Agentic Coding Boosts Software Development with Smarter SDDs

The "AutoDream" Shift: Hands-Free Coding in VS Code with Claude Code: --------------------------------------------------------------------------------- 1. Enable "AutoDream" for Structural Mapping: The standout viral feature this week is AutoDream. When you’re starting a complex build, don't just ask for code. The Workflow: Use the command palette (Cmd+Shift+P) and trigger Claude Code: Start AutoDream. The Result: Claude doesn't just write a script; it generates a structured .md plan that maps your dependencies, identifies risk areas in your existing architecture, and creates a Dispatch-compatible task list. It’s like having a Staff Engineer write your technical spec in seconds. 2. Move from "Normal" to "Auto-Accept" Mode If you trust your test suite, it’s time to stop hitting "Y" on every file change. In the VS Code Claude panel, switch your Permission Mode to Auto-Accept. Claude will now execute the AutoDream plan—modifying files, creating new components, and refactoring imports—without stopping for permission. Tip: Pair this with the new getDiagnostics tool. Claude will monitor the "Problems" tab in VS Code in real-time, automatically fixing syntax errors or linting breaks as it creates them. 3. Integrated "Computer Use" for UI Validation The latest update allows Claude to use its Visual Context Window directly within your VS Code environment. Need to verify a CSS change? Claude can now "see" your local dev server in the integrated browser, take a screenshot, and self-correct if the button alignment is off. This is the first time we’ve seen true "Visual Regression Testing" handled autonomously by the coding agent itself. 4. Headless CI/CD with "Dispatch" For the first time, you can trigger your VS Code Claude sessions remotely. Using the Dispatch feature, you can route complex refactoring tasks from your JIRA stories or GitHub Actions directly into a headless VS Code instance. It finishes the job, runs the build, and pings you when the PR is ready for a final human eye. #Leadership #GenerativeAI #AgenticAI #PromptEngineering #LLMOps #MLOps #DevOps #PlatformEngineering #CloudSecurity #FinOps #AgileLeadership #DigitalTransformation #Innovation #Terraform #Docker #Kubernetes #Git #CICD #RAG #Python #Vibecoding #Automation #AIArchitecture #MultiAgentSystems #VectorDatabase #MachineLearning #DeepLearning #Gpts #NVIDIA #OpenAI #GoogleGemini #Copilot #Anthropic #Perplexity #ClaudeCode #Cloud #AWS #GCP #AZURE

Agentic coding doesn't kill your QA tools — it needs them more than ever AI coding agents are changing how we write software. But if you're bootstrapping a greenfield web project with agents, there's something important to get right early: your existing QA tooling still matters — arguably more than before. 🧪 Unit testing Agents make mistakes. That's just the reality today. But what's remarkable is how well they can course correct — _when given the right feedback._ When an agent writes code and then runs the test suite, it sees the CLI output. It can identify what broke and fix it, often without any human intervention. This makes executable tests one of the most powerful tools in an agentic workflow — not just as a safety net for you, but as an active feedback loop for the agent itself. Practical tip: When starting a greenfield project, one of the first things you should do is make sure your agent can effortlessly run tests in the project environment. Set that up early. 🔍 Linting Why spend tokens on a large language model to catch something like an unused variable or a function that was accidentally called without being awaited, when a deterministic algorithm can do it instantly and for free? Linters are a far more efficient way to handle these low-level issues. Let the LLM focus on what it's actually good at — and let the linter handle the rest. Practical tip: Set up your linter early and make sure the agent can run it and review the results. Better yet — include your linter config file as default context for the agent. That way, it's generating code that already targets compliance from the start, rather than fixing violations after the fact. ✨ Formatting Formatters make all files look the same. That might sound trivial, but in a professional codebase, consistency removes the surprise element — and surprise when reading code is rarely a good thing. Modern LLMs can output code in a reasonably consistent style. But running a formatter on top seals the deal. A deterministic algorithm guarantees conformance in a way that probabilistic output simply can't. 🌍 The bigger picture The pattern across all three is the same: set your agent up with the right feedback loops from day one. Tests, linters, and formatters aren't relics of a pre-AI era — they're the scaffolding that makes agentic coding reliable, efficient, and professional. The best agents don't replace good engineering practices. They depend on them.

