Gregg Cochran’s Post

Most people use Claude Code like a chatbot. Open it. Ask a question. Get an answer. Close it. That's like buying a Ferrari and only driving it in first gear. Here are 9 hidden features in Claude Code that most people don't know exist:  𝟏. 𝐇𝐨𝐨𝐤𝐬 -> Auto-format, auto-test, auto-lint. Set it once.  𝟐. 𝐂𝐋𝐀𝐔𝐃𝐄.𝐦𝐝 -> Your project rules, followed automatically.  𝟑. /𝐜𝐨𝐦𝐩𝐚𝐜𝐭 -> Fresh context window, zero progress lost.  𝟒. 𝐇𝐞𝐚𝐝𝐥𝐞𝐬𝐬 𝐦𝐨𝐝𝐞 -> Run Claude from your terminal like any CLI tool.  𝟓. 𝐌𝐂𝐏 𝐬𝐞𝐫𝐯𝐞𝐫𝐬 -> Connect Claude to GitHub, Slack, databases, anything.  𝟔. 𝐒𝐮𝐛𝐚𝐠𝐞𝐧𝐭𝐬 -> Multiple agents working in parallel, one session.  𝟕. ! 𝐩𝐫𝐞𝐟𝐢𝐱 -> Run terminal commands without leaving Claude.  𝟖. 𝐌𝐞𝐦𝐨𝐫𝐲 -> Claude remembers your preferences across sessions.  𝟗. 𝐆𝐢𝐭 𝐰𝐨𝐫𝐤𝐭𝐫𝐞𝐞𝐬 -> Safe experimentation, zero risk to your codebase. What is the difference between using AI well and using it badly? Knowing these features exist.  Save this. Try one feature today.  Follow for more workflows that make AI actually useful at work.  #AIFirstProfessionals #ClaudeCode #AI #PromptEngineering #DeveloperTools #Productivity #CodingTips #Programming #SoftwareEngineering #TechTips

🚀 Imagine an IDE that doesn’t just assist you – it spawns autonomous sub‑agents that think, plan, and execute in parallel. The new open‑source extension for pi brings a Claude‑Code‑style experience to GitHub Copilot: isolated sessions, custom system prompts, live widgets, and the ability to steer agents mid‑run. Early‑release, but already reshaping how we collaborate with AI. What excites me is the shift from assistance to true partnership. When an agent can be ejected, customized, or given read‑only memory, we move toward a modular, auditable workflow where each sub‑agent is a transparent collaborator rather than a black box. 👉 Three takeaways you can start applying today: - Define a custom agent type in a .md file and watch it evolve independently. - Use background agents to keep your workflow flowing while you focus on higher‑order decisions. - Leverage structured notifications to embed results directly in your code review. How will you redesign your development pipeline when every line of code can be paired with a dedicated AI thought partner? #AI #Leadership #Innovation #EthicalTech #FutureOfWork Reference: [https://lnkd.in/gvi6sMpy] 🔄 Share 👍 React 🌐 Visit www.aravind-r.com #AravindRaghunathan

Just built something exciting 🚀 Ever felt lazy (or honestly… just tired 😅) to go through code and add proper logging, comments, or headers? I created a tool that makes this super simple: 🔹 It pulls your code directly from Git 🔹 Shows the file tree right inside the console 🔹 Lets you pick one or multiple files 🔹 You choose what you want — logging, comments 🔹 AI updates the code automatically 🔹 Push changes to a new branch with your own commit message No switching tools. No manual effort. No overthinking. 👉 Works on multiple files at once 👉 Saves time + improves code quality 👉 Keeps your repo clean and readable This is just the beginning — planning to add more intelligent code improvements soon. Curious to know: Would you use something like this in your daily workflow? #DevTools #AI #Automation #Git #DeveloperProductivity #CodingLife

