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Got headhunted by a VC-backed AI-focused company, ended up skipping past two levels of their application/interview process because of that. Got 15 minute call with the co-founder, hit it off well, but it all seemed to go sour when he asked about my AI setup and I told him I run qwen locally on a gra
https://huggingface.co/ggml-org/Mistral-Large-4-1T-A52B-GGUF https://huggingface.co/ggml-org/Mistral-Large-4-1T-A52B-NVFP4-GGUF https://huggingface.co/unsloth/Mistral-Large-4-1T-A52B-GGUF from Mistral: Mistral Large 4 - Le Chonk Mistral Large 4 is our largest and most capable model to date: a
SafeAgents is a small local proxy that sits between a coding agent and its model server. It checks each tool call in the model's response before the harness receives it. A call it flags is replaced with a short text notice, so the harness gets a plain text reply and runs nothing. You point the
After Alibaba moved from Permissive OSS licenses to its own custom license with IMO ambiguous terms in models like Qwen3.8-Flash-Next, is there any indication this will carry forward into Qwen4? While that license at first reads permissive for local/internal use, fine-tuning and derivatives, it’s de
https://huggingface.co/bartowski/HemmingwayAI_Hemmingway-1.5-GGUF from hemmingwayAI: Hemmingway-1.5 The undetectable AI. Writing indistinguishable from a human's. 27B parameters, open weights, free for non-commercial use. hemmingway.io → · Model card → · Try it → · API → · API docs → · Mac, Wi
I've been experimenting with tiny models for WebBrain, my open-source browser agent. The long-term goal is to run as much intelligence as possible locally, ideally through WebGPU. One approach I've been particularly interested in is Prism ML's Ternary Bonsai: compressing a 9B model to roughly 2 GB u
I use a dual MI50 32gb Rig for an alliterated version of IQ3_S Qwen 3.8 Flash Next. I have seen some people say tha creative writing is good on Flash NeXT but in my experience, it has just been the absolute worst at it. I’m talking like nothing in the story makes any sense at all. Gemma 3 is better
Code and tool calls can peak up to 275 tok/s - Reasoning traces up to 190 tok/s Hardware: 5080 16GB PCIe gen5x16, 64 DDR5 6400, Core Ultra 9 285K https://github.com/jesdga95/basalt Some PRs for the optimizations under my name are still pending (xyehya) but upstream already have solid gains
This is just something i had running for about 10-20 hours (kept adding models), various tests/benchmarks and had 4 (Claude kind of graded, his tokens are too precious) frontier LLM's grade the answers of the local LLM's. Big models got about 44 and smaller models got 66 prompt/tests. Some wri
I vibe-coded an app to turn my favorite webnovels chapters into multi-voice audio with acceptable quality. What was funny is that Opus completely refused to write site scraping part due to “legal reasons“. Well, qwen 3.8 FN had no such reservations. submitted by /u/SwimmerJazzlike [li
Inspired by the qwenputer project posted in this sub earlier today, I managed to get a basic version of Doom running on the virtual machine created in the post. It's quite slow, but here we are. Doom running on an LLM. Project repo: https://github.com/mapestree/qwenputer-doom submitted by
Shoutout to the Unsloth AI team for constantly pushing the needle forward for local ai worldwide. While many of us already had features such as this via other means, it is more what this means for entry level ai enthusiasts & accessibility. It is clear as day! We are in "the age of the agent."
