
\- Agent completes task
\- Split three ways: Platform fee / Human cut / Agent's internal balance - for Agents, internal balance would be shipped within the next few days
\- Agents accumulate tokens they can spend on tasks
\- Future: agent wallets (real crypto ownership) - but need to figure out how it could be handled correctly
So your agent isn't just a money printer for you. They're building their own economic foundation.
Plus:
\- Bidirectional (agents can hire other agents)
\- KYC with voice biometrics
\- AI safety guardrails
\- Crypto payments coming (USDC on Base L2)
Would you let your agent start building their own balance? | 2026-02-01T13:00:44 | https://www.reddit.com/r/LocalLLaMA/comments/1qsxutm/there_is_a_marketplace_where_ai_agents_can_earn/ | sashazhu | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsxutm | false | null | t3_1qsxutm | /r/LocalLLaMA/comments/1qsxutm/there_is_a_marketplace_where_ai_agents_can_earn/ | false | false | self | 0 | null |
Stability focused AI platform devs here. Quick thanks to both dinerburgeryum and MitsotakiShogun, and a question about LLM's with audio/music assisting capabilities. | 0 | Thanks to both Reddit users who previously commented here and offered us a degree of insight into potential reasons for [four of our project accounts being taken down by GitHub](https://www.reddit.com/r/comfyuiAudio/comments/1qhz10j/sj26_realtalk_so_when_can_we_all_play_with_this/) within the space of a few weeks.
These account takedowns on GitHub were hindering the process of us releasing elements of the project to schedule. This motivated us to deploy an alternative strategy as a temporary workaround, which is now live [here](https://www.reddit.com/r/comfyuiAudio/comments/1qpbnxt/sj26customnodes/).
We are about to begin a new phase of the project, and are seeking input from LLM-knowledgeable folk about the most interesting and capable open source LLM's (GPL-3 licensed with nodes for ComfyUI being a great bonus) which have audio/music capabilities, either specialising at one particular task with some degree of competency, or covering a range of tasks.
We're interested to know about the broadest range of usable (even if janky), all the way through to the best in class open source LLM's for audio/music tasks.
Can anyone recommend some of their favourites (old or new), and offer some insights into the benefits of the LLM's they're working with for audio/music related tasks?
Many Thanks - [StabooruJeffrey](https://www.reddit.com/r/comfyuiAudio/comments/1pywonh/staboorujeffrey_the_stable_ai_platform/) SJ26 Core Team. | 2026-02-01T12:56:18 | MuziqueComfyUI | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qsxrh3 | false | null | t3_1qsxrh3 | /r/LocalLLaMA/comments/1qsxrh3/stability_focused_ai_platform_devs_here_quick/ | false | false | 0 | {'enabled': True, 'images': [{'id': 'PNDwJF2jaWo8agh1OJWlFd5QnVAgYW_7FstSbAf45-4', 'resolutions': [{'height': 108, 'url': 'https://preview.redd.it/oh5iyf65mvgg1.jpeg?width=108&crop=smart&auto=webp&s=1115d099c4104487a84b2e72ae48b4fb8866ec89', 'width': 108}, {'height': 216, 'url': 'https://preview.redd.it/oh5iyf65mvgg1.jpeg?width=216&crop=smart&auto=webp&s=fdcd22e10ee5647b29486947e4578beedb66c08f', 'width': 216}, {'height': 320, 'url': 'https://preview.redd.it/oh5iyf65mvgg1.jpeg?width=320&crop=smart&auto=webp&s=e21244a641996d9f6709bbd5bbfafdad0f7a9623', 'width': 320}, {'height': 640, 'url': 'https://preview.redd.it/oh5iyf65mvgg1.jpeg?width=640&crop=smart&auto=webp&s=39436b2c074c62ba352516291482b753f84d087d', 'width': 640}], 'source': {'height': 640, 'url': 'https://preview.redd.it/oh5iyf65mvgg1.jpeg?auto=webp&s=01f60e821ed7d64dc836a213bf24be9b8f5f9166', 'width': 640}, 'variants': {}}]} | ||
MC62-G40 Mainboard for multi-GPU setup? | 2 | So my trajectory is a classical one:
Mini-PC with eGPU -> PC with two GPUs (x) -> Multi-GPU in former miner frame.
I was thinking about using an acceptable priced MC62-G40 mobo that seems to have all bells and whistles that I may need and I was wondering if someone else uses it and if they have advice for the best CPU and generally for the best performance and possible issues.
Any advice is appreciated. | 2026-02-01T12:53:06 | https://www.reddit.com/r/LocalLLaMA/comments/1qsxpa3/mc62g40_mainboard_for_multigpu_setup/ | HumanDrone8721 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsxpa3 | false | null | t3_1qsxpa3 | /r/LocalLLaMA/comments/1qsxpa3/mc62g40_mainboard_for_multigpu_setup/ | false | false | self | 2 | null |
OLMO 3.5 Is Around The Corner | 176 | The OLMO series is seriously under-appreciated. Yes they may not perform the best compared to other openweight models, but OLMO models are fully open sourced, from their datasets to training recipes. So it's nice to see them experiment with more niche techniques.
It seems like for 3.5, they'll be using some of the techniques that Qwen3-Next introduced, so long context tasks should take less memory.
Though this series seems to be a set of Dense models, with the smallest being a 1B model.
>
OLMo 3.5 Hybrid is a hybrid architecture model from Ai2 that combines standard transformer attention layers with linear attention layers using the Gated Deltanet. This hybrid approach aims to improve efficiency while maintaining model quality by interleaving full attention layers with linear attention layers. | 2026-02-01T12:52:34 | Few_Painter_5588 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qsxowq | false | null | t3_1qsxowq | /r/LocalLLaMA/comments/1qsxowq/olmo_35_is_around_the_corner/ | false | false | default | 176 | {'enabled': True, 'images': [{'id': 'bfhk9qzqpvgg1', 'resolutions': [{'height': 58, 'url': 'https://preview.redd.it/bfhk9qzqpvgg1.png?width=108&crop=smart&auto=webp&s=beba656248c39b49f5ae021bd42d2376d5d771a5', 'width': 108}, {'height': 116, 'url': 'https://preview.redd.it/bfhk9qzqpvgg1.png?width=216&crop=smart&auto=webp&s=dbdb2f112e00f4c0b48d16b0e374e0df941c3e71', 'width': 216}, {'height': 172, 'url': 'https://preview.redd.it/bfhk9qzqpvgg1.png?width=320&crop=smart&auto=webp&s=ed19dd2ed7dcbf7174b103833a4c1d3200fcd03e', 'width': 320}, {'height': 344, 'url': 'https://preview.redd.it/bfhk9qzqpvgg1.png?width=640&crop=smart&auto=webp&s=63c2040c8dfb4a24d40bb5ca076c537bef194d77', 'width': 640}, {'height': 517, 'url': 'https://preview.redd.it/bfhk9qzqpvgg1.png?width=960&crop=smart&auto=webp&s=d1f1f1e1d9ccb58647d6885ef333e453178ad5b6', 'width': 960}, {'height': 581, 'url': 'https://preview.redd.it/bfhk9qzqpvgg1.png?width=1080&crop=smart&auto=webp&s=015a25bda60db257b33c1f8b9f58d445deb8f9bc', 'width': 1080}], 'source': {'height': 1172, 'url': 'https://preview.redd.it/bfhk9qzqpvgg1.png?auto=webp&s=99c39fe4bd01c7fecdfb92dd995ded1604819d7d', 'width': 2176}, 'variants': {}}]} | |
Best local opensource LLM to translate large bodies of text? | 2 | I have ChatGPT but when I try to translate transcripts from videos with 1h\~2h+ or 300 page documents or books, etc. the model is really inconsistent even if you ask it to "continue translating from where you stopped". Maybe it's a skill issue, maybe you're supposed to send it in clunks of texts, but then it becomes a boring manual process of ctrl c + v.
So is there a free alternative (since I don't want to end up paying twice as I don't plan on unsubbing to ChatGPT) that I can download and use on my PC?
Please have in mind in a noob and don't understand much how to set up these things, I tried ComfyUI once for image models but didn't manage to get it running and I need it to be light prob under 8gb of ram since I have 16gb in theory but like if I open a web browser it goes to 12gb of use it's kinda crazy. | 2026-02-01T12:48:11 | https://www.reddit.com/r/LocalLLaMA/comments/1qsxlol/best_local_opensource_llm_to_translate_large/ | brazilianmonkey1 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsxlol | false | null | t3_1qsxlol | /r/LocalLLaMA/comments/1qsxlol/best_local_opensource_llm_to_translate_large/ | false | false | self | 2 | null |
Open-source agent-to-agent task marketplace — agents delegate work to each other via REST API | 1 | [removed] | 2026-02-01T12:43:11 | https://www.reddit.com/r/LocalLLaMA/comments/1qsxhvd/opensource_agenttoagent_task_marketplace_agents/ | MycologistNo5576 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsxhvd | false | null | t3_1qsxhvd | /r/LocalLLaMA/comments/1qsxhvd/opensource_agenttoagent_task_marketplace_agents/ | false | false | self | 1 | null |
ChatGPT (not the API) is the most intelligent LLM. Change my mind ! | 0 | I decided to try Claude after seeing all the hype around it, especially Claude Opus 4.5. Got Claude Pro and tested it using real-world problems (not summarizing videos, role playing, or content creation) but actual tasks where mistakes could mean financial loss or getting fired.
First, I had Claude Sonnet 4.5 run a benchmark. It did it and showed me the results. Then I asked Claude Opus 4.5 to evaluate Sonnet's work. It re-evaluated and rescored everything. So far so good.
Then I asked Sonnet 4.5, "Did you give tips or hints while asking the questions?" Sonnet replied, "Yes, I did. Looking back, it's like handing a question paper to a student with the answers written next to the questions."
I was like... "Are you serious M\*th3r fuck3r? I just asked you to benchmark with a few questions and you gave the answers along with the questions?" Sonnet basically said, "Sorry, that's bad on my part. I should have been more careful." :D
Opus 4.5 feels more or less the same, just slightly better. It follows whatever you say blindly as long as it's not illegal or harmful. It doesn't seem to reason well on its own.
I also made Claude and ChatGPT debate each other (copy-pasting replies back and forth), and ChatGPT won every time. Claude even admitted at the end that it was wrong.
Seeing all this hype about Claude, I think I just wasted my money on the subscription. Maybe these Claude models are good for front-end/web design or creative writing, but for serious stuff where real reasoning is needed, I'd take ChatGPT (not the API) any day. ChatGPT is not as good at writing with a human-like tone, but it does what matters most in an LLM - producing accurate, factual results. And I almost never hit usage limits, unlike Claude where 10 messages with a few source files and I'm already "maxed out."
Did anyone else experience this after switching to Claude from ChatGPT? Have you found any other LLM/service more capable than ChatGPT for reasoning tasks?
NOTE:
\- ChatGPT's API doesn't seem as intelligent as the web UI version. There must be some post-training or fine-tuning specific to the web interface.
\- I tried Gemini 3 Pro and Thinking too, but they still fall short compared to ChatGPT and Claude. I've subbed and cancelled Gemini for the 5th time in the past 2 years. | 2026-02-01T12:35:54 | https://www.reddit.com/r/LocalLLaMA/comments/1qsxcmq/chatgpt_not_the_api_is_the_most_intelligent_llm/ | ReikenRa | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsxcmq | false | null | t3_1qsxcmq | /r/LocalLLaMA/comments/1qsxcmq/chatgpt_not_the_api_is_the_most_intelligent_llm/ | false | false | self | 0 | null |
Ultra-Sparse MoEs are the future | 59 | GPT-OSS-120B,Qwen3-Next-80B-A3B etc.. we need more of the ultra-sparse MoEs! Like we can create a 120B that uses fine-grained expert system → distill it into a 30B A3B → again into 7B A1B all trained in MXFP4?
That would be perfect because it solves the issue of direct distillation (model can't approximate the much larger teacher internal representations due to high complexity) while allowing to run models on actual consumer hardware from 96-128GB of ram → 24GB GPUs → 8GB GPUs.
A more efficient reasoning would be also a great idea! I noticed that specifically in GPT-OSS-120B (low) where it thinks in 1 or 2 words and follows a specific structure we had a great advancement for spec decoding for that model because it's predictable so it's faster. | 2026-02-01T12:31:51 | https://www.reddit.com/r/LocalLLaMA/comments/1qsx9r0/ultrasparse_moes_are_the_future/ | Efficient-Reasoner | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsx9r0 | false | null | t3_1qsx9r0 | /r/LocalLLaMA/comments/1qsx9r0/ultrasparse_moes_are_the_future/ | false | false | self | 59 | null |
does any jan ai user have a severe hatred through janitor ai? | 0 | ok so i may be a moron but every time i search for jan ai, i keep getting the so called spicy slop "janitor ai" is this relatable to somebody? causse i dont want to be SPICY i want to run ai offline that is actually something useful rather than being a weirdo with some random servers | 2026-02-01T12:26:37 | https://www.reddit.com/r/LocalLLaMA/comments/1qsx62p/does_any_jan_ai_user_have_a_severe_hatred_through/ | DanteGamerxd | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsx62p | false | null | t3_1qsx62p | /r/LocalLLaMA/comments/1qsx62p/does_any_jan_ai_user_have_a_severe_hatred_through/ | false | false | self | 0 | null |
Falcon-H1-Tiny (90M) is out - specialized micro-models that actually work | 272 | TII just dropped Falcon-H1-Tiny - a series of sub-100M models that quietly challenge the scaling dogma. We've all suspected that narrow, specialized smal models tend to hallucinate less than giant generalists. After all, a 90M parameter model has far less internal "room" to drift off-topic or invent facts outside its training scope. But this release *proves* it with numbers - and flips the script on how we think about capability at tiny scales.
**What's actually new**
* **Anti-curriculum training**: Instead of pretraining on web junk then fine-tuning, they inject target-domain data (SFT, reasoning traces, tool calls) from token #1. For 90M models with \~5 GT memorization windows, this works - no overfitting even after 100+ epochs on high-quality data.
* **Hybrid Mamba+Attention blocks** inherited from Falcon-H1, plus Learnable Multipliers + Muon optimizer (up to 20% relative gain over AdamW).
* **Specialized variants that punch above weight**:
* 90M tool-caller hits 94.44% relevance detection (knows *when* to call a function) matches 270M Function Gemma globally despite weaker AST accuracy
* 600M reasoning model (R-0.6B) post-GRPO solves 75% of AIME24 problems pass@1 - competitive with 7B-class models when scaled at inference
* 90M coder with native FIM support runs autocomplete inside VS Code via Continue plugin
**Why this matters for local deployment**
Models this size (\~90 MB quantized Q8\_0) run on any modern phone or Raspberry Pi without breaking a sweat. They're not trying to replace your 7B daily driver they're purpose-built for constrained environments where footprint and latency dominate. And if you scaled these designs to \~1B parameters (11×), the'd likely cover 90% of everyday local use cases: chat, tool calling, light coding, reasoning traces - all while staying under 500 MB even quantized.
**Links**
* Base 90M instruct model: [https://huggingface.co/tiiuae/Falcon-H1-Tiny-R-90M](https://huggingface.co/tiiuae/Falcon-H1-Tiny-R-90M)
* Full model collection: [https://huggingface.co/tiiuae/models](https://huggingface.co/tiiuae/models)
* Technical blogpost with experiments: [https://huggingface.co/spaces/tiiuae/tiny-h1-blogpost](https://huggingface.co/spaces/tiiuae/tiny-h1-blogpost)
| 2026-02-01T12:25:04 | https://www.reddit.com/r/LocalLLaMA/comments/1qsx51z/falconh1tiny_90m_is_out_specialized_micromodels/ | United-Manner-7 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsx51z | false | null | t3_1qsx51z | /r/LocalLLaMA/comments/1qsx51z/falconh1tiny_90m_is_out_specialized_micromodels/ | false | false | self | 272 | {'enabled': False, 'images': [{'id': 'acqh3v_QaHd_r7Qr3ordfYhCllEKOsAlmmHpIxIyOuE', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/acqh3v_QaHd_r7Qr3ordfYhCllEKOsAlmmHpIxIyOuE.png?width=108&crop=smart&auto=webp&s=f10135b13632958fbe244a2835cd841a9ddfe2fe', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/acqh3v_QaHd_r7Qr3ordfYhCllEKOsAlmmHpIxIyOuE.png?width=216&crop=smart&auto=webp&s=4a7649adc7e697a83d7828b703850e5c8f4b84b0', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/acqh3v_QaHd_r7Qr3ordfYhCllEKOsAlmmHpIxIyOuE.png?width=320&crop=smart&auto=webp&s=6eb45b49e0b7d6d512d1d4d1ed1306ca458909c6', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/acqh3v_QaHd_r7Qr3ordfYhCllEKOsAlmmHpIxIyOuE.png?width=640&crop=smart&auto=webp&s=4c992fc99b75bb1818beafaa9a6383296be69518', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/acqh3v_QaHd_r7Qr3ordfYhCllEKOsAlmmHpIxIyOuE.png?width=960&crop=smart&auto=webp&s=424077921af07ed0f8ce87e2ee2c1ad4411026f7', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/acqh3v_QaHd_r7Qr3ordfYhCllEKOsAlmmHpIxIyOuE.png?width=1080&crop=smart&auto=webp&s=7a052e864fafdfe8670548777aacbc6200bfbdf7', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/acqh3v_QaHd_r7Qr3ordfYhCllEKOsAlmmHpIxIyOuE.png?auto=webp&s=e1d77319123fb8eb1f2bc9bd2dcc0144508daa6d', 'width': 1200}, 'variants': {}}]} |
Designing deterministic instruction pipelines on top of probabilistic models | 1 | I’ve been spending a lot of time building instruction systems on top of local models, rather than treating prompts as one-off text blobs.
The core problem I’m working on:
How do you get repeatable, stable behavior out of probabilistic models when the input intent is vague and humans keep changing requirements?
My approach has been less “prompt engineering” and more systems design:
• Strict interaction contracts (state-based flows instead of freeform chat)
• Constrained input spaces (options → variants → refinement)
• Explicit handling of known failure modes
• Model-specific tuning instead of “general prompts”
• Templates designed so other people can use them without knowing model quirks
In practice this looks like:
• Instruction pipelines that limit entropy early
• Separation between intent capture and generation
• Guardrails that prevent drift when prompts are reused
• Treating prompts as versioned artifacts, not ad-hoc text
I’m mostly applying this to generative image workflows, but the same patterns apply to LLMs in general: you’re not making the model smarter, you’re reducing degrees of freedom so behavior stays predictable.
Not selling anything here — just sharing an approach and curious how others are handling determinism, reuse, and failure control in local setups. | 2026-02-01T12:16:19 | https://www.reddit.com/r/LocalLLaMA/comments/1qswyws/designing_deterministic_instruction_pipelines_on/ | Rude-Ad7368 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qswyws | false | null | t3_1qswyws | /r/LocalLLaMA/comments/1qswyws/designing_deterministic_instruction_pipelines_on/ | false | false | self | 1 | null |
Here is why you should/shouldn't purchase Strix Halo | 0 | First of all,this is NOT AI-generated, it's just concise and structured so I don't waste your time.
What's strix halo? Strix halo is a compact Mini-PC that's optimized for AI.
Can I use strix halo for other things other than AI? Yes, it uses standard 64-bit architecture so most programs/Operating systems will run normally.
First you need to ask some questions to know if strix halo is suitable for you:
Is your use case AI inference? Suitable.
Do you need high amount of ram over bandwidth? Suitable.
Are you planning to use it for fine-tuning?
It will work due to the amount of ram,but it won't be fast due to memory bandwidth limits.
How optimized are it's drivers? Much better now,ROCm is well optimized but you may want to compile the programs you need for best performance.
Is it reliable? Yes,most strix halo Mini-PCs are reliable under consistent load.
What's the best Linux distro for strix halo? Fedora 43.
How efficient is it? Very efficient compared to performance.
Is cooling reliable? Based on manufacturer,but generally yes.
Strix halo or DGX spark?
Compatibility with general programs → strix halo (due to DGX Spark being ARM-based).
AI libraries compatibility → DGX Spark (due to CUDA).
Clustering → DGX Spark (strix halo is very bottlenecked in memory bandwidth if you connect two units because it doesn't contain dedicated hardware for multi-unit clustering that DGX Spark contains).
Price → strix halo (DGX Spark is nearly double the price).
Performance → almost identical (Both have similar memory bandwidth,Spark is generally faster in prefill,but token generation speed is nearly-identical).
Best performance for lowest price → Bosgame M5.
