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Products that do what A tool that turns unstructured transcripts into queryable SQL and JSON does
We built Summ, a tool that provides intelligent search and question-answering across large sets of transcripts. We turn your unstructured transcripts into queryable SQL and JSON! Try it out and read how we did it.
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I built this tool because I wanted a way to just take a bunch of URLs or domains, and query their content in RAG applications. It takes away the pain of crawling, extracting content, chunking, vectorizing, and updating periodically. I'm curious to see if it can be useful to others. I meant to launch this six months ago but life got in the way...
2024 · embedding.io
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I'm working on a tool that will probably involve querying JSON documents and I'm asking myself how to expose that functionality to my users. I like the power of `jq` and the fact that LLMs are proficient at it, but I find it right out impossible to come up with the right `jq` incantations myself. Has anyone here been in a similar situation? Which tool / language did you end up exposing to your users?
Oct 2025 · jsonquerylang.org
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2020 · github.com
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2020 · github.com
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2015 · sqlteaching.com
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2019 · github.com
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2019 · askdata.com
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2021 · github.com
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Hey, For those of you who don't know my little tool Musoq, I wanted to introduce it as a small tool that allows you to query with SQL-like syntax without any database. It allows you to query various things from niche ones like CAN DBC files, weird ones like C# code, interesting ones with Git querying to regular stuff like CSV, TSV and various others. I am quite a bit experimenting with various things so I'm hybridizing the engine with LLMs or doing other weird stuff that are more or less practical :-) I wanted also to share some recent developments in this little project as I hope it might…
2024 · github.com
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2020 · github.com
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2020 · github.com
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2021 · github.com
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Hi HN, I’m Tullie, founder of Shaped. Previously, I was a researcher at Meta AI, worked on ranking for Instagram Reels, and was a contributor to PyTorch Lightning. We built ShapedQL because we noticed that while retrieval (finding 1,000 items) has been commoditized by vector DBs, ranking (finding the best 10 items) is still an infrastructure problem. To build a decent for you feed or a RAG system with long-term memory, you usually have to put together a vector DB (Pinecone/Milvus), a feature store (Redis), an inference service, and thousands of lines of Python to handle business logic…
Jan 2026 · playground.shaped.ai
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2016 · github.com
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When building workflows that rely on LLMs, we commonly use structured output for programmatic use cases like converting an invoice into rows or meeting transcripts into tickets or even complex PDFs into database entries. The model may return the schema you want, but with hallucinated values like `invoice_date` being off by 2 months or the transcript array ordered wrongly. The JSON is valid, but the values are not. Structured output today is a big part of using LLMs, especially when building deterministic workflows. Current structured output benchmarks (e.g., JSONSchemaBench) only validate…
Apr 2026 · interfaze.ai
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2019 · github.com
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Jul 2026 · summa.josephruocco.net
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2019 · dbml-lang.org
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Hi HN, I made Lingo - the SQLite of semantic search. I'm a self-taught developer and researcher who left school at 16, and I've spent some time exploring a first-principles approach to system design for various frontier problems. In this case it's AI that challenges the 'bigger is better' transformer paradigm. Lingo is the first piece of that research, a high-performance linguistic database designed to run on-device. The full technical overview and manifesto is here: https://medium.com/@robm.antunes/bcd1e9752af6 The paper has been archived on Zenodo with a DOI:…
Sep 2025
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2020 · github.com
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