Hyperparam

Agents love grep. So we built HypGrep: grep over huge text datasets in S3, no server. Built for code and logs full of symbols and syntax that BM25 engines choke on. Index is Parquet next to your data, compute on the client. 3.2M logs, $0.38/mo. github.com/hyparam/hypgrep

Icebird v0.8.0 now supports Cloudflare R2 Data Catalog for accessing apache iceberg tables from JS environments

If you’ve got multimodal datasets, Claude Code on terminal isn’t going to work — but we’ve got you. We’re excited to be at GTC announcing multimodal support in Hyperparam: images, audio and geospatial data. Drop a dataset on Hyperparam to check it out.

Hyparquet-writer v0.12.3 with a 10%+ speed increase! JS Optimization Tip: Always prefer Uint8Array over ArrayBuffer when you can. ArrayBuffer.slice makes a copy, Uint8Array can slice for free.

Hyparquet can read ANY parquet file including recent 2025 features like Variant and Geospatial: parquet.apache.org/docs/file-form…

Apache Parquet added an Implementation Status page for parquet clients. But Hyparquet had a shameful red ❌ on Variant Encoding. But JavaScript deserves first-class Parquet support! So I stayed up late, and now Hyparquet 1.24.0 has full Variant support ✅ Peer pressure works!

Squirreling is a browser-native SQL engine built for interactive data exploration with async execution, streaming results, and no traditional backend. Kenny wrote about why existing tools struggle with this workflow, explains his thought process behind Squirreling, and gives insights into its architecture.

Someone leaked our architecture diagram 😲 What if our competitors learn that you don’t actually need a backend??

AI-assisted scoring is great for surfacing the patterns in LLM chat logs that you’d never find manually, but you still need a human looking at those patterns and deciding which ones matter. Even if you use AI for everything, the human-in-the-loop still provides that extra layer of judgment a machine can’t deliver unless you tell it what to do.

Are you really looking at your LLM data? Or just the tiny slice your tools can load? If you’ve ever wondered what’s hiding in the rows you never see (the hallucinations buried in long-form text, tone shifts after certain prompts) this is the breakdown you’ll want to read. We wrote up what we built, why we built it, and what becomes possible when you can explore AI-scale datasets in full. blog.hyperparam.app/explore-massiv…

Reasoning models now process more than half of all token usage, according to the OpenRouter 100T-token report. Especially notable has been a significant shift to coding over this past year: developers are using LLMs for everything from logic debugging to script drafting. What use case is responsible for the majority of your token use?

A tiny group of engineers already treat the browser like a first-class data workbench. The rest of the world still acts like the browser collapses if you look at it wrong.

We love AI, use it for everything. But we will NOT ship slop!

Now sponsoring bundlephobia on github. They are a doing a fantastic service for the JS community.

Browser-first data tools > Python-first. Hyperparam opens Parquet datasets in the browser with no backend, so your time-to-first-row is ~instant #BurnTheBackEnd

Today I'm excited to announce that we are launching @hyperparamapp, an AI-powered Swiss Army knife for massive LLM datasets. It lets you view, score, filter, label, and transform LLM data directly in the browser. I started Hyperparam one year ago because I knew that the world of data was changing, and existing tools like Python and Jupyter Notebooks were not built for the scale of LLM data. The weights of LLMs may be tensors, but the input and output of LLMs are massive piles of text. The training set of LLMs is a large corpus of text from various sources, meticulously cleaned and preprocessed. The output of LLMs is also text, and it’s being produced in even greater quantities than the training data. No human has the patience to sift through all that text, so we need better tools to help us understand and analyze it. That's why I built Hyperparam to be the first tool specifically designed for working with LLM data at scale. To accomplish this required rethinking how data analysis tools work. I started Hyperparam as a side project and wanted to see if I could build it entirely in the browser. No Python, no servers, just pure interactive web experience. I'm pretty excited about how it turned out. Hyperparam is fast, powerful, and easy to use. It can handle datasets with millions of rows of text and provides a rich set of tools for exploring and analyzing that data using LLMs agents for assistance. Every company is now producing volumes of LLM data. Chat logs, agent traces, coding agent logs, and more. Hyperparam is designed to help you make sense of all that data. If you're working with LLM data (and let's be honest... every company is producing LLM data now), I encourage you to give Hyperparam a try. It’s free while in beta. 🚀

We’re excited to announce the launch of Hyperparam–an AI-powered Swiss Army knife for your data. It lets you view, score, filter, label, categorize, and transform massive datasets entirely in the browser. No backend, no setup. It’s blazing fast, interactive, lets one person handle workloads that usually require a whole team, and free to use during open beta. Try it here:

Everyone working with LLM data has been there: thousands of conversations and no way to find the one that matters. Existing data tools were not built for the massive scale of LLM data. Hyperparam was built to solve this problem. Free while in beta starting 11/19.

Browser-native isn’t a toy. It’s an architectural choice that puts performance and UX at the center instead of bolting them on later. Most systems start backend first, then hand off to the frontend to make it usable. We started with the user — and built the app around them.