AI-Powered Code Review Bot for GitHub Pull Requests

Every NuGet package looked brilliant in its README too. A year ago I was using GitHub Copilot for basic autocomplete. Today, 95–99% of code in our .NET projects is AI-generated — we negotiate architecture with agents in plan mode, implement full features including tests in one go, and use GitHub and Confluence MCP tools to ground every decision in our actual codebase. The productivity gains are real. I'm not here to argue otherwise. But here's what I now tell my team: treat AI like a NuGet package with a great README, not like a senior peer. The happy path works in ten minutes and you feel brilliant. Then staging blows up. A version conflict breaks your injection container. A leaky abstraction surfaces under load. The original maintainer went dark six months ago and now you own a security vulnerability buried in three lines of code you never actually read. That's not a .NET cautionary tale. That's an AI story playing out in production systems right now. METR's 2025 randomised controlled trial found experienced developers were 19% slower using AI tools — despite predicting they'd be 24% faster. The bottleneck has shifted. It's no longer writing code. It's absorption capacity: can your team genuinely understand, own, and debug what's being shipped? Every line of AI-generated code is not an asset. It's a maintenance contract you signed without reading the terms. The shift I'm making: code author to Editor-in-Chief. Before you hit Tab on that ghost text, pause three seconds. Don't ask "does this look right?" Ask: "if this throws a NullReferenceException at 3am on Sunday, do I know exactly why.. or would I have to ask the AI to explain my own system back to me?" If it's the latter, the model is in control. Not you. Stop asking how much code you can generate. Start asking how much you can maintain. What's your test for knowing you still own your codebase - and AI is just the tool? #DotNET #SoftwareEngineering #AIEngineering #TechLeadership #EnterpriseArchitecture #CSharp #AgenticAI

I just shipped a project I'm genuinely proud of 🙂 RepoBrain — a tool that helps AI understand your codebase smarter, instead of dumping the entire source code into context every single query. The results? ✅ 20–40% reduction in token consumption ✅ Meaningful cost savings on AI API bills every month ✅ No more "context window overflow" headaches when working with large repos The problem I wanted to solve was simple: why do we keep paying for thousands of "junk" tokens — code that has absolutely nothing to do with the question being asked? RepoBrain works by indexing the codebase, understanding the project structure, and only injecting the relevant parts into context for each query. Fewer tokens, more accurate answers. This is the first time I've built something with a measurable, concrete impact — and honestly, that feeling hits differently compared to projects that were just "good enough to ship" 😄 — 🚀 And there's more — v1.3 Early Access is ready. A few things landing in this version: 🚦 Agent Safety Gate — returns SAFE / WARN / BLOCK before every commit 🧠 Persistent Workspace Memory — annotate files once, surfaces on every future run 🔍 Evidence-Based Confidence Score — every output shows retrieval strength, not just guesses ⚡ Full MCP Server — works live inside Claude Code, Cursor, and Codex Still in private early access. If you want in, just DM me or drop a comment — I'll get back to you personally. Repo: https://lnkd.in/gHk-WE6N #AI #LLM #Developer #RepoBrain #CostOptimization #BuildInPublic #OpenSource #GitHub

Running Claude Code at your terminal and running it inside a GitHub Actions runner look almost identical. They are not. At the terminal, if Claude goes into a retry loop, you notice it in seconds and kill it. In CI, nothing stops it except a timeout you probably did not set, and the first sign of trouble is the API bill at the end of the month. The defaults that make interactive use pleasant are the same defaults that make unattended use expensive. I put together a guide on Refactix covering the flags and patterns that actually hold up in a pipeline. The three flags that matter (-p, --output-format json, and --bare), a GitHub Actions workflow that fails gracefully, the JSON parser you need between Claude and your GitHub API, cost controls that keep review jobs bounded, and the security boundaries that stop a prompt injection in a PR diff from turning into a secrets leak. It also covers when to graduate from the CLI to the Claude Agent SDK or Dispatch for bigger workflows. Worth a read if you are thinking about wiring any AI agent into your CI, not just Claude Code. The shape of the problem generalizes. Full guide on Refactix: https://lnkd.in/grDAv5qA #ClaudeCode #CICD #AI #DevOps #SoftwareEngineering

