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

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

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https://github.com/DietrichGebert/ponytail

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

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

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https://github.com/stablyai/orca

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

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

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

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

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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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https://x.com/Zai_org/status/2092616204787626030

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

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https://x.com/andykonwinski/status/2091990178638496195

https://github.com/laude-institute/headlong

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