Critical engagement with papers, articles, standards, and ideas.
Discusses: Announcing the Agentic Resource Discovery specification + 1 more
ARD can help agents find capabilities across organizations, but discovery metadata must remain outside the trusted execution boundary.
Discusses: From story points to tokenmaxxing: Why engineering keeps measuring the wrong things
Counting tokens repeats an old measurement mistake unless AI-assisted work is evaluated through delivery, quality, and user outcomes.
Discusses: A paper diagram visualizer
Dependency diagrams can make mathematical papers easier to navigate, provided their inferred structure is not mistaken for verified proof evidence.
Discusses: The Open Source Agent Toolkit in 2026
Agent stacks should be selected layer by layer, with explicit contracts for state, tools, memory, evaluation, and recovery.
Discusses: How does Chain-of-Thought (CoT) Prompting work? + 5 more
Chain-of-thought prompting can improve some multi-step answers, but a plausible rationale is not evidence that an answer is correct or faithful.
Discusses: Old and new apps, via modern coding agents
Terence Tao's restored mathematical applets show where coding agents have real leverage—tasks whose outputs remain cheap for an expert to verify.
Discusses: Agentic Testing: Where Agents Fit in the E2E Testing Stack + 1 more
Slack's agent-driven E2E experiments expose a useful new execution model, but adaptation must remain separate from the authority to declare success.
Discusses: The GitHub MCP Server adds support for tool-specific configuration, and more + 1 more
GitHub MCP tool filtering reduces context and accidental capability exposure, but real least privilege also requires identity, authorization, and effect controls.
Discusses: Playwright finally hit critical mass as we enter 2026 + 2 more
Playwright's mature tooling makes it a strong default for new browser automation, but migration should be decided through a representative experiment.
Discusses: Beyond Accidental Quality: Finding Hidden Bugs with Generative Testing + 5 more
Property-based testing is more than randomized input generation; its value depends on the property, distribution, oracle, shrinking, and evidence.