Arness: Open-Source Plugin Marketplace for Software Project Lifecycle

We already have memory for AI coding agents. It’s called git. And many teams are filling it with garbage: fix bug, update styles, refactor component. These were always bad commit messages. But in the age of AI coding tools, they are worse than bad style. They are broken memory. Coding agents don’t just need code. They need intent. A diff can show what changed. But it rarely explains why the obvious solution was not used, which bug happened before, which constraint must not be broken, or which trade-off the team accepted. Git history can explain that. But only if we write it that way. This problem is not new. Bad commit messages always made projects harder to understand months later. The difference is that now the “future developer” is often a coding agent reconstructing intent from context. When git history says fix layout, the agent learns almost nothing. When it says fix mobile nav layout, it learns a little more. But when it says fix mobile nav layout to prevent CTA from jumping during hydration, it learns why the change exists. That is the difference between a label and memory. This matters because coding agents rely on broader project context: issues, PR discussions, repository instructions, and commit history. Better project memory → better agent reasoning. Many teams stop too early. They adopt Conventional Commits. And that helps. fix(header): prevent nav jump during hydration is much better than fix bug. Now tools know what kind of change happened. Sometimes they know the scope. Changelogs get easier. Breaking changes become clearer. But Conventional Commits are not enough for AI coding agents. They make git history machine-readable. They do not automatically make it understandable. They answer: what kind of change is this? But they usually do not answer: why was this needed, which bug should not come back, which constraint must be preserved, what was the goal, which trade-off was accepted? Agents need more than type and scope. They need intent. That is why we should use commit descriptions more actively. The title should stay short and scannable. The description is where memory lives. fix(header): prevent CTA layout shift on mobile Description: Why: On slow mobile devices, the header CTA moved after hydration and caused accidental taps. Goal: Keep the header stable before and after hydration. Context: The CTA must stay visible because it is the main signup entry point. Trade-off: Keep the layout CSS-only instead of recalculating header position in JS. That commit gives the agent more than a code change. It gives the agent memory of a decision. Good commit messages do not magically make LLMs smarter. They make project memory better. When coding agents use that memory as context, better memory leads to better reasoning. We used to say: write commit messages for the human developer from the future. That is still true. But now the developer from the future is often a coding agent. Stop giving AI agents broken memory.

Mastering Claude Code Goes Beyond Basic Prompts Claude Code is emerging as a powerful agentic coding tool. It excels not only at generating code but also at reading existing codebases, modifying files, executing terminal commands, and integrating with existing developer workflows across terminals, IDEs, desktops, and browsers. Often, users can achieve desired outcomes by simply describing their needs, allowing Claude Code to handle complex tasks. However, utilizing Claude Code out-of-the-box only reveals a fraction of its potential. To unlock its full capabilities, developers must understand its surrounding ecosystem, which includes custom skills, sub-agents, hooks, integrations, project-specific instructions, and reusable workflow patterns. These elements transform Claude Code from a simple assistant into a robust development system, driving significant interest in community-developed repositories and guides. The focus is shifting from mere prompts to better methods for structuring agent behavior, reducing debugging efforts, enhancing consistency, and improving effectiveness on larger projects. Here are key GitHub repositories that can aid developers in enhancing their Claude Code proficiency. The `everything-claude-code` repository serves as an excellent starting point for transforming Claude Code into a structured and advanced agentic setup. This project emphasizes a performance-oriented system for AI agent harnesses, going beyond simple prompts or configurations. It incorporates features such as agents, skills, hooks, rules, Model Context Protocol (MCP) configurations, memory optimization techniques, security scanning capabilities, and research-centric workflows. Developed with over 10 months of daily real-world application and recognized with an Anthropic x Forum Ventures hackathon award, this repository is considered a sophisticated reference for advanced Claude Code workflows. The `system-prompts-and-models-of-ai-tools` repository offers valuable insights into the broader landscape of AI tooling surrounding Claude Code, rather than focusing solely on the tool itself. This project compiles exposed system prompts, tool definitions, and model-specific details from various AI products, including Claude Code, Cursor, Devin, Replit, Windsurf, Lovable, and Perplexity. It proves particularly beneficial for those interested in prompt design, agent behavior analysis, and understanding the underlying structures of different AI coding and productivity tools, facilitating a deeper comprehension beyond the isolated use of a single product. The `gstack` repository exemplifies how Claude Code can function as a coordinated AI team instead of an individual assistant. This project showcases Garry Tan’s Claude Code configuration, demonstrating advanced implementation techniques. Full article and source link in the comments 👇 Published through a fully automated AI news system from TMC AI