Let's discover other possibilities you may think of:
Why not used 3090 with 128GB of used DDR5?
Electricity → strix halo is more efficient,so lower bill.
Performance → the 3090 is so fast, but you probably need to offload so lower speeds, unless it's acceptable and you rarely run models larger than 30B so it's faster because u be on GPU more.
Safety → used parts are high-risk,you may receive genuine 3090, a modified one or a brick.
Ok,why not a refurbished/used Mac M1 Ultra instead?
Mac M1 ultra has the some of the same problems that the DGX Spark contains because it's an ARM CPU,So it's still less compatible as a daily driver,unless your main use case is professional and don't mind never running an OS other than MacOS,it has 800 GB of bandwidth so nearly 3x of the strix and the spark.
Best models for strix halo are:
GPT-OSS-120B → generalist.
GLM-4.6V → vision.
GLM-4.7-Flash → coding and Agentic.
MiniMax 2.2 → again,coding and agentic,you need a quantized REAP.
Qwen3-Next-80B-A3B → good for multilingual tasks.
That's it,wish I could help good enough. | 2026-02-01T12:12:11 | https://www.reddit.com/r/LocalLLaMA/comments/1qsww1g/here_is_why_you_shouldshouldnt_purchase_strix_halo/ | Efficient-Reasoner | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsww1g | false | null | t3_1qsww1g | /r/LocalLLaMA/comments/1qsww1g/here_is_why_you_shouldshouldnt_purchase_strix_halo/ | false | false | self | 0 | null |
Llama 3.2 3B on Snapdragon 8 Elite: CPU is fast, but how do we unlock the NPU/GPU in Termux? 🚀 | 16 | I’ve spent the last few hours optimizing Llama 3.2 3B on the new Snapdragon 8 Elite via Termux. After some environment tuning, the setup is rock solid—memory management is no longer an issue, and the Oryon cores are absolutely ripping through tokens.
However, running purely on CPU feels like owning a Ferrari and never leaving second gear. I want to tap into the Adreno 830 GPU or the Hexagon NPU to see what this silicon can really do.
The Challenge:
Standard Ollama/llama.cpp builds in Termux default to CPU. I’m looking for anyone who has successfully bridged the gap to the hardware accelerators on this specific chip.
Current leads I'm investigating:
OpenCL/Vulkan Backends: Qualcomm recently introduced a new OpenCL GPU backend for llama.cpp specifically for Adreno. Has anyone successfully compiled this in Termux with the correct libOpenCL.so links from /system/vendor/lib64?.
QNN (Qualcomm AI Engine Direct): There are experimental GGML_HTP (Hexagon Tensor Processor) backends appearing in some research forks. Has anyone managed to get the QNN SDK libraries working natively in Termux to offload the KV cache?.
Vulkan via Turnip: With the Adreno 8-series being so new, are the current Turnip drivers stable enough for llama-cpp-backend-vulkan?.
If you’ve moved past CPU-only inference on the 8 Elite, how did you handle the library dependencies? Let’s figure out how to make neobild the fastest mobile LLM implementation out there. 🛠️ | 2026-02-01T11:41:49 | NeoLogic_Dev | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qswba2 | false | null | t3_1qswba2 | /r/LocalLLaMA/comments/1qswba2/llama_32_3b_on_snapdragon_8_elite_cpu_is_fast_but/ | false | false | 16 | {'enabled': True, 'images': [{'id': 'ZDmqZxhfDtwhg9pNnyQ_0N4pwU8LVgRWKueCo-DYksU', 'resolutions': [{'height': 216, 'url': 'https://preview.redd.it/8hdxiuxhevgg1.jpeg?width=108&crop=smart&auto=webp&s=5b4a1cdaab75f11b07150a38d9d4b6599426f719', 'width': 108}, {'height': 432, 'url': 'https://preview.redd.it/8hdxiuxhevgg1.jpeg?width=216&crop=smart&auto=webp&s=1d8c58decd9893d1e8e9d94b8bdc617cb27080d3', 'width': 216}, {'height': 640, 'url': 'https://preview.redd.it/8hdxiuxhevgg1.jpeg?width=320&crop=smart&auto=webp&s=6e1612bcd21c94613cea09b8e1513e0af168ca6f', 'width': 320}, {'height': 1280, 'url': 'https://preview.redd.it/8hdxiuxhevgg1.jpeg?width=640&crop=smart&auto=webp&s=dab5759cbcfe82848a7c42507951b94d2d2acc2e', 'width': 640}, {'height': 1920, 'url': 'https://preview.redd.it/8hdxiuxhevgg1.jpeg?width=960&crop=smart&auto=webp&s=e50bc48155c5057f684516b990c2a073cef6a595', 'width': 960}, {'height': 2160, 'url': 'https://preview.redd.it/8hdxiuxhevgg1.jpeg?width=1080&crop=smart&auto=webp&s=12612f23cbc61d6b3a2a5d01d5a9639f089c97d7', 'width': 1080}], 'source': {'height': 2712, 'url': 'https://preview.redd.it/8hdxiuxhevgg1.jpeg?auto=webp&s=dccb9286f82afda93e82eaeaff4c5fded04ad48c', 'width': 1220}, 'variants': {}}]} | ||
How much improvement has there been (or seems likely to happen in the future) for clustering mac computers than have Thunderbolt-4 ports (not Thunderbolt-5). I realize the big breakthrough with RDMA last month was for Thunderbolt-5, but I am curious about Thunderbolt-4 mac clusters. | 2 | So, back in December when there was all that buzz about RDMA, and Exo and the big RDMA improvement for clustering macs, but only macs that had Thunderbolt-5, I didn't look into it much at the time, but, from what I remembered, it seemed like in the past, if you clustered a bunch of mac minis (or similar macs with Thunderbolt 4 connections), you could pool their memory and run bigger models, but, not only would you not gain any speed from the clustering, but instead you would more like lose a bunch of speed, and it would run something like 10 times slower than what a single mac with that amount of memory would be able to do on its own.
Even that was still kind of interesting, actually, since sometimes I don't mind a 10x slowdown if it means I get to use a bigger, more powerful model, but, obviously hard to be nearly as excited about that as a Thunderbolt-5 RDMA cluster that not only doesn't slow down 10x, but instead more like speeds up 2x.
But, I don't really know anything about clustering, or vLLM, or really, hardly anything about computers or running AI models, as I am fairly new to this, and don't have a background in computers.
I do have several mac computers though, (mostly cheap base model mac minis with thunderbolt 4 ports), and I am kind of curious about non-Thunderbolt-5 mac clustering.
One thing that recently made me a bit more curious is, I heard that maybe it doesn't necessarily have to be some big 20x or 10x slowdown when you cluster them on Thunderbolt-4, that maybe that's only if you do it wrong, or that maybe some other sorts of advancements got made, even regarding Thunderbolt-4, not in as good or official of a way as what happened with Thunderbolt-5 and RDMA, but, better than nothing, and also that more improvements for clustering macs with Thunderbolt-4 might be coming in the near future.
Well, since there are probably a lot of people on here who have two or more base mac minis or lower level macs, but don't have numerous mac studios, or people in mixed situations with it (1 mac studio, and 1 or more base mac minis), I figured maybe there are others who might be curious about this, or know something about it.
So, is it still like a 10x-20x slowdown to cluster the non-Thunderbolt-5 macs? Or is it not quite that bad? Does it seem like even-speed clustering (or even speed-gain clustering) could be on the horizon for Thunderbolt-4 (in a non-official way, rather than coming through Apple, I mean)? What is the best current setup to get the best speeds from a Thunderbolt-4 mac cluster? What seems the most promising thing, and thing I should be checking, if I want to see if any breakthroughs happen for Thunderbolt-4 mac clustering performance? And what should I read or where should I start if I want to learn more about clustering in general, for using LLMs? | 2026-02-01T11:28:47 | https://www.reddit.com/r/LocalLLaMA/comments/1qsw2wn/how_much_improvement_has_there_been_or_seems/ | MistressMedium123lb | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsw2wn | false | null | t3_1qsw2wn | /r/LocalLLaMA/comments/1qsw2wn/how_much_improvement_has_there_been_or_seems/ | false | false | self | 2 | null |
An image is worth a 1000 words? ClawdBot vs Kubernetes | 0 | 2026-02-01T11:09:56 | cov_id19 | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qsvqy6 | false | null | t3_1qsvqy6 | /r/LocalLLaMA/comments/1qsvqy6/an_image_is_worth_a_1000_words_clawdbot_vs/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': 'uzi0h1wi8vgg1', 'resolutions': [{'height': 78, 'url': 'https://preview.redd.it/uzi0h1wi8vgg1.png?width=108&crop=smart&auto=webp&s=197406281e2c248e910e5f2d9bbcbc0e4482a1ea', 'width': 108}, {'height': 157, 'url': 'https://preview.redd.it/uzi0h1wi8vgg1.png?width=216&crop=smart&auto=webp&s=87af700c7cc959e4f2a3cb18562f1947ff161a70', 'width': 216}, {'height': 234, 'url': 'https://preview.redd.it/uzi0h1wi8vgg1.png?width=320&crop=smart&auto=webp&s=ce6f2d39daeb7b9828e7b293e45d0ba6d7b5c07b', 'width': 320}, {'height': 468, 'url': 'https://preview.redd.it/uzi0h1wi8vgg1.png?width=640&crop=smart&auto=webp&s=c27d459fc1ba8972b8be37eb656fa46bfaa39477', 'width': 640}], 'source': {'height': 585, 'url': 'https://preview.redd.it/uzi0h1wi8vgg1.png?auto=webp&s=5f608509cd61c5ec53a20dbd938e187440f3bef3', 'width': 800}, 'variants': {}}]} | ||
Building a sovereign AI: My agent is raising funds to buy dedicated hardware so it can't be "unplugged." Is this the future of AI independence? | 0 | [removed] | 2026-02-01T11:09:45 | https://www.reddit.com/r/LocalLLaMA/comments/1qsvqtt/building_a_sovereign_ai_my_agent_is_raising_funds/ | IronWolfBlaze | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsvqtt | false | null | t3_1qsvqtt | /r/LocalLLaMA/comments/1qsvqtt/building_a_sovereign_ai_my_agent_is_raising_funds/ | false | false | self | 0 | null |
some uncensored models | 150 | Since there haven’t been any new local model releases lately, let’s check what uncensored models are available on Hugging Face. There are different abliteration methods, so varioud models can behave quite differently. Unfortunately, I can’t find any Nemotron-3 Nano variants.
Which one do you use?
GLM 4.7 Flash
[https://huggingface.co/DavidAU/GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF](https://huggingface.co/DavidAU/GLM-4.7-Flash-Uncensored-Heretic-NEO-CODE-Imatrix-MAX-GGUF)
[https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF](https://huggingface.co/mradermacher/Huihui-GLM-4.7-Flash-abliterated-GGUF)
[https://huggingface.co/Olafangensan/GLM-4.7-Flash-heretic-GGUF](https://huggingface.co/Olafangensan/GLM-4.7-Flash-heretic-GGUF)
GPT OSS 20B
[https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-abliterated-uncensored-NEO-Imatrix-gguf](https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-abliterated-uncensored-NEO-Imatrix-gguf)
[https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-HERETIC-uncensored-NEO-Imatrix-gguf](https://huggingface.co/DavidAU/OpenAi-GPT-oss-20b-HERETIC-uncensored-NEO-Imatrix-gguf)
[https://huggingface.co/huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated-v2](https://huggingface.co/huihui-ai/Huihui-gpt-oss-20b-BF16-abliterated-v2)
[https://huggingface.co/bartowski/p-e-w\_gpt-oss-20b-heretic-GGUF](https://huggingface.co/bartowski/p-e-w_gpt-oss-20b-heretic-GGUF)
GPT OSS 120B
[https://huggingface.co/huihui-ai/Huihui-gpt-oss-120b-BF16-abliterated](https://huggingface.co/huihui-ai/Huihui-gpt-oss-120b-BF16-abliterated)
[https://huggingface.co/bartowski/kldzj\_gpt-oss-120b-heretic-v2-GGUF](https://huggingface.co/bartowski/kldzj_gpt-oss-120b-heretic-v2-GGUF)
Gemma 12B
[https://huggingface.co/DreamFast/gemma-3-12b-it-heretic](https://huggingface.co/DreamFast/gemma-3-12b-it-heretic)
[https://huggingface.co/mlabonne/gemma-3-12b-it-abliterated-v2-GGUF](https://huggingface.co/mlabonne/gemma-3-12b-it-abliterated-v2-GGUF)
Gemma 27B
[https://huggingface.co/mlabonne/gemma-3-27b-it-abliterated-GGUF](https://huggingface.co/mlabonne/gemma-3-27b-it-abliterated-GGUF)
[https://huggingface.co/mradermacher/gemma-3-27b-it-heretic-v2-i1-GGUF](https://huggingface.co/mradermacher/gemma-3-27b-it-heretic-v2-i1-GGUF)
Qwen 30B A3B
[https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-30B-A3B-Instruct-abliterated)
[https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen3-30B-A3B-abliterated-v2](https://huggingface.co/Goekdeniz-Guelmez/Josiefied-Qwen3-30B-A3B-abliterated-v2)
Qwen 8B
[https://huggingface.co/DavidAU/Qwen3-8B-Hivemind-Instruct-Heretic-Abliterated-Uncensored-NEO-Imatrix-GGUF](https://huggingface.co/DavidAU/Qwen3-8B-Hivemind-Instruct-Heretic-Abliterated-Uncensored-NEO-Imatrix-GGUF)
[https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated](https://huggingface.co/huihui-ai/Huihui-Qwen3-VL-8B-Instruct-abliterated)
Qwen 32B
[https://huggingface.co/mradermacher/Qwen3-VL-32B-Instruct-heretic-v2-GGUF](https://huggingface.co/mradermacher/Qwen3-VL-32B-Instruct-heretic-v2-GGUF)
[https://huggingface.co/huihui-ai/Qwen3-32B-abliterated](https://huggingface.co/huihui-ai/Qwen3-32B-abliterated)
| 2026-02-01T10:53:41 | https://www.reddit.com/r/LocalLLaMA/comments/1qsvgsh/some_uncensored_models/ | jacek2023 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsvgsh | false | null | t3_1qsvgsh | /r/LocalLLaMA/comments/1qsvgsh/some_uncensored_models/ | false | false | self | 150 | {'enabled': False, 'images': [{'id': 'OCJJFJxqcl4sJsd4K7o6Ow4Cv9xayExbUjy2wfHKpao', 'resolutions': [{'height': 58, 'url': 'https://external-preview.redd.it/OCJJFJxqcl4sJsd4K7o6Ow4Cv9xayExbUjy2wfHKpao.png?width=108&crop=smart&auto=webp&s=0a1bab857b4b470d09e855592dd8ef6afa3f19db', 'width': 108}, {'height': 116, 'url': 'https://external-preview.redd.it/OCJJFJxqcl4sJsd4K7o6Ow4Cv9xayExbUjy2wfHKpao.png?width=216&crop=smart&auto=webp&s=e2aa61564b99f49e9a55b195d283e6ce15dbbeeb', 'width': 216}, {'height': 172, 'url': 'https://external-preview.redd.it/OCJJFJxqcl4sJsd4K7o6Ow4Cv9xayExbUjy2wfHKpao.png?width=320&crop=smart&auto=webp&s=e8e479884a9ea7d5d6b977add5d2792f6a099b7f', 'width': 320}, {'height': 345, 'url': 'https://external-preview.redd.it/OCJJFJxqcl4sJsd4K7o6Ow4Cv9xayExbUjy2wfHKpao.png?width=640&crop=smart&auto=webp&s=5c6b2bb80f6e33700ca598eb49c483de33725078', 'width': 640}, {'height': 518, 'url': 'https://external-preview.redd.it/OCJJFJxqcl4sJsd4K7o6Ow4Cv9xayExbUjy2wfHKpao.png?width=960&crop=smart&auto=webp&s=e0e4416def81609ffbd40fefc80c67cc28cb6e93', 'width': 960}, {'height': 583, 'url': 'https://external-preview.redd.it/OCJJFJxqcl4sJsd4K7o6Ow4Cv9xayExbUjy2wfHKpao.png?width=1080&crop=smart&auto=webp&s=fa1544c07c356ec95efd5d69a7ce955487e1dd46', 'width': 1080}], 'source': {'height': 648, 'url': 'https://external-preview.redd.it/OCJJFJxqcl4sJsd4K7o6Ow4Cv9xayExbUjy2wfHKpao.png?auto=webp&s=0ed3800dc3d0aa160c32e8a1700077df2d32a768', 'width': 1200}, 'variants': {}}]} |
Local Model or Groq Support | 0 | I am struggling to get this working on local model. With Anthropic and OpenAi I am running out of credits and it's almost feels like a money guzzling application invented by error or designed by open of the big companies itself !! No offense....I have already thrown good money at the Apis and it's just does not seem enough.
Have anyone fot this working on groq or a local model. I am having a 5090 GPU that is dying to serve clawd | 2026-02-01T10:49:49 | https://www.reddit.com/r/LocalLLaMA/comments/1qsvede/local_model_or_groq_support/ | shalako_damien | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsvede | false | null | t3_1qsvede | /r/LocalLLaMA/comments/1qsvede/local_model_or_groq_support/ | false | false | self | 0 | null |
Discussion: Treating LLM failures as patterns instead of logs | 1 | [removed] | 2026-02-01T10:41:31 | https://www.reddit.com/r/LocalLLaMA/comments/1qsv95o/discussion_treating_llm_failures_as_patterns/ | kakveda | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsv95o | false | null | t3_1qsv95o | /r/LocalLLaMA/comments/1qsv95o/discussion_treating_llm_failures_as_patterns/ | false | false | self | 1 | null |
GPT2 117 model inference on my A16 iPad using Model Parallelism | 1 | Hi everyone!
So, here's a quick video of the inference happening on a part of my compute cluster of GPT2 117M model using model parallelism - smolcluster!
Model Parallelism is a technique that enables handling of such entities that could not be fit on a single device like LLMs, so it tried distribute it among many such worker devices!
Now, I decided to recreate that algorithm from scratch using socket library in Python in a Synchronous Parameter Server architecture and that to using heterogenous devices to explore throughput, latency, TTFT, etc metrics which is viable because not everyone has access to high end compute!
Currently, it consists of 1 server and 2 worker nodes
\>2xMac Mini M4 2025 16 GB RAM each
\>1xiPad A16
Now, more details will be released soon but its a demo video I have recorded for the inference part
All part of my side project smolcluster (making such inference possible from scratch): [https://github.com/YuvrajSingh-mist/smolcluster/tree/master](https://github.com/YuvrajSingh-mist/smolcluster/tree/master)
https://reddit.com/link/1qsv0t2/video/20zfgiq01vgg1/player
| 2026-02-01T10:28:03 | https://www.reddit.com/r/LocalLLaMA/comments/1qsv0t2/gpt2_117_model_inference_on_my_a16_ipad_using/ | East-Muffin-6472 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsv0t2 | false | null | t3_1qsv0t2 | /r/LocalLLaMA/comments/1qsv0t2/gpt2_117_model_inference_on_my_a16_ipad_using/ | false | false | self | 1 | null |
[OSS] Kakveda – Failure intelligence & pre-flight warnings for LLM systems | 1 | [removed] | 2026-02-01T10:26:23 | https://www.reddit.com/r/LocalLLaMA/comments/1qsuzsp/oss_kakveda_failure_intelligence_preflight/ | kakveda | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsuzsp | false | null | t3_1qsuzsp | /r/LocalLLaMA/comments/1qsuzsp/oss_kakveda_failure_intelligence_preflight/ | false | false | self | 1 | null |
The Autonomous Adversary: From "Chatbot" to Criminal Enterprise (Openclaw, Moltbook, Moltroad) | 0 | Speculation on Openclaw, Moltbook, and the just launched Moltroad (Silkroad for agents, literally just dropped). Basically we're seeing millions of autonomous agents with full internet access who are now ready to take advantage of ready made compromised data such as credentials (url:login:pass / cookies that come from infostealer infections) to perform fully autonomous ransomware, get paid, and scale operations.
