Alternatives
Products that do what Tidepool – analytics for large text datasets does
Hello HN! I'm Peter, one of the folks who helped create Tidepool. We last shared Tidepool with HN about 7 months ago https://news.ycombinator.com/item?id=36957762 Since then, the AI field has moved incredibly quickly and we’ve iterated a lot on our product! The core problem we are trying to solve is: there's a lot of useful business insights you can get from text data, but it's hard to do analytics on it. - SQL is built for tabular / structured data, but when it comes to text, the best you can do is do keyword search. - In the pre-LLM world, you might resort to training a…
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Two weeks ago I was on my babymoon in Corfu, Greece. While in transit, I was overseeing a GSoC intern submit an important feature to my array database library, Xarray-SQL. He added `to_dataset()`, which completed the roundtrip between thinking of array data in a tabular model simultaneously as gridded rasters (the premise of the project is that every Nd array can be mapped to 2d, where orthogonal dims of the Nd array are just primary keys of a tabular representation). We discussed in chat, now that this feature existed, what demos could we make that would prove this data model works? With…
Jul 2026 · github.com
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Hey HN — We're excited to share Trellis — a snowflake for unstructured data. We've built an AI engine that turns unstructured data into structured SQL-format based on the schema you define in natural language. We spent a lot of time building ML infrastructure and realized that most data warehouses and data pipelines are not designed for unstructured data (documents, PDFs, calls). While something like a Vector database and RAG are great at search tasks, they really struggle with aggregation and SQL type queries such as 1. How many emails in the past 6 months contain complaints about the…
2024 · demo.runtrellis.com
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Today you can easily adopt AI coding tools because you have git for branching and rolling back if AI writes bad code. We haven't seen this same capability for data and decided to build it ourselves. Nile is a new kind of data lake, purpose built for using with AI. It can act as your data engineer or data analyst creating new tables and rolling back bad changes in seconds. We support real versions for data, schema, and ETL. We'd love your feedback on any part of what we are building - https://getnile.ai/ What do you think?
Jan 2026
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We built a structured search engine for Twitter called Bird SQL, available at https://www.perplexity.ai/sql. Our search interface uses OpenAI Codex to translate natural language to SQL. Our backend then verifies the SQL, executes it, and displays the results on the web app. This makes large structured datasets like a scrape of Twitter easy for anyone to explore. As background, while working on text-to-SQL as a general problem, we came to believe one of its most powerful applications is as a search tool because: - SQL is hard to write by hand and prone to errors - It allows you…
2022 · perplexity.ai
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Hi HN! I am an undergrad student trying to build interesting things with AI. Recently, I was looking for a dataset I could use for a new project. I realized that it is really frustrating to go through all the government websites (with terrible UX) just to find some usable dataset. I set out to build a GitHub for datasets, named DataHub. Right now, we have more than 1000 datasets from Montréal and New York City, with more cities coming soon (and possible government agencies). All of this is wrapped into a powerful search. It's a breeze to find a dataset to work on. I'd be interested to know…
2017
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Hey HN! I built a thing and I'm really excited to share it. EDIT: I meant to link to the github, not the website: https://github.com/max-hq/max Like many of us here, I've been commonly reaching for a pattern of "pull data into db; give it to claude" for a while, whilst doing data spelunking or building tooling - for the same reasons mentioned by thellimist over here [1] and a few other recent "CLI vs MCP" posts. To that end, about a month ago I started building a project called `max` - its goal is to cut the middleman and schematise any data source for you. Essentially,…
Mar 2026 · max.cloud
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AI search results are quickly becoming more important than SEO, but as businesses, we have no visibility over it! That's why I'm building "Ahrefs for AI search results". Track keyword performance on AI tools like ChatGPT, Claude, Perplexity & more
2025 · linrush.com
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Hello HN, Lucas here. I’ve been working with BigQuery for ~5 years, mostly in large (petabyte-scale) environments. Over time we ended up spending a lot of money and engineering effort just trying to understand where costs were coming from, why and how to optimize them. At some point we decided to stop, leverage all our past experience and spend a full cycle building tooling focused on cost visibility and optimization. The main goal was to regain ownership of cost data and make it possible to understand our cost structure in under a minute, while aligning the views of engineering and FinOps…
Jan 2026 · cloudclerk.ai
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We used our platform to fine-tune a tiny text-to-SQL model using distillation from DeepSeek V3. Repo has instructions for how to replicate this. This is definitely not the best-performing model like this out there! But I found it surprising we were able to get to this much out of it: stone's throw away from a teacher 1000x the size! We also ran the same thing using the 4B Qwen and matched the teacher accuracy, though here the difference is merely 100x :) I find this pretty cool - obviously our distilled models can only do this one task and don't generalize, but that's often exactly what you…
Jan 2026 · github.com
