AI tool comparison
illumi 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
illumi
AI workspace that takes you from messy thinking to polished deliverable — and remembers the journey
75%
Panel ship
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Community
Free
Entry
illumi is an AI visual workspace designed around one thesis: "execution got cheap overnight, but comprehension didn't keep up." The founders argue that modern AI tools accelerate output production but fragment the thinking process — each conversation starts fresh, context gets lost, and knowledge workers spend more time reconstructing mental models than doing actual work. The tool maintains session continuity across work phases: raw notes and messy thinking in early sessions are preserved and connected to the polished deliverables they eventually become. AI assists at each stage — synthesizing scattered notes into structured frameworks, drafting deliverables from frameworks, and flagging when new context contradicts earlier decisions. The workspace is designed to make the evolution of a project's thinking visible, not just its final outputs. illumi launched on Product Hunt on April 21, 2026 with 92 upvotes and sparked one of the more substantive discussions of the week — a thread titled "Is AI making knowledge work harder, not easier?" resonated strongly. A two-founder indie team built it. At this stage it's an early product with a clear POV, targeting knowledge workers who feel increasingly productive but increasingly confused about their own work.
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
“The problem statement is accurate — I have a graveyard of ChatGPT conversations that led to good decisions I can no longer reconstruct. A tool that preserves the reasoning chain from messy brainstorm to shipping decision is worth trying. Whether illumi actually does that at v1 is the real question.”
“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.”
“'Session continuity' and 'preserved thinking' are features that require deep integration into how you actually work — and most people won't restructure their workflow around a new tool unless it's dramatically better from day one. The 92 PH upvotes suggest interest, not retention. Come back in six months.”
“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 'cognitive overhead of AI' problem is real and growing. We're heading toward a world where AI-generated outputs vastly outnumber human-reviewed outputs — tools that make the thinking process durable and auditable aren't productivity luxuries, they're organizational infrastructure.”
“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.”
“For content strategists and writers who live in the messy middle of multiple projects, a workspace that connects early ideation to final drafts without losing the 'why' behind every decision addresses a daily frustration. The visual approach feels right for how creative thinking actually works.”
“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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