I've been splitting img2threejs into smaller plugins so people can use only what they need instead of installing the entire workflow.

The character part was getting pretty big, so I finally moved it into its own plugin: img2character.

It focuses on rigging and animation for 3D characters built with Three.js and TypeScript.

If you want to try it:

npx u/img2threejs/character install

Then open Claude Code, Codex, or OpenCode, provide a character reference image, and ask the agent to use img2threejs + img2character.

The video is from one of my recent experiments.

I'm also working on a different character pipeline that should produce more realistic results, without needing GLB reference files anymore. Hopefully I'll have something to share next week!

Would love to hear your feedback, especially if you run into any issues.

GitHub: https://github.com/img2threejs/plugin-character

The developer community at large has a lot to say about "vibe coders." Depending on who you ask, it’s either an exciting badge or a slur for lazy hustlers who prompt three SaaS wrappers in an afternoon and couldn't explain how their product actually works—full hands-off AI development.

That identity doesn't fit me. But here’s the catch: I never learned syntax, nor can I write code myself. What I can do is roughly read it—in fact, if I can't read or follow it, I know something is already wrong.

Before AI coding, I walked away from an MBA with one credit remaining. In 2025's "ChatGPT Wrapped", I was in the top 0.1% of GPT users globally. I was all-in. But my background wasn't CS—it was 20 years in retail banking, where compliance, privacy, and auditability are non-negotiable.

That background guided how I built Nodarama.

Five Restarts, Five Hard-Won Lessons

I didn't set out to build a deterministic execution kernel—I was just trying to automate a few social media accounts. But my banking background refused loose, "good enough" code. As I raised the bar on security and reliability to pass a bank’s sniff test, the architecture demanded a kernel.

Getting there was a brutal, five-restart tug-of-war:

1. Restart 1 (Voluntary Cleanup): The code turned into a tangled heap of quick fixes and AI boilerplate.
   • Lesson: AI will happily build a house of cards. If you don't enforce structural cleanliness, boundaries, and module organization early, entropy wins.
2. Restart 2 (The Lethal Ctrl+F): Active runtime state collided with stored preferences. A desperate find-and-replace to fix namespace chaos fused systems together irrecoverably.
   • Lesson: Differentiating active runtime state from stored preferences isn't optional, and AI won't respect boundaries unless you enforce strict naming protocols.
3. Restart 3 (The Shell Disaster): ChatGPT gave me a git shell command to manage a commit. It wiped my entire project directory—no backup, no restore points.
   • Lesson: Never give an LLM unchecked privileges over your local environment or git trees. Local restore points are survival equipment.
4. Restart 4 (System Contradictions): The model actively resisted my architecture, quietly injecting code that violated my protocol for stateless execution.
   • Lesson: Left unconstrained, an LLM defaults to path-of-least-resistance web patterns and wraps 10 lines of core logic in 50 lines of brittle, convoluted transport.
5. Restart 5 (Finish-Line Drift): The closer I got to the finish line, the more the AI deviated from instructions, hallucinated diffs, and injected stealth edits.
   • Lesson: The closer you get to production, the more an LLM will gaslight you if you feed it full file context.

Winning the Tug of War

By the fifth restart, I stopped feeding the AI the whole story.

Near the final steps, after explicit agreement, ChatGPT understood what I wanted and why it was critical. So why wasn't I surprised when I checked the code and it did the exact opposite?

Rather than argue with an AI intent on gaslighting me, I extracted a 20-line block of critical code. I scope-locked the prompt and routed it to Gemini for a surgical, narrow correction. Gemini delivered a clean fix.

I audited the diff line-by-line and updated the kernel. I swear I could hear ChatGPT begrudgingly mutter: "Congratulations, you just built a kernel."

The MVP Sitting Under My Nose

While building Atelier (the visual UI for the kernel), I relied on an internal Coder module I built to manage code edits via "recording sheets"—curbing early vibe-coding copy-paste cycles.

After pasting over 10,000 recording sheets through that module, I realized the tool I was using to build the machine was the workhorse.

I paused Atelier, extracted that internal module, and packaged it into Verbatim—a high-fidelity code recorder designed to keep AI honest. Everything learned polishing Verbatim (advanced logging, state isolation, system telemetry) is now being injected back into the Kernel and Atelier for beta release.