The most underrated skill in vibe coding is planning. Most people I see trying to vibe code are hoping to one-shot their app idea. Give the agent a brief description, hope it does everything necessary to build it. While this might work for the simplest ideas, for any practical application it's almost a guaranteed failure. The most important thing you can spend time doing before you start vibe coding is planning. I've spent the last three days replanning the architecture on an app I'm building as I think about how it's going to scale. Ryan McDonnell recently shared a resource from Matt Pocock with some very helpful skills on how to go deep on your documentation and get the most out of your planning process (link in comments). This has already uncovered gaps I didn't know existed. It improved the quality of my code, my roadmap, and my database. Here's a brief overview of what I've been working through: The process: - Start with a full PRD rewrite synthesized from all existing documentation and your entire ticket backlog - Bootstrap a domain vocabulary file (CONTEXT.md) so every AI agent uses the same definitions - Write a technical architecture document derived from the new PRD - Run a schema design pass covering every planned feature across all releases - Map the current codebase against the intended architecture to find gaps - Run a deepening pass to identify shallow modules that will cause pain during implementation - Produce a change recommendations document reviewed by a second AI agent before anything is touched - Use each document as an input to the next pass, nothing skipped The skill system: - Key skills used: grill-with-docs (stress-tests plans against your domain model), improve-codebase-architecture (finds shallow modules and seams), zoom-out (maps unfamiliar code before you touch it), to-issues (breaks plans into vertical slice tickets) - Claude writes, Codex reviews, Claude resolves. Every pass has a checkpoint before anything moves forward. The documents produced: - PRD_v2.md (product requirements) - CONTEXT.md (domain vocabulary glossary) - ARCHITECTURE_v2.md (technical architecture) - SCHEMA_DESIGN.md (full database schema across all planned releases) - CODEBASE_MAP.md (current state vs intended state) - DEEPENING_REPORT.md (module depth analysis) - CHANGE_RECOMMENDATIONS.md (approved changes before any file is touched) - MILESTONES.md and RELEASE_PLAN.md (milestone and release structure) How it feeds Linear: - Every document becomes an input to ticket drafting - Tickets are written to a draft file first, reviewed by Codex, then pushed to Linear - Each ticket uses a structured agent brief format so the AI executing it has no ambiguity about what done means - Releases are sequenced by milestone with dependency order enforced before anything is created Slow down before you speed up. The foundation is everything. #AIEngineering #ProductDevelopment #VibeCoding

