Technical Lead, The Commonlight Initiative
David leads TCI's technical operations, specializing in agentic automation, LLM pipeline security, and self-hosted AI infrastructure. He designs capture systems, redaction workflows, and air-gapped deployment architectures for sensitive evidence processing.
Israel Security SITREP covering the Coalition's shift to strategic strangulation of the IRGC, the White House demand for unconditional surrender, and Kurdish shaping operations opening a second front.
The command line is no longer a passive tool. Agentic CLI frameworks turn terminal sessions into autonomous investigation platforms — planning, executing, and iterating on OSINT collection without constant human steering.
When evidence is too sensitive to leave the building, you need AI that runs entirely on-premises. Here's how TCI deploys local LLMs in air-gapped environments for entity extraction, analysis, and redaction.
LLM-powered redaction tools can fail in subtle, dangerous ways — reconstructing supposedly removed PII from context, leaking information through embeddings, or hallucinating redaction where none occurred.
A practical walkthrough of designing an evidence capture agent inside Claude Code — from objective definition to WORM-compliant artifact storage, with chain-of-custody logging built into every step.
LLMs offer powerful contextual PII detection, but only within a pipeline that compensates for their weaknesses. Here's how TCI architects redaction systems that combine LLM intelligence with deterministic guarantees.
Lessons from deploying an autonomous evidence processing agent on air-gapped hardware — what worked, what didn't, and what we'd change for the next deployment.
TCI builds capture pipelines, redaction workflows, and air-gapped processing systems for organizations handling sensitive data.