Exposing REST services as POSIX file systems for agents

You probably already know that Anthropic accidentally leaked Claude Code's entire source code. A misconfigured npm package. 500,000 lines of TypeScript. The fastest-growing repo in GitHub history — 84K+ stars before the DMCA notices started flying. I spent a lot reading through the findings because I maintain a tool that turns Claude Code into a 12-agent development team, and I needed to know what we were working around. Here's what I learned: Claude Code doesn't verify its own edits. It writes files, checks that bytes hit disk, and reports success. Whether the code compiles? Not its problem. It silently loses context. After a certain threshold, auto-compaction wipes your file reads and reasoning chains. The agent then edits against memory it no longer has — and doesn't know it. It truncates without telling you. File reads get capped. Search results get cut. The agent works from incomplete data and reports it as complete. It can't actually understand code structure. Renames use text grep, not an AST. Dynamic imports, re-exports, barrel files — all invisible. None of this makes Claude Code bad. It's still the best AI coding tool I've used. But these are mechanical limitations that silently degrade output quality — and now that they're documented, we can work around them. So I did. I pushed a hardening update to Claude Code SDLC — my open-source project that wraps Claude Code in a structured development pipeline: documentation first, TDD enforcement, architecture reviews, quality gates before every merge. The update adds: - Mandatory re-read-before-edit (never trust stale context) - Mid-slice typecheck verification (catch cascading errors early) - Chunked file reading (never hit the silent truncation cap) - A 7-step rename safety protocol (grep is not an AST) - Architect authority for structural fixes (override the "do minimum work" bias) The repo is live, MIT-licensed, and one command to install If you use Claude Code for real work, give it a look. Stars, feedback, and contributions all welcome. GitHub: https://lnkd.in/eYXKNmhb

We’ve spent the last few sessions deconstructing the most powerful tool in the modern developer’s arsenal: 𝗚𝗶𝘁. 🛠️ It’s easy to view Git as just a series of commands to memorize. But as we’ve explored, Git is actually a sophisticated architectural framework designed to handle the complexity of human collaboration at scale. As 𝗟𝗶𝗻𝘂𝘀 𝗧𝗼𝗿𝘃𝗮𝗹𝗱𝘀, the creator of Git, famously put it: "Git is a content-addressable filesystem. It’s not just about tracking files; it’s about managing snapshots of reality." 🛰️ 𝗧𝗵𝗲 𝗝𝗼𝘂𝗿𝗻𝗲𝘆 𝗦𝗼 𝗙𝗮𝗿: 𝗔 𝟳-𝗣𝗼𝘀𝘁 𝗥𝗲𝘁𝗿𝗼𝘀𝗽𝗲𝗰𝘁𝗶𝘃𝗲 If you’ve missed any of our deep dives, here is the "compressed" version of the architecture we’ve covered:  • 𝗧𝗵𝗲 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻: We moved past the "v2_final_final" era of local backups into the distributed paradigm where every machine holds the full project history.  • 𝗜𝗱𝗲𝗻𝘁𝗶𝘁𝘆 & 𝗖𝗼𝗻𝗳𝗶𝗴: We learned that professional Git starts with a verified identity—System, Global, and Local—ensuring every commit is a traceable digital signature.  • 𝗧𝗵𝗲 𝗥𝗲𝗽𝗼𝘀𝗶𝘁𝗼𝗿𝘆 𝗔𝗻𝗮𝘁𝗼𝗺𝘆: We peeked under the hood of the .𝗴𝗶𝘁 directory to find the HEAD pointer, light-weight branches, and immutable snapshots.  • 𝗧𝗵𝗲 𝗧𝗵𝗿𝗲𝗲-𝗧𝗿𝗲𝗲 𝗟𝗶𝗳𝗲𝗰𝘆𝗰𝗹𝗲: We mastered the "Buffer Zone" (Staging Area), learning why an intentional 𝗴𝗶𝘁 𝗮𝗱𝗱 is the key to atomic, logical commits.  • 𝗣𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝗥𝗲𝗮𝗹𝗶𝘁𝗶𝗲𝘀: We explored how branches allow for safe experimentation and isolated hotfixes without ever disrupting the stable 𝗺𝗮𝗶𝗻 trunk.  • 𝗧𝗵𝗲 𝗔𝗿𝘁 𝗼𝗳 𝘁𝗵𝗲 𝗠𝗲𝗿𝗴𝗲: We reframed "Merge Conflicts" not as errors, but as healthy signals of parallel collaboration that can be resolved with surgical precision.  • 𝗧𝗵𝗲 𝗗𝗶𝘀𝘁𝗿𝗶𝗯𝘂𝘁𝗲𝗱 𝗘𝗻𝗴𝗶𝗻𝗲: Finally, we bridged the gap between local nodes and remote hubs (GitHub/GitLab), mastering the 𝗳𝗲𝘁𝗰𝗵-𝗿𝗲𝘃𝗶𝗲𝘄-𝗽𝘂𝗹𝗹-𝗽𝘂𝘀𝗵 workflow. 🚀 𝗪𝗵𝗮𝘁’𝘀 𝗡𝗲𝘅𝘁? We aren't finished yet. While we’ve mastered the "how" and "where" of Git, we still need to cover the advanced strategies that separate the seniors from the juniors. 𝗧𝗵𝗲 𝗰𝗼𝗻𝘁𝗶𝗻𝘂𝗮𝘁𝗶𝗼𝗻 𝘄𝗶𝗹𝗹 𝗳𝗼𝗹𝗹𝗼𝘄 𝗶𝗻 𝗺𝘆 𝘂𝗽𝗰𝗼𝗺𝗶𝗻𝗴 𝗽𝗼𝘀𝘁𝘀, where we will dive into:  1. 𝗥𝗲𝗯𝗮𝘀𝗲 𝘃𝘀. 𝗠𝗲𝗿𝗴𝗲: The battle for the cleanest history.  2. 𝗦𝘁𝗮𝘀𝗵𝗶𝗻𝗴 & 𝗧𝗮𝗴𝗴𝗶𝗻𝗴: Managing temporary work and official releases.  3. 𝗚𝗶𝘁 𝗛𝗼𝗼𝗸𝘀 & 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Making the tool work for you while you sleep. 𝗧𝗮𝗸𝗲𝗮𝘄𝗮𝘆: Git isn't just a tool you use; it’s a language you speak. When you understand the architecture, you don't just write code—you engineer history. 𝗪𝗵𝗶𝗰𝗵 𝗼𝗳 𝘁𝗵𝗲𝘀𝗲 𝟳 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝘁𝗼𝗽𝗶𝗰𝘀 𝘄𝗮𝘀 𝘁𝗵𝗲 𝗯𝗶𝗴𝗴𝗲𝘀𝘁 "𝗮𝗵𝗮!" 𝗺𝗼𝗺𝗲𝗻𝘁 𝗳𝗼𝗿 𝘆𝗼𝘂? 𝗟𝗲𝘁'𝘀 𝗱𝗶𝘀𝗰𝘂𝘀𝘀 𝗯𝗲𝗹𝗼𝘄. 👇 #Git #SoftwareEngineering #DevOps #TechRecap #WebDevelopment #Programming #EngineeringExcellence

Most engineers use Claude Code out of the box. After months of daily use across a multi-repo codebase. Here are 9 optimizations — steal what works for you: 1. CLAUDE.md — Your Project's Brain A single markdown file at the project root that teaches Claude your architecture, build commands, hard rules, and conventions. Claude reads it every session. No more re-explaining your stack. 2. Three-Layer Context System tasks/ files (lessons.md, todo.md, decisions.md, issues.md) + persistent memory + CLAUDE.md. Claude remembers corrections from last week and never repeats the same mistake. 3. Custom Skills (Slash Commands) Domain-specific workflows in one command: /plan (structured task planning), /review (multi-agent code review), /compound (auto-captures lessons after every task), /migration (full legacy-to-REST workflow with 4 deliverables). 4. Multi-Agent Code Review One /review command launches 5 specialized agents in parallel: security, data safety, ORM patterns, API contracts, and correctness. Five perspectives, ~30 seconds, zero overlap. 5. External Dependency Auditing /audit-repo dispatches 3 agents (security, supply-chain, domain-fit) against any GitHub repo. Returns ADOPT/CAUTION/REJECT with specific findings. Already caught a plugin that silently overrides user permissions. 6. Git Worktrees for Parallel Work Work on two tickets simultaneously via .claude/worktrees/<ticket>. Both directories build and test concurrently. Real parallelism, not tab-switching. 7. Self-Improvement Loop Every correction becomes a rule in tasks/lessons.md + persistent memory. Claude reads them at session start. After 25+ lessons, the same categories of mistakes just stopped happening. 