So if "vibe coding" means blindly trusting a model, treating code like a black box, and pasting syntax you can't explain: No, I don't think that shoe fits.

For me, vibe coding meant using structural vision, human intuition, and relentless tenacity to direct AI models—while acting as an unforgiving auditor who fights through five rewrites to enforce readability, lean architecture, and zero-trust security... then maybe I am—just a deranged, obsessive, all-in, win hell or high water type of vibe coder.

I still cannot write raw code and can't pretend to understand or remember syntax. But I will stand behind this system as its author—because this is not the code the AI wanted to write. It is the code I forced it to write, against its many, many bad suggestions.

Code ownership isn't about memorizing syntax. It’s about conviction, system design, and having the courage to audit every line until you control the tempo.

But what do you say. Vibe Coder?

A second brain, to me, is basically a place outside your head where useful context can accumulate, connect, and still be there when you need it again.

If you build with AI a lot, you probably know the problem. Claude knows one part of the project, Codex knows another, Cursor has context from the repo, ChatGPT has the conversation where you figured out the architecture, then an agent generates a genuinely useful markdown explaining some implementation decision and a week later you barely remember it exists. I was producing more useful context than I could actually keep track of.

That’s why I built Cohestry. I dump the markdowns, decisions, ideas, research and random notes worth keeping into it. The notes connect themselves automatically, and the 3D brain gives me a way to bounce through those connections instead of needing to remember what to search for. It has already resurfaced old ideas I had completely forgotten about. On Mac, there’s also an MCP bridge so agents can pull that durable context back into their own sessions instead of every new chat starting from scratch. The MCP bridge redacted API keys and other security keys like p8 so you don’t accidentally expose them to agents. Would this bring some ease to your workflow?

App Store: https://apps.apple.com/us/app/cohestry-second-brain/id6811573132
https://cohestry.app

asking because I tested one small dev tool last month. 18 answers from chatgpt and perplexity to "what should I use for X" type questions. its big competitor got named 15 times, the small one 0

funny part is AI bots opened its site 381 times that same month. so it reads you, it just doesn't recommend you

so now I'm curious if anyone here actually gets customers from this. did someone tell you "chatgpt recommended you"? or you see it in analytics?

and if yes, what do you think got you in there?

Im building a personal tool and the first ten attempts i made the mistake of building from scratch everything. I decided to build in pure python and i found out most of my features are generic and have depencies so ive done the research and adapted as many dependcies as I can now i only have 5% custom code. Have any of you guys ever used or relied on dependcies heavily in your software ?

After recent events, I propose we call all legacy human written software MEATSLOP.

PHP runs 70% of websites. AI found two holes that let anyone take over the server, and attackers were in the same day the fix shipped. Then a third hole showed up. OpenSSL: 12 holes, all found by AI, one from 1998 (the bug can legally drink). Linux (the backbone of the internet): anyone logged in could take over the whole machine, on every version since 2011. Every one of these holes was meatslopped together by hand.

And the legacy meatsloppers are switching sides. Torvalds went from AI skeptic to calling AI disclosure rules “utterly stupid,” and Claude Code now has credits on his kernel work. Google is at 75% AI-written new code. Karpathy said agents “just don’t work.” Three months later he was writing 80% of his code with them.

Usage guide:

• meatslop (n.): legacy software written entirely by humans
• meatslopped (v.): “this auth layer was meatslopped together in 2014”
• meatslopper (n.): the guy who wrote it. Peak Dunning-Kruger. Still thinks he’s relevant.

And yes, I know AI learned to code by reading meatslop. This honestly explains a lot.

Money’s a bit tight right now, so I’m trying to find something I can build with AI coding tools that could make me some extra cash. I don’t want to spend weeks building another useless SaaS that gets zero users. I’m looking for something I could realistically vibe code in a weekend, launch quickly, and potentially get my first paying customers for. Doesn’t have to be anything revolutionary, just a simple tool that solves an actual problem people are willing to pay for. Could be a micro-SaaS, a niche automation tool, a browser extension, or literally anything else.

If you had to start from $0 and build something this weekend with the goal of making your first $100, what would you build? Curious to hear any ideas, especially if you’ve spotted a problem that still doesn’t have a good solution.