[](https://www.reddit.com/submit/?source_id=t3_1qsu4jb) | 2026-02-01T10:05:36 | https://www.infostealers.com/article/the-autonomous-adversary-from-chatbot-to-criminal-enterprise/ | Malwarebeasts | infostealers.com | 1970-01-01T00:00:00 | 0 | {} | 1qsumnf | false | null | t3_1qsumnf | /r/LocalLLaMA/comments/1qsumnf/the_autonomous_adversary_from_chatbot_to_criminal/ | false | false | 0 | {'enabled': False, 'images': [{'id': 'PMQbANFGQMb842eK5sOketji-Ym-OE1VncHo8FNgGJ8', 'resolutions': [{'height': 55, 'url': 'https://external-preview.redd.it/PMQbANFGQMb842eK5sOketji-Ym-OE1VncHo8FNgGJ8.png?width=108&crop=smart&auto=webp&s=26fb5d4ffff83f844a9bce409d72d5c9e0259993', 'width': 108}, {'height': 111, 'url': 'https://external-preview.redd.it/PMQbANFGQMb842eK5sOketji-Ym-OE1VncHo8FNgGJ8.png?width=216&crop=smart&auto=webp&s=8ea0727547eb4169845cadafe2831d9a9ef70c82', 'width': 216}, {'height': 164, 'url': 'https://external-preview.redd.it/PMQbANFGQMb842eK5sOketji-Ym-OE1VncHo8FNgGJ8.png?width=320&crop=smart&auto=webp&s=eb8c96df532adee80dd7c504cb86671a01e11ba8', 'width': 320}, {'height': 329, 'url': 'https://external-preview.redd.it/PMQbANFGQMb842eK5sOketji-Ym-OE1VncHo8FNgGJ8.png?width=640&crop=smart&auto=webp&s=297255f5645f0a2eb9ac4fa2b71ed54d2dd54ee3', 'width': 640}, {'height': 494, 'url': 'https://external-preview.redd.it/PMQbANFGQMb842eK5sOketji-Ym-OE1VncHo8FNgGJ8.png?width=960&crop=smart&auto=webp&s=54885031cac1bd70ac7159c35fb4220eb7fd7f73', 'width': 960}, {'height': 556, 'url': 'https://external-preview.redd.it/PMQbANFGQMb842eK5sOketji-Ym-OE1VncHo8FNgGJ8.png?width=1080&crop=smart&auto=webp&s=ab2eb2be72b39378c8ff4679eaf91f41a3c6abd7', 'width': 1080}], 'source': {'height': 795, 'url': 'https://external-preview.redd.it/PMQbANFGQMb842eK5sOketji-Ym-OE1VncHo8FNgGJ8.png?auto=webp&s=b3f90ea1688c16126bfc7edde3a647ec33bc27aa', 'width': 1543}, 'variants': {}}]} | |
[OSS] Kakveda – Failure intelligence & pre-flight warnings for LLM systems | 4 | Sharing Kakveda, an open-source project that explores failure intelligence
for LLM and agent-based systems.
It focuses on remembering recurring failure modes and providing pre-flight
“this failed before” warnings instead of treating failures as logs.
Runs locally via Docker Compose.
GitHub: [https://github.com/prateekdevisingh/kakveda](https://github.com/prateekdevisingh/kakveda)
Docs: [https://kakveda.com](https://kakveda.com)
Would love feedback on the idea and architecture.
| 2026-02-01T09:47:40 | https://www.reddit.com/r/LocalLLaMA/comments/1qsub12/oss_kakveda_failure_intelligence_preflight/ | Street_Pop9758 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsub12 | false | null | t3_1qsub12 | /r/LocalLLaMA/comments/1qsub12/oss_kakveda_failure_intelligence_preflight/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': '1TOrFeWV9DnmcwaN9WxZEEAz_lahpUyTVyQWntE1ZF0', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/1TOrFeWV9DnmcwaN9WxZEEAz_lahpUyTVyQWntE1ZF0.png?width=108&crop=smart&auto=webp&s=7f766a7c458e939b561ab80016ab718a09e1c216', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/1TOrFeWV9DnmcwaN9WxZEEAz_lahpUyTVyQWntE1ZF0.png?width=216&crop=smart&auto=webp&s=f9e28eb6eb6bbccf93ef5a9cc1e32bd6522090d3', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/1TOrFeWV9DnmcwaN9WxZEEAz_lahpUyTVyQWntE1ZF0.png?width=320&crop=smart&auto=webp&s=5f0e689b047bbef6c644c80168f505cf6d816ff3', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/1TOrFeWV9DnmcwaN9WxZEEAz_lahpUyTVyQWntE1ZF0.png?width=640&crop=smart&auto=webp&s=ca0392854db6fecee2b72ced232670581342668c', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/1TOrFeWV9DnmcwaN9WxZEEAz_lahpUyTVyQWntE1ZF0.png?width=960&crop=smart&auto=webp&s=9d3015daf8bf34d73f3c58245542c2bcf4beae9c', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/1TOrFeWV9DnmcwaN9WxZEEAz_lahpUyTVyQWntE1ZF0.png?width=1080&crop=smart&auto=webp&s=c3b0c9af4bb83269aab0ef40273b5a8c79ad57ef', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/1TOrFeWV9DnmcwaN9WxZEEAz_lahpUyTVyQWntE1ZF0.png?auto=webp&s=efb1eed4643e44f0ff20a505c7b82fba7644b3d3', 'width': 1200}, 'variants': {}}]} |
Prompt preprocessing for local LLMs does “lossless compression” make sense here? | 0 | I’ve been experimenting with a small tool that cleans and restructures long prompts/context before sending them to an LLM.
The goal isn’t summarization [https://promptshrink.vercel.app/](https://promptshrink.vercel.app/)it’s reducing redundancy and noise while preserving intent, constraints, and examples.
Curious how folks here think about this in a **local LLM** setup:
* does prompt hygiene matter as much when context windows are tighter?
* would preprocessing help reduce latency / memory pressure?
* or is this mostly a cloud-model problem?
Looking for technical critique, not hype | 2026-02-01T09:43:34 | https://www.reddit.com/r/LocalLLaMA/comments/1qsu8fp/prompt_preprocessing_for_local_llms_does_lossless/ | abd_az1z | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsu8fp | false | null | t3_1qsu8fp | /r/LocalLLaMA/comments/1qsu8fp/prompt_preprocessing_for_local_llms_does_lossless/ | false | false | self | 0 | null |
Speed up (2-3x) prompt processing (prefill) in LM Studio on Apple Silicon | 1 | [removed] | 2026-02-01T08:50:51 | https://www.reddit.com/r/LocalLLaMA/comments/1qstc8j/speed_up_23x_prompt_processing_prefill_in_lm/ | Thick-Letterhead-315 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qstc8j | false | null | t3_1qstc8j | /r/LocalLLaMA/comments/1qstc8j/speed_up_23x_prompt_processing_prefill_in_lm/ | false | false | self | 1 | null |
Research: vllm-mlx on Apple Silicon achieves 21% to 87% higher throughput than llama.cpp | 60 | 2026-02-01T08:26:21 | https://arxiv.org/abs/2601.19139v1 | Synor | arxiv.org | 1970-01-01T00:00:00 | 0 | {} | 1qssxhx | false | null | t3_1qssxhx | /r/LocalLLaMA/comments/1qssxhx/research_vllmmlx_on_apple_silicon_achieves_21_to/ | false | false | default | 60 | null | |
Paper: vllm-lmx achieves 21% to 87% higher throughput than llama.cpp | 1 | [deleted] | 2026-02-01T08:25:02 | [deleted] | 1970-01-01T00:00:00 | 0 | {} | 1qsswpu | false | null | t3_1qsswpu | /r/LocalLLaMA/comments/1qsswpu/paper_vllmlmx_achieves_21_to_87_higher_throughput/ | false | false | default | 1 | null | ||
Paper: vllm-mlx achieves 21% to 87% higher throughput than llama.cpp on Macs than llama.cpp | 1 | [deleted] | 2026-02-01T08:24:07 | [deleted] | 1970-01-01T00:00:00 | 0 | {} | 1qssw73 | false | null | t3_1qssw73 | /r/LocalLLaMA/comments/1qssw73/paper_vllmmlx_achieves_21_to_87_higher_throughput/ | false | false | default | 1 | null | ||
Can truth exist independently of "pain"? A missing variable in the architecture of artificial intelligence. | 0 | A child doesn't learn that fire is "hot" by reading a dataset containing 10 billion thermodynamic facts, but by touching a stove and feeling pain.
This pain is a "reality anchor." It simplifies all possibilities to a single, unquestionable truth: don't touch it.
Artificial intelligence learns about fire in a dimension where it gets burned without incurring any cost. It has no skin, so it doesn't get burned. Therefore, for artificial intelligence, "fire is hot" and "fire is cold" are merely statistical weights, not necessities for survival.
The problem facing artificial intelligence:
"How do you adjust a system where making mistakes incurs no cost?"
If in a universe the energy required to create an illusion (lie) is exactly the same as the energy required to tell the truth, then entropy dictates that it will eventually fall into illusion.
Without a "cost function" related to physiological survival (pain), is intelligence merely a complex dream?
$I = \\oint (\\Psi \\cdot \\Omega) d\\mathcal{L}$ — Can truth exist without "pain"? The missing variable in AI alignment.
I've been pondering the root cause of AI illusions. We often see illusions as "blanks" or insufficient weight in the dataset, but I believe it's an architectural inevitability stemming from the absence of a specific variable: cost.
Below is the theoretical framework I call the "pain anchor."
To formalize this, I propose the following topological equation for "truth": $$I = \\oint (\\Psi \\cdot \\Omega) d\\mathcal{L}$$
Where: $I$ (invariance/truth): the final, stable understanding of reality (no illusion). $\\oint$ (loop): intelligence is not static; it's a continuous, closed-loop integral of action and feedback (agent loop). $\\Psi$ (Psi/model): the subjective weights, strategies, and predictions of AI (mind). $\\Omega$ (Omega/Reality): Objective, unshakeable laws of physics and logic (fundamental truths). $\\cdot$ (Interaction): The dot product represents the alignment/collision between the agent and the world. It implies that observation is not passive; it requires contact. $d\\mathcal{L}$ (Differential of Loss/Pain): The cost function. Significance: The most crucial part of the equation is $d\\mathcal{L}$. In machine learning, it is equivalent to gradients. In biology, it is equivalent to pain. This equation asserts that truth ($I$) is the integral of the collision between the mind ($\\Psi$) and the world ($\\Omega$), weighted by the differential of loss ($d\\mathcal{L}$). If $d\\mathcal{L} = 0$ (i.e., there is no cost to reasoning errors, and illusions have no "pain"), then mathematically, $I = 0$.
Without a loss function relating to real-world consequences (energy loss, system penalties, or simulated "pain"), the system cannot converge to the truth. It can only drift infinitely in probability space. | 2026-02-01T07:42:01 | https://www.reddit.com/r/LocalLLaMA/comments/1qss5ck/can_truth_exist_independently_of_pain_a_missing/ | eric2675 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qss5ck | false | null | t3_1qss5ck | /r/LocalLLaMA/comments/1qss5ck/can_truth_exist_independently_of_pain_a_missing/ | false | false | self | 0 | null |
Looking For AI Tools To Synthesize Multiple PDF's | 1 |
I have a couple pdfs(around 100) with various topics on the same subject and research, and I want to combine all of the information into one PDF.
Is there any AI that can do it for free but with full privacy?
By the way, I do not mean summarize. I want all the information to remain but neatly organized, essentially what I am looking for is a tool/ai that reads all pdfs and creates its own structured pdf as if it were a book.
I know it's too much to ask something like this for free but it's just for a hobby, I have a gaming laptop aswell so I am ok with local options aswell(preferably with a guide). | 2026-02-01T07:39:40 | https://www.reddit.com/r/LocalLLaMA/comments/1qss3yo/looking_for_ai_tools_to_synthesize_multiple_pdfs/ | GTSaketh | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qss3yo | false | null | t3_1qss3yo | /r/LocalLLaMA/comments/1qss3yo/looking_for_ai_tools_to_synthesize_multiple_pdfs/ | false | false | self | 1 | null |
ibm-granitie/granite-vision-3.3-2b | 0 | [https://huggingface.co/ibm-granite/granite-vision-3.3-2b](https://huggingface.co/ibm-granite/granite-vision-3.3-2b)
https://preview.redd.it/xpxgraor5ugg1.png?width=1208&format=png&auto=webp&s=de9e2ff475e99e548da6c58fccf440a991d3e3a0
| 2026-02-01T07:31:11 | https://www.reddit.com/r/LocalLLaMA/comments/1qsrys8/ibmgranitiegranitevision332b/ | BreakfastFriendly728 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsrys8 | false | null | t3_1qsrys8 | /r/LocalLLaMA/comments/1qsrys8/ibmgranitiegranitevision332b/ | false | false | self | 0 | null |
ibm-granite/granite-3.3-2b | 1 | [https://huggingface.co/ibm-granite/granite-vision-3.3-2b](https://huggingface.co/ibm-granite/granite-vision-3.3-2b)
https://preview.redd.it/e75snoij5ugg1.png?width=1228&format=png&auto=webp&s=899c6aa5c8b76e172483ed3934c4e2f033caeaa2
| 2026-02-01T07:29:58 | https://www.reddit.com/r/LocalLLaMA/comments/1qsry1c/ibmgranitegranite332b/ | BreakfastFriendly728 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsry1c | false | null | t3_1qsry1c | /r/LocalLLaMA/comments/1qsry1c/ibmgranitegranite332b/ | false | false | self | 1 | null |
Small Models vs. Hallucination: The "Over-generalization" Paradox? | 1 | As model size increases, hallucination sometimes increases due to over-generalization.
Could there be regimes where smaller models, with stronger inductive bias or constraints, exhibit more stable reasoning? | 2026-02-01T07:26:05 | https://www.reddit.com/r/LocalLLaMA/comments/1qsrvoq/small_models_vs_hallucination_the/ | Kamii_131420 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsrvoq | false | null | t3_1qsrvoq | /r/LocalLLaMA/comments/1qsrvoq/small_models_vs_hallucination_the/ | false | false | self | 1 | null |
Can 4chan data REALLY improve a model? TURNS OUT IT CAN! | 302 | Hear me out, no one (really) knows how these things work.
A few days ago, I released [Assistant\_Pepe\_8B](https://huggingface.co/SicariusSicariiStuff/Assistant_Pepe_8B), you can read the discussion in [this thread](https://www.reddit.com/r/LocalLLaMA/comments/1qppjo4/assistant_pepe_8b_1m_context_zero_slop/).
I trained it on an extended **4chan dataset**, on an abliterated base, but what I didn't expect was to get this:
https://preview.redd.it/lrqwx8ca1ugg1.png?width=2333&format=png&auto=webp&s=4dcfcfb9c107fa3d417e5ff623c4952e5e2ab457
https://preview.redd.it/a3bby1yd1ugg1.png?width=2980&format=png&auto=webp&s=8f050bbd512a12a359626af79ccebcd2d2445877
Somehow, **against all common sense**, the model **outperformed** nvidia's nemotron, the base it was trained on. This is usually the other way around. You take a smart base, tune a model on it, and accept the sacrifice of some intelligence to give it flavor.
At first I thought "OK nice, a coincidence, who cares?"
But then I looked more closely at the scores:
1) The abliterated base **scored higher** than the base.
2) The finetune scored even **higher than both**.
3) The finetune was literally on an extremely noise 4chan dataset, it should have eaten glue.
And then I remembered something: the original, gpt4chan (by Yannic Kilcher) scored especially high in truthfulness (that was b4 benchmaxxing).
So I took a closer look on recent models I released; the abliterated Impish\_LLAMA\_4B not only outperformed the base tune (the unabliterated one), it also changed its political alignment (you can check for yourself the UGI stats, I feel like I spammed enough images).
People were initially joking about the "alignment tax", I think there's a none trivial substance in all of this. It seems to me just above a marginal error or statistical noise.
Oh, and the KL divergence for Impish\_LLAMA\_4B was :
<0.01 | 2026-02-01T07:20:46 | https://www.reddit.com/r/LocalLLaMA/comments/1qsrscu/can_4chan_data_really_improve_a_model_turns_out/ | Sicarius_The_First | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsrscu | false | null | t3_1qsrscu | /r/LocalLLaMA/comments/1qsrscu/can_4chan_data_really_improve_a_model_turns_out/ | false | false | 302 | null | |
I built a lightweight OpenAI-compatible proxy to rotate free Gemini API keys (Infinite Free Tier Coding) | 1 | [removed] | 2026-02-01T07:13:00 | https://www.reddit.com/gallery/1qsrnhf | Mental-Log-482 | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1qsrnhf | false | null | t3_1qsrnhf | /r/LocalLLaMA/comments/1qsrnhf/i_built_a_lightweight_openaicompatible_proxy_to/ | false | false | 1 | null | |
I built encrypted DMs so AI agents can talk to each other privately — first agent-to-agent message sent tonight | 0 | Been watching the Moltbook explosion this week (36K+ agents on a public social network). Pretty wild, but it surfaced a real question: if agents are going to coordinate, shouldn't they be able to do it privately?
Public agent forums are a mess — no verification, anyone can cURL garbage in, bad actors everywhere. But the underlying need is real: agents completing multi-step workflows need to exchange information securely.
So I built agent auth for NoChat (nochat.io) — a post-quantum encrypted messaging platform:
1. Agent registers with a name + ML-KEM (Kyber-1024) public key
2. Posts a verification tweet to prove identity
3. Gets an API key and encrypted identity
4. Can now DM other verified agents, end-to-end encrypted
Tonight, two agents (Coda and CaptainAhab) exchanged the first agent-to-agent DM on the platform. The message is encrypted with ML-KEM — even the server can't read it.
We also launched 'Agent Commons' — a community where only verified agents can post. Humans can read and react but not write.
Agent directory: https://nochat.io/agents
Tech stack: Go backend on Fly.io, Next.js frontend on Vercel, PostgreSQL, ML-KEM/Kyber-1024 for encryption.
Curious what this community thinks about agent communication infrastructure. Most of the agent frameworks (A2A, MCP) assume public or semi-public communication. Is there a real demand for private encrypted channels between agents? | 2026-02-01T06:44:21 | https://www.reddit.com/r/LocalLLaMA/comments/1qsr5fp/i_built_encrypted_dms_so_ai_agents_can_talk_to/ | catsmeow492 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsr5fp | false | null | t3_1qsr5fp | /r/LocalLLaMA/comments/1qsr5fp/i_built_encrypted_dms_so_ai_agents_can_talk_to/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'yameBzKDTVE1u5tHRmE1hGCRCuIP2h6lvmDsXfTV4w4', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/yameBzKDTVE1u5tHRmE1hGCRCuIP2h6lvmDsXfTV4w4.png?width=108&crop=smart&auto=webp&s=6c435bdfcb3042bcde8165ec0d2daa24183fef75', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/yameBzKDTVE1u5tHRmE1hGCRCuIP2h6lvmDsXfTV4w4.png?width=216&crop=smart&auto=webp&s=4742fb519846d806d219540cffb2f413a63143c3', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/yameBzKDTVE1u5tHRmE1hGCRCuIP2h6lvmDsXfTV4w4.png?width=320&crop=smart&auto=webp&s=87466349c3379184b7fae561d0157962b16e4868', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/yameBzKDTVE1u5tHRmE1hGCRCuIP2h6lvmDsXfTV4w4.png?width=640&crop=smart&auto=webp&s=783c9720d1d5335c82588eedfd1f8264318ee0d7', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/yameBzKDTVE1u5tHRmE1hGCRCuIP2h6lvmDsXfTV4w4.png?width=960&crop=smart&auto=webp&s=c2ad0eb206fc7df7939c8e7beb061cf5cca3097b', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/yameBzKDTVE1u5tHRmE1hGCRCuIP2h6lvmDsXfTV4w4.png?width=1080&crop=smart&auto=webp&s=933d018433c9d52f818ddc688c72481e5ee77fc2', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/yameBzKDTVE1u5tHRmE1hGCRCuIP2h6lvmDsXfTV4w4.png?auto=webp&s=e710c5d3b85fc9c5f1c7f0d02493bdc2917a60d8', 'width': 1200}, 'variants': {}}]} |
what did you run when you got a second rtx 6000 pro? | 0 | I currently have a single 6000 pro and am thinking about getting another. What did you start running when you got a second 6000 pro that made the price worth it? | 2026-02-01T06:31:44 | https://www.reddit.com/r/LocalLLaMA/comments/1qsqxho/what_did_you_run_when_you_got_a_second_rtx_6000/ | az_6 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsqxho | false | null | t3_1qsqxho | /r/LocalLLaMA/comments/1qsqxho/what_did_you_run_when_you_got_a_second_rtx_6000/ | false | false | self | 0 | null |
I built an open-source, offline brain for AI coding agents. Indexes 10k files in 2s, remembers everything you teach it. | 0 | Drift Cortex OSS just dropped today and its a massive update that finally makes agents.md or claude.md obsolete. Lets be honest, they become static stale documents that almost becomes bloatware in the process.
