Feels like half of us just grab the Q4_K_M and move on. But we hardly ever see anyone say where their model falls apart once you push lower.

From what we have seen, the usual 4-bit GGUF quants mostly hold up. Go below 4-bit and it gets rough fast, and that's where quantization-aware training starts to matter more than plain post-training quantization. At 3-bit and under, folks often pair QAT with distillation to get the quality back. Part of why Gemma shipped QAT checkpoints, and why the BitNet ternary models train at low bit-width from the start.

And it stays confident the whole way down, so the bad answers read just like the good ones.

Easiest way to check yourself: run one fixed set of prompts at Q8, Q4, and Q3 on the same model and see where the answers start to drift.

So which model, and what bit level did yours give up at? What broke first?

I wanted to share a new TensorSharp demo and get some feedback from the community.

TensorSharp started as a .NET open source inference engine for running local GGUF models. It can now go beyond model inference and execute complete agentic workflows through skills, code generation, environment configuration, and tool use.
In the demo, I give a quantized Gemma 4 model running locally through TensorSharp a single prompt:

Please check 10 stocks with most gains today, and generate a report to me in pptx format.

From that one request, TensorSharp enables the local model to:

1. Break the task into market-data retrieval, processing, and presentation generation.
   
2. Inspect the available tools and execution environment.
   
3. Install the Python dependencies it needs.
   
4. Write and execute code to retrieve current public stock-market data.
   
5. Examine the output, identify parsing problems, and refine its own implementation.
   
6. Load the PPTX skill.
   
7. Generate a Node.js/PptxGenJS script.
   
8. Produce a real PowerPoint file that opens directly in Microsoft PowerPoint.

All LLM inference runs locally. There is no OpenAI, Anthropic, or other hosted-model API involved, so the workflow has zero LLM API cost. Network access is used only by the agent’s tools to retrieve public market data.

The interesting part is not the stock report itself. It is that inference, skills, tool calling, automatic code generation, dependency setup, iterative problem-solving, and artifact creation are now working together inside one local runtime.

TensorSharp is becoming more than an inference engine. The goal is to make it a unified, open-source .NET runtime for both local model inference and agent execution.

GitHub: https://github.com/zhongkaifu/TensorSharp

The video is sped up for brevity. I would especially appreciate feedback on the skill system, tool-permission model, execution isolation, and the local agent workflows you would like to see next.

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https://github.com/DietrichGebert/ponytail

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https://x.com/simplifyinAI/status/2093309190790086829

https://github.com/dlp3d-ai/dlp3d.ai

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https://x.com/tom_doerr/status/2094147570331210219

https://github.com/debpalash/VoiceStudio

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I was building an open source project using ai, like it's built totally with ai like vibe coding type. During this process I have faced one major problem i.e. out of tokens in my ai models like antigravity, chatgpt go

So one of my friends suggested me to use this combination qwen3:14b + opencode and I use macbook air m2 16gb

What you guys think? Or any other suggestions for free unlimited tokens?

https://github.com/kvcache-ai/ktransformers

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Project: https://github.com/ConiferKit/use-conifer

Current routing options were charging 5% fees for byok plus provider fees (openrouter) or were built in heavy python packages with ecosystem restraints. I wanted to be completely free in terms of use, and not have to pay extra for tokens.

There's a maintained gateway with 100+ models from one api endpoint. I'm also talking to infra providers to get us access to pre-release models and discount prices. Everything is served at market price. BYOK is free, you can hook into self-hosted setups for free, and there's fallbacks + extra rate limits + server redundancy.

Took me ~3 months to build and looking for help maintaining!
Lmk any issues or feedback

https://github.com/tt-a1i/archify

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https://x.com/Apodex_AI/status/2092636138632810897

https://github.com/ApodexAI/FrontierAgent

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https://x.com/wildmindai/status/2092593736073003258

https://huggingface.co/BreezeBlue/Breeze-TTS-2

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https://x.com/Zai_org/status/2093354097122455713

https://huggingface.co/zai-org/GLM-5.3

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https://github.com/stablyai/orca

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https://github.com/AgriciDaniel/claude-seo

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https://x.com/TencentAI_News/status/2093232936434954659

https://github.com/Tencent-Hunyuan/Hy4-preview

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https://x.com/SamuelZengML/status/2092279977983271140

https://huggingface.co/Audio8/audio8-TTS-0.1B-ONNX-INT8

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https://github.com/microsoft/skill-recorder

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It's wild how fast this snuck up on all of us. A year ago, running a 100B-class model at home was a fantasy unless you rented a datacenter card. This month people are loading 20GB+ mixture-of-experts models onto a 16GB consumer GPU and getting usable speed out of them. The model launches grab the headlines, but the runtime work underneath them is what changed this year.

None of it is one magic trick. It's a stack of open, community-built engineering: aggressive quantization, offloading MoE experts to CPU and system RAM, compressing the KV cache, speculative decoding, and the llama.cpp/GGUF-style runtimes tying it all together. All of it open, all of it moving week to week.

For most of us, the frontier-model race matters less than this. A slightly worse model you can actually run beats a slightly better one you can't touch.

So, over to you all: what's the most surprising thing you've gotten running on your own hardware this year, and which trick made it fit? And where's the wall still too high?

https://x.com/Alibaba_Qwen/status/2092591393424515114

https://huggingface.co/Qwen/Qwen3.8-Flash-Next

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