𝗔𝗿𝗲 𝘆𝗼𝘂 𝘀𝘁𝗶𝗹𝗹 𝘁𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝘆𝗼𝘂𝗿 𝗢𝗽𝗲𝗻 𝗖𝗼𝗱𝗲𝘅 𝗔𝗜 𝗰𝗼𝗱𝗶𝗻𝗴 𝗮𝗴𝗲𝗻𝘁𝘀 𝗹𝗶𝗸𝗲 𝗷𝘂𝗻𝗶𝗼𝗿 𝗱𝗲𝘃𝗲𝗹𝗼𝗽𝗲𝗿𝘀 𝗼𝗻 𝘁𝗵𝗲𝗶𝗿 𝗳𝗶𝗿𝘀𝘁 𝗱𝗮𝘆? 𝗜𝘁’𝘀 𝘁𝗶𝗺𝗲 𝘁𝗼 𝘂𝗽𝗴𝗿𝗮𝗱𝗲 𝘁𝗼 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗗𝗿𝗶𝘃𝗲𝗻 𝗗𝗲𝘃𝗲𝗹𝗼𝗽𝗺𝗲𝗻𝘁 (𝗔𝗗𝗗). If you aren't using an `AGENTS.md` file in your repositories yet, you are missing out on a massive productivity boost. Already adopted by over 60,000 open-source projects, `AGENTS.md` acts as a "README for AI agents". While a standard `README.md` is for humans, `AGENTS.md` gives AI tools (like GitHub Copilot, Codex, Cursor, and Gemini) the exact context they need to succeed: your build steps, architectural rules, and strict coding conventions. Here is why this simple markdown file is a game-changer: 𝗣𝗮𝘀𝘀𝗶𝘃𝗲 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗕𝗲𝗮𝘁𝘀 𝗔𝗰𝘁𝗶𝘃𝗲 𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹: A recent Vercel study found that giving agents passive access to a compressed documentation index inside an `AGENTS.md` file resulted in a **100% pass rate** across Build, Lint, and Test tasks. When relying on agents to actively "decide" to look up documentation using "skills", the pass rate plummeted to just 53%. 𝗦𝗸𝗶𝗽 𝘁𝗵𝗲 𝗥𝗲𝗽𝗲𝘁𝗶𝘁𝗶𝗼𝗻: Instead of repeating "use TypeScript strict mode" or reminding the AI to use your specific testing frameworks in every single chat prompt, you define the "law" once in your project root. 𝗧𝗵𝗲 𝗠𝘂𝗹𝘁𝗶-𝗔𝗴𝗲𝗻𝘁 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄: With ADD, developers are moving "up the stack." We are no longer just writing boilerplate; we are orchestrating pipelines where different AI personas (like a Product Manager, Full-Stack Engineer, and QA Engineer) draft, implement, and cross-review each other's code before a human even looks at it.  𝗧𝗼𝗽 𝟯 𝗕𝗲𝘀𝘁 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗲𝘀 𝗳𝗼𝗿 𝘆𝗼𝘂𝗿 `𝗔𝗚𝗘𝗡𝗧𝗦.𝗺𝗱`: 𝗞𝗲𝗲𝗽 𝘁𝗵𝗲 𝗵𝗶𝗲𝗿𝗮𝗿𝗰𝗵𝘆 𝗳𝗹𝗮𝘁:Put a single, concise `AGENTS.md` file in your repo root for high-level rules, and point to a `.agents/skills/` folder for domain-specific instructions. 𝗦𝗲𝘁 𝘀𝘁𝗿𝗶𝗰𝘁 𝗯𝗼𝘂𝗻𝗱𝗮𝗿𝗶𝗲𝘀: Tell your agent what it should *always* do, what it must *ask first*, and what it should *never* do (like deleting failing tests or modifying database schemas without permission). 𝗨𝘀𝗲 𝗳𝗶𝗹𝗲-𝘀𝗰𝗼𝗽𝗲𝗱 𝗰𝗼𝗺𝗺𝗮𝗻𝗱𝘀: Instruct your agent to run linters or type-checkers on a per-file basis rather than running project-wide builds. This drastically speeds up feedback loops and saves tokens. Stop paying for your AI to blindly rediscover your codebase's architecture on every single prompt. Drop an `AGENTS.md` in your repo today and watch the output quality skyrocket. Have you experimented with `AGENTS.md` or AI Skills in your workflows yet? Let me know your experience in the comments! #AI #SoftwareEngineering #DeveloperTools #AGENTSmd #AgenticDrivenDevelopment #Productivity #GitHub #WebDevelopment #Claude #OpenAI #Codex #Cursor #Angular #React #NextJS #ExpressJS #SpringBoot #NestJS