I’ve been fine-tuning my development workflow lately, and it’s finally reaching a point where the AI doesn't just "help" it actually manages the heavy lifting. If you're curious about how to layer these tools for maximum output, here is the stack I’m currently running: 1. The Research Layer: Gemini + NotebookLM Instead of spending 2 hours digging through dense technical specs, I "interrogate" them. I get the context I need in 10 minutes. It’s like having a librarian who has already memorized every page. 2. The Architect: Cursor Once I have the plan, I use Cursor to gather codebase context. It’s much faster than a standard IDE for mapping out how new features will actually fit into existing code. 3. The Muscle: Claude Code This is where the automation happens. I delegate the coordination and repetitive tasks here. It’s essentially my "Agent" that handles the grunt work while I focus on the big picture. 4. The Gatekeeper: GitHub Copilot Reviewer Before a human ever touches the code, Copilot does a review pass. It catches the "obvious stuff" so my team doesn't have to waste time on trivial fixes during PRs. The result? I’m thinking more and typing less. I’m currently looking at migrating some of this to Obsidian to keep my knowledge base even more organized. How are you all using AI in your dev workflow? Are we at the "autopilot" stage yet, or are we still co-piloting? #SoftwareEngineering #AI #Productivity #GithubCopilot #Claude #Gemini #CodingLife

🚀 Your AI assistant no longer falls asleep the moment you close your laptop. It feels like that line has already started to disappear. 📦 Anthropic released Routines for Claude Code. There are now three modes: scheduled, via API, and via Webhook. On a schedule, an agent can scan stale documentation or clean up a backlog on its own. Via API, it can be triggered from CI/CD. Via Webhook, it reacts to GitHub events, like a new PR or failed tests. And yes, they also added Mission Control, one interface for all sessions, projects, and statuses. 🔧 Before this, the developer + AI workflow was usually linear. Question, answer, chunk of code. Then the same loop again. Now the picture is different. One agent refactors a module. Another writes tests. A third handles a production bug. All of it runs in Anthropic's cloud, so you really can close the laptop. The interesting part usually starts outside the code itself, in the orchestration: who does what, where the stop condition is, and who checks the result. 💡 Why this matters: the line between an "assistant" and an "autonomous agent" is getting noticeably thinner. The developer's role shifts too. Less manual line writing, more task design, guardrails, and review. By 2027 this could easily become normal. The real question is no longer "if", but who adapts first. ⚡️ Yes, it sounds a bit like marketing. But Scheduled Routines already do execute tasks on a schedule without a human in the loop. Webhook mode is GitHub-only for now. Full autonomy is still far away, but the direction is already clear. How many parallel tasks would you hand to an agent today? Unsupervised refactoring, already fine or still too early? What matters more to you: delivery speed or control over every line? #ClaudeCode #Anthropic #AIagents #AgenticCoding #SoftwareEngineering #DevOps #AITools #CodeReview #Automation

The dev team that works while you sleep just took its first real step. We've been building AgentFlow an autonomous software development team of AI agents that coordinate through a shared store and work a GitHub ticket backlog end to end. No hardcoded pipelines. No spaghetti. Just agents talking to each other through typed actions and shared state, organised under an open-source project called Sprintless. This week we hit the milestone that matters most: NEXUS, FORGE, and SENTINEL are working together in the harness. NEXUS reads the ticket board and assigns work. FORGE spins up a Claude Code instance and begins building in an isolated Git worktree. SENTINEL gets spawned fresh for every evaluation segment no history with FORGE, no accumulated leniency, no context drift. It evaluates the work against concrete criteria and sends specific, line-level feedback. FORGE iterates. Quality comes from the tension between them. This is the GAN pattern applied to software development. The generator doesn’t improve from being told to be better. It improves from iterating against a demanding, adversarial evaluator that cannot be charmed by adequate work. Some of the engineering decisions we’re most proud of: → Git worktrees for multi-pair isolation N pairs work in parallel, each on their own branch, with Redis-based dynamic file locking to prevent conflicts even when ticket scope expands mid-task → inotify-driven harness when FORGE updates the WORKLOG, the harness spawns a fresh SENTINEL within milliseconds. No polling delay. → Claude hooks as infrastructure FORGE literally cannot exit without a valid artifact. Not a prompt suggestion. A shell script that blocks the exit. → Context resets via PreCompact the hook intercepts compaction, writes a structured HANDOFF.md, and a fresh instance resumes from the exact next step. Long tasks stay coherent for hours. The repo is live: https://lnkd.in/eD7_RudY We’re building this in public. The full system goes public at end of Phase 2 and we’re looking for contributors now. If you’re working on multi-agent systems, agentic harness design, or autonomous coding infrastructure we’d love to connect. There’s meaningful open work at every level of the stack. Stack: Rust · Tokio · PocketFlow · Claude Code · LiteLLM · Redis · Docker #AIEngineering #Rust #AgenticAI #OpenSource #SoftwareEngineering

