Image created with Ideogram 3.0. Image prompt: Lower-East-Side street-corner photograph reminiscent of a late-80s album cover: weathered red-brick tenement with exterior fire-escapes, canvas awning shading racks of vintage clothes; above the awning, a hand-painted board reads ‘Llama SPORTSWEAR’; a hanging blade sign in cursive script reads ‘Llama Boutique’; a plush llama toy peers cheekily from a stack of sweaters; warm golden-hour light, subtle 35mm film grain, muted yet punchy color palette, gritty NYC vibe.
Zed just dropped the fastest Agentic code editor built in Rust. Works with Claude Sonnet 3.7, Gemini 2.5 Pro and local models via Ollama. 100% opensource. https://x.com/Saboo_Shubham_/status/1921754009221906848
MCP meets Ollama! In this video you’ll learn how build a 100% local MCP client that you can connect to any MCP server. 100% open-source code, step-by-step guide: https://x.com/akshay_pachaar/status/1921877497475485778
🚀Applications are open for the Llama Startup Program!🚀 We’re thrilled to announce the Llama Startup Program, a new initiative designed to empower early-stage startups to innovate and build generative AI applications with Llama. Why Join the Llama Startup Program? ☑️Cloud https://x.com/AIatMeta/status/1925234408187175339
This is a good overview of AI power use (small at individual level, big in aggregate). One thing that struck me: they tested LLama 3.1 405B and it averaged 3,353 joules per prompt. That is the equivalent of 2 minutes 50 seconds of human brain activity. https://x.com/emollick/status/1925178731389128744
Multimodal model support is here in 0.7! Ollama now supports multimodal models via its new engine. Cool vision models to try👇 – Llama 4 Scout & Maverick – Gemma 3 – Qwen 2.5 VL – Mistral Small 3.1 and more 😍 Blog post 🧵👇 https://x.com/ollama/status/1923139667563528347
ollama run devstral Devstral from @MistralAI and @allhands_ai is available on Ollama!”” / X https://x.com/ollama/status/1925198849263747147
Larger models benefit less from strategic prompting. While strategies improve smaller models on long-text understanding and planning. Llama3.3-70B shows only marginal gains and, in some cases, experiences performance drops due to overcautious or inefficient reasoning paths.”” / X https://x.com/omarsar0/status/1924182839081218092