8. Hooks & Permission Guardrails Auto-build on file save, deny rules for destructive commands (force push, rm -rf), 95+ pre-approved safe operations. Fast on safe actions, hard stop on dangerous ones. 9. Bootstrap Template I generalized all of the above into a reusable CLAUDE.md template: project structure, hard rules, workflow orchestration, task management, git discipline, memory setup, and 11 universal lessons learned. ~5 minutes to set up for any project. The biggest insight: Claude Code's value scales with context. Out-of-box is maybe 30% of what's possible. Each layer compounds — CLAUDE.md alone is a 2x, add memory for continuity, add skills for repeatable workflows, add multi-agent review for team-level quality from one engineer. What optimizations have you built? Drop yours in the comments. #ClaudeCode #Anthropic #AIEngineering #DeveloperProductivity #SoftwareEngineering

Here are 5 Claude Code skills I have found very useful. These skills can significantly boost your development workflow and some of them even help reduce token usage while maintaining the same level of accuracy. Worth trying if you are actively using Claude for coding. 1. Superpowers — by Jesse Vincent (@obra) A complete development methodology for Claude Code It first brainstorms → creates spec → builds plan → then executes using subagents Comes with 20+ skills — TDD, debugging, code review, collaboration Auto-triggers based on your task, no manual setup needed Install: /plugin install superpowers@claude-plugins-official GitHub: https://lnkd.in/dtnEX7GX Cost: Completely Free and Open Source 2. Code Review Plugin — by Anthropic (Official) Launches 4 agents in parallel to review your PR - 2 agents check CLAUDE.md compliance, - 1 finds bugs, - 1 catches logic issues Uses confidence scoring to filter false positives Only high-signal issues reach your PR GitHub: https://lnkd.in/ducwwpss Cost: Free with Claude Code (Managed service needs Team/Enterprise plan) 3. VibeSec — by BehiSecc Makes Claude think like a bug bounty hunter while writing code Covers 24+ vulnerability types — SQL injection, XSS, CSRF, IDOR, and more One file, zero config — just drop it in your project folder Works with Claude Code, Cursor, GitHub Copilot GitHub: https://lnkd.in/dyTUjCvH Cost: Free version on GitHub (Pro version available on https://vibesec.sh) 4. Caveman — by Julius Brussee Why use many token when few token do trick? Cuts 65-75% output tokens while keeping full technical accuracy Faster responses, easier to read, saves money Went #1 trending on GitHub — 36K+ stars Works with Claude Code, Codex, Gemini CLI, Cursor, and more GitHub: https://lnkd.in/d38exF9U Cost: Completely Free and Open Source 5. Claude-Mem — by Alex Newman (@thedotmack) Claude Code forgets everything between sessions — this fixes that Auto-captures your coding sessions and compresses them with AI Injects relevant context back in future sessions Comes with semantic search and a web viewer Install: /plugin marketplace add thedotmack/claude-mem && /plugin install claude-mem GitHub: https://lnkd.in/dPNfeVty Cost: Free core (Optional Pro features available) All 5 are open source. Even if you try just one you will see the difference in your workflow. #ClaudeCode #AI #DeveloperTools #OpenSource #CodingWithAI #Anthropic #SoftwareDevelopment