Something clicked for me a few weeks into working with Claude Code. It over-engineers. It doesn't test. It makes assumptions and buries them three files deep. It duplicates code rather than asking if a simpler abstraction already exists. It creates functions like it's getting paid by the line. And by the time it's done, it's already forgotten how it started. *I've worked with this developer before. We all have.* Every behavior is so familiar because it's the junior developer you've hired a dozen times! And for years you have worked with complicated systems designed to deal with the code-quality problem: The spec that stops them before they vanish down the wrong rabbit hole. The test harness that catches what they didn't think to check. The review pass whose entire job is to delete half of what they wrote. The difference is that this developer types four hundred words per minute. That changes everything. Those same junior developer problems -- the over-engineering, the untested assumptions, the code that forgets its own architecture -- are now arriving faster than any human QA process can absorb. The queue never empties. The bugs compound. And somewhere along the way you stopped being an architect. You are now Claude's personal human tester. Congratulations on the demotion. SLOP isn't bad code from a bad tool. It's what happens when average-quality development accelerates past the ability of human QA to improve it. Claude simply does the same thing a mediocre developer does -- at volume, burying you in poor abstractions before lunch. Steve Jobs famously hired only the best. But the rest of us tend to run real teams with average developers -- because that's how averages work. The entire discipline of software engineering was built around that reality. Specs before code. Tests before merge. Review before deploy. Not because developers are lazy, but because quality has never survived acceleration without structure. The mistake everyone is making right now is automating the developer without automating the process around the developer. You haven't solved the problem. You've just made the problem faster. Harness engineering is the realization that QA has to live inside the AI workflow, not outside it. The spec Claude writes against. The tests Claude runs before it ships. The review Claude does on its own output. Make the AI responsible for its own quality and the speed becomes an asset. (My personal evolving Claude Code development harness) https://lnkd.in/g9UAsyuc

AI is disrupting everything, and looks like there's a need to build a version control system that scales for a world of coding assistants, in particular for large monorepos. A post on GitHub's availability describes how AI-assisted coding is the reason why GitHub has been struggling with keeping their availability above three 9s, let alone five 9s.  • Five 9s (99.999%) means ~ 5 minutes and 20 seconds in downtime every year, or roughly 26 seconds per month.  • Three 9s (99.9%) uptime means ~ 8 hours and 46 minutes of downtime per year, or ~ 44 minutes per month. If you just eye-ball the charts, you can see the exponential impact of coding assistants. The numbers are quite staggering. From the charts, it looks like the first quarter of 2026 has seen as much growth for merged PRs as the previous three years together. Same for commits and number of repos. A data point that the post doesn't visualize is the rise of monorepos, and that rise appears to be "𝘢 𝘮𝘶𝘤𝘩 𝘩𝘢𝘳𝘥𝘦𝘳 𝘴𝘤𝘢𝘭𝘪𝘯𝘨 𝘤𝘩𝘢𝘭𝘭𝘦𝘯𝘨𝘦" than just a higher amount of activity. The rise of monorepos makes a lot of sense. AI-assisted development needs context. Monorepos offers exactly that, the entire codebase in one place. The coding assistant has access to every line of code a change will affect, supported by a single AI configuration file. Last year, with Opus 4.6, Anthropic introduced a 1M token context window, and removed the previous limit of 200K tokens. 1M is enough for large codebases (like with monorepos), and judging based on GitHub's post, developers certainly seem to make use of that context window by shifting from poly- to monorepos. Or at least build new monorepos. Some of scaling challenges of monorepos for git are slow git clone and git fetch operations, and for larger organizations like enterprises just the sheer amount of daily PRs to a single repo. 44 minutes per month is quite a bit of downtime, and then add that the secondary effects, e.g. from restarting your CI/CD pipelines. Facebook / Meta developed Sapling exactly for the use case of large codebases. Only very few companies operate at Meta's scale, and they probably already have their own version control system (e.g. Google). But Sapling is open source. So who knows, with the rise of AI coding and monorepos, maybe Sapling's time has come. The crazy thing is that with LLMs, the bar to build your own source control system just got lower. Original post by GitHub CTO Vladimir Fedorov here: https://lnkd.in/gfqu4dmr Less than a year ago, people would have told anyone they're crazy to adopt a monorepo. Yet here we are.

