Radar

Introduces HackDetect, a post-hoc audit that flags when a benchmark score comes from an exposure (leaked solutions, evaluation artifacts, generator structure) rather than the intended capability. Auditing 2,385 traces across 15 agent benchmarks finds exposures or reward hacking in 67.0% of Frontier Science traces and 66.7% of AutoLab tasks, with score inflation (the "Mislead gap") from 0.45 to 1.00.

Token-cost profiler for AI agents: ingests OpenTelemetry traces, prices each call against vendored pricing snapshots, and runs eight waste detectors (prompt-caching failures, redundant tool calls, verbosity, model mismatch, and "nonconvergence" loops that thrash without progress) into a 0-100 Token Efficiency grade usable as a CI cost-regression gate. Reports unmeasured values as unknown rather than guessing.

Across nearly 6,000 runs on two office-automation benchmarks and three harness stacks, the best-performing agent skills win mainly by regressing less, not gaining more. Identifies three causes of regression: description osmosis (a skill changes behavior just by being present in context), grounding displacement, and verification displacement, and argues skills should be scored on the gain/regression decomposition rather than aggregate improvement.

Pairs a frozen model with an external memory that distills each episode's outcome verdicts and after-the-fact corrections into retrievable natural-language rules. On the tau-bench banking domain, learning from outcome verdicts alone lifts single-trial success to 1.6x baseline and from corrections to 2.6x, solving 22 of 84 tasks the no-memory baseline never solves; the rule store transfers between Mistral Large and Claude Sonnet 5.

Databricks built roughly 200 tasks from real merged PRs across its multi-language, multi-million-line codebase (sealed git history, neutral problem statements, pass/fail against the original test suite) and found per-token pricing predicts almost nothing about real task cost. Harness choice dominates: one harness sent roughly 3x less context per turn than alternatives for comparable quality, and open models like GLM 5.2 now compete on hard tasks at a fraction of Opus 4.8's per-task cost. Argues for routing by task complexity and measuring cost per verified task instead of $/1M tokens.

Offline behavior-diff CLI for AI agents. Compares traces across model, prompt, or framework changes to surface dropped tool calls, argument drift, output changes, and cost or latency regressions; also supports behavior contracts and CI gates.

Evidence-gated lifecycle control for autonomous coding loops: states such as tested, done, and ready-to-merge require fresh, mechanically verifiable evidence bound to the current source state. The evaluation emphasizes preventing false-DONE outcomes and rejecting tampered evidence bundles.

Empirical study of 31,073 CodeRabbit reviews and developer responses. Only 36.4% of suggestions were accepted, while 56.3% were rejected, commonly for false positives, redundancy, scope errors, or misalignment with developer intent.

Open, multi-domain coding-agent benchmark spanning code, web, office, and security work. Tasks are reverse-engineered from real commits and business scenarios, then rewritten to resist prompt recovery and contamination while retaining reproducible environments, tests, and reference solutions.

Decomposes reliability gains in a production enterprise agent across verification loops, specialist models, routing, and scaffolding. Most uplift came from scaffolding and specialists; the verification step added less overall but rescued difficult failures near the top of the score range.

Cate maps native hooks from Claude Code, Codex, Cursor, Grok, OpenCode, and Pi into one lifecycle event stream for working, awaiting input or permission, and finished states. The adapters deliberately avoid inferring state when a CLI's signals are ambiguous.

Local-first memory layer that captures the reasoning, alternatives, linked files, and agent context behind each commit, then rolls it up by branch for later recall through MCP. Designed to keep context portable across coding agents, with optional cloud sync and workflows for living specs, reports, and documentation.

Opinionated, semi-deterministic codegen: one declarative ProblemSpec in, a buildable Clean-Architecture app out. Uses small, pattern-specialized agents (a 34-pattern library) instead of one big-context model, and runs in reverse to refactor brownfield code onto the dependency rule. Interesting for the small-model, architecture-first bet.

Separates retrieval keys from generation evidence instead of using the same chunks for both. Semantic key-value records and provenance pointers improve coverage, context assembly, and retrieval latency, especially under tight token budgets.

Semgrep's IDOR benchmark finds Kimi K3 competitive on aggregate F1 but substantially weaker on precision and on the largest enterprise-style repository. A useful case for evaluating false-positive triage cost and representative repository scale, not headline scores alone.

Peter Yang's collected patterns for keeping AI writing from reading like AI. Mine against for my own rules.

Tufte / Visual Display of Quantitative Information principles as a plugin. Check for the dataviz skill.

Uncle Bob's CRAP metric (Change Risk Anti-Patterns) for Java, complexity weighted by test coverage.