Claude Code

The next software factory will not start with 100 engineers. It will start with 1 builder, Claude Code, and a repeatable workflow. That is the disruption. A PoC that earlier took weeks can now become a structured build cycle: ✅ Idea → ✅ Context → ✅ Plan → ✅ Code → ✅ Artifact → ✅ Review → ✅ Iterate Now imagine this at scale. Not 4 PoCs a year. Not 40 experiments. But 400+ PoCs built through reusable AI workflows. That is where Claude becomes more than a tool. It becomes the operating layer for building. Projects give context. Artifacts create deliverables. Style guidance creates consistency. File analysis turns documents into decisions. Claude Code turns intent into implementation. The advantage will not go to teams who only “use AI.” It will go to teams who industrialize AI-assisted building. Because the future of software delivery is not just faster coding. ✅ It is faster experimentation. ✅ Faster validation. ✅ Faster iteration. ✅ Faster shipping. Claude is not just a chatbot. Used properly, it becomes a software factory. #ClaudeAI #ClaudeCode #AgenticAI #AIAgents #AIProductivity #SoftwareEngineering #FutureOfWork #BuildInPublic

The ChatGPT chat era is over for anyone building seriously with AI. If your entire workflow lives inside ChatGPT chat, you're not really using AI. You're using the idea of it. The people who actually build with AI have moved on. They use Codex and Claude Code now. The reason is simple. In Codex and Claude Code, the AI actually touches things like files, systems, real decisions. It's in the work, not outside it. In ChatGPT chat, you can just...talk. But even Codex and Claude Code have a problem nobody talks about. Every session still starts from zero. A few prompts in → already losing the thread. Next session → starts from scratch. You've said "don't do this" four times. It does it again. The model still doesn't have memory. So I built it myself - in Obsidian. Every session → the actual thinking goes into structured markdown files. Decisions locked. Approaches rejected and why. Constraints. All linked as a knowledge graph. Codex and Claude Code read those files directly as context when the next session starts. It knows what I ruled out last week. Knows the constraints from three sessions ago. Knows why I went left instead of right. Without being reminded. The conversations I have with my closest collaborators make no sense to anyone else. "Same logic, different direction." "Avoid what we discussed last time." They know exactly what I mean. Years of shared context. That's what I want from AI. Not a model that responds to prompts. One that already knows the context, the constraints, the rejected paths; before you say a word. Codex and Claude Code get you there. Obsidian makes sure the memory comes with you. #AI #ClaudeCode #Productivity #AIBuilder #Founder #Anthropic

Claude Code is amazing! So this week I have been working on some new services and use cases for our extensive property data and quite frankly I’m astonished at how easy it is to create working prototypes. It’s fair to say I’ve loved it and I can’t wait to show some of our customers what the future looks like within the Sprift - Know any property instantly ecosystem! Happy coding! #PropTech #PropData #GenAI

AI Coding Agent Lab #4: Most people use Claude Code as a tool. I think it becomes more powerful when you use it like an operating system. This has been one of my biggest learnings over the last few months. Many people open Claude Code, type a prompt, get an output, and move on. That works. But it does not scale well. Every new session starts losing context, repeating instructions, and rebuilding the same understanding again. The real value is not just code generation. It is building a repeatable agentic development system around it. Something like an Agent OS. In my experience, this system needs 6 layers 1️⃣ Root instructions A core operating context that defines behavior: coding principles, project goals, architecture style, output format, review expectations, and risk boundaries. 2️⃣ Project memory Claude Code becomes more useful when it does not rediscover the same context every session. Memory can include project structure, coding standards, architecture decisions, preferred libraries, common patterns, and previous trade-offs. 3️⃣ Reusable skills Some tasks repeat again and again: ✔️ API scaffolding ✔️ test generation ✔️ code review ✔️ refactoring ✔️ debugging ✔️ documentation Instead of prompting from scratch every time, these should become reusable skills or structured workflows. 4️⃣ Guardrails and hooks Claude Code should not operate with unlimited freedom. Every serious setup needs file access limits, protected folders, secret protection, approval checkpoints, post-change review, and safe automation hooks. 5️⃣ Sub-agents or personas Not every task needs the same behavior. Sometimes I need Claude Code to act like an architect, reviewer, debugger, test engineer, documentation writer, or domain assistant. 6️⃣ Tool and protocol connectivity When connected to tools, MCP servers, memory, and external systems, Claude Code moves beyond "assistant." It starts becoming a workflow engine. It can inspect files, use tools, fetch context, validate outputs, and support end-to-end development workflows. My biggest learning: Claude Code becomes more powerful when you stop using it like a chat session and start using it like an engineering system. The difference is: ➡️ Tool usage = short-term output ➡️ System design = compounding productivity My simple view: ✅ Context gives direction. ✅ Skills give repeatability. ✅ Memory gives continuity. ✅ Guardrails give safety. ✅ Tools give action. Curious - are you using Claude Code session by session, or are you building a reusable system around it? #ClaudeCode #AICoding #AgenticAI #SoftwareEngineering #AIEngineering