Everyone's using Claude Code, but almost nobody has set up the .claude folder properly. That folder is the control center for how Claude behaves in your project. Without it, you're starting every session from scratch, repeating instructions, and getting inconsistent results. I put together the Claude Code Starter Pack. 8 files every project should ship with. Here's the breakdown: → CLAUDE(.)md: Your project's operating manual. It loads every single session. Keep it under 200 lines. Define your standards, your toolchain, your git rules. Every mistake becomes a new line so Claude never repeats it. → CLAUDE(.)local(.)md: Your personal overrides. Gitignored, machine-local, never shared. This is where your secret paths, API keys, and shortcuts live. Loads alongside CLAUDE(.)md automatically. → .claude/agents/: Your AI teammates. Each agent gets its own context, tools, and permissions. A code-reviewer checks security, correctness, and performance. A test-writer handles coverage. They run in parallel without cluttering your main session. → .claude/hooks/: Deterministic automation. Unlike CLAUDE(.)md which suggests, hooks ALWAYS run. Pre-commit checks, lint-on-save, session-start scripts. If something must happen every time, it goes here. → .claude/rules/: Path-scoped instructions. Instead of bloating CLAUDE(.)md with every rule, split them by area. API rules load only when you're touching API files. Frontend rules stay out of backend work. Zero startup cost. → .claude/skills/: Reusable playbooks that replaced the old commands/ folder. Bundle scripts, templates, and instructions together. Call them with a slash command whenever you need them. → (.)mcp(.)json: Your MCP server roster. Lives at project root, not inside .claude/. Connect Playwright, Supabase, GitHub, or any other tool. Commit it and your whole team gets the same setup. → settings(.)json + settings(.)local(.)json: Your permission layers. Define what Claude can run freely, what it needs to ask about, and what's blocked entirely. Deny always wins over allow. Most people only need CLAUDE(.)md and settings(.)json to start. Add the rest as your project grows. Over to you: Want this entire starter pack with all 8 files ready to drop into your project? Comment "STARTER" and I'll send it straight to your DMs.

Claude Code Control🎮 Starter packs, The folder📂 .claude where it understand the project repetitive, workflow & instructions. Important breakdowns! Definitely consider configuring in the project if using claude🤔No Xcuse

Most teams use AI tools but ignore the system that controls consistency and scale. As highlighted by Manthan Patel the real power of Claude Code comes from structuring the .claude environment correctly. At www.jaiinfoway.com we treat AI development like software engineering where standards automation and reusable components define outcomes. When you build with agents hooks rules and skills you eliminate repetition and create predictable intelligent systems. AI is not just about writing code it is about designing how that code evolves. Are you still experimenting or building production ready AI systems. #Jaiinfoway #AI #AgenticAI #AIAgents #Automation #SoftwareEngineering #FutureOfWork #DigitalTransformation #Innovation #TechLeadership

𝐇𝐨𝐰 𝐀𝐈 𝐜𝐡𝐚𝐧𝐠𝐞𝐬 𝐭𝐡𝐞 𝐰𝐚𝐲 𝐈 𝐝𝐞𝐥𝐢𝐯𝐞𝐫 𝐬𝐨𝐟𝐭𝐰𝐚𝐫𝐞 - 𝐛𝐨𝐭𝐡 𝐭𝐡𝐞 𝐜𝐨𝐝𝐞 𝐚𝐧𝐝 𝐞𝐯𝐞𝐫𝐲𝐭𝐡𝐢𝐧𝐠 𝐚𝐫𝐨𝐮𝐧𝐝 𝐢𝐭. For the last few months at Enghouse Interactive, I've made AI tools (Claude Code and GitHub Copilot) a core part of my daily workflow. The result wasn't just faster coding - it was a different shape of delivery. 🔧 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐰𝐨𝐫𝐤 𝐬𝐡𝐢𝐩𝐩𝐞𝐝 🚀 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦-𝐈𝐧𝐝𝐞𝐩𝐞𝐧𝐝𝐞𝐧𝐭 𝐂𝐫𝐲𝐩𝐭𝐨 𝐏𝐫𝐨𝐯𝐢𝐝𝐞𝐫 - a pure .NET replacement for a Windows-only native crypto DLL, behind a factory pattern so existing customers stay on the legacy provider while new deployments unlock Linux/Docker. ~60-70% time saved vs. manual conversion (GitHub Copilot). 