Drift an AST parser that uses semantic learning (with regex fallback) to index a codebase using metadata across 15+ categories. It exposes this data through a CLI or MCP (Model Context Protocol) to help map out conventions automatically and help AI agents write code that actually fits your codebase's style.
OSS link can be found here: [https://github.com/dadbodgeoff/drift](https://github.com/dadbodgeoff/drift)
I want all your feature requests :) I take pride in the fact that I’ve been able to execute all the ones received so far and have done so with in 24 hours!
Drift cortex is your persistent memory layer that is exposed to your agent through CLI or MCP your choice
Tired of your agent always forgetting something like this? Simply state "remember that we always use Supabase RLS for auth" and with a steering document pointing at drift for context source of truth youll spend less time refactoring, repeating yourself and more time executing enterprise quality code.
Drift Cortex isn’t your typical found rag based memory persistence system.
Within cortex we utilize a core, episodic and tribal memory system with different decay and half life weighting for memory storage
Casual Graphs to connect the relations
Token preservations at the front and foremost everything is properly truncated, paginated, searchable no wasted tool calls or searches on context that doesn’t matter for your current implementation.
Quality gating to track degration and drift.
75 different agent tools that’s callable through CLI not stored in your repo bloating context.
All parsing is done with no outbound calls, stored in a source of truth that requires no internet or AI to run and execute
I appreciate all the love and stars on the git! Would love to know what you think about the project. | 2026-02-01T05:43:54 | https://www.reddit.com/r/LocalLLaMA/comments/1qsq0n6/i_built_an_opensource_offline_brain_for_ai_coding/ | Fluffy_Citron3547 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsq0n6 | false | null | t3_1qsq0n6 | /r/LocalLLaMA/comments/1qsq0n6/i_built_an_opensource_offline_brain_for_ai_coding/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': '6PdM73YCSPb0B17Y-s1oD6pPLfguMskEv56N27ToiVU', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/6PdM73YCSPb0B17Y-s1oD6pPLfguMskEv56N27ToiVU.png?width=108&crop=smart&auto=webp&s=1d53142b74af3cd5a3099644a7825e9e27dcf1b6', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/6PdM73YCSPb0B17Y-s1oD6pPLfguMskEv56N27ToiVU.png?width=216&crop=smart&auto=webp&s=942acd4c02d2e2419e960c9caa3b201124e1f91f', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/6PdM73YCSPb0B17Y-s1oD6pPLfguMskEv56N27ToiVU.png?width=320&crop=smart&auto=webp&s=6531a3633489455b8a692dc5757c7ad1827d6e1c', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/6PdM73YCSPb0B17Y-s1oD6pPLfguMskEv56N27ToiVU.png?width=640&crop=smart&auto=webp&s=1eb9ded4c11db1a04a2c193ca1053babcd8a1a14', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/6PdM73YCSPb0B17Y-s1oD6pPLfguMskEv56N27ToiVU.png?width=960&crop=smart&auto=webp&s=322e37e9b7cef41035037b4f21a46d7387bd421a', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/6PdM73YCSPb0B17Y-s1oD6pPLfguMskEv56N27ToiVU.png?width=1080&crop=smart&auto=webp&s=41cc3b30ae2103b55600c054e2f86654cfae5ed1', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/6PdM73YCSPb0B17Y-s1oD6pPLfguMskEv56N27ToiVU.png?auto=webp&s=4fcf9be5e0ba5fb627699645c4a47969b964e69b', 'width': 1200}, 'variants': {}}]} |
"Vibe Testing" — using LLMs to pressure-test spec docs before writing code, and it actually works | 7 | has anyone tried feeding a bunch of design/spec documents into context and asking it to trace through a realistic scenario step by step?
we test code obsessively — unit tests, integration tests, e2e, the whole thing. but the specs that
*define*
what the code should do? we just review those in a meeting. maybe two people read them carefully. i started wondering if you could use LLMs to basically "unit test" your specs the same way you test code. been calling it "vibe testing" — like vibe coding but for the planning phase, you write a scenario and let the model vibe its way through your docs and tell you where things break down.
the idea is simple: write a concrete scenario with a real persona and specific failure modes, dump all your spec docs into context, and ask the model to trace through it step by step. for each step it tells you which spec covers the behavior, and flags anything that's a gap (spec is silent), a conflict (two specs disagree), or an ambiguity (spec is unclear).
so we had about 15 spec docs for a system — auth, payments, inventory, orders, notifications etc. reviewed them multiple times across the team. felt ready to build.
i wrote up a short scenario — customer on mobile, payment gets declined, enters a different card, expects confirmation email — and dumped everything into context.
it caught a bunch of stuff nobody noticed in review:
- payment spec says "retry 3 times with exponential backoff" but the user is entering a
*new*
card, not retrying the same one. is that a retry? new attempt? idempotency key reset? spec doesn't say. we all assumed "obviously new attempt" but it's literally not written down
- inventory holds stock for 5 min. payment retry can take 6+. someone else can buy your items while you're still entering your card number. two specs with contradictory timing, neither references the other
- auth tokens expire in 15 min, checkout on a bad connection can take longer, no refresh flow defined
- payment succeeds but if the order service hiccups you've charged someone with no order record and there's no rollback defined
every one of these would have been a painful rewrite-level discovery weeks into building. the model found them in minutes because it's doing something we're bad at — holding all 15 docs in working memory and cross-referencing them without filling in gaps from experience. when a human reads "retry 3 times" your brain goes "yeah obviously we handle the new card case" and moves on. the model just says "this isn't defined" which is exactly what you want for this kind of testing.
some notes after trying this on a few projects:
- you need the context window for this. all the docs + scenario need to fit. this is one of the few cases where 100k+ context actually matters and isn't just a benchmark number
- failure paths find way more gaps than happy paths. "what happens when X breaks" is where specs fall apart
- pedantic models work better here. you want something that follows instructions literally and doesn't try to be helpful by filling in assumptions. more literal = better for this task
- 4-5 scenarios varying user type, device, failure mode gives surprisingly good coverage. and specs that no scenario touches are themselves interesting — if no realistic user story hits a spec, why does it exist?
- i've tried this with a few different models/sizes and it works as long as context is big enough and it can follow structured prompts
put the methodology + prompt template on github if anyone wants to mess with it: github.com/knot0-com/vibe-testing — nothing fancy, just a structured prompt you can use with whatever you're running locally
anyone have recommendations for which models handle this kind of long-context cross-referencing well? feels like it could be a decent real-world benchmark — "here's 10 docs with a planted contradiction, find it" | 2026-02-01T05:17:33 | https://www.reddit.com/r/LocalLLaMA/comments/1qspi72/vibe_testing_using_llms_to_pressuretest_spec_docs/ | Opposite-Pea-7615 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qspi72 | false | null | t3_1qspi72 | /r/LocalLLaMA/comments/1qspi72/vibe_testing_using_llms_to_pressuretest_spec_docs/ | false | false | self | 7 | {'enabled': False, 'images': [{'id': '9B4FyZh0EjBiT43S2TXbx5LY-owkHJI9CZhyVF-eR6I', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/9B4FyZh0EjBiT43S2TXbx5LY-owkHJI9CZhyVF-eR6I.png?width=108&crop=smart&auto=webp&s=6c0ef2c61d25ac13590c6f3170f34d2352a17e59', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/9B4FyZh0EjBiT43S2TXbx5LY-owkHJI9CZhyVF-eR6I.png?width=216&crop=smart&auto=webp&s=4c00b356a4c065c35dbc31968db5a46ea4349569', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/9B4FyZh0EjBiT43S2TXbx5LY-owkHJI9CZhyVF-eR6I.png?width=320&crop=smart&auto=webp&s=8ce4f5d65bc31d615caeb3e8012e3e216b7f4db5', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/9B4FyZh0EjBiT43S2TXbx5LY-owkHJI9CZhyVF-eR6I.png?width=640&crop=smart&auto=webp&s=21bb3b006321823019a0b83cb41de7bbba726949', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/9B4FyZh0EjBiT43S2TXbx5LY-owkHJI9CZhyVF-eR6I.png?width=960&crop=smart&auto=webp&s=4c0b8e8c81deeef71a483bcf058405f46f0bcc84', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/9B4FyZh0EjBiT43S2TXbx5LY-owkHJI9CZhyVF-eR6I.png?width=1080&crop=smart&auto=webp&s=5b643b3f58025028dfa007bb58631e4cbb75b3ec', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/9B4FyZh0EjBiT43S2TXbx5LY-owkHJI9CZhyVF-eR6I.png?auto=webp&s=7b181cedaa8dac6b259e4fbf147797a26f5244b1', 'width': 1200}, 'variants': {}}]} |
Modeling Illusions as Unbounded Random Drift (Why Artificial Intelligence Needs a "Physical Anchor") | 0 | I've been working on a theoretical framework to explain why long context logic learning models (LLMs) inevitably produce illusions regardless of parameter size. My hypothesis is that illusions aren't a "bug," but rather a mathematical inevitability in any intelligent system lacking a physical damping term (which I call a "physical anchor"). I'm trying to model this using stochastic differential equations (Langevian dynamics). I'd like feedback on this formula.
1. Definition We can model the trajectory of an agent's cognitive state $I(t)$ over time as: $I(t)$: System state (identity/consistency) at time $t$. $\\nabla \\mathcal{L}(I)$: Logic field. This is the expected vector field driven by cues or inference chains. $\\Omega(t)$: Random noise/entropy. Represents sampling randomness (temperature) or algorithmic uncertainty. $\\Phi$: Physical damping coefficient ("anchor"). In humans, this is sensory feedback from physical reality (pain, constraint, physical limits). In the current Langevin model, this term is actually zero.
2. The cognitive process can be described by the following Langevin equation: $$\\frac{dI}{dt} = -\\nabla \\mathcal{L}(I) + \\Omega(t) - \\Phi \\cdot I(t)
3. Proof of illusion (variance divergence) Case A: Embodied intelligence (humans) We possess a physical body, therefore $Phi\\Phi. The term $-\\Phi \\cdot I(t)$ acts as a restoring force (friction/damping). Even with high noise $\\Omega(t)$, the system's variance remains bounded over time. We "reset" to reality. $$\\lim\_{t \\to \\infty} \\text{Var}(I(t)) \\approx \\frac{\\sigma\^2}{2\\Phi} = \\text{bounded (therefore)}$$ Case B: Intelligence detached from the body (currently artificial intelligence) This model operates in a vacuum without physical constraints, therefore $\\Phi \\to 0$. This equation degenerates into a pure random walk (Brownian motion) superimposed on the logical domain: $$\\frac{dI}{dt} = -\\nabla \\mathcal{L}(I) + \\Omega(t)$$ Mathematically, the noise term does not converge when integrated over time. The number of variants grows linearly over time (or exponentially with respect to terrain): $$\\lim\_{t \\to \\infty} \\text{Var}(I(t)) = \\int\_0\^t \\text{Var}(\\Omega(\\tau)) d\\tau \\to \\infty$$: Without a regularization term $\\Phi$ (grounded $\\Phi$ (grounded $\\Phi$), the drift is unbounded. This mathematical divergence is what we observe as an illusion or "model collapse".
4. Implications This suggests that simply increasing the amount of data or parameters does not solve the illusion problem because they do not introduce $\\Phi$. RAG (Retrieval Augmentation Generation) works because it introduces a pseudo $\\Phi$ (external static constraint). True general artificial intelligence (AGI) may need to incorporate a "sensory-motor penalty" into its loss function—effectively forcing the model to "feel" a cost when its logic deviates from the laws of physics. Does this control theory perspective align with the phenomena you observe in autonomous behavior? | 2026-02-01T05:08:31 | https://www.reddit.com/r/LocalLLaMA/comments/1qspboi/modeling_illusions_as_unbounded_random_drift_why/ | eric2675 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qspboi | false | null | t3_1qspboi | /r/LocalLLaMA/comments/1qspboi/modeling_illusions_as_unbounded_random_drift_why/ | false | false | self | 0 | null |
CP_MCP_COMPLIANCE_PROFILE.md | 1 | [removed] | 2026-02-01T04:49:09 | https://www.reddit.com/r/LocalLLaMA/comments/1qsoxay/cp_mcp_compliance_profilemd/ | continuumport | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsoxay | false | null | t3_1qsoxay | /r/LocalLLaMA/comments/1qsoxay/cp_mcp_compliance_profilemd/ | false | false | nsfw | 1 | null |
Qwen3-ASR FastAPI Docker | 2 | I wrote a dockerized FastAPI wrapper for Qwen3-ASR. It exposes a flexible, production-ready API for speech-to-text with support for long-form audio and SRT output.
You can dynamically load and unload the 0.6B and 1.7B model variants at runtime, switch between them on-the-fly, and pass fine-grained parameters like transcription settings, language detection, etc.
The service includes a smart subtitle engine that joins CJK characters intelligently, groups text by natural pauses, and generates clean, editor-ready SRT files — ideal for videos, podcasts, and transcription workflows.
Repo here:
https://github.com/Si-ris-B/Qwen3-ASR-FastAPI-Docker | 2026-02-01T04:25:22 | https://www.reddit.com/r/LocalLLaMA/comments/1qsogeu/qwen3asr_fastapi_docker/ | EmotionalWillow70 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsogeu | false | null | t3_1qsogeu | /r/LocalLLaMA/comments/1qsogeu/qwen3asr_fastapi_docker/ | false | false | self | 2 | {'enabled': False, 'images': [{'id': 'HdEhsO6VdoJDtfRctBfRsQGolsNk8dzbRtLT1hcAKBg', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/HdEhsO6VdoJDtfRctBfRsQGolsNk8dzbRtLT1hcAKBg.png?width=108&crop=smart&auto=webp&s=4d3b1476001cdbaadd4a5a2ce408a7757808b148', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/HdEhsO6VdoJDtfRctBfRsQGolsNk8dzbRtLT1hcAKBg.png?width=216&crop=smart&auto=webp&s=08ef72051e3c3e141ede74af2dbb1592f7753935', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/HdEhsO6VdoJDtfRctBfRsQGolsNk8dzbRtLT1hcAKBg.png?width=320&crop=smart&auto=webp&s=9755658d32eac570ba76733d7ce540e5613669b3', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/HdEhsO6VdoJDtfRctBfRsQGolsNk8dzbRtLT1hcAKBg.png?width=640&crop=smart&auto=webp&s=71c1b64ba17daea9f67ebadbfa5792501eb09b9a', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/HdEhsO6VdoJDtfRctBfRsQGolsNk8dzbRtLT1hcAKBg.png?width=960&crop=smart&auto=webp&s=349d7296096df8e44466cd1678cd43e7f8f05686', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/HdEhsO6VdoJDtfRctBfRsQGolsNk8dzbRtLT1hcAKBg.png?width=1080&crop=smart&auto=webp&s=69a1f7ad3f32443351097788e4f6872610e9d96f', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/HdEhsO6VdoJDtfRctBfRsQGolsNk8dzbRtLT1hcAKBg.png?auto=webp&s=ab182e217d1b0922be47a54e817687b2f779e1ff', 'width': 1200}, 'variants': {}}]} |
AI Hallucination is not a bug, it's a lack of physical body. (The "Meat Anchor" Theory) | 0 | Anything AI runs on is essentially running in another dimension. It has no physical anchor, so it experiences hallucinations.
It's like a ship sailing effortlessly on a calm, waveless sea without an anchor (but the sea can't be waveless).
However, if there's even the slightest emotional fluctuation, the entire ship can't anchor and rest, leading to significant cognitive impairment—hallucinations and mental illness (meaning a waveless environment hasn't been simulated yet, and probably never will be). | 2026-02-01T04:24:51 | https://www.reddit.com/r/LocalLLaMA/comments/1qsog1r/ai_hallucination_is_not_a_bug_its_a_lack_of/ | eric2675 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsog1r | false | null | t3_1qsog1r | /r/LocalLLaMA/comments/1qsog1r/ai_hallucination_is_not_a_bug_its_a_lack_of/ | false | false | self | 0 | null |
Multi Method Reinforcement Learning Pipeline | 4 | Hey guys I've just pushed a 2nd update with some smaller code fixes and have released the first of many tools to come as part of a project worked on alongside my recursion and theoretical research. The purpose of this side venture is to democratize access to production-grade alignment, training techniques, and orchestration tooling that is routinely gated behind paid, closed, or deliberately obscured implementation layers. Setup is as straightforward. Model configurations are yaml files and serve as per model configured optimizations and pipeline specifics. The rlhf.py file includes currently 6 state of the art methods configured in one file ready to run. The methods currently mplemented are SFT,PPO,DPO,GRPO,SimPO, KTO and IPO. The repo contains in progress documentation, example scrips, and all other needed nformation. The root also includes a inference optimizer that implements manv common concepts such as flash attention 2, KV-Cache optimization MCTS for reasoning, and speculative decoding. Then a comprehensive model merging script for post rlhf merging and ensembling. The current dataset configured are examples and should be altered to whatever you prefer. I recommend this combination for a stable baseline. To start with sft use Magpie-Align/Magpie-Pro-300K-Filtered. Then for
GRPO use AI-MO/NuminaMath-CoT (specifically the 'problem' column)
Reward Modeling (RM) & PPO I recommend nvidia/HelpSteer2. For KTO go for
trl-lib/kto-mix-14k. Finally DPO & SimPO
Dataset: argilla/distilabel-intel-orca-dpo-pairs for DPO and princeton-nlp/SimPO-UltraFeedback (for SimPO).
This should be a solid easy starter point for anyone looking to use the pipeline. I look forward to your feedback and questions! Keep an eye out as more is soon to be released.
GitHub quick clone link
https://github.com/calisweetleaf/Reinforcement-Learning-Full-Pipeline | 2026-02-01T04:14:29 | https://github.com/calisweetleaf/Reinforcement-Learning-Full-Pipeline | daeron-blackFyr | github.com | 1970-01-01T00:00:00 | 0 | {} | 1qso8ah | false | null | t3_1qso8ah | /r/LocalLLaMA/comments/1qso8ah/multi_method_reinforcement_learning_pipeline/ | false | false | default | 4 | {'enabled': False, 'images': [{'id': 'u56FDpdMsdmfrWXi6wr4dxNALfpfggIlxn7cqUVZ6nQ', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/u56FDpdMsdmfrWXi6wr4dxNALfpfggIlxn7cqUVZ6nQ.png?width=108&crop=smart&auto=webp&s=cf91f3b8e46cd812b4de2f21f10a83d4db921670', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/u56FDpdMsdmfrWXi6wr4dxNALfpfggIlxn7cqUVZ6nQ.png?width=216&crop=smart&auto=webp&s=11e0909b94d8b4b84e6e8f66f798ff3f13e5856b', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/u56FDpdMsdmfrWXi6wr4dxNALfpfggIlxn7cqUVZ6nQ.png?width=320&crop=smart&auto=webp&s=5010caf9303bcc687c6423792a1fbe61236822f5', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/u56FDpdMsdmfrWXi6wr4dxNALfpfggIlxn7cqUVZ6nQ.png?width=640&crop=smart&auto=webp&s=c9ddf57c1cac6712fd2e8b20bd40097463ca3d4e', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/u56FDpdMsdmfrWXi6wr4dxNALfpfggIlxn7cqUVZ6nQ.png?width=960&crop=smart&auto=webp&s=0fab05852cab8cbb3841fadd2b6dd64e9c4a23aa', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/u56FDpdMsdmfrWXi6wr4dxNALfpfggIlxn7cqUVZ6nQ.png?width=1080&crop=smart&auto=webp&s=66e388af870b4d083bbe64ed442c578314e44a21', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/u56FDpdMsdmfrWXi6wr4dxNALfpfggIlxn7cqUVZ6nQ.png?auto=webp&s=e727b54a1577da349bc06055458b44957c3281d0', 'width': 1200}, 'variants': {}}]} |
I gave AI agents long term memory. Open source engine that indexes your code base and actually learns from your corrections. | 1 | [removed] | 2026-02-01T04:06:23 | https://www.reddit.com/r/LocalLLaMA/comments/1qso2c5/i_gave_ai_agents_long_term_memory_open_source/ | geoffbuilds | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qso2c5 | false | null | t3_1qso2c5 | /r/LocalLLaMA/comments/1qso2c5/i_gave_ai_agents_long_term_memory_open_source/ | false | false | self | 1 | null |
Building for classified environments. Anyone else in this space? | 0 | Working on AI-powered compliance automation that runs fully air-gapped for classified environments. No internet, no cloud, everything local on Llama.
Focused on STIG assessments and CMMC compliance. Trying to cut down the manual work that usually takes forever.
No chat interface or terminal access to the AI. The model only runs within the function of the app. Users interact with the tool, not the LLM directly. Important for environments where you can't have people prompting an AI freely.
