We built an AI tool for working with massive LLM chat log datasets
Details
- External ID
- 45981930
- Source
- HN
- Company
- —
- Product
- We built an AI tool for working with massive LLM chat log datasets
- Website domain
- hyperparam.app
- Launched
- Nov. 19, 2025
- Cohort
- —
- Upvotes
- 16
- Upvotes percentile
- 0.6299126637554585
- Tags
- —
- Fetched at
- Sept. 7, 2026, 9:25 p.m.
- Updated at
- Sept. 7, 2026, 9:25 p.m.
Description
There’s an important problem with AI that nobody’s talking about. AI’s entire lifecycle is tons of data in for training, and an even larger amount of text data out. Traditional tools can’t handle the sheer volume of text, leaving teams overwhelmed and unable to make their data work for them.Today we’re launching Hyperparam, a browser-native app for exploring and transforming multi-gigabyte datasets in real time. It combines a fast UI that can stream huge unstructured datasets with an army of AI agents that can score, label, filter, and categorize them. Now you can actually make sense of AI-scale data instead of drowning in it.Example: Using the chat, ask Hyperparam’s AI agent to score every conversation in a 100K-row dataset for sycophancy, filter out the worst responses, adjust prompts, regenerate, and export your dataset V2. It all runs in one browser tab with no waiting and no lag.It’s free while it’s in beta if you want to try it on your own data.
Enrichment
- Theme
- task-specific ai agents and assistants
- Vertical
- Horizontal
- Function
- Analytics & BI
- Audience
- Developer
- AI stance
- AI-native
- Project type
- Commercial product
- Normalized one-liner
- llm chat log dataset analysis tool
- Manually corrected
- False
Could you build this?
No Analyzing massive conversational datasets straight from cloud buckets using Apache Iceberg, hybrid keyword/vector search, and browser-driven analytics is a heavy data infrastructure challenge.
What it would actually take: The architecture requires an open-table data lakehouse (Apache Iceberg / Parquet) stored in S3/GCS, a client-side or distributed query engine (e.g., DuckDB-Wasm or custom columnar scanner), and a hybrid indexing pipeline combining vector embeddings and full-text search. Building scalable log compaction, distributed token/cost aggregation over gigabytes of unstructured JSON traces, and interactive sub-second in-browser query execution requires advanced data engineering and database internals expertise.
Discussion
1 comment analyzed.
Competitors mentioned: Python, Jupyter Notebooks
Competitors
Other products that read as similar to this one — 318 launches clear the similarity bar, closest 8 shown.
Attention rank: #121 of 319 (itself plus its competitors, highest first — normalized so YC and Product Hunt are compared fairly).
Launched 20 days after the earliest competitor.
- AI Chatbot LLM · ph · 2026-09-28 · 1 upvotes · similarity 0.52
- CoChat · hn · 2025-12-02 · 6 upvotes · similarity 0.51
- ChatIndex · hn · 2025-11-26 · 17 upvotes · similarity 0.50
- AI-Augmented Memory for Groups · hn · 2025-12-16 · 10 upvotes · similarity 0.44
- Unsiloed AI: Make Unstructured Data LLM-Ready · yc · 2025-10-31 · 52 upvotes · similarity 0.44
- I built a tiny LLM to demystify how language models work · hn · 2026-04-06 · 915 upvotes · similarity 0.43
- Sylvian: Data for LLMs through Competition · yc · 2025-11-04 · 36 upvotes · similarity 0.42
- Hyper · hn · 2026-03-11 · 16 upvotes · similarity 0.42
Other launches for this product
- No other launches for this product.
Same idea, different domain
Nobody's really built a analytics & bi tool for Legal yet.