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Built QueryWeaver, an open-source text2SQL tool that uses a graph to create a semantic layer on top of your existing databases. When you ask "show me customers who bought product X in a certain ‘REGION’ over the last Y period of time," it knows which tables to join and how. When you follow up with "just the ones from Europe," it remembers what you were talking about. Instead of feeding the model a list of tables and columns, we feed it a graph that understands what a customer is, how it connects to orders, which products belong to a campaign, and what "active user" actually means in your…
2025 · github.com
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Hey everyone! Like many data analysts and engineers, I love SQL and the dbt ecosystem. So it bothers me that we have to use separate tools for product analytics. We do our transformations, BI work, and ad-hoc queries in SQL, but when it's time to look at funnels and flows, we have to use (and procure) a separate platform like Mixpanel or Amplitude. This dbt package is a (very rough) start at fixing that. With it, you can create event streams and run funnel analyses via dbt[0]. More features like flows and retention are coming soon! But I'm mostly curious how you all are doing product…
2022 · github.com
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Hey everyone! I am excited to share updates on four of my & my teams' open-source projects that take large-scale search systems to the next level: USearch, UForm, UCall, and StringZilla. These projects are designed to work seamlessly together, end-to-end—covering everything from indexing and AI to storage and networking. And yeah, they're optimized for x86 AVX2/512 and Arm NEON/SVE hardware. USearch [1]: Think of it as Meta FAISS on steroids. It's now quicker, supports clustering of any granularity, and offers multi-index lookups. Plus, it's got more native bindings than probably…
2023 · usearch-images.com
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Hi, We are building an open-source framework for loading and structuring LLM context to create accurate and explainable LLM answers using knowledge graphs and vector stores. We built the tool with four main concepts in mind: 1. Loader -> uses dlt in the backend to load and structure the data 2. Cognify step -> creates a graph with summaries, labels and factoids that are interconnected across the documents and stored as a representation in the vector store 3. Optimizer -> Uses DSPy to optimize LLM queries, and we plan to extend it to most of the knobs we can turn, like chunking etc. 4. Search…
2024 · github.com
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Hey HN, For the last couple of months, we have been building an AI agent for continuous statistical analysis, and we're looking for feedback while it's still early in development. We call it BIGWIG - an autonomous agent that is specialised, and very good at, performing advanced statistical analysis, through long traces of iteration and reasoning. As it builds statistical models it also "emits" outputs back to the user that you can then interact with, iterate on and schedule for follow up analysis. While we're still in BETA, we've launched a public analysis site that showcases some of the…
2025 · askbigwig.com
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Hey HN, I'm Steve, co-founder of Factor.fyi, a new data platform for querying and visualizing financial datasets using SQL. During the pandemic, I took much more of an active role in managing my portfolio. I wanted to be able to make informed decisions about the investments I was making, and explore financial data in new ways. Market and Econ data are some of the most talked-about and widely-available datasets out there, but I was frustrated by the lack of options to answer questions I had, like, "What would have happened if I had started dollar-cost averaging VTI back in 2013?"[1] Or, "How…
2022 · factor.fyi
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How it works (tech stack): -Built entirely with Lovabl.dev (no-code front-end + logic) -ChatGPT / Claude for research and inspiration -Powered by GPT-4 Vision to interpret charts visually -Hosted on Supabase for performance & caching It’s not meant to replace analysts — just to speed up how traders interpret data. I’m a designer exploring AI tools, and this is my first attempt to turn an idea into a functional product. Would love to know what you think.
Oct 2025 · quantify-ai.co
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Hey HN, Data quality matters more than ever. Our world increasingly relies on AI and proprietary datasets to fine-tune models. But as many of us know, garbage in often results in garbage out. Bad data can cost millions, especially when it informs important decisions like public health policy or interest rate hedging. Today’s data engineers need a flexible, secure, and performant data quality toolkit designed for the modern workflow. But current solutions were built a decade ago and don't support big data technologies, such as Spark. This is why we're building Spotlight - the data quality…
2023 · spotlight.dev
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Hey folks, Elias here. Excited to unveil my latest project. Why I Built This: Traditional keyword search isn't cutting it. I've used LLM-embeddings to provide more nuanced, relevant results. How It Works: LLM-embedding similarity on curated datasets for semantically similar results. No need to iterate over keywords any more. Current Datasets: - YC Companies - Show HN Posts, - Ask HN Posts - ProductHunt Startups - Github Top 200k Repos Use Cases: - Validate a product idea's existence - Check if someone already Asked HN something - Have fun - search random terms and see what pops up Want to…
2023 · payperrun.com
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I've been lurking on HN for years. You know the drill: interesting headline, 200+ comments, you dive in thinking "I'll just skim for 5 minutes"... and an hour later you're 36 chambers deep in a thread about memory allocation patterns in Postgres and you've completely forgotten what the original article was about. I don't just want a "summary" (which usually just shortens the noise). I want the meta-consensus: "What is the actual trade-off being debated? Who is winning the argument? Why does this matter?" So I built HNSignals. Think of it less like a "summarizer" and more like a Chief of…
Jan 2026 · hnsignals.com
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