This is beautiful, I can't deny that. But the claim that Claude Code is a fixed set of tools is quite wrong. Claude Code regularly generates new code to carry out tasks, and you can choose to promote those tasks to skills, workflows, etc.
Much of the world believed in geocentrism for a very long time, yet we've known better for a long time now. Currently, this "Ptolemaic" idea of human intelligence being the center of everything sounds similar. What if human intelligence is not at the center? What if other forms, such as AI, emerge as the new "center"?
Basically, as long as you are using an Anthropic library or tool, you can use your OAuth credentials. For example, you can use the Claude Agent SDK with your OAuth credentials. This is sweet because I can prototype all sorts of agents with Claude Code embedded inside, at a predictable monthly cost. One nice use case is turning skills into standalone tools or apps.
You can also do convoluted things like run Claude Code within tmux and send input to it and read the output.
MCP Channels are interesting too for bidirectional communication between your app and a running Claude Code instance, with an MCP server sitting in between. It's slow, but allows for some interesting use cases when you want to step out of an existing CLI session to do work that is easier in a graphical interface, have Claude Code respond and do work, then when you're done, go back to the CLI session and continue, never losing context.
1. playwright-cli for exploration and ad-hoc scraping, in order to determine what works.
2. playwright code generation based on 1, which captures a repeatable workflow
3. agent skills - these can be playwright based, but in some cases if I can just rely on built-in tools like Web Search and Web Fetch, I will.
playwright is one of the unsung heroes of agentic workflows. I heavily rely on it. In addition to the obvious DOM inspection capabilities, the fact that the console and network can be inspected is a game changer for debugging. watching an agent get rapid feedback or do live TDD is one of the most satisfying things ever.
Browser automation and being able to record the graphics buffer as video, during a run, open up many possibilities.
"Test harness is everything, if you don't have a way of validating the work, the loop will go stray"
This is the most important piece to using AI coding agents. They are truly magical machines that can make easy work of a large number of development, general purpose computing, and data collection tasks, but without deterministic and executable checks and tests, you can't guarantee anything from one iteration of the loop to the next.
"To come back to my earlier comment, though, my main approach makes sense in this context. I let Opus do the abstract thinking, and then OpenAI's models handle the fine details."
Very interesting. I'm going to try this out. Thanks!
Thanks, I'll try those out. I've used Codex CLI itself on a few small projects as well, and fired it up on a feature branch where I had it implement the same feature that Claude Code did (they didn't see each other's implementations). For that specific case, the implementation Codex produced was simpler, and better for the immediate requirements. However, Claude's more abstracted solution may have held up better to changing requirements. Codex feels more reserved than Claude Code, which can be good or bad depending on the task.
Hey HN! In 2025, I've spent more time than ever conversing with AI coding agents, particularly Claude Code. These conversations are an intimate look into how we think and solve problems. Every chat with the agent contains valuable solutions, patterns, decisions, and mistakes. So being able to search, analyze, and learn from those interactions isn't just convenient, it's becoming essential.
To help me do this, I built a tool to process Claude Code conversations:
* Import and search your entire conversation history across projects
* Analyze sessions, choosing from over 300 LLM models, via OpenRouter, to extract insight and patterns (decisions made, error patterns, how you use AI agents)
* Share insights as GitHub Gists (as long as the text passes a security scan)
* View basic aggregate statistics on Claude Code usage
The tool is built with Python, Streamlit, SQLite with FTS5, OpenRouter, and Gitleaks.
I made this for myself, and sharing it in case it helps you too. Once your conversations are in a database, you can start asking questions like “What were the key technical decisions on this project?”, “How did the agent help to research and prototype this feature?”, "What steps did I take to implement this?" and “What errors does the agent commonly make?”
It’s a work in progress, and I'm planning on adding more features. Currently only tested on macOS 14.7 with Claude Code 2.0.21. If you’re curious what your Claude Code sessions may reveal, take it for a spin!
But the AI coding agent can then ask you follow up questions, consider angles you may not have, and generate other artifacts like documentation, data generation and migration scripts, tests, CRUD APIs, all in context. If you can reliably do all that from plain pseudo code, that's way less verbose than having to write out every different representation of the same underlying concept, by hand.
Sure, some of that, like CRUD APIs, you can generate via templates as well. Heck, you can even have the coding agent generate the templates and the code that will process/compile them, or generate the code that generates the templates given a set of parameters.
I expect less time spent on boilerplate and documentation, and more time spent on iterating, experimenting, and increasing customer satisfaction. I also wouldn't accept "I don't know how to do that" as an answer. Instead, I'd encourage "I don't know how to do that, but I can use AI to learn faster, and also seek out someone with experience to help review my work".
Add LLM-powered chat to your app. Translate English into executable JSON commands using just TypeScript, Node, and OpenAI—no frameworks! Start extremely simple and get better at prompt engineering, before delving into things like tool calls and MCP servers.
That is funny, because I was a Java developer for many years, then Scala for a few years, and these days I mainly write Python, but the last thing I go for is creating a class. That's generally only when a set of functions in a single responsibility module need to share / mutate some state, and it's more self-documenting than passing around dictionaries.