AI tool comparison
Stet 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.
Productivity
Stet
Open-source macOS dictation that sounds like you, not a corporate AI
75%
Panel ship
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Community
Free
Entry
Stet is a minimalist, open-source macOS voice input app that transcribes speech and cleans it up without stripping away your natural voice. Named for the editorial term "let it stand," it's built on the principle that AI transcription should preserve your phrasing — not homogenize it into corporate-speak. The app listens locally, then optionally passes transcripts through an AI cleanup layer (OpenAI or Groq) to fix filler words and false starts. You can bring your own API key for completely free usage, or pay $6.99/month for the hosted cloud version. A Supabase backend enforces zero data retention, so nothing is stored after processing. Stet is the work of a single indie developer who noticed that every dictation tool on the market either sounds robotic or aggressively rewrites your words. At 66 Product Hunt upvotes on launch day (April 22, 2026), it's a quiet success that fills a real gap for writers, developers, and anyone who types a lot and is tired of Dragon-era dictation software.
AI Productivity
Sup AI
Runs 339 LLMs in parallel and downweights the hallucinating ones.
50%
Panel ship
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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
“Open-source, BYOK, and local-first listening? This is how voice input should work. The Groq integration makes transcription near-instant. I've been using it for commit messages and code comments — genuinely faster than typing for longer explanations.”
“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.”
“Apple's built-in dictation has gotten surprisingly good, and it's free with no BYOK setup. The 'preserves your voice' pitch is compelling but subjective — I'd want a side-by-side blind test. Solo indie developer + $7/mo hosted tier raises long-term sustainability questions.”
“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.”
“We're entering an era where voice is the primary interface for AI-assisted work. Tools that get the human-voice preservation problem right now will have a head start when voice input becomes default. Stet's philosophy is the right one.”
“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.”
“As a writer, dictation tools that rewrite me drive me insane. Stet is the first one that feels like a scribe rather than an editor. The zero-retention policy means I can dictate client-sensitive notes without anxiety. This is the one.”
“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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