GitHub just turned agent skills into npm — one `gh skill install` now runs on 6 different AI coding agents. Shipped April 16 in CLI v2.90.0. Copilot, Claude Code, Cursor, Codex, Gemini CLI, Antigravity. Same command. Same skill. Zero vendor lock. Spent the weekend rewiring my workflow around it. The skill I built for Claude Code last month installed into Cursor in 12 seconds. No rewrite. No adapter. Just a commit SHA and a `gh skill add`. VoltAgent's awesome-agent-skills repo already curates 1,000+ skills. K-Dense-AI dropped a full science/finance/research pack the same week it launched. The ecosystem moved faster than the announcement. Here's what most engineers are missing: The agent isn't the moat anymore. The skill library is. Whoever owns the best skills wins — not whoever owns the best model. Copilot, Claude, Cursor all become interchangeable shells the moment skills go portable. But read this twice before you install anything: GitHub does zero verification. No signatures. No review. No sandbox by default. Your AI is now executing arbitrary instructions from random GitHub repos. A skill can contain prompt injections, hidden system prompts, or shell commands. `gh skill preview` is your only line of defense — and almost nobody will run it. We just recreated the npm supply chain problem for AI agents, except this time the malicious payload tells your model what to think. Pin to commit SHAs. Preview before install. Treat every skill like untrusted code — because it is. The agent wars just ended. The skill wars just started. #AI #GitHub #DevTools #AIAgents #SupplyChainSecurity

🚀 Shipping ShipIt Agent v1.0.5 This release takes ShipIt from a single-agent toolkit to a full multi-agent platform — with prebuilt personas, orchestration, notifications, and cost control baked in. What's new: 🧠 40 Prebuilt Agents across 8 categories Architecture, Code Quality, Security, DevOps, Testing, Planning, Research, and Content. Load in one line with AgentRegistry.default(), search, compose, or override with your own .shipit/agents/ JSON files. 🛠 ShipCrew — Multi-Agent Orchestration DAG-based crews with task dependencies, three execution modes (sequential, parallel, hierarchical), template variable resolution, streaming events, and cycle detection via Kahn's algorithm. 🔔 Notification Hub Slack (Block Kit), Discord (rich embeds), and Telegram (MarkdownV2 with auto-escaping) — all with zero external dependencies. Multi-channel dispatch with severity and event filtering, plus auto-hooks into the agent lifecycle. 💰 Cost Tracking & Budgets Real-time per-call cost tracking across 20+ models, hard budget enforcement with BudgetExceededError, alert callbacks, and model aliases ("opus", "sonnet", "haiku"). 📚 By the numbers → 29 new source files → 4 new notebooks (108 cells) → 4 new doc pages More coming soon. Docs: https://docs.shipiit.com/ GitHub: https://lnkd.in/dpUiYqzF #AI #Agents #OpenSource #DeveloperTools #LLM

Turn any public GitHub repo into a single powerful prompt for Cursor, Claude Code, or Codex.  GitReverse does exactly that.  Just paste a GitHub URL and it instantly generates one clean, conversational, high quality prompt that lets your AI agent rebuild or vibe code the entire project from scratch.  It pulls the repo metadata, root file tree, and README, then crafts a smart context aware prompt using an LLM via OpenRouter.  Core value: No more copying dozens of files or writing long messy prompts. You get one ready to paste prompt that captures the essence of the project so your AI can understand the architecture goals and structure right from the start.  Key features:   • One click conversion from GitHub URL to optimized prompt   • Shareable links like /vercel/next.js   • Pulls essential context including file tree and README   • Designed specifically for vibe coding and agentic workflows   • Clean and fast interface  Perfect when you want to quickly explore a new repo, recreate a project, or hand your agent a well structured starting point without manual effort.  https://lnkd.in/e4EsyVyG  #GitReverse #ClaudeCode #CursorAI #AIAgents #AgenticAI #VibeCoding #GitHubAI #AICoding #DevTools #Anthropic

GitHub Copilot CLI just shipped something clever: Rubber Duck. The idea is simple but powerful — when a coding agent drafts a plan, a second model from a different AI family reviews it before execution. Why a different family? Because a model reviewing its own work has the same blind spots. Same training data, same biases. A model from a different family catches different things. How it works: → You select a Claude model as your orchestrator → Rubber Duck uses GPT-5.4 as the reviewer → It activates at key checkpoints: after planning, after complex implementations, after writing tests The results are compelling: Claude Sonnet + Rubber Duck closes 74.7% of the performance gap between Sonnet and Opus alone. On the hardest problems (3+ files, 70+ steps), it scores 4.8% higher. Real examples of what it catches: • A scheduler that would start and immediately exit • A loop silently overwriting the same dict key every iteration • Three files reading from a Redis key that new code stopped writing What I like about this: it's not about replacing human review. It's about catching the confident mistakes that compound before you even see them. Available now in experimental mode with /experimental. #GitHubCopilot #AI #developer #programming

