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Tyler Jennings's avatar

This is one of the most cogent takes on the current state of AI coding tools. I instantly subscribed. Good work, man!

It seems like we're getting closer to another layer of abstraction emerging, where ultimately there will be, basically, an AI coding language designed to optimize token usage and minimize power consumption.

Until such time, were all just trying to figure out how best to use these tools, and honestly it's kind of an amazing time to be around to see this new tech emerging.

Looking forward to following you and learning more. I am curious if you have used the bmad method tools for a more orchestration approach to using basically any model. Projects like that are definitely going to be instrumental in the next wave of innovation.

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Jove Zhong's avatar

Nice post, Shrivu! I learned a few new things, but I am not sure I agree all of them. Happy to be the first committer. I’ll just respond in the order you laid things out

1. “AI Cannot Read Your Docs” — catchy title, but not fully true. AI can read docs if you wire it up right: Context7 MCP, llmstxt, or even just letting an LLM search or fetch content. The bigger point, which I agree with, is that we should think about redesigning the software itself

2. Showing CLI output with object details and next steps is super useful. Not just for AI but for beginners trying to understand what’s happening or track the execution history

3. Error messages should always explain what went wrong and how to fix it, not just say “something went wrong”

4. With AI agents, good code comments are critical. The harder part is keeping them fresh when you refactor or redesign. Some people even go all the way, treating prompts as the only real “source” and having LLMs generate the actual Java/C/TypeScript code

5. CLI really is a great interface. That’s basically why Claude Code exists as a terminal tool instead of a web UI or IDE plugin. Even macOS apps are increasingly scriptable — maybe that’s a growing trend

6. Building interfaces that feel familiar, like pytest or pandas, helps adoption. Reinventing syntax for no reason is usually a barrier

7. Not sure about organizing code strictly by feature. In practice, backends might be Java/Go/Rust, frontends TypeScript, middleware in Go. You wouldn’t put those in the same folder like “addEmail.” Maybe with LLM coding, microservices could make more sense to have feature-oriented structure but today it feels messy

Overall, great post and worth a wide read. Would you be ok if I put together some visuals or infographics to share it further?

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