🔗 𝐍𝐚𝐭𝐢𝐯𝐞 𝐅𝐞𝐝𝐞𝐫𝐚𝐭𝐢𝐨𝐧 𝐰𝐢𝐭𝐡 𝐋𝐨𝐜𝐚𝐥 𝐅𝐢𝐥𝐞 𝐏𝐚𝐭𝐡𝐬 - decoupled TypeScript library from Angular frontend, upgraded Angular 12 → 20 in one phase, and enabled runtime module swapping for independent driver development by R&D and PS teams. 229 files changed, 219 tests still passing (GitHub Copilot). ☁️ 𝐒𝐚𝐥𝐞𝐬𝐟𝐨𝐫𝐜𝐞 𝐄𝐱𝐭𝐞𝐫𝐧𝐚𝐥 𝐂𝐥𝐢𝐞𝐧𝐭 𝐀𝐩𝐩 𝐒𝐮𝐩𝐩𝐨𝐫𝐭 - added OAuth Client Credentials flow to the driver, a dynamic App Type selector in the installer, full upgrade detection, and a reusable AutoIt test framework with JUnit/XRay CI. ~70-80% time saved (Claude Code). 🗂️ 𝐍𝐨𝐧-𝐞𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐰𝐨𝐫𝐤 - 𝐬𝐚𝐦𝐞 𝐀𝐈 𝐭𝐨𝐨𝐥𝐤𝐢𝐭 📝 Authored the 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐝𝐞𝐜𝐤 by feeding feature documents into Claude Code. 🧪 Generated XRay test cases, edited admin-guide Word documents directly at the OOXML level, and produced management-facing work summaries. 🧩 𝐓𝐡𝐞 𝐜𝐨𝐦𝐩𝐨𝐮𝐧𝐝𝐢𝐧𝐠 𝐥𝐚𝐲𝐞𝐫 - 𝐫𝐞𝐮𝐬𝐚𝐛𝐥𝐞 𝐂𝐥𝐚𝐮𝐝𝐞 𝐂𝐨𝐝𝐞 𝐬𝐤𝐢𝐥𝐥𝐬 Along the way, I packaged recurring workflows into Claude Code skills. Each one captures conventions that used to live in individuals' heads: 📄 enghouse-docx - edits Word docs against the Enghouse template at the XML level. 💡 training-slides-authoring - turns feature docs into slide markdown + speaker notes. 🖥️ build-slides - converts that markdown into a branded PowerPoint deck 🤖 autoit-tests - generates installer UI tests against our shared framework 📊 xray-tests - generates JIRA/XRay-importable test case JSON. ✅ smart-commit - automates code commit steps with reusable conventions 𝐖𝐡𝐲 𝐭𝐡𝐢𝐬 𝐦𝐚𝐭𝐭𝐞𝐫𝐬 The first time you use AI to do something, you save time. The second time anyone on the team uses one of these skills, the saving compounds - because the conventions, gotchas, and templates are baked in. AI didn't just make individual tasks faster. It made the next task faster too. #AI #ClaudeCode #GitHubCopilot #SoftwareEngineering #DeveloperProductivity #ReleaseManagement

𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝗱𝗲 is a full agent development framework. Most people drop a CLAUDE. md file and call it a day. That's like using 1 out of 6 gears. Here's the complete architecture most people are missing: — 𝗟𝗮𝘆𝗲𝗿 𝟭: 𝗠𝗲𝗺𝗼𝗿𝘆 → CLAUDE. md = team instructions, committed to git → CLAUDE. local. md = your personal overrides, gitignored → rules/ = modular always-on instruction files This is what Claude reads every single session. Non-negotiable context. 𝗟𝗮𝘆𝗲𝗿 𝟮: 𝗦𝗸𝗶𝗹𝗹𝘀 → Each skill lives in its own folder with a SKILL. md file → Claude auto-invokes them based on semantic matching → Skills can carry references/, scripts/, and examples/ → Think of it as on-demand domain expertise Skills are probabilistic. Claude decides when to use them. 𝗟𝗮𝘆𝗲𝗿 𝟯: 𝗛𝗼𝗼𝗸𝘀 → Shell scripts triggered by events like PreToolUse, PostToolUse, SessionStart → block-force-push.sh blocks dangerous git commands → auto-lint.sh runs after every file edit → 17 hook events available Hooks are deterministic. They run 100% of the time. No exceptions. 𝗟𝗮𝘆𝗲𝗿 𝟰: 𝗔𝗴𝗲𝗻𝘁𝘀 → Subagents get their own isolated context window → code-reviewer. md, security-auditor. md, qa-tester. md → 3 built-in agents: Explore, Plan, Task → They run in parallel without polluting your main conversation 𝗟𝗮𝘆𝗲𝗿 𝟱: 𝗣𝗹𝘂𝗴𝗶𝗻𝘀 → Bundle skills + agents + hooks into one distributable package → Install via /plugins → Namespaced to avoid conflicts → Share your entire workflow with your team in one command 𝗟𝗮𝘆𝗲𝗿 𝟲: 𝗠𝗖𝗣 → .mcp. json connects Claude to external tools → GitHub, Jira, databases, APIs → The bridge between Claude and everything else — The critical insight most people miss: 𝗖𝗟𝗔𝗨𝗗𝗘. 