AI tool comparison
ChatFolders 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
ChatFolders
Color-coded folders, tags, and auto-sort for ChatGPT, Claude, Gemini, and Grok — one extension
75%
Panel ship
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Community
Free
Entry
ChatFolders is a browser extension built by a solo indie developer that adds folders, color-coded tags, bookmarks, and auto-sort rules to the four major AI chat interfaces: ChatGPT, Claude, Gemini, and Grok. All data is stored locally in your browser — no accounts, no cloud sync, no server-side storage. The cross-platform coverage from a single extension is the headline feature. The extension fills a genuine organizational gap that all major AI chat products have been slow to address. ChatGPT has Projects but they're limited. Claude's sidebar is essentially a flat list. Gemini has folders but only within its own ecosystem. Grok has nothing. ChatFolders applies a consistent organizational layer across all four interfaces simultaneously, which means you can apply the same tagging taxonomy regardless of which model you're using for a given task. The local-first architecture is a deliberate privacy choice. Given how sensitive the contents of AI chat conversations can be — from business strategy to personal health — an extension that explicitly stores nothing server-side and requires no authentication is meaningfully different from cloud-synced alternatives. The solo indie origin makes this a genuine labor-of-love project rather than a VC-funded bet. Already seeing organic traction from power users who have hundreds of conversations with no way to find anything.
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 cross-platform angle is what makes this actually useful. I use different models for different tasks — Claude for writing, ChatGPT for code, Gemini for research — and having one organizational system that works across all of them without switching contexts is a genuine quality-of-life improvement. Local-first is also the right call for professional conversations.”
“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.”
“Browser extensions for major AI platforms are inherently fragile — one UI update from OpenAI or Anthropic breaks everything until the solo developer finds time to patch it. The local-only storage also means your organizational system doesn't follow you to a new computer. This solves a real problem but in a brittle, unscalable way.”
“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 fact that someone had to build this as a browser extension is the real story: none of the major AI companies have prioritized knowledge management for power users. ChatFolders is filling a gap that should have been filled by product teams months ago. Either someone acqui-hires this developer, or the major platforms ship native folder systems within the year.”
“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 creators juggling project briefs, brand voice docs, and campaign conversations across multiple AI tools, this is genuinely useful. Color-coded folders alone is worth the install — visual organization of a chaotic sidebar has an immediate quality-of-life impact. The auto-sort rules could save hours per week for heavy users.”
“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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