LLaMA leak mixed blessing for Facebook AI - techhq.com
LLaMA leak mixed blessing for Facebook AI techhq.com
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llamav0.14.20
Release Notes [2026-04-03] llama-index-agent-agentmesh [0.2.0] fix vulnerability with nltk ( #21275 ) llama-index-callbacks-agentops [0.5.0] chore(deps): bump the uv group across 50 directories with 2 updates ( #21164 ) chore(deps): bump the uv group across 24 directories with 1 update ( #21219 ) chore(deps): bump the uv group across 21 directories with 2 updates ( #21221 ) fix vulnerability with nltk ( #21275 ) llama-index-callbacks-aim [0.4.1] fix vulnerability with nltk ( #21275 ) llama-index-callbacks-argilla [0.5.0] chore(deps): bump the uv group across 58 directories with 1 update ( #21166 ) chore(deps): bump the uv group across 24 directories with 1 update ( #21219 ) chore(deps): bump the uv group across 21 directories with 2 updates ( #21221 ) fix vulnerability with nltk ( #21275 )
I Built a Visual Spec-Driven Development Extension for VS Code That Works With Any LLM
I Built a Visual Spec-Driven Development Extension for VS Code That Works With Any LLM The Problem If you've tried GitHub's Spec Kit , you know the value of spec-driven development: define requirements before coding, let AI generate structured specs, plans, and tasks. It's a great workflow. But there's a gap. Spec Kit works through slash commands in chat. No visual UI, no progress tracking, no approval workflow. You type /speckit.specify , read the output, type /speckit.plan , and so on. It works, but it's not visual. Kiro (Amazon's VS Code fork) offers a visual experience — but locks you into their specific LLM and requires leaving VS Code for a custom fork. I wanted both: a visual workflow inside VS Code that works with any LLM I choose . So I built Caramelo . What Caramelo Does Caramelo
MixtureOfAgents: Why One AI Is Worse Than Three
The Problem You send a question to GPT-4o. It answers. Sometimes brilliantly, sometimes wrong. You have no way to know which. What if you asked three models the same question and picked the best answer? That is MixtureOfAgents (MoA) — and it works. Real Test I asked 3 models: What is a nominal account (Russian banking)? Groq (Llama 3.3): Wrong. Confused with accounting. DeepSeek: Correct. Civil Code definition. Gemini: Wrong. Mixed with bookkeeping. One model = 33% chance of correct answer. Three models + judge = correct every time . The Code async function consult ( prompt , engines ) { const promises = engines . map ( eng => callEngine ( eng , prompt ) . then ( r => ({ engine : eng , response : r , ok : true })) . catch ( e => ({ engine : eng , error : e . message , ok : false })) ); ret
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Google DeepMind s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts
Designing algorithms for Multi-Agent Reinforcement Learning (MARL) in imperfect-information games — scenarios where players act sequentially and cannot see each other s private information, like poker — has historically relied on manual iteration. Researchers identify weighting schemes, discounting rules, and equilibrium solvers through intuition and trial-and-error. Google DeepMind researchers proposes AlphaEvolve, an LLM-powered evolutionary coding agent [ ] The post Google DeepMind s Research Lets an LLM Rewrite Its Own Game Theory Algorithms — And It Outperformed the Experts appeared first on MarkTechPost .
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