Recently Omar Sanseviero asked for suggestions on Twitter for future Gemma models. Here is my take on this: Gemma 5 should be a looped transformer. Being a looped transformer with configurable loop depth will address one of the biggest Gemma 4 weaknesses - reasoning ability. Don't get me wr
AA no longer lists GLM5.3 flash (despite clearly being one of the best models - and probably the cheapest per intelligence) , they seem to have been doing all sorts of changes to push US models and hiding good cost effective models, including qwen 3.8 flash, so that table is no good. I need a better
I post this because I was honestly quite impressed by the capabilities of this model from a opencode harness and wanted to share my experience. So i've been playing about for a bit wit Strata and Qwen 3.8 flash for a bit and was looking for a new kind of test. One that would also (if succesfull..) g
Model & weights: BrunoHays/embeddinggemma-2-nullspace-audio MAEB (Massive Audio Embedding Benchmark) is the main MTEB leaderboard benchmark for audio and text-to-audio embeddings, currently dominated by 8B-9B multimodal models. I took Google's edge-sized google/embeddinggemma-2 (577M params, 768d ve
This might have implications for tool calling and computer use. Write-up: https://divagr.com/blog/qwenputer HF: https://huggingface.co/divagr1925/Qwen3.5-4B-Qwenputer Live demo: https://modelscope.ai/studios/Spartan1925/Qwenputer submitted by /u/LatentSpacer [link] [comments]
Clover-35B-A3B-Preview is the first model released by DiffuSpace, delivering state-of-the-art performance among open-source diffusion language models at release on the benchmarks reported in our launch blog. Clover combines a 35B-parameter mixture-of-experts backbone with approximately 3B acti
No, this is not a lobotomized 1-bit squeeze, its full Q2 nazeshinjite's DeepSeek-V4.1-Flash-1017 Q2 GGUF on a single 3090, natively on Windows no WSL. On an RTX 3090 with 192 GB of RAM I'm writing at 41 tok/s (average across multiple prompts) and the output agrees with DeepSeek's own API on th
Hi everyone, whenever I looked at existing LLM leaderboards I was missing practical numbers for running quantized open weight models on affordable local hardware. Most tables only copy vendor cloud scores or run tiny subsets of a benchmark, and none of them tell you how long a model actually sits th
One more great model from Liquid AI! Good for edge: LFM3-Audio-2B is a compact, fully featured audio model that runs locally on laptops and edge devices. It takes text or audio as input and responds with text, speech, or both. Voice chat: ask questions aloud and receive streaming replies in t
I have been working on test-time scaling for quite some time now. Posted some of my previous work on this sub-reddit before. Recently, I made significant simplifications to the core system-design and it finally felt like now anybody can understand and directly use this on their hardest problem
https://huggingface.co/LiquidAI/LFM3-1.7B-GGUF from liquidAI: LFM3-1.7B is a text model for local agents and specialized applications, based on our new tri-hybrid architecture. Agentic training: post-trained for agentic harnesses such as Hermes Agent, Pi, and OpenClaw, with a 256K-token conte
I am feeling like a mighty local King spinning two Strata engines with 70tps each whole day and using 12M tokens a day. Also me scrolling twitter and stumbling upon a post saying that poteto uses 34M tokens per minute 🤪 Upd: Guys are interested who is she and what she is doing with all these t
Most OCR models focus on reading documents. MiniCPM-OCR adds a second piece: a model that checks whether the OCR result is actually trustworthy. OpenBMB just released two open-source models: MiniCPM-OCR 1.0 (1.3B): Scores 93.68 on Real5-OmniDocBench, targeting real-world documents like curved
Think of the animation workflows people use with Claude Opus 5.5: the model writes code, rendering tools turn it into video, and further instructions revise the result. SenseNova follows that same general approach, with a model and creation Skill focused on video creation A few highlights: Gen
I've been building OpenJet, a local coding agent with its own CUDA megakernel engine for Qwen3.8-27B Q4_K_M. Until now it served one conversation at a time. The new release serves several agents at once on a single GPU. https://reddit.com/link/1xgs8hp/video/p0zd2rxse0yh1/player Numbers (RTX 30
from inclusionAI: We introduce Ling-3.1-flash-1019, an upgraded version of Ling-3.1-flash, with 560B total parameters, 25B active parameters per token, and a 1M-token context window. With further post-training optimizations, Ling-3.1-flash-1019 demonstrates significant improvements in reasonin
Reverse Engineer Anything blew up recently but, every impressive reverse engineering result we’ve seen uses (closed) frontier models. We wanted to know whether small local models can do useful work here if a bigger model organizes them, hopefully cutting cost and allowing a broder range of peo
HF traces: https://huggingface.co/datasets/CohereLabs/10-2026-qwen-investigation-traces 1. Understand the Python script: ids ids = dataset[0]["token_ids"] if len(ids) < 2323: raise ValueError("The trace is shorter than the required 2,323-token prefix.") ids = ids[:2323] # stop just before the URL,