


DeepSeek released DeepSeek-V3.2-Exp build on top of previously released V3.1-Terminus model. It is 50% cheaper and slightly better at search benchmarks. Deep dumping

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DeepSeek released DeepSeek-V3.2-Exp build on top of previously released V3.1-Terminus model. It is 50% cheaper and slightly better at search benchmarks. Deep dumping
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Trackio is very minimalist and hackable i think you will like it
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people think ai creativity is magic. it paints, writes, composes – so we assume it’s “thinking” like us. but that’s not what’s happening under the hood. researchers recently showed that creativity in models comes from architecture, not imagination. the wiring matters more than
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ai doesn’t create because it “wants to.” it creates because we wired it to notice patterns. architectural choices like locality and equivariance force the model to capture structure in the data. that’s why it can remix old knowledge into something that looks new. creativity in
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Stay tuned for more such interesting posts → @Saboo_Shubham_ I have created 100+ AI Agents and RAG tutorials, 100% free and opensource. P.S: Don't forget to star the repo to show your support

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oLLM is a lightweight Python library for local large-context LLM inference. Run gpt-oss-20B, Qwen3-next-80B, Llama-3.1-8B on ~$200 consumer GPU with just 8GB VRAM. And this is without any quantization – only fp16/bf16 precision. 100% Opensource.

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RAGLight: Open Source RAG Library A lightweight Python library for building production-ready RAG systems, featuring LangGraph-powered agent pipelines and multi-provider LLM support with built-in GitHub integration and CLI tools. Check out RAGLight now!
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Awesome work by Anuar Sharafudinov! Here's the repo:
↳ https://
github.com/Mega4alik/ollm

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Ace. oLLM is a lightweight Python library for LLM inference built on top of transformers Run qwen3-next-80B, GPT-OSS, Llama3, on consumer hardware. ↳ Handle 100k tokens on an 8GB GPU
↳ Works with contracts, logs, reports
↳ No quantization, just fp16/bf16 Repo in ↓

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7. Language Models that Think, Chat Better A simple recipe, RL with Model-rewarded Thinking, makes small open models “plan first, answer second” on regular chat prompts and trains them with online RL against a preference reward.