Alternatives
Products that do what ←INTELLI•GRAPHS→ does
The Semantic Wiki For Human + AI Collaboration
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Build the semantic layer that makes AI analytics trustworthy
Mar 2026 · metabase.com
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2020 · crossminds.ai
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I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet. It runs locally in ~/.wuphf/wiki/ and you can git clone it out if you want to take your knowledge with you. The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard. I…
Apr 2026 · github.com
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Hey HN, we want to share HelixDB (https://github.com/HelixDB/helix-db/), a project a college friend and I are working on. It’s a new database that natively intertwines graph and vector types, without sacrificing performance. It’s written in Rust and our initial focus is on supporting RAG. Here’s a video runthrough: https://screen.studio/share/szgQu3yq. Why a hybrid? Vector databases are useful for similarity queries, while graph databases are useful for relationship queries. Each stores data in a way that’s best for its main type of query (e.g.…
2025 · github.com
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Hi HN, We’re Daniel and Mark, the creators of TrustGraph (https://github.com/trustgraph-ai/trustgraph). TrustGraph is an open source, full end-to-end AI infrastructure that automates knowledge graph building and querying along with modular agent integration. A unique aspect of TrustGraph is that the graph building is a one-time process that builds reusable knowledge cores that can be stored, shared, and reloaded. You can read more about TrustGraph knowledge cores here (https://trustgraph.ai/docs/cores/). Throughout our careers, we’ve been faced…
2024 · github.com
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Hey HN! This is Steve from integrate.ai (https://integrate.ai). Our platform unlocks a range of machine learning and analytics capabilities on data that would otherwise be difficult or impossible to access due to privacy, confidentiality, or technical hurdles. Traditional approaches to machine learning and analytics require centralization and aggregation of data sources. Given the increasingly distributed nature of data - across organizations, across borders, and across connected devices - centralizing the data necessary for machine learning and analytics often requires complex…
2022
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2016 · the-codex.net
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TrustGraph now supports automatic knowledge graph construction guided by OWL ontologies. You provide an ontology (OWL/Turtle format or build one in the Workbench editor), point it at your documents, and it extracts entities and relationships that conform to your schema. The problem this solves: generic GraphRAG approaches extract whatever relationships an LLM thinks are relevant, which often misses domain-specific semantics. If you're working in healthcare, finance, or intelligence analysis, you likely already have ontologies (or can adapt standards like SOSA, FIBO, etc.) that define…
Nov 2025
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Small experiment of visualization of wikipedia articles as a graph using d3.js.<p>Articles with more traffic are bigger. I computed the semantic similarity using LSI with python (gensim) You have to scroll down/right a bit!<p>http://similarityapi.appspot.com/graph/?title=blade%20runner<p>There is also a JSON api: http://similarityapi.appspot.com/api/v1/?limit=100&title=blade%20runner<p>All feedback is appreciated:<p>@lucamartinetti [email protected]
2012 · similarityapi.appspot.com
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Nov 2025 · github.com
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2020 · github.com
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