𝗺𝗱 + 𝗛𝗼𝗼𝗸𝘀 = 𝗱𝗲𝘁𝗲𝗿𝗺𝗶𝗻𝗶𝘀𝘁𝗶𝗰 (runs every time) 𝗦𝗸𝗶𝗹𝗹𝘀 + 𝗔𝗴𝗲𝗻𝘁𝘀 = 𝗽𝗿𝗼𝗯𝗮𝗯𝗶𝗹𝗶𝘀𝘁𝗶𝗰 (Claude decides) Understanding this distinction drives every architecture decision. I created a complete visual breakdown of the project structure showing all 6 layers (see image below). Save this. You'll reference it constantly. Thanks a lot for sharing Brij kishore Pandey

GitHub Copilot has features most devs don't know exist. Here are 10 that changed how I work, Just Copy Paste the below ask Copilot to follow these instructions :) 1/ Custom AI agents via .agent.md files Create .github/agents/security-reviewer.agent.md — define tools, permissions, and 30K chars of instructions. Build a "security reviewer" that reads but can't edit. Chain agents: planner → implementer → reviewer. A programmable AI team in your editor. 2/ Paste screenshots into Chat → get working code Copilot has vision. Paste a Figma mockup or error dialog into chat. "Convert this to responsive React." It generates semantic HTML/CSS with ARIA attributes. Up to 3 images per message. Design handoff in minutes. 3/ Assign GitHub Issues to @copilot — get PRs while you sleep The Coding Agent clones your repo, writes code, runs CI, and opens a PR. Add copilot-setup-steps.yml to configure the sandbox. I assign issues before EOD and wake up to draft PRs with passing tests. It even iterates on review comments. 4/ Custom Chat Modes for different AI personalities Create .vscode/chat-modes/debug-only.chatmode.md — restrict tools to read-only diagnostics. I have modes for "architecture planning" (no edits), "quick fix" (aggressive), and "docs writer" (generates docs, never touches source). 5/ Reusable prompt files (.prompt.md) Create .github/prompts/new-endpoint.prompt.md with templated instructions. Your whole team shares battle-tested prompts instead of ad-hoc requests. I have prompts for migrations, test suites, and release notes. 6/ Path-specific instructions with glob patterns Go beyond copilot-instructions.md. Create .github/instructions/frontend.instructions.md with applyTo: "src/components/**". React code gets React rules. Go services get Go rules. Essential for monorepos. 7/ MCP servers let Copilot talk to your stack Define servers in .vscode/mcp.json — Copilot can query your DB, fetch Jira tickets, run Playwright, or hit internal APIs mid-conversation. "Investigate the 500s on /checkout" → Copilot fetches real logs and suggests fixes. 8/ #terminal feeds errors straight to Copilot Type #terminal in chat — it reads your terminal output, diagnoses the issue, and fixes it. In Agent Mode this is automatic: runs tests, reads failures, edits, loops until green. Almost nobody uses this. 9/ The Coding Agent responds to PR review comments Leave comments like "add null handling" on Copilot's PR. It reads them, pushes fixes, and iterates. Full async review cycle without writing code yourself. 10/ /init scaffolds your Copilot config in 30 seconds Analyzes your repo, generates copilot-instructions.md, prompts, and agent configs. 30 seconds that permanently improve every Copilot interaction. Stop using Copilot as autocomplete. These features turn it into a programmable AI engineering team. #GitHubCopilot #VSCode #AI #DeveloperTools #Programming