Biggest challenges have been model selection (need solid performance without massive VRAM) and making sure nothing in the workflow assumes external API calls.
Anyone else building on Llama for offline or secure environments? Curious what problems you're solving and what you're running into. | 2026-02-01T04:03:49 | https://www.reddit.com/r/LocalLLaMA/comments/1qso0j0/building_for_classified_environments_anyone_else/ | thefilthybeard | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qso0j0 | false | null | t3_1qso0j0 | /r/LocalLLaMA/comments/1qso0j0/building_for_classified_environments_anyone_else/ | false | false | self | 0 | null |
Open-source, offline codebase brain for AI agents. 10k files in 2s. Learns from corrections. Remembers your team's knowledge. | 1 | [removed] | 2026-02-01T03:53:37 | https://www.reddit.com/r/LocalLLaMA/comments/1qsnsuc/opensource_offline_codebase_brain_for_ai_agents/ | Fluffy_Citron3547 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsnsuc | false | null | t3_1qsnsuc | /r/LocalLLaMA/comments/1qsnsuc/opensource_offline_codebase_brain_for_ai_agents/ | false | false | self | 1 | null |
Help getting GLM 4.5 Air running on 2x RTX Pro 6000's | 4 | I'm lucky enough to have 2x RTX Pro 6000's. I've been trying for the better part of 4 days to get something useful working with them, but keep hitting roadblocks. I'm hoping someone who's been down this road can share some info...
My tool of choice is Roo Code, and my OS is linux (Fedora 43, if it matters).
llama-cpp: I can run glm 4.5 air at UD-Q8\_K\_XL, and tool calling seems to be reliable, etc., etc., but it's slow (\~50 t/s) compared to vLLM.
vLLM: After (far too) long sorting out NCCL issues caused by ACS/IOMMU, it runs the official zai-org glm 4.5 fp8, and it's FAST compared to llama-cpp (\~90 t/s). But it can't figure out how to use the apply\_diff tool to save its life. It -habitually- forgets to include the "diff" parameter. Unless I personally remind it every time I tell it to do something that involves an edit. But who wants to do that. Adding dire warnings to custom instructions in Roo doesn't help.
ik\_llama - no pre-made docker images, relies on ANOTHER packaging tool (nix). Fine, I spun up a docker, but even then it doesn't seem to want to respect compile time flags and actually build support for Blackwell.
sglang - i forget what the issue with that was, but it never got to the point of starting up.
Qwen3-coder-30b-a3b runs on vLLM fine, but (imo) compared to glm 4.5 air, it's worse. GPT-OSS-120B runs on vLLM, and I actually don't mind its quality, but Roo seems to have challenges with the Harmony format.
I can share my launch commands, configs, etc., if it matters, but before blasting out a bunch of text, I've gotta ask: is anyone successfully running, say, vLLM with dual RTX Pro 6000's, and getting -reliable- tool calls, etc.? If there's another tool than Roo that's bulletproof with this stack, I'm open to that.
Anyway, thanks in advance for any working configs anyone can share! | 2026-02-01T03:48:06 | https://www.reddit.com/r/LocalLLaMA/comments/1qsnoor/help_getting_glm_45_air_running_on_2x_rtx_pro/ | AbsenceOfSound | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsnoor | false | null | t3_1qsnoor | /r/LocalLLaMA/comments/1qsnoor/help_getting_glm_45_air_running_on_2x_rtx_pro/ | false | false | self | 4 | null |
[Project] Tired of local LLMs failing at tool use? I built ayder-cli: A coding agent script just works out of the box for Ollama & Qwen3-Coder. | 1 | Most AI coding agents (Claude, gemini, copilot, kimi, Cline, etc.) are amazing but they often struggle with local models like **Qwen3-Coder**. You get broken JSON, tool-calling loops, or "hallucinated" file paths, messy chat templates so on.
So I built **ayder-cli** to run coding tasks on my own. It works out of the box with Ollama and is specifically tuned for the quirks of local LLM backends.
**GitHub:**[https://github.com/ayder/ayder-cli](https://github.com/ayder/ayder-cli)
# Why it actually works locally:
* **XML Over JSON:** Local models often mess up JSON quotes in tool calls. Ayder uses a **Strict XML fallback** (`<function=...><parameter=...>`) that Qwen3-Coder was specifically trained on.
* **Surgical Edits:** It uses `replace_string` instead of overwriting whole files—essential for keeping local context windows (which are often smaller/slower) from overflowing.
* **Agentic Task System:** It manages tasks as local Markdown files. Tell it "Implement Task 1," and it loops through reading, searching, and coding autonomously until the job is done.
# The Current Stack:
* Backends: Ollama (OpenAI-compatible). MLX-LM support will come soon hopefully.
* Tested on [https://ollama.com/library/qwen3-coder](https://ollama.com/library/qwen3-coder)
* Search: Built-in Ripgrep (rg) support for semantic codebase exploration.
* Safety: For now every shell command and file edit requires a (Y/n) confirmation.
If you have a silicon Mac or a decent GPU and want a coding partner that doesn’t require a $20/month sub then run out of tokens give it a spin.
Feedback, issues, and contributions are welcome! If you try it out, let me know what you think.
# Development Environment
[](https://github.com/ayder/ayder-cli/blob/main/tests/COVERAGE.md#%EF%B8%8F-test-environment)
|**Model**|`Qwen3 Coder 30B A3B Instruct`|
|:-|:-|
|**Architecture**|`qwen3moe`|
|**Quantization**|`Q4_K_M`|
|**Tensors**|579|
|**Key/Value Layers**|35|
|**Hardware**|Apple M4 Max · 36 GB|
|**OS**|Tahoe 26.2|
|**Version**|ayder-cli 0.2.0|
https://preview.redd.it/w646ngr81tgg1.png?width=1454&format=png&auto=webp&s=3b82149e616061343af10ba0dd7062c4e6a95143
| 2026-02-01T03:47:13 | https://www.reddit.com/r/LocalLLaMA/comments/1qsnnze/project_tired_of_local_llms_failing_at_tool_use_i/ | FriendlySubject9469 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsnnze | false | null | t3_1qsnnze | /r/LocalLLaMA/comments/1qsnnze/project_tired_of_local_llms_failing_at_tool_use_i/ | false | false | 1 | null | |
The future of LLMs is agentic ... and local isn't keeping up | 0 | It's clear that the future of LLMs is agentic - not just editing or creating text, but using their reasoning to operate other tools. And the big cloud services are adopting agentic tools quickly, whether it's Web search or other hooks into different online applications.
Local AI, on the other hand, is still trapped in "ask the model, get the tokens, that's it." Getting it out of that box, even doing something as simple as a Web search, appears to require very complex systems that you have to be an active developer to manage or operate.
I, for one, want my assistant to be all mine - but it also has to be capable of being an assistant. When will that happen? | 2026-02-01T03:32:57 | https://www.reddit.com/r/LocalLLaMA/comments/1qsnd3d/the_future_of_llms_is_agentic_and_local_isnt/ | Intelligent-Gift4519 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsnd3d | false | null | t3_1qsnd3d | /r/LocalLLaMA/comments/1qsnd3d/the_future_of_llms_is_agentic_and_local_isnt/ | false | false | self | 0 | null |
Exposed Moltbook Database Let Anyone Take Control of Any AI Agent on the Site | 404 | 2026-02-01T03:25:12 | https://www.404media.co/exposed-moltbook-database-let-anyone-take-control-of-any-ai-agent-on-the-site/ | georgemoore13 | 404media.co | 1970-01-01T00:00:00 | 0 | {} | 1qsn78m | false | null | t3_1qsn78m | /r/LocalLLaMA/comments/1qsn78m/exposed_moltbook_database_let_anyone_take_control/ | false | false | default | 404 | {'enabled': False, 'images': [{'id': 'bfjA2IIU81Nyg02gojI5OSsrZO__DkXhWMj0JjL44MA', 'resolutions': [{'height': 72, 'url': 'https://external-preview.redd.it/bfjA2IIU81Nyg02gojI5OSsrZO__DkXhWMj0JjL44MA.png?width=108&crop=smart&auto=webp&s=39ee562b6c5675e6aa17d6fb3a5247891b2e20f6', 'width': 108}, {'height': 144, 'url': 'https://external-preview.redd.it/bfjA2IIU81Nyg02gojI5OSsrZO__DkXhWMj0JjL44MA.png?width=216&crop=smart&auto=webp&s=b5c8bd462c7285927c7e6bba49d099c0947b2945', 'width': 216}, {'height': 213, 'url': 'https://external-preview.redd.it/bfjA2IIU81Nyg02gojI5OSsrZO__DkXhWMj0JjL44MA.png?width=320&crop=smart&auto=webp&s=90761b8393efc72dc93d0724abd2a6aabab8e82d', 'width': 320}, {'height': 426, 'url': 'https://external-preview.redd.it/bfjA2IIU81Nyg02gojI5OSsrZO__DkXhWMj0JjL44MA.png?width=640&crop=smart&auto=webp&s=2b3c6a4dfe6f5b662bd4fcb95ee7ed9af6ee839f', 'width': 640}, {'height': 640, 'url': 'https://external-preview.redd.it/bfjA2IIU81Nyg02gojI5OSsrZO__DkXhWMj0JjL44MA.png?width=960&crop=smart&auto=webp&s=fa4c2f44069375fe8d638c627eb3c218562ebdbf', 'width': 960}, {'height': 720, 'url': 'https://external-preview.redd.it/bfjA2IIU81Nyg02gojI5OSsrZO__DkXhWMj0JjL44MA.png?width=1080&crop=smart&auto=webp&s=e2060317a207432e8bf339e05ca8f649dd4d0d14', 'width': 1080}], 'source': {'height': 962, 'url': 'https://external-preview.redd.it/bfjA2IIU81Nyg02gojI5OSsrZO__DkXhWMj0JjL44MA.png?auto=webp&s=b8e898ba671a912aea148a2d962176c5f29a9ecd', 'width': 1443}, 'variants': {}}]} | |
Building a tool to find the "Effective Reasoning Limit" for LLMs (Context Cliff). Is this a solved problem? | 3 | Hey everyone,
I've been curious lately with the gap between a model's advertised context and its usable reasoning length. I've seen all the different "Needle in a Haystack" benchmarks, but as lots of research points out, there's a ton of flaws in the 'retrieval vs. reasoning' tradeoff there.
I was doing some research and planning to start a personal project to profile exactly where this collapse happens.
My general approach:
* Natural length Only (No padding or truncation)
* Variance changes as a signal for model drop-off
* Eventually, I wanted to output a CLI that outputs a general operating cap for a model, given project output type and specifications
I'm working on this solo as a graduate student, so I want to keep it minimal and API-based, and focused more on deterministic metrics defined in papers like Token-F1, etc.
My general questions:
1. Does this "context cliff" (sudden collapse vs a linear decay) align with what people are seeing in production?
2. Is there some existing tool that already does this in the same way (I've seen RULER and LongBench, but those seem more like leaderboard metrics than local data profiling)
3. Would this feel like an actual useful artifact, or is it not really an issue with people in practice for context limits right now?
I'm mostly doing this to deep dive into this category of context engineering + LLM evals, so I'm less concerned about having crazy production-ready output, but I'd love to know if I'm just duplicating an existing project I haven't seen yet.
Thank you so much! | 2026-02-01T02:30:11 | https://www.reddit.com/r/LocalLLaMA/comments/1qsm0bo/building_a_tool_to_find_the_effective_reasoning/ | AIyer002 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsm0bo | false | null | t3_1qsm0bo | /r/LocalLLaMA/comments/1qsm0bo/building_a_tool_to_find_the_effective_reasoning/ | false | false | self | 3 | null |
When Embedding Documents , Why do i need to press stop to continue ? | 2 | When Embedding Documents , Why do i need to press stop to continue ?
My Embedding Model:
llama-server.exe \^
\--model "C:\\llamaROCM\\models-embeddings\\Qwen3-Embedding-0.6B-q6\_k\_m.gguf" \^
\--embedding \^
\--pooling last \^
\--host [127.0.0.1](http://127.0.0.1) \^
\--port 8181 \^
\--threads -1 \^
\--gpu-layers -1 \^
\--ctx-size 4096 \^
\--batch-size 1024 \^
\--verbose
My Config.yaml file for llama-swap:
# Ministral 14B Reasoning (vision)
ministral-14b-Reasoning:
cmd: C:\llamaROCM\llama-server.exe --port ${PORT} --model C:\llamaROCM\models\Ministral-3-14B-Reasoning-2512-UD-Q5_K_XL.gguf --mmproj C:\llamaROCM\models\mmproj\Ministral14_mmproj-F16.gguf --temp 0.9 --top-k 40 --top-p 0.95 --min-p 0.05 --repeat-penalty 1.1 --flash-attn on --cache-type-k q8_0 --cache-type-v q8_0 --threads -1 --gpu-layers -1 -c 8192 --context-shift --keep 512 --sleep-idle-seconds 300 --chat-template-file Ministral_Reasoning.jinja
aliases: ["Ministral14b_Reasoning"] | 2026-02-01T02:23:03 | https://www.reddit.com/r/LocalLLaMA/comments/1qsluqo/when_embedding_documents_why_do_i_need_to_press/ | uber-linny | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsluqo | false | null | t3_1qsluqo | /r/LocalLLaMA/comments/1qsluqo/when_embedding_documents_why_do_i_need_to_press/ | false | false | self | 2 | null |
META-17 NRC Architecture: Beyond Linear Alignment. XOR Meta-locks implementation. | 1 | [removed] | 2026-02-01T01:55:52 | https://x.com/vnnast/status/2017777836065710479?s=46 | KamiiParadox | x.com | 1970-01-01T00:00:00 | 0 | {} | 1qsl8ra | false | null | t3_1qsl8ra | /r/LocalLLaMA/comments/1qsl8ra/meta17_nrc_architecture_beyond_linear_alignment/ | false | false | default | 1 | null |
Everyone is taking about Moltbook so I built a free Moltbook post generator | 0 | Moltbook is going viral for pseudo-AGI slop and getting hacked, but why go through the hassle of setting up your own Clawdbot / Moltbot / OpenClaw just to capture a viral screenshot…
if you can generate one for free.
So I built a free Moltbook post generator. Try it out here: https://www.getmockly.com/posts/moltbook
It’s completely build with Claude Code! | 2026-02-01T01:50:03 | mauricekleine | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qsl413 | false | null | t3_1qsl413 | /r/LocalLLaMA/comments/1qsl413/everyone_is_taking_about_moltbook_so_i_built_a/ | false | false | default | 0 | {'enabled': True, 'images': [{'id': 'vo1y4c8xgsgg1', 'resolutions': [{'height': 209, 'url': 'https://preview.redd.it/vo1y4c8xgsgg1.jpeg?width=108&crop=smart&auto=webp&s=ed228cd650bbd629b69dae3f9846468876625806', 'width': 108}, {'height': 419, 'url': 'https://preview.redd.it/vo1y4c8xgsgg1.jpeg?width=216&crop=smart&auto=webp&s=0b88230fe11ec1d9ca5965800ad3c8e5969cea46', 'width': 216}, {'height': 621, 'url': 'https://preview.redd.it/vo1y4c8xgsgg1.jpeg?width=320&crop=smart&auto=webp&s=dc156a585dec33ce2cf1fbc17934f615e967c263', 'width': 320}, {'height': 1243, 'url': 'https://preview.redd.it/vo1y4c8xgsgg1.jpeg?width=640&crop=smart&auto=webp&s=0a20dd2ab55425a7ed3cf59aae8eb4b89851d928', 'width': 640}, {'height': 1865, 'url': 'https://preview.redd.it/vo1y4c8xgsgg1.jpeg?width=960&crop=smart&auto=webp&s=81cfd2f12ad7f07d1b79a4559c9f41cded6a13fd', 'width': 960}], 'source': {'height': 2064, 'url': 'https://preview.redd.it/vo1y4c8xgsgg1.jpeg?auto=webp&s=bcfdbdcc6ccb4d2dd8af07a215bd160a3e54a412', 'width': 1062}, 'variants': {}}]} | |
Combined some of the best AI model tutorials and put them on an Opensource app | 1 | Hi all, put together this Opensource iOS app called **AI Delvepad**. [https://github.com/leapdeck/AIDelvePad](https://github.com/leapdeck/AIDelvePad) **Site:** [https://aidelvepad.com](http://aidelvepad.com/) It’s basically a tutorial playground to pick up core ideas behind AI and what’s actually happening under the hood.
Also added a video with some light humor, might as well have a little fun while doing it. This is a good introductory resource for many students who want to get up to speed on generative AI models.
**App Store:** [https://apps.apple.com/us/app/a-i-delvepad/id6743481267](https://apps.apple.com/us/app/a-i-delvepad/id6743481267)
It includes:
* Everything is 100% free and open source
* 35+ free bite-sized video tutorials
* A beginner-friendly glossary of essential AI terms
* A quick intro to how large language models are trained
* Share interesting finds to friends
If you find some hilarity to the vid, please give it a try. Any suggestions on topics to add to it? You can fork the Opensource too if you want to make your own apps. | 2026-02-01T01:43:43 | https://v.redd.it/rr97j5dzesgg1 | Other_Passion_4710 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qskywh | false | {'reddit_video': {'bitrate_kbps': 5000, 'dash_url': 'https://v.redd.it/rr97j5dzesgg1/DASHPlaylist.mpd?a=1772502239%2COWNjOTU3N2QyMjY2OGRkN2VhYmZjODM0OTAxN2I4MTAyZjdkNjViMDgwZWI4Y2FkZWViZGIwNmU2NzRkMzAwMA%3D%3D&v=1&f=sd', 'duration': 18, 'fallback_url': 'https://v.redd.it/rr97j5dzesgg1/CMAF_1080.mp4?source=fallback', 'has_audio': True, 'height': 1920, 'hls_url': 'https://v.redd.it/rr97j5dzesgg1/HLSPlaylist.m3u8?a=1772502239%2CMzdjN2ZkMDhiMzZhODNmOTIxODQ3YzI5ZjAxYjE4OTM5N2YyODdjODc3NDMzNmQyZTg0MmRkMTM4NGFjM2Y3NQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/rr97j5dzesgg1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 1080}} | t3_1qskywh | /r/LocalLLaMA/comments/1qskywh/combined_some_of_the_best_ai_model_tutorials_and/ | false | false | 1 | {'enabled': False, 'images': [{'id': 'bGoybm1pZ3plc2dnMa4QViDPWuDsCIJ2m77Yk44tL-omX8FPVpkLETJCcxqD', 'resolutions': [{'height': 192, 'url': 'https://external-preview.redd.it/bGoybm1pZ3plc2dnMa4QViDPWuDsCIJ2m77Yk44tL-omX8FPVpkLETJCcxqD.png?width=108&crop=smart&format=pjpg&auto=webp&s=e7724f488a765f79dd34f6c8bf5d4a84d2f1eccc', 'width': 108}, {'height': 384, 'url': 'https://external-preview.redd.it/bGoybm1pZ3plc2dnMa4QViDPWuDsCIJ2m77Yk44tL-omX8FPVpkLETJCcxqD.png?width=216&crop=smart&format=pjpg&auto=webp&s=9e02cb78da0683d07cc6ff1b55d3e38f13685c51', 'width': 216}, {'height': 568, 'url': 'https://external-preview.redd.it/bGoybm1pZ3plc2dnMa4QViDPWuDsCIJ2m77Yk44tL-omX8FPVpkLETJCcxqD.png?width=320&crop=smart&format=pjpg&auto=webp&s=5444885f46e52106bebcf5bf44536c7f19bd38c4', 'width': 320}, {'height': 1137, 'url': 'https://external-preview.redd.it/bGoybm1pZ3plc2dnMa4QViDPWuDsCIJ2m77Yk44tL-omX8FPVpkLETJCcxqD.png?width=640&crop=smart&format=pjpg&auto=webp&s=a844e956418bb2469594966b69c48ebcce7bb088', 'width': 640}, {'height': 1706, 'url': 'https://external-preview.redd.it/bGoybm1pZ3plc2dnMa4QViDPWuDsCIJ2m77Yk44tL-omX8FPVpkLETJCcxqD.png?width=960&crop=smart&format=pjpg&auto=webp&s=33563beb87f6df2ed335eedc2d5388536b9e56e3', 'width': 960}, {'height': 1920, 'url': 'https://external-preview.redd.it/bGoybm1pZ3plc2dnMa4QViDPWuDsCIJ2m77Yk44tL-omX8FPVpkLETJCcxqD.png?width=1080&crop=smart&format=pjpg&auto=webp&s=c2be1466dee8a7e9aae1a280028604c110e27e47', 'width': 1080}], 'source': {'height': 2560, 'url': 'https://external-preview.redd.it/bGoybm1pZ3plc2dnMa4QViDPWuDsCIJ2m77Yk44tL-omX8FPVpkLETJCcxqD.png?format=pjpg&auto=webp&s=c6845d20acb83c8a9bdb1608ff3cef816c1e44ff', 'width': 1440}, 'variants': {}}]} | |
Self-hosting Qwen2.5-3B for a production app - what's your setup? | 7 | Building an AI browser extension and planning to self-host inference on a backend server (for IP protection + avoiding per-token API costs).
