Dr. Pascal Giessler’s Post

Most developers treat Claude Code like a magic box. 🪄 You type a half-baked instruction and hope for the best. 🤞 But the "magic" is actually a series of highly specific instructions. 📜 I found a repository that extracts every single system prompt Claude uses under the hood. 🔎 You can find the full list here: 👉 [https://lnkd.in/d47GsiYE) If you are building software with AI, this is a gold mine for three reasons: 1. It reveals the mechanics of how Claude actually works. You can see the specific strings it uses to plan tasks, explore your codebase, and even write git commits. 🏗️ 2. It uncovers the "sub-agent" strategy. You can read how Claude spawns smaller agents to handle security reviews and conversation summaries. 🤖 3. It stays current. They track changes across 57+ versions, so you can see exactly how the model's logic evolves over time. 📈 Don't just use the tool. Understand the logic behind the tool. 🧠 Have you tried reading the system prompts of the AI tools you use daily? 💬 ♻️ Repost this to help your network stop guessing and start mastering AI development. ➕ Follow Deven Goratela [https://lnkd.in/dQwsb2jA) to stay ahead in the world of AI and automation. #ClaudeCode #SoftwareDevelopment #AIAutomation #CodingTips #LLM #DevTools

🤖 From Rigid Scripts to Reasoning Agents: The New Era of Automation I’m convinced we’re finally seeing the decline of the "brittle automation" era. For years, we’ve been building frameworks that are essentially glass houses. We’d write these rigid scripts, hard-code every possible logical path, and spend half our lives writing try-catch blocks for every conceivable API exception. But the moment something "weird" happened such as a field name changed or a response came back in a slightly different shape the whole thing shattered. It wasn't just frustrating; it was an architectural dead end. When we entered the LLM ReAct and MCP paradigm, this is where things got interesting. We’re moving away from telling the code exactly what to do at every micro-step. Instead, we’re building systems that can dynamically self-reflect. If a call returns something unexpected, the framework doesn't just throw its hands up and fail. It looks at the output, reasons through the discrepancy, and takes the appropriate action on the fly. We’re finally giving our automation a "brain" to handle the messiness we used to have to hard-code manually. "But what about the cost?" It’s a valid concern. We don't want to burn our entire budget on frontier model tokens just to check a status code. But the solve here might be simple: Small, local LLMs. Using a lightweight model locally can give us that reasoning layer without the massive API bill or the latency. We’re not just writing scripts anymore; we’re designing resilient loops. Is it less "predictable" than a hard-coded line of code? Maybe. But may be some cases it's better to take a tool that can think its way through a minor change over a script that breaks every time a dev breathes on the backend. #Automation #SoftwareEngineering #AI #LLM #MCP #DevOps #FutureOfCode

Developers feel 20% more productive with AI-generated code. Data shows they are actually 19% slower. This 39-point gap is an important figure in software development today. By 2026, 51% of all code on GitHub will be AI-assisted. We are releasing features faster, but human review times for pull requests have tripled. SD Times calls this the "2026 Quality Collapse," and I think that fits well. Here’s what’s happening: AI writes code quickly, but humans take their time to review it. This only makes sense if teams trust the code without fully understanding it. Most teams do, as slowing down would make them less efficient. So, they commit the code. It works in testing and staging, but 60 days later, it fails in production. This happens because the team does not fully understand the logic behind the AI-generated code. One developer shared that he had to rewrite 60% of the code produced by an AI agent on a recent project. He didn’t do this because the code was wrong, but because it passed tests while violating long-term design principles that showed up under heavy use. The role of senior developer has changed; they are no longer the main authors but now act as "guardrail managers." Research shows that 48% of AI-generated code has security vulnerabilities. By 2027, up to 30% of new security problems may come from AI-generated logic that hasn’t been thoroughly reviewed. We promised increased speed, and we delivered. However, the codebase wasn’t informed. Here are three key questions to CTOs and engineering leads: 1. How much of your current codebase was generated by AI and not reviewed by someone who understood it? 2. Do your developers feel productive, or are they truly productive? 3. When technical debt appears, who in your organization will have enough context to fix it? Link to article in comments. If you want to hear more news, click follow. #SoftwareDevelopment #AI #CTO #EngineeringLeadership #CodeQuality #AIinDev

