Hey everyone.
Radomir here.
And here's the origin story of Suprmind.
Last summer I was writing an article about a specific model of a sporting rifle and needed one closing detail - a professional shooter who actually uses it.
I asked an AI.
It handed me a name. Well-known Polish long-range competitive shooter, the competitions he won, the whole career. Awesome.
I went looking for a photo as a final touch. There were none.
So I dug a bit further and found out why. There was no guy.
The AI had hallucinated a person with an entire professional sports career, and I was one photo away from publishing it.
That was the loud version of something quiet I did every day anyway.
Copy an analysis out of ChatGPT. Paste it into Claude to poke holes in the logic. Paste it into Gemini or Perplexity to check whether anybody made up a number.
I was the mailroom trainee in a room full of very confident AIs.
So I built Suprmind.
One conversation thread, five frontier models - GPT, Claude, Gemini, Grok, and Perplexity - all reading the same context and each other.
The mode I live in is Sequential. The models answer in a chain (hence the name) and each one sees everything said before it, so the fourth model can call out what the first one got wrong. The one that hallucinated has to sit through the whole circle before it gets a chance to defend itself. Watching that happen live is still my favorite part of the product.
Reading five models argue takes time, though. When I do not have it, I switch to Super Mind. All five answer in parallel, then a fresh model takes their replies plus the user's original prompt, project data and history and merges everything into one answer.
It takes about double what a single AI like Claude or ChatGPT takes to finish. Not blazing fast, but fast. There are six modes in total, but those two do most of my work.
If I feel confident in my latest business idea, then I run it through the Red Team mode, and usually afterward I don't feel that confident anymore 🥲
These guys can be harsh. Fair but harsh.
None of this makes hallucinations completely disappear, but our latest feature (soon on production), descriptively named AI Anti-Hallucinogen, makes it really difficult for them to reach users's decisions.
Happy to answer anything - the orchestration mechanics, pricing, what has not worked so far (plenty). And if you have your own hallucination horror story, let's laugh and cry together 😀
LOL, finally someone who gets me! For years, when people talked about the brilliance and complete stupidity of a single AI model, I said "Just search ChatGPT, Claude, and Gemini, then compare their answers." Everyone looked at me like I was insane. Over time, I've automated that multi-model search, and I honestly think you're the first person I've "met" who has gone down a similar path. I'd love to compare notes.
Hahaha there are more of us every day, which is great and sad at the same time. It's still unbelievable to me that a good percentage of people have no idea that AIs are fabricating stuff, and they are doing it rather frequently.
We are continuously updating our aggregated AI hallucinations statistics and rates page, and it's worse and worse every month. The smarter they are, the bigger the chance that they will hallucinate :D
Happy to compare notes anytime.
Well, I like it!
I like this
I'm impressed, Radomir! Below is a snippet from the output of Suprmind based on my early usage. I can vouch for its recommendation of Lovable -> GitHub -> Cursor or Claude Code (though OpenCode with allium is my current go-to in this slot) -> Playwright -> GitHub Actions -> Vercel -> Sentry. I personally think it would be helpful for Indie Hackers' Vibe Coding Tools page to have information along the lines of what's offered below.
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Why a small stack usually beats a single “best” tool
Most serious indie projects do not need one tool that does everything. They need a few tools that each optimize a different loop:
Idea to demo
Idea to code
Multi-file execution
Risk discovery
Testing
Deployment
User feedback
Billing and operations
A practical stack might look like this:
This workflow uses an app generator for rapid exploration, a real repository for long-term ownership, an editor or terminal agent for production work, and automated tools for verification.
A broader production stack might include:
GitHub for version control
Cursor or Claude Code for implementation
GitHub Actions for CI
Vercel or Railway for deployment
Sentry for error monitoring
PostHog for product analytics
Stripe for payments
The exact products are less important than the connections between them. Code should eventually connect to tests, deployments, logs, analytics, and revenue—not remain trapped inside a generation tool.
well that was an interesting read
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