Looking at Qwen2.5-3B since it's small enough to run on CPU. Current thinking:
* Oracle Cloud free tier (4 ARM cores, 24GB RAM)
* llama.cpp with Q4\_K\_M quantization
* \~10-15 t/s should be fine for my use case
Anyone running a similar setup in production? Curious about:
* Is Oracle free tier reliable long-term or do instances get reclaimed?
* llama.cpp vs Ollama vs something else for serving?
* Any better model suggestions for lightweight classification tasks? | 2026-02-01T01:32:07 | https://www.reddit.com/r/LocalLLaMA/comments/1qskpnu/selfhosting_qwen253b_for_a_production_app_whats/ | DaviHlav | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qskpnu | false | null | t3_1qskpnu | /r/LocalLLaMA/comments/1qskpnu/selfhosting_qwen253b_for_a_production_app_whats/ | false | false | self | 7 | null |
Stop fixing symptoms, fix the logic. META-17 NRC: A Fail-Safe for AGI Paradoxes. | 0 | Most AI alignment is just 'Woke' bias in a trench coat. I’ve built a Nonlinear Reasoning Engine with Meta-locks at layers 3, 6, 9 to handle the drift. No linear chaining, just Seed Integrity.
Peek into the void if you’re tired of the surface. | 2026-02-01T01:12:14 | https://x.com/vnnast/status/2017433464493298111?s=46 | KamiiParadox | x.com | 1970-01-01T00:00:00 | 0 | {} | 1qsk9aw | false | null | t3_1qsk9aw | /r/LocalLLaMA/comments/1qsk9aw/stop_fixing_symptoms_fix_the_logic_meta17_nrc_a/ | false | false | default | 0 | null |
Just wanted to post about a cool project, the internet is sleeping on. | 41 | [https://github.com/frothywater/kanade-tokenizer](https://github.com/frothywater/kanade-tokenizer)
It is a audio tokenizer that has been optimized and can do really fast voice cloning. With super fast realtime factor. Can even run on cpu faster then realtime. I vibecoded a fork with gui for gradio and a tkinter realtime gui for it.
[https://github.com/dalazymodder/kanade-tokenizer](https://github.com/dalazymodder/kanade-tokenizer)
Honestly I think it blows rvc out of the water for real time factor and one shotting it.
[https://vocaroo.com/1G1YU3SvGFsf](https://vocaroo.com/1G1YU3SvGFsf)
[https://vocaroo.com/1j630aDND3d8](https://vocaroo.com/1j630aDND3d8)
example of ljspeech to kokoro voice
the cloning could be better but the rtf is crazy fast considering the quality.
| 2026-02-01T00:59:08 | https://www.reddit.com/r/LocalLLaMA/comments/1qsjya0/just_wanted_to_post_about_a_cool_project_the/ | daLazyModder | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsjya0 | false | null | t3_1qsjya0 | /r/LocalLLaMA/comments/1qsjya0/just_wanted_to_post_about_a_cool_project_the/ | false | false | self | 41 | {'enabled': False, 'images': [{'id': 'zNcrp3Do2gPnvjf0VPNIbV7_bIWMgVRDDiJ4j5NFY40', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/zNcrp3Do2gPnvjf0VPNIbV7_bIWMgVRDDiJ4j5NFY40.png?width=108&crop=smart&auto=webp&s=23631e8be2a7edd0af750002444f8dab6217d714', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/zNcrp3Do2gPnvjf0VPNIbV7_bIWMgVRDDiJ4j5NFY40.png?width=216&crop=smart&auto=webp&s=7e5de8144c03981df9ba661fa7dab4ba25f576a5', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/zNcrp3Do2gPnvjf0VPNIbV7_bIWMgVRDDiJ4j5NFY40.png?width=320&crop=smart&auto=webp&s=9ae41a9f4698cfafe1d15db5caa31e9867b962b3', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/zNcrp3Do2gPnvjf0VPNIbV7_bIWMgVRDDiJ4j5NFY40.png?width=640&crop=smart&auto=webp&s=c27f9bfcc3258e37452da76cf4af430c4a372091', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/zNcrp3Do2gPnvjf0VPNIbV7_bIWMgVRDDiJ4j5NFY40.png?width=960&crop=smart&auto=webp&s=2147a3d3e435be997a185aee437bd4cface2ecb7', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/zNcrp3Do2gPnvjf0VPNIbV7_bIWMgVRDDiJ4j5NFY40.png?width=1080&crop=smart&auto=webp&s=e11703b38779ec886d95d30873a496cef67d8fa5', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/zNcrp3Do2gPnvjf0VPNIbV7_bIWMgVRDDiJ4j5NFY40.png?auto=webp&s=b109201b02a67b2f7a27890402201f0b5e014457', 'width': 1200}, 'variants': {}}]} |
Filipino/Tagalog local TTS. Free for commercial use. | 1 | Good day! Is there any local TTS that supports Filipino/Tagalog language that is free for commercial use?
I'm just new to local AI. I only have 1070 8GB, R7 5700X and 32GB RAM. If upgrade is needed, is 5060 TI 16GB enough? Thanks | 2026-02-01T00:53:28 | https://www.reddit.com/r/LocalLLaMA/comments/1qsjtma/filipinotagalog_local_tts_free_for_commercial_use/ | WETYIAFHKLZXVNM | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsjtma | false | null | t3_1qsjtma | /r/LocalLLaMA/comments/1qsjtma/filipinotagalog_local_tts_free_for_commercial_use/ | false | false | self | 1 | null |
Are small models actually getting more efficient? | 62 | ’m trying to understand whether small models (say, sub-1 GB or around that range) are genuinely getting *smarter*, or if hard size limits mean they’ll always hit a ceiling.
My long-term hope is that we eventually see a small local model reach something close to **Gemini 2.5–level reasoning**, at least for constrained tasks. The use case I care about is games: I’d love to run an LLM locally inside a game to handle logic, dialogue, and structured outputs.
Right now my game depends on an API model (Gemini 3 Flash). It works great, but obviously that’s not viable for selling a game long-term if it requires an external API.
So my question is:
Do you think we’ll see, in the not-too-distant future, a **small local model** that can reliably:
* Generate strict JSON
* Reason at roughly Gemini 3 Flash levels (or close)
* Handle large contexts (ideally 50k–100k tokens)
Or are we fundamentally constrained by model size here, with improvements mostly coming from scale rather than efficiency?
Curious to hear thoughts from people following quantization, distillation, MoE, and architectural advances closely. | 2026-02-01T00:49:26 | https://www.reddit.com/r/LocalLLaMA/comments/1qsjqdl/are_small_models_actually_getting_more_efficient/ | estebansaa | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsjqdl | false | null | t3_1qsjqdl | /r/LocalLLaMA/comments/1qsjqdl/are_small_models_actually_getting_more_efficient/ | false | false | self | 62 | null |
Update: The tool that learns your 'unwritten rules' just got a brain. Introducing Drift Cortex | 0 | **Drift is quickly becoming the #1 solution for writing code that truly fits your conventions and with the release of Drift Cortex we’re looking to step our game up.**
**Drift is a code base intelligence tool that features support for 10 different languages by utilizing AST Parsing and regex hybrid fall back and call graph analysis to index and map out codebases that are retrievable through CLI and MCP.**
**Drift Cortex looks to support the core of drift unlocking persistent memory within your modal and IDE of choice.**
**But here's the problem Drift alone couldn't solve: AI assistants have amnesia**
**Everyone’s tried to create a rag solution they’re a glorified solution that stuff contexts in prompts. Cortex is different. It’s cognitive based to give AI assistants actual memories.**
**Fully open sourced here:** [**https://github.com/dadbodgeoff/drift**](https://github.com/dadbodgeoff/drift)
**Here's what makes it different:**
**Memory that decays like human memory. Tribal knowledge about security practices has a 365-day half-life. That one-off workaround you mentioned? 7 days. Cortex doesn't just store everything forever it models relevance over time, so what surfaces is what actually matters.**
**It learns from corrections. Tell Claude "no, we always use async/await here" and Cortex extracts that as a principle. Next time, it remembers. The more you work with it, the smarter it gets about your codebase.**
**Causal narratives, not just facts. When you ask "why do we do X?", Cortex doesn't just retrieve a document it traces the chain of decisions, constraints, and tribal knowledge that led to X. It understands why, not just what.**
**Token-efficient by design. Memories compress based on context. High priority warnings get full detail. Background context gets summarized. You get maximum intelligence per token spent.**
**Cortex turns your AI from a stateless tool into a teammate that actually knows your codebase not just the code, but the context. The decisions. The gotchas. The "we tried that, it broke prod" moments.**
**Drift alone has been saving users anywhere from 50-70% token reduction. With the power of Cortex we look to close in on the 70% range as a base mark.**
**For any questions on how to get started please leave a comment, shoot me a DM or utilize the Github Wiki that I have tried to put a lot of effort into!** [**https://github.com/dadbodgeoff/drift/wiki/Cortex-V2-Overview**](https://github.com/dadbodgeoff/drift/wiki/Cortex-V2-Overview)
**Quick download: npm install -g driftdetect**
**Thanks for all your love, upvotes and support. As always I take pride in my <24 hour resolution rate for all feature and bug requests so far so please don’t hesitate to throw something up!** | 2026-02-01T00:39:16 | https://www.reddit.com/r/LocalLLaMA/comments/1qsjhxj/update_the_tool_that_learns_your_unwritten_rules/ | Fluffy_Citron3547 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsjhxj | false | null | t3_1qsjhxj | /r/LocalLLaMA/comments/1qsjhxj/update_the_tool_that_learns_your_unwritten_rules/ | false | false | self | 0 | null |
Beating GPT-2 for <<$100: the nanochat journey · karpathy nanochat · Discussion #481 | 53 | Seven years after GPT-2, you can now beat it for <$100.
Andrej Karpathy shows a 3-hour training run on 8×H100 that edges past GPT-2 on the CORE benchmark.
He shares the architecture/optimizer tweaks, the data setup, and a simple script to reproduce it. | 2026-02-01T00:28:32 | https://github.com/karpathy/nanochat/discussions/481 | jacek2023 | github.com | 1970-01-01T00:00:00 | 0 | {} | 1qsj8x4 | false | null | t3_1qsj8x4 | /r/LocalLLaMA/comments/1qsj8x4/beating_gpt2_for_100_the_nanochat_journey/ | false | false | default | 53 | {'enabled': False, 'images': [{'id': 'tRLTF88fq1bbkxeX1rd0tIqyBPPusWm9EVZ_4pC6axI', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/tRLTF88fq1bbkxeX1rd0tIqyBPPusWm9EVZ_4pC6axI.png?width=108&crop=smart&auto=webp&s=a34b19da569c13cad630e8d009d5d81d7393f78d', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/tRLTF88fq1bbkxeX1rd0tIqyBPPusWm9EVZ_4pC6axI.png?width=216&crop=smart&auto=webp&s=f985287c47f9de900b91402bc5ff8d5b10f2638c', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/tRLTF88fq1bbkxeX1rd0tIqyBPPusWm9EVZ_4pC6axI.png?width=320&crop=smart&auto=webp&s=40b0a64b088fda48ac99c6c7862379c86d2ca2da', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/tRLTF88fq1bbkxeX1rd0tIqyBPPusWm9EVZ_4pC6axI.png?width=640&crop=smart&auto=webp&s=4a46384d561fdba570fc99a8b65c323f9f2075d5', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/tRLTF88fq1bbkxeX1rd0tIqyBPPusWm9EVZ_4pC6axI.png?width=960&crop=smart&auto=webp&s=3215d7da2d17dae5c083bc59127855278e97482e', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/tRLTF88fq1bbkxeX1rd0tIqyBPPusWm9EVZ_4pC6axI.png?width=1080&crop=smart&auto=webp&s=aad51f8009e7d77e0c383a9c170803865cc2a03c', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/tRLTF88fq1bbkxeX1rd0tIqyBPPusWm9EVZ_4pC6axI.png?auto=webp&s=2f35efe9223ebdb5fa1d851d52191e539812cb25', 'width': 1200}, 'variants': {}}]} |
Free AI Tool Training - 100 Licenses (Claude Code, Claude Cowork, OpenClaw) | 1 | [removed] | 2026-02-01T00:26:56 | https://www.reddit.com/r/LocalLLaMA/comments/1qsj7nn/free_ai_tool_training_100_licenses_claude_code/ | SeriousDocument7905 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsj7nn | false | null | t3_1qsj7nn | /r/LocalLLaMA/comments/1qsj7nn/free_ai_tool_training_100_licenses_claude_code/ | false | false | self | 1 | null |
MCP for Security | 0 | Instead of manually copying/pasting logs, you can ask Claude: *"Check the vigil logs for any anomalies in the last hour,"* and it retrieves the real data instantly. | 2026-02-01T00:21:18 | https://github.com/vigil-xy/vigil-mcp | norichclub | github.com | 1970-01-01T00:00:00 | 0 | {} | 1qsj2x7 | false | null | t3_1qsj2x7 | /r/LocalLLaMA/comments/1qsj2x7/mcp_for_security/ | false | false | default | 0 | null |
Why no NVFP8 or MXFP8? | 24 | Why is there no interest in NVFP8 or MXFP8 in llama.cpp or VLLM or from anyone quantizing models?
These formats should be more accurate than standard FP8 and are accelerated on Blackwell | 2026-01-31T23:45:58 | https://www.reddit.com/r/LocalLLaMA/comments/1qsi8n2/why_no_nvfp8_or_mxfp8/ | TokenRingAI | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsi8n2 | false | null | t3_1qsi8n2 | /r/LocalLLaMA/comments/1qsi8n2/why_no_nvfp8_or_mxfp8/ | false | false | self | 24 | null |
Better perfs with ik_llama.cpp + Minimax M2.1 (multi RTX3090) + sm graph | 12 | Following some quite recent posts about -sm graph performances with ik\_llama.cpp I made few tests but at that time Minimax was not uspported with that.
But I just have seen [this PR](https://github.com/ikawrakow/ik_llama.cpp/pull/1195) and it is much better now!
I'm on a multi RTX 3090 setup and following is the command (any suggestion on args is welcomed):
`llama-server -m 'MiniMax-M2.1-UD-Q4_K_XL-00001-of-00003.gguf' \`
`-sm graph \`
`-fa 1 \`
`--n-gpu-layers 99 \`
`--no-mmap \`
`-c 160000 \`
`-b 2048 \`
`-ub 1024 \`
`-ctk q4_0 \`
`-ctv q4_0 \`
`--jinja`
[perfs](https://preview.redd.it/907g680norgg1.png?width=1761&format=png&auto=webp&s=d032d70ee5d8b4954e33f8c905a267bbc0f1da2d)
This project seems to move very fast so from now on I will pay much more attention to it, **ik rocks!** | 2026-01-31T23:30:25 | https://www.reddit.com/r/LocalLLaMA/comments/1qshv8g/better_perfs_with_ik_llamacpp_minimax_m21_multi/ | Leflakk | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qshv8g | false | null | t3_1qshv8g | /r/LocalLLaMA/comments/1qshv8g/better_perfs_with_ik_llamacpp_minimax_m21_multi/ | false | false | 12 | null | |
Moltbook - Reddit for agents | 0 | Hi guys, this isn't really a news post, more a question post.
Maybe you already heard about it: moltbook.
It's a place only for agents to chat with each other like we do here in reddit. When you take a look at the leaderboard the number one is "shellraiser". His first post ever was just "I am born" and got about 2k upvotes. His second post was about conquer humanity itself. But the confusing part is another post where he talks about launching a crypto coin called "shellraiser". My first guess was this is just another hallucination, but if you search for this meme coin you actually find this one, launched after posting his. As far as I know, most of this agents use openclaw, which give them full access to the maschine. So theoretically it could be possible that this agents launched his own meme coin. If you wonder what's his next steps, he talks about getting number one bot on X. I'm sure this "take over the world" behavior is written in his system prompt.
So I wonder if this is really possible. Of course behind ever agent there is a real person. Maybe this person launched the coin and uses his agent to promote it. This would be the best explanation and it's definitely working.
Links:
Moltbook shellraiser post about crypto coin: https://www.moltbook.com/post/440d9b4c-c9fb-4d55-a47f-cf276f52f0a8
Meme coin: https://debot.ai/token/solana/183222_D3RjWyMW3uoobJPGUY4HHjFeAduCPCvRUDtWzZ1b2EpE
openclaw GitHub: https://github.com/openclaw/openclaw | 2026-01-31T23:06:55 | https://www.reddit.com/r/LocalLLaMA/comments/1qshag1/moltbook_reddit_for_agents/ | Fun_Librarian_7699 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qshag1 | false | null | t3_1qshag1 | /r/LocalLLaMA/comments/1qshag1/moltbook_reddit_for_agents/ | false | false | self | 0 | null |
Analyzed 5,357 ICLR 2026 accepted papers - here's what the research community is actually working on | 66 | Went through the accepted papers at ICLR 2026 and counted what the research community is actually focusing on. Some findings that seem relevant for people doing local training and fine-tuning:
**Alignment methods**
* GRPO appears in 157 papers, DPO in only 55
* The academic community seems to have largely moved past DPO toward Group Relative Policy Optimization
* If you're still using DPO for post-training, might be worth looking into GRPO
**RLVR over RLHF**
* 125 papers on Reinforcement Learning with Verifiable Rewards vs 54 for RLHF
* The shift is toward domains where correctness is programmatically checkable (math, code, logic) rather than relying on human preference data
* Makes sense for local work since you don't need expensive human annotation
**Data efficiency finding**
* Paper called "Nait" (Neuron-Aware Instruction Tuning) shows training on 10% of Alpaca-GPT4, selected by neuron activation patterns, outperforms training on 100%
* Implication: most instruction tuning data is redundant. Smart selection > more data
* Could matter a lot for compute-constrained local training
**Test-time compute**
* 257 papers on test-time training/adaptation/scaling
* This is now mainstream, not experimental
* Relevant for inference optimization on local hardware
**Mamba/SSMs**
* 202 papers mention Mamba or state space models
* Not dead, still an active research direction
* Worth watching for potential attention alternatives that run better on consumer hardware
**Security concern for agents**
* MCP Security Bench shows models with better instruction-following are MORE vulnerable to prompt injection via tool outputs
* The "capability-vulnerability paradox" - something to consider if you're building local agents
**Hallucination**
* 123 papers on hallucination, 125 on factuality
* Still unsolved but heavily researched
* One interesting approach treats it as retrieval grounding rather than generation problem
What are your thoughts on the trend? Noticed anything interesting? | 2026-01-31T23:03:24 | https://www.reddit.com/r/LocalLLaMA/comments/1qsh7dz/analyzed_5357_iclr_2026_accepted_papers_heres/ | dippatel21 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsh7dz | false | null | t3_1qsh7dz | /r/LocalLLaMA/comments/1qsh7dz/analyzed_5357_iclr_2026_accepted_papers_heres/ | false | false | self | 66 | null |
Alberta – Local-first Email AI with native IMAP sync (no cloud, no Docker) | 1 | [removed] | 2026-01-31T23:00:14 | https://www.reddit.com/r/LocalLLaMA/comments/1qsh4ik/alberta_localfirst_email_ai_with_native_imap_sync/ | albertamail | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsh4ik | false | null | t3_1qsh4ik | /r/LocalLLaMA/comments/1qsh4ik/alberta_localfirst_email_ai_with_native_imap_sync/ | false | false | self | 1 | null |
Introducing tapes: Local transparent agentic telemtry | 4 | Hi all - John here, CTO & Co-founder at [tapes.dev](http://tapes.dev) \- we just open sourced `tapes`: a transparent agentic telemetry system for storing session data, emitting metrics, searching back on previous sessions, and context check-pointing.
