We built an AI tool for working with massive LLM chat log datasets
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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.
HN · launched Nov 19, 2025 · 16 upvotes (top 37%) · 0 views
Niche: ai intelligence and analysis tools · 141 launches, 17 in the last 7 days ▲ +13% · Explore this niche →
Category and tags
- Horizontal
- Analytics & BI
- Developer
- AI-native
- Commercial product
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More like this: all 141 launches in ai intelligence and analysis tools →
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 : 1062 launches clear the similarity bar, closest 8 shown.
Attention rank: #388 of 1063 (itself plus its competitors, highest first, normalized so YC and Product Hunt are compared fairly).
Launched 20 days after the earliest competitor.
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