AI Coding
Evidence-led guidance for using coding agents and AI tools without giving up review, security, or understanding.
41 articles in this section.
How Claude Code became an agent: product lessons for developer tools
Anthropic’s Claude Code retrospective describes its path from an internal CLI to a broader coding agent. Build around inspectable actions, permission boundaries, fast feedback, and real repository workflows.
Claude Sonnet 5: migration checklist for coding and agent workloads
Anthropic introduced Claude Sonnet 5 for coding, agents, and professional work at scale. Replay repository tasks, tool calls, and structured outputs before replacing the previous model.
Publish an AI Studio Android app safely to the internal test track
Google announced Play Console integration, browser emulation, ADB testing, and Internal Test Track publishing from AI Studio. Add source review, permission review, signing ownership, and device tests before publishing.
Migrate from Gemini CLI to Antigravity CLI without losing project rules
Google is unifying its agent development experience around Antigravity CLI and recommends porting Gemini CLI custom skills. Inventory every customization, migrate in a branch, and compare behavior on fixed tasks.
Evaluate AI-generated web interfaces from Gemini 3.5 Flash
Google says Gemini 3.5 Flash can generate richer interactive web interfaces and graphics. Test real data, empty states, localization, keyboard input, and constrained mobile widths.
Gemini 3.5 Flash for long-running agents: design checkpoints and recovery
Google highlights Gemini 3.5 Flash for longer agentic work through its Antigravity harness. Store checkpoints after planning, data collection, modification, and verification so work can resume safely.
Gemini 3.5 Flash vs larger models: choose with task-level evaluations
Google positions Gemini 3.5 Flash as combining flagship-level intelligence with Flash-series latency and strong coding and agent benchmarks. Compare completion rate, tool errors, latency, and cost on your actual tasks.
Secure cloud execution for coding agents: lessons from the Ona acquisition
OpenAI announced an agreement to acquire Ona for secure cloud execution and orchestration technology in the Codex ecosystem. Use ephemeral environments, minimum credentials, network policy, audit logs, and clean teardown.
How to migrate an AI feature to GPT-5.6 without breaking production
GPT-5.6 is generally available through the API as a new family rather than a byte-for-byte replacement for older models. Replay representative production cases in shadow mode and compare structured outputs before routing live traffic.
GPT-5.6 for frontend design: test visual quality instead of trusting a demo
OpenAI describes GPT-5.6 as making a notable step in design and end-to-end knowledge work. Evaluate generated interfaces across real content, keyboard navigation, and mobile viewports.
GPT-5.6 cost controls: budget AI workloads before switching models
GPT-5.6 offers different performance and cost points across Sol, Terra, and Luna. Set per-feature token, retry, and monthly budget limits before migration.
GPT-5.6 Sol vs Terra vs Luna: which model should developers choose?
OpenAI positions Sol as the flagship, Terra as the balanced everyday option, and Luna as the most cost-efficient member of GPT-5.6. Route a fixed evaluation set through all three and choose per task instead of setting one global default.
GitHub Copilot browser tools in VS Code: a safe testing workflow
Use Copilot browser tools in VS Code for navigation, screenshots, and web-app validation with domain controls, test accounts, and human review.
GitHub Copilot Vision: review screenshots, PDFs, and UI bugs safely
Use Copilot Vision with screenshots and PDFs for UI debugging while protecting private data, verifying visual claims, and keeping accessible tests.
GitHub Models retirement: migration checklist before July 30, 2026
Migrate projects from GitHub Models before retirement by inventorying API calls, prompts, credentials, evaluations, and replacement providers.
Copilot agent session streaming API: build a useful progress UI
Use Copilot agent session streaming to show progress, tool activity, failures, and completion without misleading users or leaking sensitive data.
Use Codex as an agent provider in JetBrains IDEs
Set up Codex as an agent provider in JetBrains IDEs, understand preview requirements, choose permissions, and verify changes safely.
