AI tool comparison
Claudian 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
Claudian
Claude Code as an AI collaborator inside your Obsidian vault
75%
Panel ship
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Community
Free
Entry
Claudian is an Obsidian plugin that embeds Claude Code directly into your knowledge vault — not as a chat sidebar, but as a full agent capable of reading, creating, editing, and linking notes with tool use and multi-step reasoning. It's the first plugin to bring genuine agent capabilities to Obsidian rather than wrapping a chat API. Once installed, Claudian can scan your vault for related notes, synthesize information across documents, create new notes with proper backlinks, and run user-defined workflows as repeatable commands. It understands Obsidian-specific constructs like frontmatter, tags, dataview queries, and the graph — treating your vault as a structured knowledge base rather than a folder of text files. The plugin is open source and was built by a solo developer experimenting with Obsidian's plugin API and Claude's tool-use capabilities. It's gaining traction fast in the PKM and second-brain communities, where the idea of a genuinely capable AI collaborator embedded in a private, offline-first knowledge base is a compelling alternative to cloud-native tools.
AI Productivity
Sup AI
Runs 339 LLMs in parallel and downweights the hallucinating ones.
50%
Panel ship
—
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
“Giving Claude Code actual read-write access to an Obsidian vault — not just chat context — is the right model. The ability to run multi-step workflows that create linked notes and run dataview queries puts this well ahead of any chat plugin.”
“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.”
“An agent with write access to your personal knowledge base is a trust cliff. A hallucinated backlink or an overwritten note could quietly corrupt months of organized thinking. The vault backup discipline required to use this safely isn't mentioned in the README.”
“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.”
“Obsidian's graph is one of the few personal knowledge structures rich enough to give an AI agent meaningful context. Claudian points at a future where your second brain and your AI collaborator are genuinely the same system, not two tools awkwardly integrated.”
“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 writers and researchers who already live in Obsidian, this is the most exciting release in months. Ask it to synthesize three interview notes into a first-draft outline, with backlinks intact — that alone pays for the setup time.”
“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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