AI tool comparison
Cabinet 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
Cabinet
Free open-source AI-first knowledge base and startup OS — runs locally
75%
Panel ship
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Community
Free
Entry
Cabinet is a free, open-source knowledge base and 'startup operating system' that stores everything as markdown files on disk — no database, no vendor lock-in, no subscription. It scaffolds a full AI team (CEO agent, Editor agent, Marketer agent, etc.) around your company context in five minutes, with cron-based automation for recurring tasks like competitor monitoring and newsletter drafts. The 'everything is markdown on git' philosophy makes it genuinely portable. You can spin up a web terminal inside a folder, link a git repo for source code, run Kanban boards, and embed HTML apps — all without leaving the interface. AI agents have access to your entire knowledge base, not just a retrieval snippet. For solo founders and small teams who want to avoid SaaS subscriptions for wikis, project management, and AI tooling, Cabinet bundles everything into a single `npx create-cabinet my-startup` command. It's one of the rare tools where 'free and open-source' isn't a stripped-down version of something paid.
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
“Git-backed markdown with a built-in web terminal and AI agents that can actually schedule tasks — this is what Notion should have been for developer-founders. The `npx create-cabinet` scaffold makes setup genuinely fast. The lack of a hosted SaaS tier means you own your data forever.”
“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.”
“Self-hosting a knowledge base plus AI agents plus task automation is three different categories of ops burden for a founder whose main job is building product. The AI agent 'budget controls' mention suggests costs can spike, and there's no mention of how model API credentials are secured. For a solo founder, Notion + one AI tool is genuinely less work.”
“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.”
“The 'startup OS' framing is exactly right — as AI agents become capable of autonomously running business functions, the knowledge base IS the company's operating layer. Cabinet is an early prototype of what every small business will run in five years: a context-aware, agent-staffed operational core.”
“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.”
“Scheduled AI drafts for newsletters while I sleep, competitor monitoring that writes its own briefs, a Kanban linked to my git repo — all free and local. For a content-first founder this is almost too good to be real. The WYSIWYG editor with markdown toggle is a small thing that matters a lot day-to-day.”
“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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