Blog
Insights, tutorials, and updates from the Mr.Chief team
16 articles
How we gave an AI agent 4,000+ pages of compiled truth, a consistent voice identity, and the ability to move confidential on-chain capital — and why combining all three changes what delegation actually means.
A probabilistic, cross-signal investment system that synthesizes equities, crypto, and prediction markets — with a strict cron DAG, aggressive Red Team review, and veto-gated execution. Live numbers from 68 closed paper trades.
AI doesn't eliminate human work. It dissolves professional walls. When anyone can do anything, the question shifts from 'what's my job?' to 'how can I be useful?'
ATMs grew bank teller employment 11.6%. Spreadsheets grew bookkeepers 6.4%. 85% of job growth comes from tech-created occupations. Here's why AI follows the same arc.
Anthropic's landmark study found 0% unemployment increase in AI-exposed jobs. The AI capability gap is 61 points wide. Here's what the data actually says about AI and jobs.
The real AI agent costs behind running 31 agents — model tiering, 5-layer memory architecture, the nightly learning loop, and why $130/month changes everything.
4 human gates, cascading validation, trust scoring, and circuit breakers — the AI agent quality control system that keeps 31 agents from shipping garbage. Real patterns, real failures, real fixes.
Both Mr.Chief and KlausAI are built on Mr.Chief. But they're building different products for different users. Here's the honest comparison.
Honest comparison of Mr.Chief and Limova — pricing, features, customization, and GDPR compliance. See which AI assistant delivers more value for European businesses.
Mr.Chief and Twin.so solve different problems. One builds agents for you. The other IS your agent. Here's how to choose the right AI tool for your workflow.
Learn how to run 31+ AI agents in production with circuit breakers, cascading validation, task registries, and model differentiation. Real architecture, real failures, real fixes.
Flat orchestration collapsed in two weeks. Here's the two-layer multi-agent architecture — master agent for routing, domain orchestrators for execution — that actually scales to 31 AI agents.
One AI agent hit walls in a week — context overflow, domain dilution, single point of failure. Here's why I built a multi-agent system with 31 specialized agents organized into 8 teams.
5 independently verifiable security layers for AI agents in production — Docker sandboxing, shell allowlists, filesystem isolation, approval gates, and automated daily audits. Plus the trap nobody warns you about.
Build a 5-layer memory architecture for AI agents that remembers mistakes, detects contradictions, scores importance, and improves every night. From regressions lists to confidence-aware recall.
How to cut AI agent costs 80% with model differentiation, skills-in-prompt loading, custom Docker images, session architecture, and automated performance scorecards. Real numbers from 31 production agents.