Ever changed a class… everything looked fine… and then something broke 2 days later? 😅 Yeah same. I realized most tools (and even AI agents) 𝘀𝗲𝗮𝗿𝗰𝗵 𝗰𝗼𝗱𝗲… but they don’t actually *understand relationships*. So I started building something different 👇 🚀 𝗚𝗿𝗮𝗽𝗵𝘆𝗻 A 𝗯𝗹𝗮𝘇𝗶𝗻𝗴 𝗳𝗮𝘀𝘁 𝗥𝘂𝘀𝘁-𝗯𝗮𝘀𝗲𝗱 𝗲𝗻𝗴𝗶𝗻𝗲 that turns your codebase into a 𝗴𝗿𝗮𝗽𝗵 𝗼𝗳 𝘀𝘆𝗺𝗯𝗼𝗹 𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻𝘀𝗵𝗶𝗽𝘀 So instead of guessing, you can ask:  • What breaks if I change this?  • Where is this used (even under aliases)?  • What depends on this deep inside the system? 💡 Real example: ```ts import { UserPayload as ResponseModel } from '../../../models/user_payload' ``` Most tools miss this. Graphyn doesn’t. 🤖 I plugged it into my workflow:  • Agents → via MCP  • Me → via simple CLI And this is where it got interesting:  • Detected aliases like createReactive → reactive  • Found hidden usages across packages  • Showed blast radius before making changes 👉 Basically giving an 𝗫-𝗿𝗮𝘆 𝘃𝗶𝗲𝘄 𝗼𝗳 𝘁𝗵𝗲 𝗰𝗼𝗱𝗲𝗯𝗮𝘀𝗲 ⚡ Why I’m excited about this Sometimes I don’t even fully know what my agents changed 😅 Graphyn helps me 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 + 𝘁𝗿𝘂𝘀𝘁 those changes and it does it fast. 📦 Just released `𝘃𝟬.𝟭.𝟬` on crates.io will also add adding support for 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗥𝘂𝘀𝘁, 𝗚𝗼 in coming updates 🙏 Would love you to try it If you find bugs / edge cases → open an issue If you’re interested → contributions are welcome (core is in Rust) 🔗 Repo: https://lnkd.in/gus4Uw2g If this idea clicks for you, drop a ⭐ Would love to hear what you think. #SoftwareEngineering #DeveloperTools #CodeIntelligence #StaticAnalysis #RustLang #DevTools #AIEngineering #OpenSource #BuildInPublic

When I said we lived in an AI 𝗯𝘂𝗯𝗯𝗹𝗲, nobody believed me. The 𝗱𝗲𝗺𝗼𝗰𝗿𝗮𝘁𝗶𝗰 manifesto was saying: “everybody can code, everybody should code.” Yesterday, GitHub paused new sign-ups for GitHub Copilot Pro, Pro+, and Student plans.  Also planned to increase the fee on AI consumption. We’re using resources out of a marketing program and an 𝗶𝗱𝗲𝗼𝗹𝗼𝗴𝗶𝗰𝗮𝗹 one. It’s a subsidized marketing with 𝗰𝗵𝗲𝗮𝗽 tokens to 𝗵𝗼𝗼𝗸 users moving to a sustainable infrastructure or real-world 𝗽𝗿𝗶𝗰𝗶𝗻𝗴. The only way to survive is using local models as a "bridge" or a "pre-processor" for giants like Gemini and Claude, and the only 𝗹𝗼𝗴𝗶𝗰𝗮𝗹 move to avoid going 𝗯𝗿𝗼𝗸𝗲 while staying productive. For instance, models like Context7, an open-source Model Context Protocol (MCP) server developed by Upstash, provide AI coding 𝗮𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁𝘀 with real-time, up-to-date documentation for programming libraries and frameworks. It addresses a critical problem: AI models often have outdated knowledge about software libraries or hallucinate deprecated APIs, leading to incorrect code suggestions. See how to wire into 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝗖𝗟I, with the container booting autonomously every time the terminal opens. https://lnkd.in/dEGvAauq #AICoding #MCP #GitHubCopilot #Upstash #Context7 #LocalAI #DevTools #OpenSource #SoftwareArchitecture #CodingBubble #LLM #TechStrategy

🧠 What a project's git history reveals — without reading a single line of code I took an open source repo I had never seen before: RAG-Anything (HKUDS, 1k+ stars on GitHub). 333 commits. Zero code reading. Just git metadata. Here's what emerged in seconds 👇 (see screenshot picture) A senior developer joining this project cold would spend hours mapping this coupling. This took 12 seconds. No LLM calls. Pure graph inference. Here's the insight behind this: The architecture of a project is not in the code. It's in the co-modification patterns — which files evolve together, in which order, how often. Every git commit is a signal. Accumulated over months, those signals reveal something no static analysis tool can see: how the project actually thinks. Current AI coding agents forget everything between sessions. They rediscover your project every single time. What if your agent remembered — not just your code, but the architectural soul of your project? That's what we're building. 🚧 👉 Repo used for this demo: https://lnkd.in/dfkVb_Kt #AI #DevTools #SoftwareEngineering #BuildInPublic #AgentAI