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This week's open-source highlights: AI2's hybrid architecture proves transformers need help, autoresearch automates ML experiments overnight, and local inference gets serious upgrades.
Nvidia open-sources a 30B-parameter reasoning model that runs on consumer GPUs with a million-token context window. Here's what makes it different.
AI2 and Lambda trained a hybrid transformer-RNN model that's twice as data-efficient as pure transformers. But can you actually run it locally?
Chinese AI startup MiniMax has released M2.5, an open-weights model matching Claude Opus performance for coding and agentic tasks while costing 95% less to run
GLM-5, Qwen 3.5, DeepSeek V3.2, and MiniMax M2.5 are rewriting the rules. Here's what they actually deliver on consumer hardware.
Ollama's new OpenClaw integration lets you run AI agents locally through WhatsApp, Telegram, or Slack. Here's how it works, what you need, and the security risks nobody mentions.
Veea releases a sub-millisecond security proxy for AI agents under MIT license as new research shows 88% of organizations have experienced agent security incidents.
Clone voices locally with Chatterbox TTS. Free, open-source alternative to ElevenLabs that actually wins blind tests. Docker setup included.
This week's biggest open-source AI developments: Alibaba's efficient new model outperforms its massive predecessor, Mistral releases a 675B frontier model under permissive license, and local inference adoption accelerates
Forget 700B parameter flagships you can't run. Here are the open-weight models that deliver real performance on consumer hardware - with actual benchmarks.
Alibaba's new 35B model matches Claude Sonnet 4.5 on benchmarks while running locally on an RTX 4090. Here's what you need to know.
Ecotone AI's open-source framework makes population-scale DNA analysis economically viable for the first time.
Former Google and Stripe security head Niels Provos built an open source sandbox that assumes AI agents will go rogue. Here's how it works.
Ollama delivers 40% faster inference while llama.cpp finds a permanent home at Hugging Face. Two developments that secure the future of running AI on your own hardware.
The popular local inference tool now installs and configures OpenClaw automatically, giving desktop users access to AI agents running Kimi-K2.5 and GLM-5 with a single command.
A Toronto startup is etching LLM weights directly into transistors, achieving 17,000 tokens per second. The catch: you can't change the model.
This week's biggest open-source AI developments: llama.cpp finds a permanent home at HF and Z.ai ships GLM-5 under MIT license
Step-by-step guide to building a private document search system that runs entirely on your computer, no cloud services required
Zhipu AI releases GLM-5 under MIT license, a frontier model rivaling Claude and GPT-5 while proving China can build top-tier AI without NVIDIA hardware.
llama.cpp's creators join Hugging Face to keep the project open and sustainable while local AI competes with cloud inference.
Forget the marketing - here's how the latest open-weight models actually perform on your GPU, from 8GB budget cards to 24GB workstations.
A CVSS 9.8 flaw in vLLM allows unauthenticated remote code execution through malicious video URLs. Patch now if you run multimodal models.
A tier-by-tier comparison of the top open-weight LLMs you can run locally, from 8GB laptops to 24GB gaming GPUs to Apple Silicon Macs.
Step-by-step guide to running a private, local AI chatbot that rivals ChatGPT - no subscription, no data collection, no internet required.
