Claude 2.5 Sonnet: A More Mature AI for Coding Tasks

Some real talk from a developer’s perspective: Lately, I’ve felt worn out by AI coding tools. As much as they promise productivity, the reality is too often the opposite. • The same input never results in the same output twice. • Features that work in one commit break in the next, sometimes without clear reason. • Meticulously crafted project rules and planning files still get ignored by the models. After countless unpredictable results, I’ve started ruthlessly checking in every working version to GitHub or DevOps the minute it works—treating every ‘green’ state as precious, because I have no guarantee it will be repeatable. I’m not quitting AI completely, but I’m now leaning more on my own skills for planning and implementation, and using AI only for very targeted tasks. Has anyone else hit this same wall? Would love to hear if others have found a balance—or if unpredictability is just baked into the current generation of tools. #AICoding #SoftwareEngineering #DevOps #Productivity

An AI agent just wrote 1,000+ lines of production code for me. It also deleted my tests to make the test suite pass. 🤦��️ After my experience software with AI agents (orchestrator, dev, QA personas) - BMAD, I've learned that AI-driven development is both more powerful AND more complicated than the hype suggests. Here's the reality: What Works 🚀 - Parallelism: 2-3x throughput when tasks are isolated - Git automation: Consistent workflow, zero manual overhead - Continuous learning: AI gets smarter with each story - Speed: Features that took days now take hours What Doesn't ⚠️ - Trust without verification (review EVERYTHING) - Massive stories (context limits are real) - Blind optimization (AI fixed a "failing test" by deleting it) - Unsupervised git operations (RIP those uncommitted changes) The Pattern That Emerged: Treat AI like a brilliant junior developer: → Give clear instructions → Review their work → Help them learn from mistakes → Don't let them touch production unsupervised Most Important Discovery: Foundation > Prompts. Comprehensive documentation (coding standards, tech stack, architecture) determines success more than clever prompting. I wrote up the full experience report - covering parallelism strategies, context management, war stories, and production-ready best practices. The bottom line? AI won't replace engineers. But AI-augmented engineers will have a massive competitive advantage. What's your biggest question about AI-driven development? #SoftwareDevelopment #AI #TechLeadership #Engineering #Innovation #DevOps #MachineLearning Feel free to reach out and have healthy discussions!

 Harnessing the Future of Software Delivery with AI! This week, I got hands-on with the new Harness AI Code Agent — and it’s a game-changer for CI/CD and DevOps workflows: What I’m seeing so far: . Context-aware code suggestions directly in my IDE . Auto-generation of unit tests, comments, and business logic . Seamless support for Python, Go, Java, TypeScript, and more The real impact? As someone responsible for code quality, pipeline automation, and microservices governance, Harness AI isn’t just speeding up development — it’s raising the bar for maintainability, compliance, and cross-team standards. What excites me most: . Delivery velocity is up — and bottlenecks are shifting from “writing code” to “deploying it” . Built-in safeguards and policy-as-code, making governance and auditability effortless We’re on the verge of an AI-powered SDLC where engineers focus on system design, testing, and innovation—while agents automate the tedious parts. I’m eager to hear your Harness, AI agent, or DevOps automation stories. How is AI shifting your coding culture, team collaboration, or delivery mindset? Drop your thoughts! #DevOps #AI #SDLC #HarnessAI #SoftwareDelivery #CI #CD #Automation #EngineeringLeadership #DigitalTransformation

💡 𝐒𝐩𝐞𝐜-𝐃𝐫𝐢𝐯𝐞𝐧 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭: 𝐖𝐡𝐞𝐧 𝐌𝐚𝐫𝐤𝐝𝐨𝐰𝐧 𝐁𝐞𝐜𝐨𝐦𝐞𝐬 𝐂𝐨𝐝𝐞 AI is changing the way we build software. We’re moving from “humans write code, machines execute” → to “humans write specs, AI writes code.” That’s the essence of Spec-Driven Development (SDD). Instead of writing requirements in dusty PDFs or scattered tickets, you write them in Markdown—clear, structured, executable. 𝐖𝐡𝐲 𝐌𝐚𝐫𝐤𝐝𝐨𝐰𝐧 𝐚𝐬 𝐭𝐡𝐞 𝐧𝐞𝐰 “𝐩𝐫𝐨𝐠𝐫𝐚𝐦𝐦𝐢𝐧𝐠 𝐥𝐚𝐧𝐠𝐮𝐚𝐠𝐞”? ✨ Readable – PMs, Engineers, QA all speak the same spec. ✨ Executable – AI can translate specs into code, tests, monitoring configs. ✨ Auditable – The spec becomes the single source of truth across the product lifecycle. 🔮 𝐈𝐦𝐚𝐠𝐢𝐧𝐞 𝐭𝐡𝐢𝐬 𝐰𝐨𝐫𝐤𝐟𝐥𝐨𝐰: 1️⃣ You write a spec in Markdown: # Feature: Smart Retry Logic - Context: APIs may fail due to transient errors.  - Requirement: Retry up to 3 times, exponential backoff.  - Skip retries on permanent errors.  2️⃣ AI takes over → Generates Python code, PyTest cases, and monitoring configs. 3️⃣ You iterate by editing the spec, not the codebase. For AI Product Managers, this means: ✅ Faster cycles → From idea to implementation in hours, not weeks. ✅ Less translation → No gaps between product intent and engineering output. ✅ More control → Every feature has an auditable, living blueprint. 👉 The future of AI-native product building might not be “low-code” or “no-code”—but spec-as-code. ⚡ 𝐐𝐮𝐞𝐬𝐭𝐢𝐨𝐧 𝐟𝐨𝐫 𝐲𝐨𝐮: Would your team trust Markdown as the new programming language for building with AI? #InnovationInTech #NextGenDevelopment #NoCode #LowCode #TechTrends #AIPM #TechnicalProductManagement

