AI Development: Balancing Speed with Customer Understanding

I tried using Claude for real coding workflows. Not just “write a function”… But actual development tasks. Here’s what I observed: Claude is strong at: – understanding large codebases – writing structured, readable code – explaining logic clearly But breaks when: – context becomes fragmented – requirements are ambiguous – multiple steps aren’t orchestrated properly That’s when I realized: The problem isn’t the model. It’s how we design the system around it. Good AI usage = prompts Great AI systems = orchestration + context + control Still experimenting with where Claude fits best in dev workflows. #buildinpublic #claude #ai #softwareengineering

When I build a larger feature with AI, I don’t start with code anymore. I start with a plan. I use a bunch of agents to break the idea down into something very detailed, both from a product and technical perspective. Then I: - save that plan in a doc - turn it into high level diagrams so others can understand it - create a detailed implementation plan From there I implement it in small iterations. Instead of one massive PR, I end up with: - smaller, focused changes - easier reviews - better understanding of what’s actually going on AI didn’t replace coding for me. It changed how I approach building things.

Everyone’s excited about AI writing code, but the real bottleneck is PR review. If AI is going to ship thousands of changes, humans can’t review every single one. That’s why I’ve been working on a PR Reviewer Agent , one that goes beyond linting and actually understands diffs, context, behavior, and repo conventions. The big question is architecture: ReAct vs Plan and Execute, Flexibility vs control. This layer is going to matter a lot as AI moves deeper into engineering workflows. I wrote a quick breakdown here - https://lnkd.in/gn73Crwh

Most teams are still optimizing code. But in AI systems, code is only part of the equation. What really shapes outcomes is everything around it: the data you expose, the context you provide, and the signals the model interprets. That’s where things start to get interesting (and harder). In this blog, Michael Scranton explores how AI-native engineers are shifting their mindset, from writing logic to designing context. 👉 Dive deeper here: https://lnkd.in/d6kK4YDK #Coderio #softwareengineering #ainative #innovation

Much of the conversation around AI in engineering is centered on coding speed. In practice, we’ve seen something more impactful: reduced friction across the entire delivery lifecycle. From faster troubleshooting and solution design to more consistent documentation, AI is improving how work gets done, not just how quickly code is written. In this blog, we share practical observations from real delivery work and where AI is providing the most value. Read the blog: https://lnkd.in/ehXgfa7Z

This week's GitHub trending repos are all about making AI work harder for you. From NousResearch's Hermes Agent framework that learns your workflow, to Andrej Karpathy's single-file coding upgrade, to a personalized AI tutor, a platform that turns agents into teammates, and Google's on-device ML showcase — these five repos are shaping where dev tools are headed. Whether you're building agents or just want to code smarter, this roundup has something for you. Follow OPC Community for more: opc.community https://lnkd.in/edxeZR4p

everyone's coding with AI now but almost nobody reads the docs anymore you prompt → you get code → you ship it but here's the thing: that "working" code might use deprecated APIs or stale patterns... reading docs = understanding why, not just what the best engineers I know still read docs first, then use AI to accelerate not replace thinking. you can vibe-code a feature in 10 min and spend 3 hours debugging it because the library changed its auth flow 2 versions ago or you could've spent 5 min on the migration guide. read the docs. let AI help you write better code, not just more code.

The terminal is graduating 🎓. It’s no longer just a passive execution engine; it’s becoming an active AI collaborator. Integrating agents like GitHub Copilot and Claude Code directly into the command line—as shown here—represents a monumental shift in developer workflow. We aren't just 'typing commands' anymore. We are orchestrating intelligence. Context switching is dying, and real-time co-creation is rising. The IDE was the first frontier; the CLI is the future for GenAI. Are you ready for an autonomous command line? #FutureOfDev #AI #GenerativeAI #DeveloperExperience #CLI #GitHub #Anthropic https://lnkd.in/e3vpAgKj

If humans have a future coding along side AI, there’s a whole discipline I think that needs to emerge. Infrastructure for how humans think alongside agents. It’s so exciting for people (me) to be able to produce at this velocity and I think we’re hoping that the models just get good enough that it works at scale because right now we have people vibe coding small things, but the coherence falls apart at scale. Maybe something like coherence management at AI-assisted velocity

Recently, I came across an interesting statement from the head of Claude code at Anthropic, who mentioned that he hasn't written code by hand in months. In just two days, he shipped 49 features, all generated by AI. This raises a thought-provoking question: if one of the top engineers in the field isn't writing code anymore, what reasons do other engineers have for not fully embracing and utilizing Claude code and similar tools in their own production environments? We have reached an inflection point where the barrier to entry is no longer the skill set of coding, but rather the ability to architect and prompt effectively. #tech #ai #swe