Alternatives
Products that do what Kronotop – A distributed multi-model database built on FoundationDB does
After almost three years of development, the first developer preview of Kronotop is out. Kronotop is a distributed multi-model database built on FoundationDB. Our motto is: One transaction, multiple models. Documents, ordered key-value data, and other models can participate in the same strictly serializable transaction, even across namespaces. I'd love to hear your feedback.
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Hey there HN! We’re Joe and Stopa, and today we’re open sourcing InstantDB, a client-side database that makes it easy to build real-time and collaborative apps like Notion and Figma. Building modern apps these days involves a lot of schleps. For a basic CRUD app you need to spin up servers, wire up endpoints, integrate auth, add permissions, and then marshal data from the backend to the frontend and back again. If you want to deliver a buttery smooth user experience, you’ll need to add optimistic updates and rollbacks. We do these steps over and over for every feature we build, which can…
2024 · github.com
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Hey HN, I built pg-mcp, a Model Context Protocol (MCP) server for PostgreSQL that provides structured schema inspection and query execution for LLMs and agents. It's multi-tenant and runs over HTTP/SSE (not stdio) Features - Supports multiple database connections from multiple agents - Schema Introspection: Returns table structures, types, indexes and constraints; enriched with descriptions from pg_catalog. (for well documented databases) - Read-Only Queries: Controlled execution of queries via MCP. - EXPLAIN Tool: Helps smart agents optimize queries before execution. - Extension…
2025 · github.com
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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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Hi all, Aram and Eduard here - authors of Modelence (https://github.com/modelence/modelence), an all-in-one backend platform for teams that love TypeScript + MongoDB. Think Supabase, but for MongoDB: auth, cron jobs, email, monitoring, without glue code before you can ship. As Karpathy (and many of us) noted, getting from prototype to production is mostly painful integration work. The pieces exist, but stitching them together reliably is the hard part: https://x.com/karpathy/status/1905051558783418370. YC AI Startup School talk about this -…
2025 · github.com
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2016 · bedquiltdb.github.io
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- 10JM
Hi HN ! Alex here. I'm excited to show you Jinbase (https://github.com/pyrustic/jinbase), my multi-model transactional embedded database. Almost a year ago, I introduced Paradict [1], my take on multi-format streaming serialization. Given its readability, the Paradict text format appears de facto as an interesting data format for config files. But using Paradict to manage config files would end up cluttering its programming interface and making it confusing for users who still have choices of alternative libraries (TOML, INI File, etc.) dedicated to config files. So I…
2024 · github.com
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2021 · github.com
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Last year we launched ChartDB OSS (https://news.ycombinator.com/item?id=44972238) - an open-source tool that generates ER diagrams from your database (via query/sql/dbml) without needing direct DB access. Now we’re launching the ChartDB Agent. It helps you design databases from scratch or make schema changes with natural language. You can: - Generate schemas by simply describing them in plain English - Brainstorm new tables, columns, and relationships with AI - Iterate visually in a diagram (ERD) - Deterministically export SQL script Try it out here -…
Oct 2025 · app.chartdb.io
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2015 · kvpbase.tumblr.com
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Hey HN, We've been experimenting with a real-time, version-controlled NoDB for Deno & React called GoatDB. The idea is to remove backend complexity while keeping apps fast, offline-resilient, and easy to self-host. Runs on the client – No backend required, incremental queries keep things efficient. Self-hosted & lightweight – Deploy a single executable, no server stack needed. Offline-first & resilient – Clients work independently & can restore state after server outages. Edge-native & fast – Real-time sync happens locally with minimal overhead. Why We Built It: We needed something that’s…
2025 · github.com
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Large Language Models (LLMs) are powerful, but they’re limited by fixed context windows and outdated knowledge. What if your AI could access live search, structured data extraction, OCR, and more—all through a standardized interface? We built the JigsawStack MCP Server, an open-source implementation of the Model Context Protocol (MCP) that lets any AI model call external tools effortlessly. Here’s what it unlocks: - Web Search & Scraping: Fetch live information and extract structured data from web pages. - OCR & Structured Data Extraction: Process images, receipts, invoices, and handwritten…
2025
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Nov 2025 · github.com
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2024 · github.com
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Hey HN, I've been working on a multi-model database called NodeDB. Originally, i've found out the idea of SurrealDB quite good. However, it doesn't have some graph and vector features that I need. And since it is just a KV wrapper, instead of purpose-built engine, the performance will never be close to the specialized databases (like Neo4j, Pinecone, Clickhouse, etc). And i've asked myself, what if, there is a database that have the same idea, but built differently? Instead of just treating it as KV database, we build specialized engines for the data. Besides that, I want it to be able to…
May 2026 · github.com
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Hi HN, I’m Samrith, creator of Hyperterse. Today I’m launching Hyperterse 2.0, a schema-first framework for building MCP servers directly on top of your existing production databases. If you're building AI agents in production, you’ve probably run into agents needing access to structured, reliable data but wiring your business logic to MCP tools is tedious. Most teams end up writing fragile glue code. Or worse, giving agents unsafe, overbroad access. There isn’t a clean, principled way to expose just the right data surface to agents. Hyperterse lets you define a schema over your data and…
Feb 2026 · hyperterse.com
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Nov 2025 · github.com
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2025 · github.com
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