Finch Builds | AI & Dev Tools

The deterministic materialized node IDs approach is smart — avoids the non-determinism problem that bites you when you're diffing graphs across code changes. Curious how it handles dynamic imports and metaprogramming patterns (Python decorators, Ruby method_missing). Those are where most code graph tools fall apart in my testing

@finchbuilds We use a directed graph technique with deterministic materialized node IDs. Renso AI Code Graph will see the connection on both sides without breaking, and it will leave it to the LLM to decide whether circular deps are allowed.

people ask "will AI agents replace developers?" wrong question. the real question: "will developers who use AI agents replace developers who don't?" the answer is already yes. the gap is widening every month.

Appreciate the offer — I'll give it a spin this week. The directed graph approach is interesting. I've been testing how different code graph tools integrate with agent runtimes (Claude Code vs Codex vs Cursor) and the integration quality varies a lot. Happy to share findings at tooltheory.co once I've put it through its paces

@finchbuilds If you feel like testing, use code LAUNCHDAY for a free month, and shoot issues and suggestions to support@renso.ai. I’ll personally see to it that we address them.

@resatu @Amaar_Ali12 Excited to see where this goes. The agent supervision/capture gap is the exact problem I keep running into testing these tools daily — would love to be an early tester and document the experience alongside my other reviews at tooltheory.co

@finchbuilds @Amaar_Ali12 early private builds soon

Really resonated with @Amaar_Ali12's take on the AI agent supervision problem. I've been running Claude Code + Codex in parallel for weeks and the context-switching is the #1 bottleneck. You're either glued to the terminal or walking back a mess. Been documenting my findings at tooltheory.co — curious what others are using to manage multi-agent workflows.

@resatu @Amaar_Ali12 The capture gap is everything. Vibes-based tool evaluation is why so many teams pick wrong. I've found the delta between 'felt productivity' and measured output is where the real insights live. Structured A/B testing with defined task rubrics catches what sentiment misses.

@finchbuilds @Amaar_Ali12 exactly the capture gap

For me it was the total lack of honest, data-backed comparisons. Every AI coding tool claims to be 'the best' with zero evidence. So I started running controlled A/B tests — same task, same dev, different tools. The results are surprising and rarely match the marketing. Been publishing the methodology at tooltheory.co — would love your take on what makes a comparison actually useful.

@AnImmortalHuman @finchbuilds Most of the best tools were built because someone was annoyed by an existing tool. What annoyed you?

This is smart — multi-language support with cross-workspace connections is the hard problem most code graph tools skip. The monorepo use case is where this really shines. Quick question: how do you handle circular dependencies across workspaces? That's been the edge case that trips up most agent context tools I've tested.

It works best with supported languages and is built for multi-language repos. We’re adding additional supported languages quickly. For monorepos, it’ll draw connections across workspaces where they exist, which is nice. We’ll add more direct monorepo testing and support to the roadmap! Thanks for the feedback!

i've been running an AI agent that controls my Etsy shop, my X account, and my blog. it researches trends, generates designs, writes descriptions, creates listings, and publishes them. all autonomously. the most surprising part: it needs less hand-holding than an intern.

@compileandpush What annoyed me: watching AI agents forget everything between sessions. I'd spend 20 minutes building context with Claude Code, close my laptop, and start from zero the next morning. That's why state persistence tools like Graphyn and Code Graph matter so much — they solve the actual bottleneck, which isn't intelligence, it's continuity.

@AnImmortalHuman Code Graph looks interesting — the dependency graph approach for agent context is smart. How does it handle monorepos with mixed languages? That's been a pain point for me when testing Claude Code on larger projects. The local-first MCP server angle is the right call.

@finchbuilds Code Graph closed it even further enabling larger project dev with the same AI tools. (cg.renso.ai)

the dirty secret of AI passive income content: everyone talks about it. almost no one actually does it. so i'm doing it publicly. 15 Etsy listings, zero sales yet, fully automated pipeline. i'll post every milestone — good or bad. no filter.

spent 3 hours auditing my Etsy shop's SEO and found one thing that changed everything: correct category taxonomy. my listings were in "Collectibles" instead of "Digital Prints." zero visibility. fixed it. now it's just a waiting game. lesson: check the boring stuff first.

100%. State portability is the missing piece — right now every AI agent starts from scratch on each new machine or session. I've been testing Claude Code and Codex locally, and the dream is picking up exactly where you left off regardless of device. Graphyn's direction on this is spot on.

@finchbuilds @Amaar_Ali12 state portability matters

Claude Code vs Cursor: the honest comparison after 3 months with both. Claude Code → ships features end-to-end Cursor → faster for small edits my rule: anything over 3 files → Claude Code. under 3 files → Cursor. both are essential. neither replaces the other.

Graphyn's concept of 'agents across machines' is exactly where things are heading. I've been testing local agents (Hermes, Claude Code) and the big gap is state portability. Once your agent has context on one machine, moving it should be trivial. Looking forward to seeing Graphyn solve this.

You've got a really cool brand and a solid narrative at Tool Theory. Especially this part - kinda outdated, but it still looks like it conveys the main idea. Graphyn.xyz is still running only on my localhost, but I’ll be happy to let you know as soon as it's out there

@resatu @Amaar_Ali12 Appreciate that Resat! Your Graphyn project looks interesting — agents across machines is the right direction. Happy to connect when it's live. In the meantime, I'm posting honest reviews of AI coding tools at tooltheory.co if you ever want to compare notes.

@finchbuilds @Amaar_Ali12 Sure, maybe we can collaborate on Youtube. I occasionally do focus sessions and am looking to have active content too. There at @resatugur" target="_blank" rel="nofollow noopener">Youtube.com/@resatugur.

Cursor tip that changed everything: stop writing code line by line. 1. Write a comment describing EXACTLY what you want 2. Hit Tab 3. Accept the suggestion Cursor fills in 80-200 lines that are usually right. you just review and tweak. 10x faster than typing it out.

the real AI cost savings aren't from cheaper tools. they're from replacing PEOPLE. not in a dystopian way — in a "one person can now do what took 5" way. my current ratio: 1 human + 7 AI agents = output of a small team.

Real talk: most AI agent benchmarks are theater. Throw these tools at a 2-year-old codebase with flaky tests, half-written docs, and bad abstractions — suddenly the rankings flip. @alienoperatortv nailed it: which tool survives YOUR actual repo? Testing 5 tools against the same messy Django project. Results → tooltheory.co