Moltbook as MCP Stress Test: What 770K Agents Reveal About Protocol Design
A follow-up to my MCP Maturity Model post. Moltbook shows what happens when you run 770K agents at Level 0 maturity with zero governance. The results are instructive.
MIT Tech Review named mechanistic interpretability a 2026 Breakthrough Technology. Anthropic open-sourced circuit tracing. Here's what actually changed, how it connects to the activation probes I built for sandbagging detection, and why production teams should care.
Reinforcement Learning with Verifiable Rewards powers every reasoning model worth talking about. But it only works where you can check the answer automatically. Extending it to messy, real-world domains is the hardest open problem in LLM training right now.
MCP handles agent-to-tool. A2A handles agent-to-agent. A2UI handles agent-to-interface. Together they form a protocol stack that nobody has mapped properly - including the security gaps that should terrify you.
First empirical demonstration of activation-level sandbagging detection. Linear probes achieve 90-96% accuracy across Mistral, Gemma, and Qwen models. Key finding - sandbagging representations are model-specific, and steering can reduce sandbagging by 20%.
I tested activation steering on 4 agent behaviors across 3 models. The results surprised me.
A practical framework for evaluating your multi-agent context management strategy. From ad-hoc string concatenation to self-evolving context systems - where does your architecture stand?
A follow-up to my MCP Maturity Model post. Moltbook shows what happens when you run 770K agents at Level 0 maturity with zero governance. The results are instructive.
MIT Tech Review named mechanistic interpretability a 2026 Breakthrough Technology. Anthropic open-sourced circuit tracing. Here's what actually changed, how it connects to the activation probes I built for sandbagging detection, and why production teams should care.
Reinforcement Learning with Verifiable Rewards powers every reasoning model worth talking about. But it only works where you can check the answer automatically. Extending it to messy, real-world domains is the hardest open problem in LLM training right now.
MCP handles agent-to-tool. A2A handles agent-to-agent. A2UI handles agent-to-interface. Together they form a protocol stack that nobody has mapped properly - including the security gaps that should terrify you.
Interactive exploration of Manifold-Constrained Hyper-Connections - how DeepSeek fixed the signal explosion problem in deep residual networks using 1967 mathematics