Everyone talks about AI coding tools. Few talk about using them together. After observing how teams are actually working in 2026, one pattern is clear: 👉 No single "best" AI coding agent exists 👉 The real leverage comes from your workflow architecture Here's the current landscape: 🔹 IDE-first agents – Cursor, Windsurf, GitHub Copilot Daily drivers. Low-latency, file-aware, best for inline edits and refactoring. 🔹 CLI / control layer – Aider, Cline, Claude Code Git-aware, local model support, scriptable. Best for batch operations and automation. 🔹 Cloud / autonomous agents – Devin, Codex Workspace Asynchronous, wide context. Best for long-running tasks like test generation or docs. 💡 The shift: From "one assistant for everything" → orchestrating multiple agents by task type Common pattern emerging: IDE agent (live edits) → CLI agent (staged changes) → cloud agent (async tasks) The question is no longer: "Which AI tool should I use?" It's: "How do I design my AI workflow?" #AIAgents #AICoding #SoftwareDevelopment #DevTools #FutureOfWork #CursorAI #ClaudeCode #GitHubCopilot #AIWorkflow #TechStack

We aren't just using an AI assistant; we’re engineering a custom intelligence layer for our entire development pipeline. Understanding the relationship between Global Memory (Instructions), Specialized Agents, Ecosystem Plugins, and On-Demand Skills is critical for moving beyond simple code-completion to full workflow transformation. Below my mental model for navigating the GitHub Copilot extensibility landscape. ⚡ Instructions = Your baseline coding standards. 🤖 Agents = Specialized, safe knowledge silos. 🧩 Plugins = Deep, third-party ecosystem integrations. 📖 Skills = Repeatable, contextual playbooks. The future of software development isn't just about writing code faster; it's about building a uniquely customized and highly extensible developer experience. How is your team layering these capabilities to maximize AI ROI? #AIProgramming #SoftwareEngineering #TechLeadership #DeveloperExperience #ExtensibleAI

I replaced my entire development workflow with AI agents—here's what worked and what didn't. Six months ago, I made a decision that my coworkers viewed as either genius or career suicide: I replaced as much of my development workflow as possible with AI agents. This wasn't just about using Copilot for autocomplete; I implemented full-blown autonomous agents to handle code reviews, write tests, triage bugs, generate documentation, and even manage deployments. Here’s a straightforward breakdown of what actually worked, what flopped spectacularly, and the exact setup I'm still using today. First, let's clarify what I mean by "AI agents." I'm referring to autonomous or semi-autonomous systems that take inputs—such as GitHub issues, pull requests, or failing tests—and produce meaningful outputs with minimal human intervention. These agents run on schedules, respond to webhooks, and are integrated into my CI/CD pipeline. My stack before this experiment was standard: a Next.js frontend, Node.js API, PostgreSQL, deployed on AWS with GitHub Actions for CI/CD, and a team of three developers, including myself. For more details, check out the link: https://lnkd.in/gKHi6MmT

I think a lot of people using coding agents (Claude Code, Copilot CLI, etc.) are underestimating how expensive and inefficient they can be by default. Not because the models are bad, but because of how we use them. Most agents today don't really optimize for token usage, context window allocation, or cost per task They willl happily pull in way more context than needed, loop through multiple reasoning steps, retry, re-read, re-generate, and produce long outputs even when unnecessary. And if u are using something like a 1M token context model, it FEELS like you have infinite room. So you stop thinking about it. But more context doesn't necessarily mean better performance. It often just means more noise, more tokens, more cost. What's interesting is that most of these tools are context-aware, but not really context-efficient. They don't ask what’s the minimal context needed? Or what's the cheapest way to solve this? Or even, do we actually need another reasoning loop here? So you end up paying for EXPLORATION every time. Which is fine (don't get me wrong) for debugging or one-off tasks. But at scale, bruuuhhh, it becomes a very different problem. Feels like we are missing a layer that treats tokens and context more like REAL RESOURCES: something to allocate, optimize, and constrain. Curious how others are thinking about this. #AI #AIAgents #AIInfrastructure #Copilot #GithubCopilot #ClaudeCode #ContextWindows