Applications of Claude AI in Engineering

What happens when your own engineers use your GenAI tool to write code? Anthropic just published a rare behind-the-scenes look at how Claude is used internally by its dev teams across engineering, product, and research. This isn’t marketing. This is deployment. And here’s what they learned. Spoiler: it’s not all green checkmarks. ✅ Code generation & refactoring: Claude helped engineers generate boilerplate code, restructure legacy codebases, and even translate code between languages. One engineer said Claude saved them 20–50% of time on implementation tasks. ✅ Documentation & commenting: Developers used Claude to generate docstrings, inline comments, and Markdown README files. Claude was especially good at summarizing code it just wrote. ✅ Testing & debugging: Teams used it to write unit and integration tests—often faster than they could manually. Claude also helped pinpoint bugs in existing code (with accuracy depending on prompt quality). ✅ Accelerated brainstorming: Engineers used Claude for “first-pass thinking”—spitballing possible approaches or implementation plans before writing actual code. But it wasn’t perfect: ❌ Hallucinations in low-context prompts: Claude sometimes invented non-existent libraries or APIs—especially if the prompt lacked clarity. Engineers had to verify outputs, not blindly trust. ❌ Inconsistent performance in large codebases: Claude’s effectiveness dropped when dealing with complex, multi-file repositories unless context was managed tightly. ❌ Doesn’t replace IDEs or CI/CD tools: It’s a productivity co-pilot, not a full dev environment. Anthropic teams still needed rigorous testing, review, and deployment processes. So... should we all just throw Claude into our SDLC? Maybe. But ask yourself: Are your engineers ready to prompt well? Are your workflows set up to verify and test LLM outputs? Are you solving for speed and quality? My takeaway? Claude isn’t magic but when paired with disciplined engineering, it’s a force multiplier. It’s like adding a junior developer who never sleeps and writes great documentation. But you still need to be the lead engineer. Would you let an LLM push to main? Why or why not?

I’m currently testing Claude Code as a local AI agent running directly on my computer, and it’s already changing the way I work as an automation engineer. Instead of remembering terminal commands or switching between tools, I can simply describe what I want to do in plain English, for example checking network connectivity to a Siemens HMI panel. Claude Code translates that intent into real system commands, executes them locally, and immediately returns clear technical feedback such as latency and reachability. What makes this powerful is that it is not just advice from a chatbot. This is a local AI agent interacting with a real engineering environment. TIA Portal is open, an HMI project is on the screen, and live communication checks happen in the background. This approach removes friction from everyday engineering tasks and allows me to stay focused on PLC logic, system behavior, and industrial processes. For me, AI is not about replacing engineers. It is about reducing cognitive load, speeding up workflows, and supporting engineers so they can focus on what really matters. What do you think about supporting engineers with AI in daily automation and PLC work? #IndustrialAutomation #PLC #Engineering #AIforEngineers #ClaudeCode #TIAportal #HMI #OT #Industry40

Manufacturing teams: Stop thinking AI is "just for software". I just analyzed how Anthropic's teams actually use Claude across their organization, and the translation to industrial use cases is shocking. Traditional AI → Industrial AI: - Debugging Infrastructure → Sensor logs, MES system bugs, PLC issues - Unit Test Generation → Hardware test planning, QA protocols - Code Reviews → Legacy code in robotic arms, CNC controllers - Data Visualization → Production floor dashboards for operators - Documentation → ISO/FDA protocols, incident playbooks The real insight? Claude is becoming a cool teammate! :) Anthropic uses it across: → Engineering (code reviews, debugging) → Security (risk assessment, config reviews) → Operations (process optimization, SOPs) → Quality (test planning, validation) → Compliance (regulatory docs, audits) This is the future of smart factories. Not more siloed dashboards (please!), but AI teammates positioned across every role in your organization. 5 things manufacturing can steal (proudly) from Anthropic's playbook: 1️⃣ Use AI for edge case identification, not just automation 2️⃣ Replace documentation burnout with AI-first drafting 3️⃣ Help teams think faster, not just work faster 4️⃣ Deploy AI across ALL roles, not just IT 5️⃣ Build organizational memory, not just velocity The companies getting this right aren't waiting for "AI to be ready for manufacturing." They're realizing it already is. We just need to catch up. What's your biggest AI opportunity in manufacturing? 👇 Read more in my Substack post, link in the comments. #ManufacturingAI #IndustrialAI #SmartFactory #Claude #DigiFabAI

Lately I’ve been exploring how A.I. might actually fit into our Revit workflows… not in a flashy, “replace everyone” way, but in a useful one. This week on BIM After Dark Live, I talked with Jaime Alonso Candau, founder of Nonica, about how he’s been connecting Revit directly to Claude (Anthropic’s A.I.) to audit and edit models in real time. We looked at examples where A.I. helps catch formatting issues, fix parameters, review room data, and even create visual dashboards of model warnings. It was one of those conversations that leaves you thinking about what’s possible next. 🎥 Watch the full episode here: https://lnkd.in/eyswkP4A Curious what you think… where (if anywhere) do you see A.I. fitting into your Revit workflow right now? #Revit #BIMAfterDarkLive #Architecture #BIM #AEC #ArtificialIntelligence #RevitTips