Cocoon

What it does

We profile tables: this is the first step to understanding the table and identifying any anomalies. Many small decisions require semantics by LLMs. For example, an age of 100 is acceptable, but -1 is impossible! Upload a CSV and we will send back the profile.

GenAI fails because enterprise data is large and messy with 1000+ tables. Cocoon unlocks robust and scalable GenAI by cataloging your data warehouse for RAG. Build a semantic catalog to organize your data, ready forfrom cocoon-data-transformation.github.io

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  An open-source tool that semantically profiles your data using LLMs2024 · github.com · ▲10

  The problem we solve is profiling tables: this is the initial step where you need to understand the table and identify any anomalies. During the process, many small decisions require semantic understanding. For example, missing values are normal for 'deathdate' (still alive) but abnormal for 'name.' For outliers, 100 for ages is fine, but some are -1, which is impossible! We use LLMs to semantically understand your tables and detect anomalies. You can try it by uploading a CSV, and we will email back the profile: https://cocoon-data-transformation.github.io/page/ Let me…

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  QuickCompare by TrismikApr 2026 · trismik.com · ▲231

  Compare LLMs on your data, measure, and pick the best.

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  Knowledge graph of restaurants and chefs, built using LLMs2025 · theophilecantelob.re · ▲201

  Hi HN! My latest side project is knowledge graph that maps the French culinary network using data extracted from restaurant reviews from LeFooding.com. The project uses LLMs to extract structured information from unstructured text. Some technical aspects you may be interested in: - Used structured generation to reliably parse unstructured text into a consistent schema - Tested multiple models (Mistral-7B-v0.3, Llama3.2-3B, gpt4o-mini) for information extraction - Created an interactive visualization using gephi-lite and Retina (WebGL) - Built (with Claude) a simple Flask web app to clean and…

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  Humans vs LLMs2025 · aipageready.com · ▲138

  Hi HN, We just launched [AI Page Ready](https://aipageready.com) — a tool to check if your web pages are discoverable and understandable by AI tools like ChatGPT, Claude, Gemini, and Perplexity. It runs 40+ checks across six areas: Discoverability & Crawlability, Structured Data & Semantics, Content Quality, Answer-Ready Formatting, Accessibility & Rendering, and Deep AI LLM-specific checks. Would love your feedback — especially if you work in eCommerce, SEO, content, SaaS, or are trying to get traffic from AI tools. (Also open to ideas on what else to add to the tool!)

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  Graphiti – LLM-Powered Temporal Knowledge Graphs2024 · github.com · ▲142

  Hey HN! We're Paul, Preston, and Daniel from Zep. We've just open-sourced Graphiti, a Python library for building temporal Knowledge Graphs using LLMs. Graphiti helps you create and query graphs that evolve over time. Knowledge Graphs have been explored extensively for information retrieval. What makes Graphiti unique is its ability to build a knowledge graph while handling changing relationships and maintaining historical context. At Zep, we build a memory layer for LLM applications. Developers use Zep to recall relevant user information from past conversations without including the entire…

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  Core – open source memory graph for LLMs – shareable, user owned2025 · github.com · ▲112

  I keep running in the same problem of each AI app “remembers” me in its own silo. ChatGPT knows my project details, Cursor forgets them, Claude starts from zero… so I end up re-explaining myself dozens of times a day across these apps. The deeper problem 1. Not portable – context is vendor-locked; nothing travels across tools. 2. Not relational – most memory systems store only the latest fact (“sticky notes”) with no history or provenance. 3. Not yours – your AI memory is sensitive first-party data, yet you have no control over where it lives or how it’s queried. Demo video:…

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