🚀 Supercharge Your Dev Workflow: Claude Code Plugins Claude Code is arguably the smartest coding agent available right now. But let’s be honest: it can be forgetful, mute, and impulsive. Every session starts from zero. It doesn’t remember yesterday’s architecture, it can’t "hear" your ideas, and it often jumps into code without a plan. To hit a 100x productivity boost, you need to bridge the gap between raw AI intelligence and structured execution. Here is the 5-part stack to transform Claude into a high-earning engineering partner. 🧠 1. Memory: Never Explain Your Project Twice Tool: claude-mem Stop wasting 30 minutes every morning re-explaining your naming conventions. This tool captures session context and injects it into future sessions automatically. • The Win: "Endless Mode" keeps context sharp even in marathon sessions. • Install: /plugin install claude-mem 📂 2. Second Brain: The Vault That Organizes Itself Tool: obsidian-mind While code lives in the editor, your intent lives here. It automatically files meeting notes, decisions (ADRs), and project goals into an Obsidian vault. • The Win: Use /standup to let Claude scan your vault and tell you exactly where you left off. 🎙️ 3. Voice: Talk Faster Than You Type Tool: VoiceMode Debugging is high-bandwidth. VoiceMode uses local Whisper (STT) and Kokoro (TTS) so you can brainstorm out loud while keeping your hands on the keyboard. • The Win: 100% offline and private. No API keys, no data leaks. 🛡️ 4. Discipline: Think Before You Code Tool: superpowers Claude’s biggest flaw is jumping into code too fast. Superpowers forces a "Think-First" framework and TDD (Test-Driven Development) by default. • The Win: You get solutions that fit your architecture, not just snippets that "look" right. 📈 5. Strategy: Compound Engineering Tool: compound-engineering-plugin Invert technical debt. This ensures every unit of work makes the subsequent one easier. It shifts the focus: 80% planning, 20% execution. • The Win: Use /ce:plan to launch research agents that audit your repo before writing a single line. 🛠️ The "All-in-One" 60-Second Setup Run these in your Claude Code terminal to level up: /plugin install claude-mem /plugin install voicemode /plugin install superpowers /plugin install compound-engineering The Final Verdict: Don't just use AI to write code; use it to manage the complexity of your engineering. Stop repeating yourself and start shipping at a scale you didn't think was possible. #ClaudeCode #ClaudeAI #SoftwareEngineering #GenerativeAI #Productivity #Coding #AIWorkflow

What if your AI coding assistant could also manage multiple software applications, with milestones and tasks? I've been experimenting with something with Claude Code. Instead of just using AI to write code, I built a coordination layer on top of Claude Code CLI that handles task management, code review, and progress tracking across multiple projects.                                  AI Company, a human gives direction, an AI Coordinator breaks work into milestones and tasks, and AI Workers execute autonomously. The whole system runs on markdown files and git. No database, no custom framework. https://lnkd.in/dFcMjynW I drop docs/SOWs into a folder (or reused existing project with git history). The Coordinator reads it, asks questions, plans milestones, and assigns workers. Code gets reviewed, revisions get tracked, and I only step in for decisions that need attention. The internal operating model is just structured markdown files in a git repo:  - 𝐂𝐎𝐌𝐌.𝐦𝐝 — current task, status, and worker notes per project  - 𝐌𝐈𝐋𝐄𝐒𝐓𝐎𝐍𝐄𝐒.𝐦𝐝 — milestone breakdown with task progress      - 𝐑𝐄𝐕𝐈𝐄𝐖_𝐋𝐎𝐆.𝐦𝐝 — every code review verdict and feedback     - 𝐂𝐄𝐎_𝐈𝐍𝐁𝐎𝐗.𝐦𝐝 — escalations and action items that need my     attention                            - 𝐑𝐄𝐆𝐈𝐒𝐓𝐑𝐘.𝐦𝐝 — which worker is on which project right now Github Repo: https://lnkd.in/dFcMjynW #ClaudeCode #AINative #SoftwareDevelopment