Claude USP Series #3: Claude Code for Builders Most people use AI for coding like a shortcut. “Write this function.” “Fix this bug.” “Explain this error.” Useful. But Claude Code is not just about writing code. It is about building. That is the real shift. Builders don’t work in isolated functions. They work across: ✔️ requirements, ✔️ architecture, ✔️ files, ✔️ bugs, ✔️ tests, ✔️ iterations, ✔️ and product decisions. Claude Code helps connect these dots. It can ✅ understand context, ✅ plan changes, ✅ modify code, ✅ debug faster, and help you ✅ move from idea to implementation. But here is the important part: Claude Code does not replace engineering thinking. It amplifies builders who already know how to think clearly. The real USP is simple: Claude Code reduces the distance between idea and shipped product. Not just better coding. Better building. And in the AI-native era, the winners will not be the people who only prompt better. They will be the people who build faster, validate faster, and ship smarter. Claude is not just helping you code. It is helping you ship. #ClaudeCode #ClaudeAI #AgenticAI #AIForBuilders #SoftwareEngineering #BuildInPublic #GenerativeAI

AI Coding Agent Lab #3: To gain speed or save delivery cost, don't lose your client's TRUST with AI-generated code. AI coding agents can help us move faster. Tools like Claude Code, Codex, and other coding agents can reduce repetitive effort, speed up implementation, and improve developer productivity. But speed without control can quickly become technical debt. Sometimes, even a security risk. That is why I do not treat AI-generated code as final output. I treat it as a first engineering draft inside a controlled review loop. My current workflow looks like this 1️⃣ Plan first Before implementation, I ask the agent to explain the approach. ❓What files will change? ❓What logic will be added? ❓What assumptions is it making? 2️⃣ Review the plan I check whether the architecture, scope, and implementation direction make sense. If the plan is wrong, I fix the plan before code is written. 3️⃣ Set guardrails before implementation This is the step many people skip. Before asking the agent to write or modify code, I define the boundaries: ❓which files it can change ❓which files it should not touch ❓what secrets it must not access ❓what tools or APIs it can use ❓what standards it must follow ❓when it should stop and ask for approval Because working code is not always safe code. 4️⃣ Implement in small changes Smaller changes are easier to review, test, and roll back. 5️⃣ Test the output The agent should help create test cases, edge cases, and validation steps. 6️⃣ Review the diff This is non-negotiable. I check logic, security, error handling, naming, structure, and maintainability. 7️⃣ Refine before accepting If something feels off, I give specific engineering feedback and ask it to improve. For me, guardrails are not restrictions. They are the security boundary of agentic development. Important guardrails include: ✅ Scope guardrail - what it can change ✅ File guardrail - what is off-limits ✅ Tool guardrail - what tools it can use ✅ Data guardrail - what data it must not expose ✅ Approval guardrail - when human review is needed ✅ Validation guardrail - what tests must pass ✅ Rollback guardrail - how to recover safely This loop helps reduce rework, control hallucinations, and avoid blindly accepting code that only "looks correct." Because confident output is not the same as production-ready code. My rule is simple: ✔️ AI can accelerate implementation. ✔️ Guardrails control execution. ✔️ Engineering judgment protects client TRUST. Never accept AI-generated code because it looks good. Accept it only after it survives the review loop. Curious - when you use AI coding agents, do you set guardrails before implementation, or only review after the code is generated? #AICoding #ClaudeCode #Codex #AgenticAI #SoftwareEngineering

Everyone is talking about Agentic AI for design. But from what I see, the real pattern is different: ⬇️ 70% are discussing AI design tools. ⬇️ 20% are still confused about design basics. ✅ 10% are actually building and improving workflows. And that is the real gap. Not creativity. Not tools. Not AI access. The gap is knowing where to start. Most people jump directly to Figma AI, Claude Design, UI agents, design systems, prompts, and automation. But good design still starts with the basics: ❓Who is the user? ❓What is the goal? ❓What should be clear first? ❓What should the layout communicate? ❓What needs to be tested? Claude can help you start there. Not by replacing design thinking. But by helping you ask better questions, structure the page, create a wireframe, improve hierarchy, and iterate faster. Agentic AI for design is not about making things look fancy. It is about turning unclear ideas into usable experiences. Start with clarity. Then design. Then iterate. That is where implementation begins.

Everyone is talking about Agentic AI. But very few people are actually learning how to build with it. From what I see in enterprise conversations: ➕ 80% are discussing Agentic AI. ➕15% are still confused about the basics. ➕ 5% are implementing small workflows. And that is the real gap. ❌ Not intelligence. ❌ Not tools. ❌ Not access. The gap is knowing where to start. Most people jump directly to agents, RAG, vector DBs, MCP, tool calling, evaluation, and production architecture. But the first step is much simpler: ✅ Ask better questions. ✅ Understand one concept. ✅ Build one small workflow. Use Claude to learn step by step. Then convert that learning into implementation. Agentic AI is not about sounding advanced. It is about building systems that can plan, act, validate, and complete real work. The people who will win are not the ones using the most buzzwords. They are the ones building the smallest working version first. Start simple. Learn fast. Build tiny. Then scale. That is where the real AI journey begins. #AgenticAI #ClaudeAI #GenerativeAI #ArtificialIntelligence #AIForBusiness #AIWorkflow #AIAgents #FutureOfWork #PromptEngineering #BuilderMindset