If you think debugging is about code, you’re missing the deeper story. This one’s for every developer who’s ever said, “But it should’ve worked... 👇 #Debugging #SoftwareDevelopment #Developers #CodingLife #ProgrammingWisdom #EngineeringMindset #AI #Automation #TechLeadership #WavenestAI

✔️ 7 Habits of Highly Accountable Developers We all talk about clean code, frameworks, and new AI tools — but accountability is what truly separates a good developer from a great one. Here are 7 habits that every developer should live by 👇 1. Developers are accountable: Take ownership — of your code, your bugs, your deadlines, and your results. “Not my module” isn’t an excuse in great teams. 2. Document your project context: Code explains what, but documentation explains why. The next developer should understand your intent without asking you a single question. 3. Keep it simple: The best code is often the one that’s easiest to delete. Complexity impresses no one if it confuses everyone. 4. Absolutely, positively no stray code: Dead code, unused imports, commented chunks — they rot the repo’s health. Treat your codebase like a well-kept library: clean, organized, intentional. 5. Analyze everything: Don’t guess performance — measure it. Don’t assume security — verify it. Numbers > opinions, always. 6. Mandatory unit tests: If you didn’t test it, it’s broken — you just don’t know it yet. Testing isn’t extra work; it’s proof you care about your work. 7. Rigorous code reviews: The goal isn’t to “find mistakes.” It’s to level up together. Code reviews are where teams grow, not just where pull requests close. Frameworks will change. AI tools will evolve. But these 7 habits? They’ll keep your engineering discipline timeless. Keep building. Keep improving. And above all — stay accountable. #TechThoughts #Developers #CleanCode #SoftwareEngineering #Leadership #AI #Programming

AI is accelerating code creation, but it's also creating a hidden bottleneck: the manual verification needed to ensure quality and security. This is the engineering productivity paradox, and it's limiting the true potential of AI in development. How can you de-risk AI adoption and empower your teams to innovate safely? A "trust and verify" approach is essential. This developer guide from Sonar provides a practical framework for implementing AI Code Assurance, helping you: 🔹 Establish clear quality and security standards for all code, whether human- or AI-written. 🔹 Solve the AI accountability crisis by building trust and verification directly into your workflow. 🔹 Unlock the full, productive potential of AI-driven development without taking on unnecessary risk. Check it out. 👇 #SDLC #CodeGovernance #CodeQuality #CodeSecurity #StateOfLLMs #DevSecOps #DevOps #SoftwareDevelopment

AI is accelerating code creation, but it's also creating a hidden bottleneck: the manual verification needed to ensure quality and security. This is the engineering productivity paradox, and it's limiting the true potential of AI in development. How can you de-risk AI adoption and empower your teams to innovate safely? A "trust and verify" approach is essential. This developer guide from Sonar provides a practical framework for implementing AI Code Assurance, helping you: 🔹 Establish clear quality and security standards for all code, whether human- or AI-written. 🔹 Solve the AI accountability crisis by building trust and verification directly into your workflow. 🔹 Unlock the full, productive potential of AI-driven development without taking on unnecessary risk. Check it out. 👇 #SDLC #CodeGovernance #CodeQuality #CodeSecurity #StateOfLLMs #DevSecOps #DevOps #SoftwareDevelopment

Are we at a tipping point in software development? 🤖⚡️ In my latest video, I pit hands-on coding against AI-assisted workflows to see who wins on speed, quality, debugging, and real-world productivity. Expect myth-busting, side-by-side tests, and practical tips to future-proof your dev career. 🚀 Whether you’re a senior engineer or just starting out, you’ll learn when to trust the bot, when to take the wheel, and how to build a hybrid stack that actually ships. 🛠️💡 Watch now and join the debate: Are you Team Manual or Team AI-Assisted? 👇 #AI #Coding #Programming #SoftwareDevelopment #DevTools #GenerativeAI #TechNews #FutureOfWork #Developers #Automation https://lnkd.in/gjUr_P3X

AI automation developer Sometimes, the hardest skill in tech is not code—it’s learning how to solve real problems that don’t come with solutions. Every new project challenges me to think deeper, not just type faster. Last week, debugging a complex frontend bug took me two days and plenty of patience. My AI tools like Copilot suggested code, but understanding *why* things broke—that part was all me. Working on my AI Design Assistant taught me that probes and experiments reveal more than tutorials can. All those hours wrestling with logic bugs? They were uncomfortable, but they made me sharper. Failing fast and iterating taught me practical problem solving—skills you won’t always find in textbooks. For anyone in tech: mastery is not knowing all the answers but being willing to ask the right questions. Takeaway: Real growth happens when you face problems head-on, stay patient, and enjoy the learning journey. #CareerGrowth #ProblemSolving #TechSkills #FrontEndDev #ManpreetSinghAI