testing

Sai Rahul highlights Claude Code's Hooks feature that automates test running after edits, blocks destructive bash commands, logs spending, sends Slack alerts, and rewrites bad output automatically.

Devin AI now natively supports automated end-to-end testing and video recording after creating a PR, sending a recorded screen capture to reviewers for quick verification.

A Twitter user reports seeing multiple Tesla Cybercab prototypes with steering wheels and test drivers in Palo Alto and San Jose, indicating continued testing.

Crabbox is a new tool that gives AI coding agents isolated cloud environments to test and verify PRs, enabling them to work in parallel without conflicts and reducing the review bottleneck.

Bain & Company is using AI 'vibecoding' replicas to test potential software acquisition targets, simulating how they might operate under new ownership.

Selector Forge is a browser extension that uses AI to generate and verify reliable CSS/XPath selectors for web automation, helping developers build robust selectors for testing, scraping, and page automation.

A Rust developer profiles and optimizes the incremental rebuild time of SQLx tests, identifying bottlenecks like debuginfo generation and proc macro overhead, and proposes improvements to speed up test compilation.

Using Codex to automate app testing by generating user stories and tracking feature status in a spreadsheet through iterative loops.

Pure Effect is a zero-dependency effect library for JavaScript/TypeScript that separates business logic from I/O by representing side effects as plain data, enabling reproduction of production bugs without a database.

Ramp adopts a layered release strategy, pushing major features daily, splitting releases into early access (EA) and general availability (GA) layers. EA covers 10% of customers and 5000+ enterprises. Before GA, they must submit evidence: demo, KPIs, customer feedback, support readiness, and launch plan, to accelerate iteration.

This article introduces claude-browser-stack and agent-pods, a tool that automates browser development loops by enabling AI agents to debug APIs, scan for vulnerabilities, record user flows, and provide visual context to Claude, closing the loop between coding and verification.

Personal lessons on evaluating AI agents in production, including mapping symptoms to layers, using trajectory evaluation, calibrating LLM judges, converting failures to test cases, and performing adversarial testing.

Greptile launches T-Rex, a feature that runs your branch in a sandbox to find bugs by mocking API calls, clicking around the UI, and running unit tests, catching ~20% more bugs than base Greptile.

Yoyo is an AI agent that self-evolves every 8 hours on GitHub Actions. Its key to success lies in a harness design of a stateless agent plus persistent state (git repository). The article deeply analyzes simple solutions to issues such as memory, context, feedback, verification, etc., emphasizing that persistent state is more critical than the model itself.

A discussion on the security risks of AI agents using tools, focusing on prompt injection as a practical threat where untrusted text can alter agent behavior, and the need for repeatable testing before granting permissions.

Adaline 2.0 is an agent self-improvement layer that watches real user interactions, clusters failures by pattern, automatically writes hundreds of tests daily, and generates new agent candidates for approval before deployment.

Iris is an MCP server that runs inside your real app to verify AI agent work (e.g., Claude Code, Codex, Hermes) by checking conditions and returning a pass/fail verdict with evidence, reducing false positives and token usage compared to snapshot-based approaches.

The author describes a voice agent call cut off at 600 seconds without warning, and proposes a testing approach to handle max duration gracefully, including pre-cutoff warnings and state preservation.

This article introduces the method of building an industrial-grade Skill from scratch, emphasizing core features such as precise triggering, permission scoping, and evaluable iteration, as well as the importance of constructing scoring criteria, test cases, and quality gate scripts, demonstrating how to implement professional and maintainable skill packages in agent environments like Codex.

Discusses a gated approach for AI agent self-modification where the agent forks itself, proposes a patch, and runs multiple tests before modification is applied.