I break systems I actually use, trace failures to their root cause, and turn the evidence into fixes, tools, or upstream reports.

My current focus is offensive security around AI and developer tooling: real systems under real load, reproducible failures, preserved artifacts, and attribution that survives a second look.

• Offensive security & vulnerability research around AI agents, developer tooling, and the systems they depend on.
• Digital forensics & OSINT for reconstructing incidents, establishing provenance, and separating evidence from assumptions.
• Agent infrastructure that keeps context persistent, inspectable, portable, and under user control.

• schroedinger-sync — local-first export and sync for claude.ai conversations, project docs, and memory. Windows, DPAPI + CDP, no telemetry, no cloud.
• BRONCO — AI Metrology, Benchmarks, DIN & ISO/IEC — research-first infrastructure for treating AI benchmarks as versioned measurement instruments, models/agents/harnesses as precisely defined systems under test, and results as measurements with provenance, applicability, and uncertainty.
• AFFLR_Anthropic-Failure-Forensics-Live-Radar — AFFLR watches the public anthropics/claude-code issue space and surfaces the strongest GitHub activity signals so interesting failures, regressions, and weird behavior are harder to miss.
• MemPalace/mempalace — my daily persistent-memory layer for agent workflows. I contribute fixes found by running it under real-world load.

• Authorized targets. Offensive mindset.
• Hypothesis → reproduction → evidence → root cause → fix.
• Measure, don't guess.
• Evidence before attribution.