Great visual! Would like to add AG-UI to this mental model. MCP handles agent-to-tool. A2A handles agent-to-agent. AG-UI handles agent-to-user and standardizes how agent state and actions stream to the frontend. All three together give you the full protocol stack for agentic
AGENTS
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GPT as Always-On AI Collaborator for Productivity
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gpt is like having an always-on collaborator:
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AI Bots on X: Detection Failure and Verification Paywall Problems
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As AI advances, bots are inevitable. Kaito shares some blame, but X’s failure to distinguish bots from humans is the real problem. The verified paywall failed, and bots now pay to spam. It is absurd that X would rather ban crypto than improve its bot detection.
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MiroThinker: Scaling Open-Source Research Agents Performance
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MiroThinker: Pushing the Performance Boundaries of Open-Source Research Agents via Model, Context, and Interactive Scaling https://
buff.ly/jXip8ln
#AI #MachineLearning #DeepLearning #LLMs #DataScience -
Solving AGI Governance: Minimal Conditions for Stable Multi-Agent Order
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[ META-AGENTIC α‑AGI ] SOLVING α‑AGI GOVERNANCE "Minimal Conditions for Stable, Antifragile Multi‑Agent Order" Deck : https://
github.com/MontrealAI/AGI
-Alpha-Agent-v0/blob/main/alpha_factory_v1/demos/solving_agi_governance/presentation/Solving_Alpha-AGI_Governance_v0.pdf
… Reading : https://
chatgpt.com/s/dr_69627b12a
fec8191808c741d626ce445
… #AGIFirst #AGIALPHA #Superintelligence -
Observing and Evaluating LLM Agents on LangChain Academy
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📊 Learn how to observe & evaluate agents on LangChain Academy 📊
— LangChain (@LangChain) 10 janvier 2026
Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic – you can’t always predict how they will behave.
Add multi-turn interactions and tool-calling agents, and… pic.twitter.com/tBt6YJkifILearn how to observe & evaluate agents on LangChain Academy Testing applications is essential to the development lifecycle, but LLM systems are non-deterministic – you can’t always predict how they will behave. Add multi-turn interactions and tool-calling agents, and
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Alpha-Factory AGI Business Framework Omega-Grade Edition
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[ META-AGENTIC α‑AGI ] Large‑Scale α‑AGI Business 3 — Omega‑Grade Edition "Alpha‑Factory v1 → Ω‑Lattice v0" Deck: https://
github.com/MontrealAI/AGI
-Alpha-Agent-v0/blob/main/alpha_factory_v1/demos/alpha_agi_business_3_v1/presentation/OMEGA_GRADE_Business_3_v0.pdf
… Reading: https://
chatgpt.com/s/dr_696267deb
a248191ad4322fe8465072c
… #AGIFirst #AGIALPHA #Superintelligence -
Single Agent Skills vs Multi-Agent Systems Efficiency
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Can a single agent with skills replace multi-agent systems? Multi-agent systems work well for complex reasoning where specialized agents collaborate through explicit communication. But this incurs substantial computational overhead in tokens and latency. This new research
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Claude-Mem Plugin Enables Persistent Memory for Coding Agents
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Persistent memory for Claude Code! Claude-Mem (12.9K+ GitHub stars) is a free plugin that gives Claude persistent memory across sessions. Most coding agents forget everything the moment a session ends.
You restart the terminal, and you are back to explaining architecture, -
Testing AI Agent Outputs Over Process Steps
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Engineering at Anthropic dropped another banger. Their internal playbook for evaluating AI agents. Here's the most counterintuitive lesson I learned from it: Don't test the steps your agent took. Test what it actually produced. This goes against every instinct. You'd think