Readit Newsstym06 commented on Show HN: Cheapest Managed OpenClaw hosting – 30s setup (agent37.com) agent37.com/openclaw... · Posted by u/azfar0007
Nice write-up and very clear explanation of the economics. This seems perfect for people who just want to try OpenClaw quickly. Probably not ideal for production yet, but great for experimentation
nice
stym06 commented on Show HN: Kepler - An Open-source text-to-SQL platform github.com/stym06/kepler... · Posted by u/stym06
Introducing Kepler
An open-source text-to-SQL platform that lets you query data warehouses in plain English. It currently supports ClickHouse.
https://github.com/stym06/kepler
1. Ask a question in natural language 2. Kepler finds relevant tables and recalls past learnings 3. The AI agent generates SQL, executes it read-only, and validates results 4. Results appear with suggested charts when appropriate
It features persistent memory - it learns corrections, schema notes, and patterns across sessions, implemented using Qdrant + Ollama (nomic-embed-text).
Based on a recent research paper published by OpenAI https://openai.com/index/inside-our-in-house-data-agent/
stym06 commented on Show HN: Doomscrolling Research Papers openpaperdigest.com/... · Posted by u/davailan
Thanks!
What topics are you interested in?
Two different directions I'm thinking of for Open Paper Digest:
- either some recommendation algorithm that figures out which topics you are interested in and serves you papers based on that. Would need a good way to get signals though. That's why I'm now bootstrapping the process with Huggingface Trending Papers, but that immediately constrains the topics.
- or more search driven, where you type "I'd like to read about X" and it starts your feed
With regards to anti-bubbling: interesting thought, a "reverse" recommendation algorithm...
you could just rank the papers, and show trending ones as a separate tab.
for filters, create a set of pre-defined tags and let the LLM choose one of your pre-defined tags from the paper's summary.
How's it different from existing open source data catalogs like amundsen.io?
If a human had done this, these would be at a museum
The idea is simple: take any GitHub URL, add “quick” before github.com, and get AI-generated system design documentation in under 60 seconds.
github.com/vercel/next.js → quickgithub.com/vercel/next.js
The generated docs include a system overview, architecture breakdown, key modules, tech stack, entry points, and dependencies.
There’s also a Q&A chat to ask follow-up questions about the repo.
Why I built this:
I think we’re heading toward a world where most code is written by AI agents, and the bottleneck shifts from writing code to understanding what was written. Traditional docs assume a human author. I wanted something that could explain any codebase instantly, regardless of who (or what) wrote it.
Some details: * Each repo is indexed once and cached permanently * Login required via GitHub OAuth (one free repo per account) * All generated docs are public by default * It’s open source: github.com/stym06/quickgithub
Would love feedback on the quality of the generated docs.
Try it on a repo you know well and tell me where it gets things wrong. that’s the most useful feedback I can get.