Insights on AI orchestration, code understanding, and building maintainable software
The GAO documented a crisis: $2B+ in duty drawback claims lack proper review because CBP cannot manage multi-month investigations. EAPA cases span 300-360 days with critical context loss. Focused Assessment responses require reconstructing prior analysis within 30-day deadlines. We're solving investigation memory for customs compliance—then expanding to M&A due diligence and beyond.
Read the full strategy →50-100x faster investigation resumption (research goal). Revolutionary checkpoint architecture reduces context restoration from 20-40 hours to 15-30 minutes by capturing reasoning provenance, confidence trajectories, and dead-end documentation alongside execution state. Researchers lose 30% of time re-exploring rejected approaches—knowledge-augmented checkpoints prevent this. No competitor offers this capability.
Read the full technical breakdown ->From a frozen mammoth in my hometown of Kikinda, Serbia to building AI systems that preserve research context across decades. Why we named our platform after one of Europe's best-preserved mammoths—and what prehistoric preservation teaches us about research intelligence for the long haul.
Read the origin story ->Mamut Lab evolves to universal agentic platform. We've successfully integrated Project Continuum's research intelligence capabilities—adding Layer 11 (Research Intelligence), Temporal Knowledge Substrate for multi-year investigations, and Graduated Autonomy framework.
Read the integration announcement ->Announcing neurosymbolic reasoning as Mamut Lab' foundational implementation. Combining neural AI with symbolic logic for formal verification, explainability, and regulatory compliance.
Read more ->In Ursula K. Le Guin's Earthsea, knowing something's true name gives you power over it. AI generates thousands of lines of code without knowing a single true name.
Read more ->Humans have two thinking systems and sophisticated context management. AI agents have neither—which is why they delete databases and enter infinite loops.
Read more ->AI systems can lose 90% accuracy when learning new tasks. Understanding task-incremental, domain-incremental, and class-incremental learning is critical for production AI.
Read more ->Pure LLMs hallucinate invalid code. Pure symbolic systems can't learn from data. Neuro-symbolic AI combines both—achieving 3-10x better performance without catastrophic forgetting.
Read more ->Claude generates code. Codex reviews it. Tests fail. Now what? Learn how Mamut Lab handles multi-step AI workflows with automatic rollback and workspace cleanup.
Read more ->Your workflow needs a human decision, an AI recommendation, and data from a 20-year-old SOAP service. How do you coordinate that? Coordinated Space architecture has answers.
Read more ->Research shows automation can reduce productivity by 18% and quality by 13% once AI is removed. Evidence-based strategies to stay sharp while moving fast.
Read more ->AI can generate 500 lines in seconds. But six months later, can you maintain it? We explore the growing problem of "code slop" and why understanding matters more than speed.
Read more ->Just like human code reviews, AI models catch different issues. Learn why Claude + Codex working together produces more reliable code than either alone.
Read more ->