AI tool comparison
Clicky 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
Clicky
AI assistant that lives next to your cursor and reads your screen
75%
Panel ship
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Community
Free
Entry
Clicky is a Mac application that surfaces an AI assistant inline — directly adjacent to your cursor — without requiring you to switch windows or paste context manually. The app maintains persistent screen awareness, reading what's in front of you and using that context to answer questions, guide tasks, and make suggestions relevant to what you're doing in any application. Unlike clipboard-based AI tools that require explicit copy-paste workflows, Clicky works through ambient screen reading: you invoke it with a hotkey, it understands the current screen context automatically, and responds inline. The approach is closer to GitHub Copilot's ghost-text model than a chat sidebar — the assistant lives where your attention already is. The indie approach prioritizes a single, focused Mac use case rather than trying to be a cross-platform agent platform. Early Product Hunt reception highlighted the overlay UI and the speed of context capture as standout experiences. For knowledge workers who context-switch constantly between reference material, documentation, and writing tools, the cursor-adjacent model reduces the friction of asking a question by eliminating the need to describe what you're looking at.
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 screen-aware context capture is the killer feature — I'm tired of pasting error messages into chat windows. If Clicky accurately reads terminal output and stack traces without me doing anything, that alone justifies the install. The hotkey-invoke pattern feels like the right UX for async assistance.”
“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.”
“Persistent screen reading is a significant privacy surface. What data is captured, where it goes, and how it's retained are crucial questions that indie tools often underspecify. This space is also crowded — Cursor, Copilot, and a dozen similar tools already compete for this workflow. What's Clicky's durable advantage?”
“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.”
“Cursor-adjacent AI is the right mental model for ambient assistance. We've been training users to alt-tab to a chat window for 3 years; tools like Clicky train the reflex that AI is contextually available wherever attention lands. This interaction paradigm will win.”
“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 someone who constantly switches between design specs, documentation, and writing tools, cursor-adjacent AI is genuinely useful. No more describing a UI element in a chat window — Clicky can just see it. The overlay aesthetic is clean and the indie origin means it'll iterate fast on creator feedback.”
“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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