💡 lyogavin/airllm just hit the trending charts — here's why it matters.

🔗 https://github.com/lyogavin/airllm
📝 AirLLM 70B inference with single 4GB GPU
──────────────────────────────

AirLLM is a game-changer for large language models, enabling you to run massive models like 405B Llama 3.1 on a single 8GB GPU and 671B DeepSeek-V3 on ~12GB. This is achieved without quantization, distillation, or pruning. The key to this magic is that AirLLM only keeps one layer on the GPU at a time, reducing the required VRAM significantly.

To get started, you can install the airllm package using pip install airllm. Then, initialize the model using AutoModel.from_pretrained(), passing in the Hugging Face repo ID or local path of the model. You can also enable model compression for up to 3x inference speedup by specifying the compression argument.

AirLLM supports a wide range of models, including Llama, Qwen, DeepSeek, Mistral, and many more. It's perfect for developers and researchers who want to work with large language models without breaking the bank on GPU hardware.

In short, AirLLM is a powerful tool that makes large language models accessible to everyone. With its ease of use and impressive performance, you can now run huge models on relatively small GPUs - unlocking new possibilities in NLP research and development.

──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe

💡 KnockOutEZ/wigolo just hit the trending charts — here's why it matters.

🔗 https://github.com/KnockOutEZ/wigolo
📝 The go-to web for your AI coding agent — local-first search, fetch, crawl & research over MCP. No API keys, no cloud, $0/query. Public beta.
──────────────────────────────

Meet wigolo, a local-first web intelligence platform designed for AI agents. It provides a durable surface for everything web-related, including search, fetch, crawl, extract, cache, and research, all without requiring API keys or incurring metered bills.

Key features include multi-engine web search, tiered routing for fetching pages, structured data extraction, and a memory that compounds with each query. wigolo runs wherever your agent runs, whether as an MCP server, a REST/MCP endpoint, or embedded through an SDK.

To get started, simply run npx wigolo init --agents=<your-agent> to set up the local engine. You can then use various tools like search, fetch, and research to gather information.

Technical highlights include on-device models, direct adapters for public engines, and transparent per-result scoring. wigolo is designed for agents, not humans, and provides honest output with surfaced degradation and self-flagged junk results.

Audience: This platform is ideal for developers and users of AI agents, including those using Claude Code, Cursor, Codex, and other popular AI tools.

In short, wigolo is a powerful, private, and free web intelligence platform that empowers your AI agents to gather information without incurring costs or relying on third-party services. The takeaway: with wigolo, your AI agents can search, fetch, and research the web without breaking the bank or sacrificing privacy.

──────────────────────────────
🧠 Channel: https://t.iss.one/GithubRe

🚀 Meet codecrafters-io/build-your-own-x: a gem from today's GitHub trending list.

🔗 https://github.com/codecrafters-io/build-your-own-x