An engineering and verification platform for trustworthy enterprise ontologies and knowledge graphs.
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Latest capture 2026-09-18 03:04
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Claude Code
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Catch silent failures in agent workflows by verifying real system state
The harness layer for Claude Code — a reference implementation of harness engineering with hook-enforced dual review, state-machine gates that survive context compaction, and fail-closed safety where it counts. Quality gates that AI can't skip.
Anti-laziness skill for AI agents. Core: the Depth Tree method, which splits a task N layers deep and gives every leaf the full time budget of the whole task, so effort multiplies with depth. Grounded in 2025-2026 research on model laziness, underthinking and premature completion.
VE Harness — run Claude Code for hours without babysitting it. A hosted task board that survives the session, hooks that deny the commit until a review ran, real-time shared memory for your team's agents, and a Docker worker that works your queue overnight.
LLM-as-a-Verifier is a general-purpose framework that provides fine-grained feedback for any agent without requiring additional training. It achieves SOTA performance across coding, robotics, and medical agentic benchmarks.
AI-Driven Full-Text Screening and Data Extraction for Systematic Reviews and Evidence Synthesis