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

𝗪𝗲 𝗱𝗶𝗱𝗻’𝘁 𝗵𝗮𝘃𝗲 𝗮 𝗰𝗼𝗱𝗶𝗻𝗴 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 - 𝗪𝗲 𝗵𝗮𝗱 𝗮 “𝘄𝗵𝗲𝗿𝗲 𝗱𝗼 𝗜 𝗲𝘃𝗲𝗻 𝘀𝘁𝗮𝗳𝘁?” 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 Many tasks begin with unclear instructions. You spend 20-30 minutes only to find out what is needed. Then you search the codebase for the right place to start. If the area is new, you lose 1-2 hours before writing code. Or you ask someone who knows. This is fine but does not scale. This happened often. We paid for the same things repeatedly: - Understanding context - Figuring out patterns - Re-learning how things are done It was not complex work. It was repeated work. It depended on who already knew the system. We did not try to generate code faster. We focused on understanding and execution as one flow. We built a structured entry point with: - Architecture and patterns - Conventions for code, testing, logging - How to extend the system - Risky areas and common pitfalls This removed the need to start from scratch. It is not only about generating code. A prompt guides the process: 1. Understand the task: type, scope, intent 2. Estimate complexity 3. Decide if an agent should solve it 4. Find relevant codebase parts 5. Propose implementation options 6. Build a step-by-step plan 7. Highlight risks 8. Generate a verification checklist If the task is too complex, the prompt splits it into smaller parts. For low or medium complexity, the prompt suggests using the agent. The agent then has system context, correct patterns, and a clear plan. The agent generates: - Code that fits the repository - Tests for new features - Regression tests for existing behavior It is not perfect. But it is good enough to remove most routine work. Tasks estimated at 6-8 hours often take 2-3 hours. Simple tasks drop from 1 hour to minutes. You spend less time figuring things out before starting. This works because: - Context is given upfront - Implementation is partially handled - Common mistakes are reduced The main change is: - Less mental overhead - Faster start - Fewer interruptions Takeaway: Most teams do not have a coding problem. They have a "time-to-understanding" problem. If every task starts with digging through the system and re-learning patterns, this is the real bottleneck. AI helps when: - It understands your system - It is used selectively I see two common patterns: Teams either do not use AI. Or they use AI too much. Neither works well. I see this pattern in many teams. How do you approach it? Source: https://lnkd.in/gps7qSd6 Optional learning community: https://t.me/GyaanSetuAi

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

Most AI coding tools help you write code faster. But shipping software is not just writing code. It is turning an idea into a production-ready solution that is tested, stable, scalable, and verifiable. Whether you are an entrepreneur shaping a new product idea, a developer building on an existing codebase, or a DevOps engineer wiring up infrastructure, the journey from concept to production touches far more than code. Discovery, architecture, specs, planning, execution, review, deployment. Most tools accelerate one of those steps. None of them connect the full chain. So I built Arness. Yes, I dropped the H on purpose 😊. Today I'm open-sourcing it. Arness is a plugin marketplace for Claude Code by Anthropic that covers the entire software project lifecycle. Three plugins, each independently installable, but together they form a single pipeline from first idea to production: Spark takes a raw idea through product discovery, persona generation, competitive research, brand naming (with real WHOIS and trademark checks), architecture evaluation, full use case specs, and clickable prototypes you can present to a customer or stakeholder. Every artifact feeds directly into the coding phase. Code is a development pipeline that scales process to scope. A quick bug fix gets minimal ceremony. A cross-cutting feature gets full spec, plan, multi-agent execution, and review with parallel execution across Git worktrees. It works on new and existing codebases, learning your patterns automatically. Infra handles containerisation, IaC, CI/CD, environment promotion, secrets, and monitoring with the same structured change management as the dev pipeline. It knows you. Arness captures your experience, skills, and preferences on first use and carries them across every session. Your idea, your codebase patterns, your target audience, your skill set. It all persists without you having to repeat yourself. 88 skills and 46 specialist agents behind the scenes, but you only need three entry points: /arn-brainstorming, /arn-planning, and /arn-infra-wizard. From there, each plugin drives itself. Integrates with GitHub, Jira, Bitbucket, and optionally Figma and Canva. Tested with about two dozen colleagues over the past several months. Their feedback shaped every rough edge. Their enthusiasm gave me the confidence to share it more widely. To all the fellow engineers, entrepreneurs, and builders I have met during my career: this is for you. MIT license, fully open source. Arness was built with Arness. All 134 components went through its own pipeline. https://lnkd.in/eVd6whVS If you try it, I would genuinely love your feedback. And if it resonates, a star on GitHub goes a long way for an open-source project just getting started. #OpenSource #AI #AgenticAI #DevTools #SoftwareEngineering