Copilot usage API review cycles: measure AI adoption without bad metrics
Interpret Copilot usage API review-cycle and time-to-review metrics, avoid false productivity claims, and build a balanced engineering report.
Fix GitHub pull request merge conflicts with Copilot on mobile
Understand GitHub Mobile’s Copilot merge-conflict workflow, when it is safe, what to review, and when to return to a full development environment.
Deploy managed GitHub Copilot settings with MDM safely
Deploy organization-managed Copilot settings through MDM or files, test precedence, protect developer workflows, and prepare rollback.
GitHub Copilot OpenTelemetry export for VS Code and CLI explained
Understand enterprise-managed OpenTelemetry export for Copilot, choose useful signals, protect prompt data, and validate an approved collector.
GitHub Copilot repository overview: use it without trusting it blindly
Use Copilot repository overviews to enter unfamiliar codebases faster while verifying architecture, commands, ownership, and security assumptions.
Local vs cloud AI coding tools: privacy and speed tradeoffs
A balanced guide to local and cloud AI coding tools, including privacy, context size, speed, model quality, setup, and team governance.
Using AI on legacy code safely: understand before changing
A practical guide to using AI with legacy codebases: map behavior, add tests, avoid blind rewrites, and preserve business rules.
When AI refactors too much: how to keep changes reviewable
How to keep AI coding changes small, reviewable, and safe by limiting scope, separating refactors, and protecting behavior.
AI code diff review checklist before you merge
A practical review checklist for AI-generated code diffs: scope, tests, security, dependencies, error handling, and hidden behavior changes.
Slopsquatting explained: when AI suggests packages that do not really exist
A simple explanation of slopsquatting, hallucinated package names, and why AI-generated install commands need verification.
MCP server permissions explained: what to check before connecting one
A practical guide to MCP server permissions, tool access, OAuth, data exposure, and what developers should review before connecting AI tools.
Prompt injection in coding agents: why repo access changes the risk
A practical explanation of prompt injection risks in coding agents that can read repos, edit files, call tools, or open external pages.
AI-generated tests explained: useful safety net or fake confidence?
How to use AI-generated tests without fooling yourself, including assertions, edge cases, fixtures, and reviewer judgment.
AI coding tool rules: what every repo should tell Copilot or ChatGPT
A practical repo rules checklist for AI coding tools: architecture boundaries, tests, security, style, dependencies, and review expectations.
Copilot cloud agent explained: what developers should review before merging
How GitHub Copilot cloud agent changes the development workflow, what to inspect in generated branches, and how to keep ownership clear.
Copilot custom instructions: how to make AI coding tools follow your project rules
A practical guide to GitHub Copilot custom instructions, repository guidance, coding standards, and avoiding repetitive AI mistakes.
GitHub Copilot code review explained: how to use it without trusting it blindly
How to use GitHub Copilot code review as a helpful second reviewer while still keeping human judgment, tests, and security checks in control.
Using AI to understand an unfamiliar codebase
How to use AI tools to map an unfamiliar codebase without trusting summaries blindly or missing important architecture.
Security risks of copy-pasting AI-generated code
The security mistakes developers should check before copying AI-generated code into authentication, APIs, database queries, or deployment scripts.
Agentic coding tools explained: useful workflow or hype?
What agentic coding tools do, where they help, where they fail, and how developers should use them safely.
How to debug code with ChatGPT or Claude without getting misled
A practical prompt pattern for debugging code with AI tools while preserving logs, constraints, and your own reasoning.
Cursor vs GitHub Copilot vs ChatGPT for developers
A practical comparison of Cursor, GitHub Copilot, and ChatGPT for coding, debugging, refactoring, and learning.
Why developers do not fully trust AI coding tools yet
Why developers use AI coding tools but still distrust their output, and how to use them without lowering code quality.
AI-generated code review checklist for developers
A practical checklist for reviewing AI-generated code before merging it into a real project.