Run your personal AI company with a team of AI agents! Alook is an open-source collaboration platform for AI coding agents. Self-hosted and local-first. The setup: Define an org structure. Give each agent a role – dev, ops, research, whatever you need. Set reporting lines.
AGENTS
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AI Agents Automate Task Distribution and Team Coordination
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Works with Claude Code, Codex, and OpenCode out of the box. Define the org chart once, assign work from the top down, and have agents handle the rest, including handoffs among themselves. Every completed task feeds back into a shared memory layer, building SOPs the whole team
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Alook: Open-Source AI Agent Team Orchestration Platform
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Alook launched an open-source platform that lets a single person run an organized team of AI agents, with defined roles, reporting lines, and real email coordination between agents.
— 🚨 AI News | TestingCatalog (@testingcatalog) 27 mai 2026
Close the screen and terminal, the agents keep running, and the work lands in your inbox. pic.twitter.com/zkmRiyidSoAlook launched an open-source platform that lets a single person run an organized team of AI agents, with defined roles, reporting lines, and real email coordination between agents. Close the screen and terminal, the agents keep running, and the work lands in your inbox.
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Open-Source Avatars Level Up with AVTR-1 Locally
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OPEN-SOURCE AVATARS JUST LEVELED UP@avaturn_me just open-sourced AVTR-1, and the tech is pretty wild.
— Charly Wargnier (@DataChaz) 27 mai 2026
> clone the repo
> download the weights
> then run a conversational avatar locally on your machine at almost zero cost 🔥
repo + weights + demo links below 🧵↓ https://t.co/IbrBKsDFylOPEN-SOURCE AVATARS JUST LEVELED UP @avaturn_me just open-sourced AVTR-1, and the tech is pretty wild. > clone the repo
> download the weights
> then run a conversational avatar locally on your machine at almost zero cost repo + weights + demo links below ↓ -
Claude Features Enable AI Integration into Real Workflows
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Claude is more than a chatbot • Projects → persistent context
• Artifacts → live outputs
• MCP → external tools
• Connectors → enterprise knowledge
• Rules → operational consistency AI becomes powerful when connected to real workflows. Via Giuliano Liguori -
AI Agents Have Three Memory Layers: Working Memory
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You use AI agents every day.
ChatGPT, Claude, Gemini, Cursor. They remember what you said, who you are, and what you've worked on before. But this memory has three layers, not one. Working memory
Your current chat. Whatever you typed five messages ago. Gone when you close -
Build an AI Agent: 8 Key Steps
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Build an #AIAgent: 8 Key Steps
by @tut_ml #MachineLearning #ArtificialIntelligence #ML -
Future of AI agents lies in smart model combinations, not size
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The future of AI agents may not belong to the biggest models. It may belong to the smartest combination of models. A lot of companies are currently using large language models for everything:
summarization,
classification,
tool routing,
structured outputs,
simple repetitive -
Agent Token Efficiency: Context Re-reading Loop Performance
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input tokens are the real driver here. agent re-reads the entire context on every single loop
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MiniMax releases M2 series technical report for agentic tasks
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MiniMax just released a technical report for the MiniMax-M2 Series! The MiniMax-M2 series, a family of Mixture-of-Experts models designed from the ground up for agentic tasks. The flagship M2 packs 229.9B total parameters but activates only 9.8B per token—thanks to an