🚨 Stop writing code. Start writing specs. 🚨 I mean it. ✋ The most important skill in software development right now isn't knowing React, Rust, or whatever framework dropped this morning ☕ It's knowing how to THINK before you build. 🧠💡 As Joshua Field put it — "I write the proof before I write the code." 🎯🔥 Welcome to Specification-Driven Development (SDD) 📋✨ 🔄 The OLD way: Vague requirements → write code → debug forever → ship something that kinda works → rewrite it 😅💀 ✅ The NEW way: Crystal-clear spec → constraints defined → AI generates → validation gates catch drift → ship with confidence 🚀 The spec IS the product. The code is just the output. 📝➡️⚙️ Here's where it gets interesting 👀 Jordan E. called this — software factories will increasingly become ingestors of spec-driven documentation. 🏭📄 We're approaching a world where you hand a structured specification to an AI-powered factory and it produces production-grade software. No vibe coding. No guessing. 🎲❌ The factory doesn't need your clever variable names. It needs your CLARITY. 💎 And he made another point living rent-free in my head 🧠 — as Eric Schmidt declares the end of human coding, the real insight is that AI-assisted development will turn into a niche art 🎨 A NICHE ART. 🖼️✨ The people who thrive will be the ones who can: 📐 Architect systems with precision 📋 Write specs that leave zero ambiguity 🎯 Define constraints that guide AI toward correct solutions 🤝 Bridge human intent and machine execution This is the new literacy. 📚 The developers who survive aren't the fastest coders. They're the clearest THINKERS. 🧠⚡ What I've changed since embracing this: 1️⃣ Every project starts with a spec, not a code editor 2️⃣ Success criteria defined BEFORE the first function 3️⃣ AI gets a detailed blueprint, not vibes 🏗️ 4️⃣ 70% thinking, 30% building — 10x better output 📈 This post was even built collaboratively with Claude — I'll share the conversation link so you can see SDD in action 🔗🤖 The future belongs to spec writers. Architects. People who translate messy human problems into structured, machine-readable clarity. 🌍🔮 Are you still writing code first? Or are you writing the proof? 🤔 👇 Drop your thoughts below 🔗 Referenced posts linked in comments #SpecDrivenDevelopment #SDD #AIEngineering #SoftwareFactories #FutureOfCoding #SpecFirst #BuildSmarter https://lnkd.in/eMHBvEfp

Friday morning reflection - In spec-driven development, does the developer write more specs than code? The short answer: yes. And that's exactly the point. Spec-driven development flips the productivity equation. The expensive part is no longer typing syntax — the AI handles that. The expensive part is thinking clearly enough to describe what you want. That's a tax most developers haven't paid before. We were trained to figure things out as we went. The code was the thinking. Now the spec is the thinking, and the code is the output. "Writing specs is just documentation." No. Writing specs is engineering. A good spec isn't a requirements list. It's a precise statement of intent — covering inputs, outputs, edge cases, failure modes, and the assumptions baked into each decision. That takes time. It takes more words than the function it describes. And here's the uncomfortable truth: most developers resist it — not because it's hard, but because it makes vagueness visible. Vague thinking produces vague specs. Vague specs produce confident-looking code that's wrong in subtle ways. The developers who thrive in this model aren't necessarily better coders. They're better explainers of their own intent. That's a different skill. It's closer to technical writing than to programming. So yes — in spec-driven development, you write more specs than code. The ratio is the feature, not the bug. What's your experience — is the spec-writing discipline holding teams back, or unlocking them?