Superpowers: The Engineering Discipline Framework The Gist: Created by Jesse Vincent (obra), Superpowers is an open-source framework that forces AI coding agents (like Claude Code, Cursor, or Copilot) to follow a rigorous, senior-level software engineering methodology. Instead of letting an agent "guess and code," it mandates a structured workflow of planning, testing, and reviewing. The Highlights: - Mandatory "Skills": It installs a library of "skills" (instruction sets) that trigger automatically when the agent detects a task. - The 7-Step Workflow: -> Brainstorming: Socratic refinement of your idea into a concrete spec. -> Git Worktrees: Automatically moves work to an isolated branch to prevent "clobbering" your main code. -> Writing Plans: Breaks implementation into 2–5 minute tasks with exact file paths. -> TDD (Test-Driven Development): Enforces a "Red-Green-Refactor" loop—it won't let the agent write features until a failing test exists. -> Subagent Execution: Dispatches fresh "worker agents" for each task to keep context clean and fast. -> Systematic Review: A separate "reviewer agent" must approve the code against the original plan. -> Finishing: Verifies the final state and merges/cleans up the worktree. - Platform Agnostic: While highly optimized for Claude Code, it supports Cursor, GitHub Copilot CLI, and Gemini. Core Philosophy: "Write tests first, always" and "Verify before declaring success." The Bottom Line: Superpowers turns "yolo-coding" agents into disciplined engineers. It’s designed to stop agents from hallucinating or making sloppy mistakes by wrapping them in a strict, battle-tested process. https://lnkd.in/d3dEeh3x

Vibe coding isn’t a fad anymore – it’s quickly becoming the default way we explore, prototype, and ship software with AI as a first-class collaborator. Naveen has been building a product that can tell you how risky your career is and how much time you have to upgrade yourself. Besides analyzing risk, this will help diagnose CV and LinkedIn profile to develop a new career narrative. That’s exciting. But yes, vibe coding comes with risks that leaders can’t ignore. So, what is vibe coding! Vibe coding is building software primarily through natural-language prompts, letting AI agents generate, refactor, and stitch together most of the code while humans steer by intent and feedback. Instead of wrestling with syntax, we describe the “vibe” of the feature and iterate conversationally until it works. This shift impacts not only developers, but also PMs, analysts, and non‑technical founders who are now shipping scripts, micro‑apps, and internal tools. Popular vibe coding tools: Cursor – AI‑native IDE that deeply understands your codebase, great for power developers who want speed without losing control. Mature with large user base (Fortune 500+ support). Windsurf – more autonomous multi‑step execution for large changes across many files, ideal for advanced coders and big projects. Claude Code – an agentic coding companion that reads your repo, plans tasks, edits files, and runs commands from natural language. Google AI Studio - Generate web apps (React/Next.js/Angular) from prompts, add DB/auth (Firebase built-in), secure API keys, collaborative, deployable. Free to use; supports production apps. Open AI Codex: Multi-agent workflows, background tasks, automated refactors, code review integration v0 by Vercel – turns UI prompts into production‑ready React/Next.js front‑ends you can export and harden in your own stack. Replit/Lovable – browser‑based environment where you can prompt, build, and deploy full‑stack apps from a single collaborative workspace. Why vibe coding is powerful Speed: Idea → prototype in hours instead of weeks, especially for CRUD apps and internal tools. Accessibility: Non‑coders can participate directly in building and iterating on software. Leverage: Senior engineers focus on architecture, reviews, and hard problems while agents handle boilerplate. The downside we must manage: Shadow apps: Unreviewed AI‑built tools creeping into production without security or compliance. Fragility: Systems that “work in the demo” but are hard to debug, extend, and reliably operate. Skills erosion: Over‑reliance on vibes instead of fundamentals in testing, observability, and design. Vibe coding isn’t going away. The organisations that win won’t be those that vibe the hardest, but those that pair this new creative superpower with strong engineering standards, governance, and a culture of responsible experimentation. Preeth Sumeet Piyush Agilemania Vikram Ashwinee #vibecoding #AI #AIDevelopment #AISM #AIPO