Most developers are sleeping on Claude Code. It's not just another AI coding tool - it's closer to having a senior engineer who never gets tired. Here's what changes when you actually use it right: • Stop typing code. Start describing outcomes. Claude Code handles the implementation while you stay in strategy mode. • Connect it to the Anti-Gravity Sub-Agent MCP Sahu and watch your agents start talking to each other. Your workflows go from linear to parallel - instantly. • Use the "think" or "plan" mode before any complex task. Claude Code maps the entire problem before touching a single file. Less chaos. Cleaner output. • MCP Sahu integration means your sub-agents inherit context. No more copy-pasting between tools or re-explaining your stack every session. • The real unlock? Using Claude Code as your planning layer, not just your coding layer. Architecture decisions. Dependency mapping. Breaking tasks into agent-ready chunks. Most people use it like a fancy autocomplete. The ones winning with it use it like a co-founder who codes. There's a difference. --- If you're building with Claude Code or experimenting with MCP integrations - what's the wildest workflow you've set up so far? Drop it below. Genuinely curious. #claudecode #aiagents #mcpintegration

AI-generated code has 1.7x more bugs than human-written code. And your team is reviewing 20% more PRs this year than last. CodeRabbit analyzed 470 GitHub pull requests. AI-generated PRs average 10.83 issues each. Human PRs average 6.45. 1.75x more logic errors. 1.57x more security findings. 1.4x more critical issues. PR volume is up 20% year over year. Incidents per PR up 23.5%. More code. Worse code. More time cleaning it up I built a tool that runs AI agents overnight to improve codebases automatically. Dozens of sequenced improvement passes. No human in the loop while it runs. That tool has 900+ tests and 90% statement coverage. Fully automated CI across multiple platforms. And the code is almost entirely AI-generated. It works because I wrote a comprehensive PRD before the AI touched a single file. Backend architecture. Frontend behavior. Auth flows. Error handling. Recovery patterns. Git safety. Every decision documented before line one. The teams drowning in AI bugs aren't using the wrong model. They're skipping the part where you define what the code should actually do. In detail. Before anything gets built. A thorough spec isn't overhead. It's the only reason AI-assisted code ships clean. What does your pre-coding process look like before you hand work to an AI agent? https://lnkd.in/gBZ5kgRx #AIEngineering #SoftwareDevelopment #CodeQuality #DeveloperProductivity #BuilderCulture

The Claude Code Engineering Platform. From requirement to production grade code planned tested and verified. Pilot Shell is a complete structured engineering environment for Claude Code. While Claude Code can write code very fast it often skips tests loses context and delivers inconsistent results. Other frameworks add dozens of agents and heavy complexity without meaningfully better output. Pilot Shell is different. It brings real software engineering discipline to AI coding. Key highlights:   • spec mode for full end to end feature development with proper planning implementation and TDD verification   • fix mode for intelligent bug fixing using TDD workflow   • prd mode to turn vague ideas into clear well researched requirements   • Enforced quality gates that run linting formatting type checking and tests on every change   • Persistent context engineering that preserves important decisions and knowledge across sessions   • Code intelligence with semantic search and code knowledge graph   • Strong token optimization that can reduce costs by 60 to 90 percent   • Console dashboard with real time notifications and session management   • Pilot Bot for 24 7 scheduled automations and background jobs  If you want your AI coding agent to produce reliable high quality production ready code instead of just generating fast output Pilot Shell is built for exactly that. https://lnkd.in/eq5nKprj #ClaudeCode #PilotShell #AICoding #AgenticAI #SoftwareEngineering #TDD #DevTools #Anthropic #AItools

