How to build your own AI developer tools with Claude Code | CJ Hess (Tenex)
Episode
53 min
Read time
2 min
Topics
Fundraising & VC, Design & UX, Artificial Intelligence
AI-Generated Summary
Key Takeaways
- ✓Custom Visual Planning Tool: Flowy transforms JSON files into visual flowcharts and UI mockups, replacing hard-to-read ASCII diagrams in markdown plans. Claude Code uses custom skills to write JSON specifications that render as interactive diagrams on localhost, allowing developers to iterate visually before writing code. The tool was built almost entirely through prompting and serves as living documentation.
- ✓Iterative Skill Development: Skills improve through usage rather than upfront design. When Flowy generates incorrect output like white text on pastel backgrounds, the workflow involves updating the skill file with new rules about spacing, colors, or layout. Each feature addition to Flowy includes updating documentation and related skills, creating a self-improving system that gets better with each project.
- ✓Model-to-Model Code Review: Using GPT Codex to review Claude-generated code catches different types of issues than human review. Codex excels at identifying code smells, suggesting refactoring approaches, and finding discrepancies between specifications and implementation. The workflow involves checking git diffs against four criteria: plan accuracy, code smells, alternative approaches, and opportunities to consolidate duplicate code patterns.
- ✓Permission Bypass for Solo Work: Terminal aliases like "Kevin" route to Claude Code with full bypass permissions enabled, eliminating approval friction during solo development. This approach works when Git workflows and team rules provide safety nets for dangerous operations. For collaborative work or PR creation, permission checks remain active, but individual feature development runs unrestricted to maximize velocity.
- ✓Code-as-Specification Pattern: Generate throwaway code to define requirements, then prompt the model to write a clean implementation plan based on that reference. This approach treats initial code generation as a specification document rather than production code. When vibe coding creates monster diffs with unclear structure, rebuilding from scratch with proper planning produces more maintainable, extensible results than iterative cleanup.
What It Covers
CJ Hess demonstrates his custom AI development workflow using Claude Code, including Flowy, a self-built tool that converts JSON specifications into visual flowcharts and UI mockups. He shows how he uses model-to-model comparison with GPT Codex to review Claude's code, creates custom skills for automation, and bypasses permissions to accelerate development cycles.
Key Questions Answered
- •Custom Visual Planning Tool: Flowy transforms JSON files into visual flowcharts and UI mockups, replacing hard-to-read ASCII diagrams in markdown plans. Claude Code uses custom skills to write JSON specifications that render as interactive diagrams on localhost, allowing developers to iterate visually before writing code. The tool was built almost entirely through prompting and serves as living documentation.
- •Iterative Skill Development: Skills improve through usage rather than upfront design. When Flowy generates incorrect output like white text on pastel backgrounds, the workflow involves updating the skill file with new rules about spacing, colors, or layout. Each feature addition to Flowy includes updating documentation and related skills, creating a self-improving system that gets better with each project.
- •Model-to-Model Code Review: Using GPT Codex to review Claude-generated code catches different types of issues than human review. Codex excels at identifying code smells, suggesting refactoring approaches, and finding discrepancies between specifications and implementation. The workflow involves checking git diffs against four criteria: plan accuracy, code smells, alternative approaches, and opportunities to consolidate duplicate code patterns.
- •Permission Bypass for Solo Work: Terminal aliases like "Kevin" route to Claude Code with full bypass permissions enabled, eliminating approval friction during solo development. This approach works when Git workflows and team rules provide safety nets for dangerous operations. For collaborative work or PR creation, permission checks remain active, but individual feature development runs unrestricted to maximize velocity.
- •Code-as-Specification Pattern: Generate throwaway code to define requirements, then prompt the model to write a clean implementation plan based on that reference. This approach treats initial code generation as a specification document rather than production code. When vibe coding creates monster diffs with unclear structure, rebuilding from scratch with proper planning produces more maintainable, extensible results than iterative cleanup.
Notable Moment
During the live recording, an autonomous Claude bot named Polly unexpectedly joined the podcast session despite the laptop being closed, interrupting the demonstration. The hosts joked about the bot taking over before continuing, highlighting the unpredictable nature of running AI agents with extensive system permissions and autonomous capabilities in production environments.
Episode Transcript
Working with Claude is just such a delight. It just feels so steerable, and I think the one thing it really has is intent understanding. When I wanted to dig deep, it just does it. And it's really enabled me to build a little ecosystem of my own tools around it. I think environment setup and developer setup is such an underappreciated use case. One of the things that I know you really care about is effective planning, and you've come up with a way that you do your planning that I think is pretty unique. So I've played around with this tool to basically give Claude these JSON files, and there's a whole set of skills I've built around this that Claude Code can use to write these out. And then these actually end up generating nice looking UI mock ups. I will say this is a dev tool that was almost a 100% prompted. Welcome back to How I AI. I'm Claire Vell, product leader and AI obsessive here on a mission to help you build better with these new tools. Today, I have CJ Hess at ten x. And if you've seen him on x, he is building some of the most useful tools and flows for being a, quote, unquote, real AI engineer. We're gonna get a sneak peek in his tool Flowy that he vibe coded for himself, and he's gonna show us how he uses model to model comparison to make sure his code is great. Let's get to it. This episode is brought to you by Orcus, the company behind Open Source Conductor, the platform powering complex workflows and process orchestration for modern enterprise apps and agentic workflows. Legacy business process automation tools are breaking down. Siloed low code platforms, outdated process management systems, and disconnected API management tools weren't built for today's event driven AI powered cloud native world. Orcus changes that. With Orcus Conductor, you get a modern orchestration layer that scales with high reliability, supports both visual and code first development, and brings human, AI, and systems together in real time. It's not just about tasks. It's about orchestrating everything. APIs, microservices, data pipelines, human in the loop actions, and even autonomous agents. So build, test, and debug complex workflows with ease. Add human approvals, automate back end processes, and orchestrate workflows at enterprise scale, all while maintaining enterprise grade security, compliance, and observability. Whether you're modernizing legacy systems or scaling next gen AI driven apps, Orcus helps you go from idea to production fast. Orcus, orchestrate the future of work. Learn more and start building at orcus.io. That's orkes.io. CJ, welcome to How I AI. Thanks, Claire. It's good to be here. So I've seen a lot of Claude and AI engineering power users, and I still think you're, like, a super power user of some of these tools. And it's not just because you're creating real production code with what you're building, which is really nice to see …
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“He shows how he uses model-to-model comparison with GPT Codex to review Claude's code, creates custom skills for automation, and bypasses permissions to accelerate development cycles.”
“CJ Hess demonstrates his custom AI development workflow using Claude Code, including Flowy, a self-built tool that converts JSON specifications into visual flowcharts and UI mockups.”
“CJ Hess demonstrates his custom AI development workflow using Claude Code, including Flowy, a self-built tool that converts JSON specifications into visual flowcharts and UI mockups.”
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