AI tool comparison
Devaito vs Sup AI
Which one should you ship with? Here is the side-by-side panel verdict, pricing read, reviewer split, and community vote comparison.
Business Tools
Devaito
AI autopilot that launches your whole business and keeps running it
50%
Panel ship
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Community
Free
Entry
Devaito is an all-in-one AI business launcher that deploys a website, online store, mobile app, SEO infrastructure, blog, and social media automation from a single prompt — then keeps AI agents running continuously in the background to attract customers, answer support questions, and generate content. The pitch is 'launch everything, then let it work for you.' Where traditional no-code builders like Webflow or Squarespace give you a static site you have to maintain, Devaito deploys a full business stack including a sales pipeline and customer support layer, then runs agents on top of it indefinitely. The founding team is small (Symo Lahlou and two others), building with a product-led growth model. The risk is that this is a lot of surface area for a small team to maintain. But for solo founders or tiny teams trying to ship an online business without hiring, the pitch is compelling: one tool, everything running, no ongoing management required.
AI Productivity
Sup AI
Runs 339 LLMs in parallel and downweights the hallucinating ones.
50%
Panel ship
—
Community
Free
Entry
Sup AI is an ensemble AI assistant that runs your query through 339 language models simultaneously, measures per-segment confidence across all responses, and synthesizes a final answer that amplifies agreement and suppresses likely hallucinations. The team claims a 52.15% score on Humanity's Last Exam (HLE) — 7.41 percentage points above the single best model — which, if verified, would make it the highest-scoring system on the benchmark to date. The underlying mechanism works like an LLM panel: each model votes on sub-claims within the response, confidence is estimated by agreement density, and the final output surfaces high-confidence segments while flagging uncertain ones. It's designed to reduce hallucination rate on factual tasks, not improve reasoning per se — the models in the ensemble aren't doing collaborative chain-of-thought, they're voting on outputs. Sup AI was built by Ken Mueller (Stanford, CEO) and Scott Mueller (AI Research Scientist) and launched on Product Hunt today. Pricing starts with $10 in free credits, no auto-charge, with a credit card required to start. The HLE benchmark claim is the headline and will face scrutiny — if verified, this is a meaningful research result. If it's cherry-picked, it's still a usable product with a differentiated architecture.
Reviewer scorecard
“The integrated approach — site, store, SEO, and support all in one system with shared context — could genuinely outperform stitching together Webflow + Shopify + Buffer + Intercom. If the AI agents actually stay on-brand, this is a massive time saver for solo builders.”
“The HLE claim needs independent verification, but the underlying ensemble approach is architecturally sound for factual Q&A tasks. Running 339 models is expensive — pricing will be the gating factor for production use. The $10 free credit is a fair trial.”
“A three-person team promising to replace your website, store, app, SEO, blog, social, CX, and sales pipeline is wildly ambitious. Each of those is a VC-funded company on its own. The risk of the agents drifting off-brand, generating bad content, or the startup shutting down is very real.”
“Extraordinary claims require extraordinary evidence. A 7.41 point jump on HLE via ensembling — without publishing methodology — smells like benchmark gaming. The latency of running 339 models in parallel is also a real concern for anything other than async research tasks.”
“This is the logical conclusion of the 'one-person billion-dollar company' thesis. If the agent layer is solid, you're looking at the first truly autonomous business operating system. The ambition is exactly right even if the execution is early.”
“Model ensembling is an underexplored direction in the race to reduce hallucination. If Sup AI's approach scales, it could be more durable than fine-tuning individual models — you get the wisdom of the crowd across model families, training data, and architectures simultaneously.”
“I love the concept but AI-generated social posts and blog content need a strong editorial voice to not feel generic. Until I can audit and tune the agents' brand voice deeply, I'd be worried about everything sounding like it came from the same ChatGPT template.”
“For creative work, ensemble outputs tend to regress toward the mean — you get the most-agreed-upon version of something, which is usually the least interesting version. This is a tool for factual accuracy, not creativity. I'd stick with a single strong model for writing.”
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