Agentic Coding Best Practices: Level Up Your SDD for Smarter, Faster Development In the era of AI agents, Agentic Coding isn't just hype—it's transforming how we build software. But without solid Software Design Documents (SDDs), it's chaos. Here's how to make SDDs your secret weapon for end-to-end excellence, with a heavy focus on testing (manual and automated). 1. Craft Full-Functional User Stories in Your SDD Start with crystal-clear user stories that cover every functional angle. This isn't a vague outline—it's your blueprint. Agents thrive on specificity, turning stories into executable plans. 2. Master Testing: Manual First, Then Hyper-Automate Manual Testing as the Foundation: Use your SDD's BDD specs (Given-When-Then) for targeted manual exploratory testing. Humans excel at UX intuition, edge-case discovery, and subjective validation—run quick sessions to validate agent-generated code against real-world flows. Limit to 20% of effort; agents handle the rest to avoid bottlenecks. Automated Testing Revolution: Leverage AI agents for test case generation straight from SDD stories—scanning requirements to auto-create comprehensive suites (unit, integration, E2E) with 70% less manual work. Tools like agentic frameworks expand one story into dozens of scenarios via NLU and systematic variations. Implementation with Selenium or Playwright:   - Playwright (preferred 2026): 2-3x faster execution (45s vs 90s per suite), 60% fewer flakes, built-in parallelism, and auto-waits. Ideal for cross-browser/device E2E tests.[3] - Selenium: Mature for complex Grid setups, but higher resource use (500MB memory vs 250MB).   Integrate into CI/CD: Agents write/run tests, enforcing 100% coverage. Result? 50% faster releases, 40-60% fewer bugs in prod. 3. Build Custom Skill Agents per Tech Stack Don't use one-size-fits-all agents. Create specialized agents for React, Node.js, AWS, etc.—trained on your SDD. They handle stack-specific tasks like optimizing queries or securing endpoints, slashing context-switching by 50%. 4. Design Guardrails & Gardens Around Your Framework Define strict guardrails (e.g., "Never use raw SQL; always ORM") and sandbox gardens (safe zones for experimentation). Agents respect these, ensuring compliance while fostering innovation. Your SDD enforces this—no more rogue code. 5. End-to-End Process: From SDD to Prod - Plan: SDD with stories + BDD specs.   - Test Manually: Quick human validation.   - Auto-Generate & Run Tests: Agents + Playwright/Selenium.   - Code/Review/Deploy: Human-in-loop for guardrails.  This flow boosts productivity 3x for teams—agents handle boilerplate/testing, you focus on architecture. Engineering teams love it: Less burnout, more shippable features. Bonus: Cost Savings Cut engineering hours by 40-60% (automated tests replace manual toil), reduce rework, and scale without headcount bloat. #AgenticCoding #SoftwareEngineering #AITesting #Playwright #Selenium #Automation

The 10x AI Coding Playbook 𝗕𝗲𝗰𝗼𝗺𝗲 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝘁 𝗔𝗜 𝗶𝗻 𝗷𝘂𝘀𝘁 𝟭 𝗺𝗶𝗻𝘂𝘁𝗲 𝗮 𝗱𝗮𝘆. 𝗚𝗲𝘁 𝘁𝗵𝗲 𝗔𝗜 𝗻𝗲𝘄𝘀𝗹𝗲𝘁𝘁𝗲𝗿 𝘀𝗺𝗮𝗿𝘁 𝗹𝗲𝗮𝗱𝗲𝗿𝘀 𝗿𝗲𝗮𝗱. 𝗦𝗶𝗴𝗻 𝘂𝗽 𝗳𝗿𝗲𝗲 𝗻𝗼𝘄 → aiforleaders.com Original post: __________ Here's the playbook I use every day to run 5+ Claude Code sessions at once and actually 10x my output (without sacrificing code quality). Because just running more coding agents in parallel doesn't instantly "10x" you! They just step on each other toes. It takes a system to make it work reliably. Here's mine, starting with the 5 pillars: 1. Issue as spec. Every implementation starts from a GitHub issue (or something like a Jira ticket). Every validation starts from the pull request. Real auditable artifacts that you can create in batch and handle in batch. 2. Git worktrees for isolation. Each agent gets its own copy of the codebase. Claude Code supports this natively: `claude -w issue-10`. Five agents, five worktrees, zero collisions. 3. Plan, build, validate inside each worktree. Use whatever AI coding process you already have (BMAD, GitHub Spec Kit, custom skills, etc.). The only thing that changes is the input: point it at the issue number. 4. Fresh context for review. The reviewer should never see the writer's chat. Always start a fresh session for PR/code review. Using the same context window is like asking a kid to grade their own homework. Bonus: have Codex (or another agent) do an adversarial review in a completely separate session. 5. Self-healing AI layer. When a bug surfaces in review, don't just fix the bug. Fix the system that allowed it. Update context, skills, CLAUDE.md, workflows, etc. Fewer bugs per batch over time, smaller human bottleneck. Parallel coding agents come with their own challenges though. It takes real engineering to solve for these: - Port conflicts: unique port per worktree, derived from the worktree name - Dependency cost: install packages when the worktree is created, not mid-run - Database conflicts: Database branching gives each worktree its own data copy instantly. Per-worktree SQLite works too. That's the system I use to go from 5 issues to 5 shipped features before lunch. Full 25-minute walkthrough with live demos on YouTube - link in the comments! Credit to Cole Medin. Follow him for more.