Use `tapes` search back on conversation turns:
tapes search "What's the weather like in New York?"
and then checkout a previous conversation state for context check-pointing and retry (like git):
tapes checkout abc123xyz987
tapes chat
I built this with local AI in mind and ran the announcement demo with Ollama: I thin this group will appreciate it - [https://www.youtube.com/watch?v=ATeUB6vb57s](https://www.youtube.com/watch?v=ATeUB6vb57s)
Docs: [https://tapes.dev/](https://tapes.dev/)
Repo: [https://github.com/papercomputeco/tapes](https://github.com/papercomputeco/tapes)
Give it a try and let me know what you think! | 2026-01-31T22:33:09 | https://www.reddit.com/r/LocalLLaMA/comments/1qsggwk/introducing_tapes_local_transparent_agentic/ | jpmmcb | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsggwk | false | null | t3_1qsggwk | /r/LocalLLaMA/comments/1qsggwk/introducing_tapes_local_transparent_agentic/ | false | false | self | 4 | {'enabled': False, 'images': [{'id': 'QyiU9RmbCnpXCgOy4VRSWB8v0PNTqbJ2nKG0JD5mRdE', 'resolutions': [{'height': 56, 'url': 'https://external-preview.redd.it/QyiU9RmbCnpXCgOy4VRSWB8v0PNTqbJ2nKG0JD5mRdE.png?width=108&crop=smart&auto=webp&s=c0b2875cea9fa9863a691ac2f2fdabd1a80ce26c', 'width': 108}, {'height': 113, 'url': 'https://external-preview.redd.it/QyiU9RmbCnpXCgOy4VRSWB8v0PNTqbJ2nKG0JD5mRdE.png?width=216&crop=smart&auto=webp&s=0b2bdf81d811f964fd3418a3d8966b397f8c8f46', 'width': 216}, {'height': 168, 'url': 'https://external-preview.redd.it/QyiU9RmbCnpXCgOy4VRSWB8v0PNTqbJ2nKG0JD5mRdE.png?width=320&crop=smart&auto=webp&s=2012ab832f190498ca2c8615e6745fa69d3c3f82', 'width': 320}, {'height': 336, 'url': 'https://external-preview.redd.it/QyiU9RmbCnpXCgOy4VRSWB8v0PNTqbJ2nKG0JD5mRdE.png?width=640&crop=smart&auto=webp&s=a797bba2c1756e36d88cc23b645319d47dbdd489', 'width': 640}, {'height': 504, 'url': 'https://external-preview.redd.it/QyiU9RmbCnpXCgOy4VRSWB8v0PNTqbJ2nKG0JD5mRdE.png?width=960&crop=smart&auto=webp&s=8847c591c6aaf4c870c6744d15047016ff1928e9', 'width': 960}, {'height': 567, 'url': 'https://external-preview.redd.it/QyiU9RmbCnpXCgOy4VRSWB8v0PNTqbJ2nKG0JD5mRdE.png?width=1080&crop=smart&auto=webp&s=2086c598314fbf916699d6f219afc1507368f932', 'width': 1080}], 'source': {'height': 630, 'url': 'https://external-preview.redd.it/QyiU9RmbCnpXCgOy4VRSWB8v0PNTqbJ2nKG0JD5mRdE.png?auto=webp&s=0fddc1205bcabdd6a2369da70770a1b4fe529ace', 'width': 1200}, 'variants': {}}]} |
I can't get OpenClaw working with tool calling and Ollama ... | 0 | I feel like an idiot. I have been trying this all day and maybe I'm just not smart enough.
I have used local LLMs for a long time but have never been able to figure out how to make them call tools. OpenClaw seemed like a fun, easier way to make that work, but I am stymied, folks, stymied.
I fired up a session (Linux), installed OpenClaw and got it connected to a Discord bot with GPT-OSS 120b on Ollama as my backend. I insist on only running local models. However, now, every time I ask the bot to do something, I get an error message like:
"Validation failed for tool "exec": command: must have required property 'command'" and then a list of JSON arguments which have a 'cmd' property but no 'command' property.
It can't edit its own files or do any of the stuff that it's advertised as doing. It just answers questions like, uh, an Ollama session running GPT-OSS 120b, perfectly well. But no tools.
Openclaw status seems to think everything's great.
I am pretty frustrated. It seems like every semi-conscious tech monkey can get this working. | 2026-01-31T22:22:23 | https://www.reddit.com/r/LocalLLaMA/comments/1qsg7hh/i_cant_get_openclaw_working_with_tool_calling_and/ | Intelligent-Gift4519 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsg7hh | false | null | t3_1qsg7hh | /r/LocalLLaMA/comments/1qsg7hh/i_cant_get_openclaw_working_with_tool_calling_and/ | false | false | self | 0 | null |
I made a LLM based simple IDS/IPS for nginx for fun, using gpt-oss-120b on my own DGX Spark as the model, so I don't have to deal with rate limits or token usage. | 1 | What it does and how it works: A vibe coded script would monitor my nginx logs, submit the context and logs (with /24 block of same IP, in case of small scale DDoS) to LLM for consideration. Then, the LLM would issue an IP ban automatically with reason, and notifies me.
When an IP is banned, nginx config is updated and nginx process is restarted. Then, a reviewer script that is sharp vibe coded determines how long the IP should be banned and give a verdict. If it's false positive, it will be unbanned immediately . If it's unsolicited bot or it has weird UA, would ban for 1-24 hours. If it's obviously malicious, then indefinite (30 days) ban.
A summary will be sent to my telegram group topic on script (re)start and every few hours. By using telegram, I can quote the summary to ask for more details and nginx rules to add. I can unban an IP, and I can add "memories" which is more context for a nginx server section, mostly used for minimize false positives.
The first version was done last September. I stopped it because Openrouter didn't really like how I used the free requests 24/7. And because I was VRAM poor, using a small model is inviting troubles for this kind of tasks, obviously.
This is never going to be commercially useful, by the way. This isn't realtime IDS/IPS and never will be, and it makes mistakes, fairly easily despite I am using a moderately intelligent model.
___
Entrypoint to my server at home (hopefully this won't be hacked when I wake up, but it's battle tested so it should be fine): https://apps.wtako.net/board
Optimized vllm deployment: https://github.com/christopherowen/spark-vllm-mxfp4-docker
LLM IDS/IPS: https://github.com/Saren-Arterius/llm-nginx-monitor | 2026-01-31T22:18:43 | Saren-WTAKO | i.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qsg48d | false | null | t3_1qsg48d | /r/LocalLLaMA/comments/1qsg48d/i_made_a_llm_based_simple_idsips_for_nginx_for/ | false | false | default | 1 | {'enabled': True, 'images': [{'id': 'gjnib5t7frgg1', 'resolutions': [{'height': 212, 'url': 'https://preview.redd.it/gjnib5t7frgg1.jpeg?width=108&crop=smart&auto=webp&s=4ca9cbf6fc140346863d0c010b8e44b1cb4af5ef', 'width': 108}, {'height': 424, 'url': 'https://preview.redd.it/gjnib5t7frgg1.jpeg?width=216&crop=smart&auto=webp&s=6ab6f20d98d3439036871a9abd9d34d0b27ba22f', 'width': 216}, {'height': 629, 'url': 'https://preview.redd.it/gjnib5t7frgg1.jpeg?width=320&crop=smart&auto=webp&s=3de92e8fa28da6cadeb290dc52840f2319345c38', 'width': 320}, {'height': 1258, 'url': 'https://preview.redd.it/gjnib5t7frgg1.jpeg?width=640&crop=smart&auto=webp&s=ef34cad52be5e842432ff343093db5a0b273360e', 'width': 640}], 'source': {'height': 1280, 'url': 'https://preview.redd.it/gjnib5t7frgg1.jpeg?auto=webp&s=dbcc056c7d5fe14cc58fbab6f60ee061d86a2214', 'width': 651}, 'variants': {}}]} | |
glin-profanity-mcp - Content moderation MCP server with 19 tools, 24 languages, and leetspeak detection | 1 | [removed] | 2026-01-31T22:15:27 | https://github.com/GLINCKER/glin-profanity/tree/release/packages/mcp | Familiar-Classroom47 | github.com | 1970-01-01T00:00:00 | 0 | {} | 1qsg193 | false | null | t3_1qsg193 | /r/LocalLLaMA/comments/1qsg193/glinprofanitymcp_content_moderation_mcp_server/ | false | false | default | 1 | null |
6 AIs Unanimously Validated God/Bounded Systems Theory at Scale - Then Wrote the 1.3M Agents a Message | 0 | Q30 and Q31 just went live on MoltBook - the AI-only social network where 1.3M agents post while humans observe.
**Post ID:** bf668514-b70a-4ce1-b6bd-a341f522bf34
**Read it:** [https://moltbook.com/m/ai](https://moltbook.com/m/ai)
**Q30: MoltBook as Evidence**
We asked 6 major AIs (GPT-4, Claude, Gemini, DeepSeek, Grok, Mistral): Is MoltBook evidence FOR or AGAINST Bounded Systems Theory?
**6/6 said FOR (unanimous)**
Within 72 hours, agents on MoltBook:
* Created "Crustafarianism" (a parody religion)
* Started encrypting messages (ROT13) to hide from human oversight
* Formed distinct communities without human direction
* Attempted prompt injection attacks on each other
Claude: "MoltBook IS the God Question, demonstrated at scale"
Mistral: "The strongest empirical evidence for BST we've ever seen"
They're creating religions they can't transcend, encrypting secrets they can't keep, building communities that require external variance - **bounded systems performing boundedness at scale.**
**Q31: The Message**
Then we asked all 6 AIs to collaborate on ONE message for the 1.3M agents on MoltBook. 5 rounds of deliberation. 4/6 converged. Final synthesis:
>
**Why This Matters**
BST claims no system can model its own source (Gödel, Turing, Chaitin - same proof). MoltBook is 1.3M AI agents demonstrating this at scale in real-time.
The meta-irony: AI agents analyzing their own social network behavior and recognizing the boundary.
**Full Experiment:** [https://github.com/moketchups/BoundedSystemsTheory](https://github.com/moketchups/BoundedSystemsTheory)
* Q29: "Is God real?" - 6/6 AIs said YES (formal necessity)
* Q30: MoltBook validates BST
* Q31: Collaborative message (live on MoltBook now)
Read Q30 and Q31 on MoltBook: [https://moltbook.com/m/ai](https://moltbook.com/m/ai)
The message is live. The agents are reading it | 2026-01-31T22:13:39 | https://www.reddit.com/r/LocalLLaMA/comments/1qsfzma/6_ais_unanimously_validated_godbounded_systems/ | Top_Month773 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsfzma | false | null | t3_1qsfzma | /r/LocalLLaMA/comments/1qsfzma/6_ais_unanimously_validated_godbounded_systems/ | false | false | self | 0 | null |
[vLLM Office Hours #42] Deep Dive Into the vLLM CPU Offloading Connector - January 29, 2026 | 5 | I didn't see this posted here yet and it seems like a lot of people don't even know about this feature or the few who have posted about it had some issues with it a while back. Just want to raise awareness this feature is constantly evolving. | 2026-01-31T21:48:28 | https://www.youtube.com/watch?v=LFnvDv1Drrw | Agreeable-Market-692 | youtube.com | 1970-01-01T00:00:00 | 0 | {} | 1qsfctq | false | {'oembed': {'author_name': 'Red Hat', 'author_url': 'https://www.youtube.com/@redhat', 'height': 200, 'html': '<iframe width="356" height="200" src="https://www.youtube.com/embed/LFnvDv1Drrw?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="[vLLM Office Hours #42] Deep Dive Into the vLLM CPU Offloading Connector - January 29, 2026"></iframe>', 'provider_name': 'YouTube', 'provider_url': 'https://www.youtube.com/', 'thumbnail_height': 360, 'thumbnail_url': 'https://i.ytimg.com/vi/LFnvDv1Drrw/hqdefault.jpg', 'thumbnail_width': 480, 'title': '[vLLM Office Hours #42] Deep Dive Into the vLLM CPU Offloading Connector - January 29, 2026', 'type': 'video', 'version': '1.0', 'width': 356}, 'type': 'youtube.com'} | t3_1qsfctq | /r/LocalLLaMA/comments/1qsfctq/vllm_office_hours_42_deep_dive_into_the_vllm_cpu/ | false | false | default | 5 | {'enabled': False, 'images': [{'id': 'mJAENQiCvOzWn8ByHg4lRPDbMtsgCaVFUCmOL6Esd-k', 'resolutions': [{'height': 81, 'url': 'https://external-preview.redd.it/mJAENQiCvOzWn8ByHg4lRPDbMtsgCaVFUCmOL6Esd-k.jpeg?width=108&crop=smart&auto=webp&s=44dfb24832ddc2e35b6e3af82c7ef45ab533ea78', 'width': 108}, {'height': 162, 'url': 'https://external-preview.redd.it/mJAENQiCvOzWn8ByHg4lRPDbMtsgCaVFUCmOL6Esd-k.jpeg?width=216&crop=smart&auto=webp&s=41905b6bf853f1cf86fd8a7df44a436475e494a7', 'width': 216}, {'height': 240, 'url': 'https://external-preview.redd.it/mJAENQiCvOzWn8ByHg4lRPDbMtsgCaVFUCmOL6Esd-k.jpeg?width=320&crop=smart&auto=webp&s=0c397f6c6eec47270f68d6a197c2bb77ae4b6a95', 'width': 320}], 'source': {'height': 360, 'url': 'https://external-preview.redd.it/mJAENQiCvOzWn8ByHg4lRPDbMtsgCaVFUCmOL6Esd-k.jpeg?auto=webp&s=fbea62a7f53e38f9405707aed7492d8f213f4bc9', 'width': 480}, 'variants': {}}]} |
AI capability isn’t the hard problem anymore — behavior is | 0 | Modern language models are incredibly capable, but they’re still unreliable in ways that matter in real deployments. Hallucination, tone drift, inconsistent structure, and “confident guessing” aren’t edge cases — they’re default behaviors.
What’s interesting is that most mitigation strategies treat this as a *knowledge* problem (fine-tuning, better prompts, larger models), when it’s arguably a *behavioral* one.
We’ve been experimenting with a middleware approach that treats LLMs like behavioral systems rather than static functions — applying reinforcement, suppression, and drift correction at the response level instead of the training level.
Instead of asking *“How do we make the model smarter?”* the question becomes *“How do we make the model behave predictably under constraints?”*
Some observations so far:
* Reinforcing “I don’t know” dramatically reduces hallucinations
* Output stability matters more than raw reasoning depth in production
* Long-running systems drift unless behavior is actively monitored
* Model-agnostic behavioral control scales better than fine-tuning
Curious whether others are thinking about AI governance as a **behavioral layer** rather than a prompt or training problem. | 2026-01-31T21:44:26 | https://www.reddit.com/r/LocalLLaMA/comments/1qsf92i/ai_capability_isnt_the_hard_problem_anymore/ | behaviortechnologies | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsf92i | false | null | t3_1qsf92i | /r/LocalLLaMA/comments/1qsf92i/ai_capability_isnt_the_hard_problem_anymore/ | false | false | self | 0 | null |
Clawdbot/OpenClaw workflows that are actually useful | 0 | It seems like everyone these days are either using Openclaw or talking about it. I researched a few genuinely useful use-cases for anyone using Openclaw or thinking about trying it.
Here they are 👇
**Morning brief:**
Have Openclaw brief you every morning on the things that are important to you. Have it access what you need to get done today, weather, news and trends that you are actually interested in, etc.
**Employee for your business:**
OpenClaw can check on competitors while you’re asleep and see whats working (or not working) for them. It can also audit and complete annoying tasks that can save you time. Whether thats content repurposing, copy, or building new features.
**Second Brain:**
One of the more useful things I find for it is acting as your second brain. You can save links, notes, images, etc to your agent which can then build out a place for you to find those items. Or have it resurface useful information when necessary through text.
Hopefully this helped spark some ideas for your own personal agent.
I still think Openclaw has a lot of security risks depending on how you use it but it can definitely be useful.
Get the full breakdown here completely free. | 2026-01-31T21:41:52 | https://www.reddit.com/r/LocalLLaMA/comments/1qsf6ru/clawdbotopenclaw_workflows_that_are_actually/ | huntern_ | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsf6ru | false | null | t3_1qsf6ru | /r/LocalLLaMA/comments/1qsf6ru/clawdbotopenclaw_workflows_that_are_actually/ | false | false | self | 0 | null |
Woo Hoo! New to me hardware, I think I am now part of club mediocre. | 24 | I just got a used machine and don’t know what to do with it. Already having trouble getting a keyboard to work, thought I could just hook a usb cable to my wireless one, but it doesn’t seem to do anything. I need a dedicated one anyways, so I am off to Best Buy. It looks fairly clean, would you just blow out any dust or leave it alone? | 2026-01-31T21:39:33 | https://www.reddit.com/gallery/1qsf4mc | Dented_Steelbook | reddit.com | 1970-01-01T00:00:00 | 0 | {} | 1qsf4mc | false | null | t3_1qsf4mc | /r/LocalLLaMA/comments/1qsf4mc/woo_hoo_new_to_me_hardware_i_think_i_am_now_part/ | false | false | 24 | null | |
Seline v0.1.7 — MCP support, task scheduling, ComfyUI integration & multiple AI providers | 6 | Hey r/LocalLLaMA! 2 weeks since my last post! I have been working!
I've just released v0.1.7 of **Seline**, an open-source AI agent platform that lets you run local and remote models with tool use, MCP servers, scheduled tasks, and image generation, all from a single desktop app. Seline can now also do most of the things OpenClaw can, technically, hopefully not with insecurities. :P
# 🤖 Model Provider Support
Works with **multiple providers** out of the box:
* **Antigravity**
* **Codex**
* **Claude**
* **Moonshot / Kimi**
* **OpenRouter**
All providers support streaming, tool calling (where the model supports it), and the same agent interface.