I used to think working code was enough. Then I shipped my first production system. It passed every test. Clean APIs. Solid data flow. I was proud of it. Then real users showed up. Latency spiked. Edge cases appeared. Data inconsistencies surfaced. The system I trusted started behaving in ways I never anticipated. That experience changed how I think about engineering entirely. Not "does it work?" — but "will it keep working when it matters most?" Over time I noticed that the systems that survived production shared four traits: → Observable — you can't fix what you can't see → Resilient — failures are inevitable, so handle them gracefully → Scalable — designed to grow without breaking → End-to-end — built as a system, not a collection of isolated parts This applies even more to AI systems. We spend so much time evaluating model output quality. But in production, what matters equally is reliability, consistency, and how well the system integrates with everything around it. Software doesn't live in a vacuum. It lives in unpredictable environments, with real users, real constraints, and real consequences. "It works" is just the starting point. I wrote about this in my latest piece — the full story of what production systems taught me about building reliable AI and backend infrastructure. Link in the comments 👇 #SoftwareEngineering #AIEngineering #SystemDesign #BackendEngineering #ProductionSystems 🔗 Read here: https://lnkd.in/ebqs4ztD

𝗖𝗼𝗱𝗶𝗻𝗴 𝗪𝗮𝘀 𝗡𝗲𝘃𝗲𝗿 𝘁𝗵𝗲 𝗛𝗮𝗿𝗱 𝗣𝗮𝗿𝘁 The biggest misconception about software development is that the hard part is writing code. It isn’t. The hard part is everything around the code. Understanding how the system works. Making architectural decisions. Clarifying requirements. Ensuring changes don’t break something else. Maintaining documentation and knowledge. AI makes writing code easier, but those problems don’t disappear. In fact, they become more important. Because faster code generation means faster system change. And systems break when change isn't governed. #MLOps #AI #B2BSoftware #AIIntegration #SoftwareDevelopment

So much to say about this.... 💡 Invest in an #aidriven #engineeringsystem 💡 Retool observability systems for AI vs. humans 💡 Recognize that more code != better code; velocity and quality need to be designed together 💡 Pinpoint where teams are getting stuck and fix the system ☠️ If even the top software engineering shops are finding critical bugs in production....that should tell you something. ☠️ Your lack of an #aidriven #engineeringsystem will cost you in operational bugs and security issues

🏗️ Technical Debt is no longer a life sentence. "We can't refactor it," they’d say. "The original architects left in 2019, the documentation is a myth, and if we touch the core, the whole stack collapses." In 2026, Legacy Code is no longer a liability. It's an AI training set. If you are a leader at a company with a "mature" codebase, you are sitting on a goldmine—if you know how to use Agentic Refactoring. 🛠️ The "Modernization" Workflow (The Architect's Secret): The old way of refactoring took 6 months of manual mapping. My current AI-Augmented workflow looks like this: 1. Context Ingestion: AI doesn't "forget" how a function in File A affects a hook in File Z. We map the entire repo dependencies in minutes. 2. Intent-Driven Decoupling: Prompting agents to "Identify all synchronous database calls and propose an asynchronous pattern across these 40 files." 3. Automated Documenting: The AI writes the technical docs while it refactors. You get a clean codebase and a manual at the same time. 4. Agentic Unit Testing: Deploying specialized agents to "attack" the refactored code with integration tests before a human review. 🏢 To the Executives: Don't let your legacy stack be the reason you can't pivot. The cost of "doing nothing" is now higher than the cost of an AI-driven refactor. You don't need a 20-person "Migration Team"; you need a Strategic Architect who knows how to orchestrate the right agents. Is your legacy code holding you back, or are you using it as a springboard? 👇 #LegacyCode #TechLeadership #Refactoring #AI #SoftwareArchitecture #CTO