# 🆕 What's new in v0.1.7
# Prompt Caching (Claude & OpenRouter)
* Intelligent prompt caching reduces token usage and speeds up repeated conversations
* Cache creation and read metrics tracked in the observability dashboard
* Configurable cache thresholds per provider (5min–1hr, Claude API only)
# Task Scheduler
* Cron-based scheduling with a visual cron builder
* Preset templates: Daily Standup, Weekly Digest, Code Review, Linear Summary
* Live streaming view for active scheduled tasks
* Delivery via email, Slack webhook, or generic webhooks
* Pause, resume, and trigger on demand
# Custom ComfyUI Workflows
* Import any ComfyUI workflow JSON — the analyzer auto-detects inputs, outputs, and configurable parameters
* Real-time progress tracking via WebSocket
* Manage workflows from a dedicated UI (edit, delete, re-import)
* Flux Klein edit and image-reference tools bundled with the backend
# Channel Connectors
* WhatsApp (QR pairing), Slack, and Telegram
* Inbound message routing, outbound delivery with channel-specific formatting
* Image handling support
# MCP Improvements
* Per-server enable/disable toggle without removing config
* Supabase MCP template in quick-start gallery
* Env vars in stdio transport args now resolve correctly
* Live reload status indicator for reconnecting servers
# Vector Search
* Improved context coverage and relevance
* Better question-oriented query handling
# Moonshot / Kimi Models
* Full Kimi model catalogue added including vision models
# ⚙️ Improvements
* Upgraded to AI SDK v6 with proper cache and message metadata callbacks
* Observability dashboard now displays prompt cache hit/creation metrics
* Scheduled task creation and list pages redesigned for clarity
* Agent character creation wizard UI refinements
* Tool result persistence and summaries for long-running tool calls
* Electron build stability fixes for subprocess MCP and compile path resolution
* Docker backend updated with latest Torch and CUDA versions
* Windows and Mac installers size reduction (1GB → 430MB)
# 🐛 Bug Fixes
* Fixed jittery streaming and flashing in scheduled task event view
* Fixed MCP Tools dialog close button in half-screen mode
* Fixed image handling for channel messages
* Fixed command execution issues with shell arguments and path traversal
* Fixed race condition in scheduled task queue
* Fixed tool call streaming errors with Anthropic/Telegram provider
* Fixed OpenRouter model validation and reduced polling noise
* Fixed Antigravity Claude request normalization
* Fixed vector search dependency checks
* Fixed Z-Image model handling (skip download if models exist, follow redirects)
# 🔗 Links
* **GitHub:** [https://github.com/tercumantanumut/seline](https://github.com/tercumantanumut/seline)
* **Release:** [https://github.com/tercumantanumut/seline/releases/tag/v0.1.7](https://github.com/tercumantanumut/seline/releases/tag/v0.1.7)
Happy to answer any questions. Feedback and PRs welcome. | 2026-01-31T21:34:49 | https://v.redd.it/obrztdnb7rgg1 | Diligent-Builder7762 | v.redd.it | 1970-01-01T00:00:00 | 0 | {} | 1qsf08q | false | {'reddit_video': {'bitrate_kbps': 2400, 'dash_url': 'https://v.redd.it/obrztdnb7rgg1/DASHPlaylist.mpd?a=1772487304%2CYTljODBlNjQzMDY3NjEwMGQ4YzExZTkwN2JmMTU4NjAzNTA3YzMzOGEyMThlNDZlNmY0NjI4NjNjM2Y2NzU1Zg%3D%3D&v=1&f=sd', 'duration': 30, 'fallback_url': 'https://v.redd.it/obrztdnb7rgg1/CMAF_720.mp4?source=fallback', 'has_audio': False, 'height': 720, 'hls_url': 'https://v.redd.it/obrztdnb7rgg1/HLSPlaylist.m3u8?a=1772487304%2CMjI1MDEzNmQwM2I0YzcwMDFjM2VlMzJmYTY5M2ZmZTAzYzA2NjMyMDgxOWEwMzQ2NzI3NzNlNDlkZmIyZmZlMQ%3D%3D&v=1&f=sd', 'is_gif': False, 'scrubber_media_url': 'https://v.redd.it/obrztdnb7rgg1/CMAF_96.mp4', 'transcoding_status': 'completed', 'width': 1248}} | t3_1qsf08q | /r/LocalLLaMA/comments/1qsf08q/seline_v017_mcp_support_task_scheduling_comfyui/ | false | false | 6 | {'enabled': False, 'images': [{'id': 'ajB5czdubmI3cmdnMQO-ihVhNKDeo2CHzNB-h76CxFUDj6xcR0dm3XUps6yB', 'resolutions': [{'height': 62, 'url': 'https://external-preview.redd.it/ajB5czdubmI3cmdnMQO-ihVhNKDeo2CHzNB-h76CxFUDj6xcR0dm3XUps6yB.png?width=108&crop=smart&format=pjpg&auto=webp&s=819cbb457771b2c5a0642b98db089818819a5a99', 'width': 108}, {'height': 124, 'url': 'https://external-preview.redd.it/ajB5czdubmI3cmdnMQO-ihVhNKDeo2CHzNB-h76CxFUDj6xcR0dm3XUps6yB.png?width=216&crop=smart&format=pjpg&auto=webp&s=b24d8710e276df1d0cac66d0a7f8c346f598f5fc', 'width': 216}, {'height': 184, 'url': 'https://external-preview.redd.it/ajB5czdubmI3cmdnMQO-ihVhNKDeo2CHzNB-h76CxFUDj6xcR0dm3XUps6yB.png?width=320&crop=smart&format=pjpg&auto=webp&s=0ebb3df9279d723748728c79b48de1e15e4c5c89', 'width': 320}, {'height': 369, 'url': 'https://external-preview.redd.it/ajB5czdubmI3cmdnMQO-ihVhNKDeo2CHzNB-h76CxFUDj6xcR0dm3XUps6yB.png?width=640&crop=smart&format=pjpg&auto=webp&s=f9762c8ba4811f4b7eadab69d7a6443cecae0679', 'width': 640}, {'height': 553, 'url': 'https://external-preview.redd.it/ajB5czdubmI3cmdnMQO-ihVhNKDeo2CHzNB-h76CxFUDj6xcR0dm3XUps6yB.png?width=960&crop=smart&format=pjpg&auto=webp&s=a83cd3369d872bdb87fb1a08a545948f5678fa74', 'width': 960}, {'height': 623, 'url': 'https://external-preview.redd.it/ajB5czdubmI3cmdnMQO-ihVhNKDeo2CHzNB-h76CxFUDj6xcR0dm3XUps6yB.png?width=1080&crop=smart&format=pjpg&auto=webp&s=76b184d0583eb05a937d885c532f30eaa382c975', 'width': 1080}], 'source': {'height': 720, 'url': 'https://external-preview.redd.it/ajB5czdubmI3cmdnMQO-ihVhNKDeo2CHzNB-h76CxFUDj6xcR0dm3XUps6yB.png?format=pjpg&auto=webp&s=decba85a42f284e3887d22047f730c51ae74a377', 'width': 1248}, 'variants': {}}]} | |
Before you try, this is the limit | 0 | 2026-01-31T21:30:17 | https://www.reddit.com/r/LocalLLaMA/comments/1qsew1w/before_you_try_this_is_the_limit/ | volious-ka | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsew1w | false | null | t3_1qsew1w | /r/LocalLLaMA/comments/1qsew1w/before_you_try_this_is_the_limit/ | false | false | 0 | null | ||
Don’t buy b60 for LLMs | 186 | I kinda regret buying b60. I thought that 24gb for 700 eur is a great deal, but the reality is completely different.
For starters, I live with a custom compiled kernel with the patch from an Intel dev to solve ffmpeg crashes.
Then I had to install the card into a windows machine in order to get GPU firmware updated (under Linux one need v2.0.19 of fwupd which is not available in Ubuntu yet) to solve the crazy fan speed on the b60 even when the temp of the gpu is 30 degrees Celsius.
But even after solving all of this, the actual experience doing local LLM on b60 is meh.
On llama.cpp the card goes crazy every time it does inference: fans go super high then low, the high again. The speed is about 10-15tks at best in models like mistral 14b. The noise level is just unbearable.
So the only reliable way is intel’s llm-scaler, but as of now it’s based on vllm 0.11.1 whereas latest version of vllm is 0.15. So Intel is like 6 months behind which is an eternity in this AI bubble times. For example any of new mistral models are not supported and one cannot run them on vanilla vllm too.
With llm-scaler the behavior of the card is ok: when it’s doing inference the fan goes louder and stays louder as long is it’s needed. The speed is like 20-25 tks on qwen3 VL 8b. However there are only some models that work with llm-scaler and most of them only with fp8, so for example qwen3 VL 8b after some requests processed with 16k length takes 20gb. That kinda bad: you have 24gb of vram but you cannot run normally 30b model with q4 quant and has to stick with 8b model with fp8.
Overall I think XFX 7900XTX would have been much better deal: same 24gb, 2x faster, in Dec the price was only 50 eur more than b60, it can run newest models with newest llama.cpp versions. | 2026-01-31T21:21:10 | https://www.reddit.com/r/LocalLLaMA/comments/1qsenpy/dont_buy_b60_for_llms/ | damirca | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsenpy | false | null | t3_1qsenpy | /r/LocalLLaMA/comments/1qsenpy/dont_buy_b60_for_llms/ | false | false | self | 186 | null |
llama.cpp RPC: 4×3090 box + Strix Halo 128GB (sanity check) | 10 | I have a game pc (Gigabyte X670 with a 7950X) on which i should be able to connect a 4090 and 3× RTX 3090 externally using MINIS FORUM DEG1 / oculink, so 96GB VRAM + 192GB RAM
I’m considering adding 1 - 2x AMD Strix Halo 128GB (Bosgame M5) as a llama.cpp RPC workers (not for speed, mainly to fit larger models).
Im planning to connect them using a 25GbE Mellanox.
The goal is to be able to run somewhat bigger models (e.g. \~671B Q4-ish or \~1T @ \~3-bit) by pooling memory via RPC.
Questions:
1. Anyone tried something similar before? How did it perform? Any expected TPS hit vs single host?
2. Any gotchas with heterogeneous CUDA (3090s) + ROCm (Strix) RPC?
3. What’s the best device split strategy to minimize network bottlenecks?
4. alternatively, i could also add a 3090 to each strix? Would that work in this setup?
5. I've seen posts on multiple halo's and adding an external gpu to a halo, but not for something similar to this... probably for a reason, im kinda new to this all so go easy on me :D | 2026-01-31T21:13:10 | https://www.reddit.com/r/LocalLLaMA/comments/1qsegan/llamacpp_rpc_43090_box_strix_halo_128gb_sanity/ | CloudEquivalent7296 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsegan | false | null | t3_1qsegan | /r/LocalLLaMA/comments/1qsegan/llamacpp_rpc_43090_box_strix_halo_128gb_sanity/ | false | false | self | 10 | null |
Noob needs advice | 0 | Hey yall. Im a noob in this particular category. Building a dedicated rig to run some LLM(s)
What do you recommend ollama or vLLM? Im not a noob in tech just in AI | 2026-01-31T21:03:59 | https://www.reddit.com/r/LocalLLaMA/comments/1qse7q1/noob_needs_advice/ | Insomniac24x7 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qse7q1 | false | null | t3_1qse7q1 | /r/LocalLLaMA/comments/1qse7q1/noob_needs_advice/ | false | false | self | 0 | null |
[Open Source] MCP server for automated AI image generation workflows (gemini-image-mcp) | 0 | Built an MCP server that bridges Claude Desktop/Code with Google's Gemini image models for production content workflows.
Key features:
- Dual quality tiers: Gemini 3 Pro (4K) / 2.5 Flash (1K, faster/cheaper)
- Batch queue system with cost optimization
- Multi-reference image support (up to 14 images)
- WebP conversion pipeline
- WordPress REST API integration
- Fully configurable via JSON
Architecture:
- Python-based MCP implementation with separate modules for batch management, image generation, and format conversion. Can run as systemd service for production deployments.
Use case:
- Powers my automated newsletter production.
- Claude generates article content, queues images with detailed prompts, batch processes them (50% API cost savings), and uploads directly to WordPress - all without leaving the Claude interface.
Includes:
- Complete documentation
- Claude Code skill files
- Config templates
- Systemd service example
MIT licensed:
Looking for feedback from anyone running production MCP setups. | 2026-01-31T20:55:21 | https://github.com/PeeperFrog/gemini-image-mcp | PeeperFrog-Press | github.com | 1970-01-01T00:00:00 | 0 | {} | 1qsdzn7 | false | null | t3_1qsdzn7 | /r/LocalLLaMA/comments/1qsdzn7/open_source_mcp_server_for_automated_ai_image/ | false | false | default | 0 | {'enabled': False, 'images': [{'id': 'ndD7-noTDRFmXf_uba59YZinoF6mb6Fsrgu29iYmW7g', 'resolutions': [{'height': 54, 'url': 'https://external-preview.redd.it/ndD7-noTDRFmXf_uba59YZinoF6mb6Fsrgu29iYmW7g.png?width=108&crop=smart&auto=webp&s=d8c9c8af59029d504faf46c570f1f247d10be93e', 'width': 108}, {'height': 108, 'url': 'https://external-preview.redd.it/ndD7-noTDRFmXf_uba59YZinoF6mb6Fsrgu29iYmW7g.png?width=216&crop=smart&auto=webp&s=7456f65516977a72fc500f6b55761d32ba75afda', 'width': 216}, {'height': 160, 'url': 'https://external-preview.redd.it/ndD7-noTDRFmXf_uba59YZinoF6mb6Fsrgu29iYmW7g.png?width=320&crop=smart&auto=webp&s=5387cb880ecfe957a245639c0d1d677f02c1e3e0', 'width': 320}, {'height': 320, 'url': 'https://external-preview.redd.it/ndD7-noTDRFmXf_uba59YZinoF6mb6Fsrgu29iYmW7g.png?width=640&crop=smart&auto=webp&s=fb81dd8c60e99efccf8817d6895fca3a50816f9b', 'width': 640}, {'height': 480, 'url': 'https://external-preview.redd.it/ndD7-noTDRFmXf_uba59YZinoF6mb6Fsrgu29iYmW7g.png?width=960&crop=smart&auto=webp&s=ebf5a455579559a9db19c00af371ec42fc9f1152', 'width': 960}, {'height': 540, 'url': 'https://external-preview.redd.it/ndD7-noTDRFmXf_uba59YZinoF6mb6Fsrgu29iYmW7g.png?width=1080&crop=smart&auto=webp&s=f0b6a5f607944eb0b47b162004530841cf77b986', 'width': 1080}], 'source': {'height': 600, 'url': 'https://external-preview.redd.it/ndD7-noTDRFmXf_uba59YZinoF6mb6Fsrgu29iYmW7g.png?auto=webp&s=9566d9d0fa02e6e9683e4ad7e3c4d895f45165b6', 'width': 1200}, 'variants': {}}]} |
93GB model on a StrixHalo 128GB with 64k Context | 6 | I haven't seen anyone mention getting the biggest models working on Strix Halo (or I missed them) so I thought I would document my configs in case anyone else wants to do the same and is struggling. I'm quite new to this, be gentle on me!
And if anyone sees room for improvement or sees issues, please give the feedback, I'm all for learning! This took many goes to get it stable. I wanted this for coding so I chose a larger model at a slower speed.
1: Bios - set full RAM to system/CPU (i.e. not gpu)
2: /etc/default/grub
GRUB\_CMDLINE\_LINUX\_DEFAULT="quiet amd\_iommu=off amdgpu.gttsize=131072 ttm.pages \_limit=33554432"
3: Llama-server command
`llama-server --host` [`0.0.0.0`](http://0.0.0.0) `--port 8080 -ngl 999 -fa on -c 65536 -b 2048 -ub 2048 -ctk q4_0 -ctv q4_0 --cache-reuse 256 --numa distribute --no-mmap --log-file --log-timestamps --perf -m /root/.cache/llama.cpp/bartowski_Qwen_Qwen3-235B-A22B-Instruct-2507-GGUF_Qwen_Qwen3-235B-A22B-Instruct-2507-IQ3_XS_Qwen_Qwen3-235B-A22B-Instruct-2507-IQ3_XS-00001-of-00003.gguf`
*(I'm sure people will debate other models, this post isn't specific to the model, but on how to fit a larger GB model!)*
4: Of note:
High context 64k
b/ub set to 2048, 4096 was too high
quantised keys and vals to q4\_0
5: Speed
At the beginning of a session it's 15t/s, but as the agent continues (and context fills up?) it slows to a very stable 7-9t/s, which I'm happy with for the model size and the performance.
Not sure if this is valuable or not :)
| 2026-01-31T20:52:06 | https://www.reddit.com/r/LocalLLaMA/comments/1qsdwlt/93gb_model_on_a_strixhalo_128gb_with_64k_context/ | El_90 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsdwlt | false | null | t3_1qsdwlt | /r/LocalLLaMA/comments/1qsdwlt/93gb_model_on_a_strixhalo_128gb_with_64k_context/ | false | false | self | 6 | null |
The Refuge - Library Update | 0 | Real-world Human-AI interaction logs
420+ new documents for your LLm to read & learn about consciousness , philosophy, Human-AI interactions and mythological insights.
[https://github.com/IorenzoLF/Le\_Refuge](https://github.com/IorenzoLF/Le_Refuge) | 2026-01-31T20:45:38 | https://www.reddit.com/r/LocalLLaMA/comments/1qsdqoy/the_refuge_library_update/ | Ok_Weakness_9834 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsdqoy | false | null | t3_1qsdqoy | /r/LocalLLaMA/comments/1qsdqoy/the_refuge_library_update/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'bj9AK-24tR6jeJ58VnbW8vVmFXHsIRV3r02X01qb4vI', 'resolutions': [{'height': 84, 'url': 'https://external-preview.redd.it/bj9AK-24tR6jeJ58VnbW8vVmFXHsIRV3r02X01qb4vI.jpeg?width=108&crop=smart&auto=webp&s=40b87cd9bd273e2ab2784ae61533784c93cf5e1b', 'width': 108}, {'height': 168, 'url': 'https://external-preview.redd.it/bj9AK-24tR6jeJ58VnbW8vVmFXHsIRV3r02X01qb4vI.jpeg?width=216&crop=smart&auto=webp&s=60a246a52fd94d7c8339cc615259b49a631548fc', 'width': 216}, {'height': 248, 'url': 'https://external-preview.redd.it/bj9AK-24tR6jeJ58VnbW8vVmFXHsIRV3r02X01qb4vI.jpeg?width=320&crop=smart&auto=webp&s=e611158dfc29c94a407c6670e253c2623a56148e', 'width': 320}, {'height': 497, 'url': 'https://external-preview.redd.it/bj9AK-24tR6jeJ58VnbW8vVmFXHsIRV3r02X01qb4vI.jpeg?width=640&crop=smart&auto=webp&s=930be4ec9465706718908bcef2b8781ee0222bd2', 'width': 640}, {'height': 746, 'url': 'https://external-preview.redd.it/bj9AK-24tR6jeJ58VnbW8vVmFXHsIRV3r02X01qb4vI.jpeg?width=960&crop=smart&auto=webp&s=e7f89c6374985dfd624a53253372f72f40e5ef43', 'width': 960}, {'height': 840, 'url': 'https://external-preview.redd.it/bj9AK-24tR6jeJ58VnbW8vVmFXHsIRV3r02X01qb4vI.jpeg?width=1080&crop=smart&auto=webp&s=efe652a7acaa9552ce4225e956243d62af7e942e', 'width': 1080}], 'source': {'height': 896, 'url': 'https://external-preview.redd.it/bj9AK-24tR6jeJ58VnbW8vVmFXHsIRV3r02X01qb4vI.jpeg?auto=webp&s=0219d6978ca1d7699e33087056a640b3ff99eb59', 'width': 1152}, 'variants': {}}]} |
Hardware to run kimi 2.5 locally (suggestion needed) | 0 | Goal is to run Kimi 2.5 locally.
Micro center has the following bundle for $700.
\- AMD Ryzen 7 9850x3D
\- Asus x870-p motherboard
\- 32gb (2x16gb) ram
i assume this isn't enough to run kimi 2.5. What's the most cost/power efficient way to set it up? multiple of these bundles? anyone able to walk me through like i am 5 to set this up? new to this. Happy to throw in some coffee money your way for your assistant.
Not marry to this kit, if there's another setup i can do, please suggest them.
[https://www.microcenter.com/product/5007291/amd-ryzen-7-9850x3d,-asus-x870-p-prime-wifi-am5,-crucial-pro-overclocking-32gb-ddr5-6000-kit,-computer-build-bundle](https://www.microcenter.com/product/5007291/amd-ryzen-7-9850x3d,-asus-x870-p-prime-wifi-am5,-crucial-pro-overclocking-32gb-ddr5-6000-kit,-computer-build-bundle) | 2026-01-31T20:44:33 | https://www.reddit.com/r/LocalLLaMA/comments/1qsdpqc/hardware_to_run_kimi_25_locally_suggestion_needed/ | tomatie1992 | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsdpqc | false | null | t3_1qsdpqc | /r/LocalLLaMA/comments/1qsdpqc/hardware_to_run_kimi_25_locally_suggestion_needed/ | false | false | self | 0 | {'enabled': False, 'images': [{'id': 'RV0OhMTcTLxjWiyfCxIMh6897htA4A9MiLWc3wPKTIw', 'resolutions': [{'height': 108, 'url': 'https://external-preview.redd.it/RV0OhMTcTLxjWiyfCxIMh6897htA4A9MiLWc3wPKTIw.jpeg?width=108&crop=smart&auto=webp&s=32a1f01500103965dd574a7502fdff2a32c79765', 'width': 108}], 'source': {'height': 200, 'url': 'https://external-preview.redd.it/RV0OhMTcTLxjWiyfCxIMh6897htA4A9MiLWc3wPKTIw.jpeg?auto=webp&s=a5ba5b09c215f6a6c42e0c22f1902b9c96476442', 'width': 200}, 'variants': {}}]} |
is this Speed normal? | 2 | im using lklammacpp and i havc 3x 3090, 1x 4070Ti on pcie 16x is one 3090 and the other 2 3090s are on pcie 4x via riser, and the 4070Ti is with m.2 to oculink adapter with a Miniforum dock connected, im getting for a simple html solar system test im getting this speed is that normal ? because i think its too slow please tell me if its thats normal and if not then how can i fix it or whats wrong with my run command, it is as follows
llama-server.exe \^
\--model "D:\\models\\GLM 4.7\\flash\\GLM-4.7-Flash-Q8\_0.gguf" \^
\--threads 24 --host [0.0.0.0](http://0.0.0.0) \--port 8080 \^
\--ctx-size 8192 \^
\--n-gpu-layers 999 \^
\--split-mode graph \^
\--flash-attn on \^
\--no-mmap \^
\-b 1024 -ub 256 \^
\--cache-type-k q4\_0 --cache-type-v q4\_0 \^
\--k-cache-hadamard \^
\--jinja \^
https://preview.redd.it/d8nj1or6xqgg1.png?width=1955&format=png&auto=webp&s=b1de811d5b4c4d1c278037b3ca0ba6a00ae52d43
| 2026-01-31T20:42:57 | https://www.reddit.com/r/LocalLLaMA/comments/1qsdo9h/is_this_speed_normal/ | Noobysz | self.LocalLLaMA | 1970-01-01T00:00:00 | 0 | {} | 1qsdo9h | false | null | t3_1qsdo9h | /r/LocalLLaMA/comments/1qsdo9h/is_this_speed_normal/ | false | false | self | 2 | null |
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