HackerReposA FIELD GUIDE TO OPEN SOURCE
ProSponsor the shelf♧ 0
← Back to top all-time
simonw
datasette
Python2017–2026
simonw / datasette

datasette

An open source multi-tool for exploring and publishing data

36Distinct people
77Mentions
1Explicit recommenders
11.6KGitHub stars
REPOSITORY AT A GLANCE

What to know before exploring

What it is

An open source multi-tool for exploring and publishing data

GitHub

Where it is used

No documented product use was verified in the last source check.

What people are saying

My favorite incarnation of this idea is probably Datasette. https://github.com/simonw/datasette

rpdillon used a recommendation phrase. One indexed comment matches. This is a phrase match, not a verdict.

No classified warning is attached to this page. Read the discussions before treating silence as approval.

Also in these discussions

Mentioned in the same Hacker News threads. That is not proof they are substitutes.

This week on Hacker News

Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 1:49:46 PM UTC.

No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.

Mentions over time

HN mentions by year77 dated mentions

Observed mentions: 2017-11-14 07:56:02 UTC to 2026-08-05 02:35:12 UTC.

051015202017-01-01 00:00:00 UTC to 2018-01-01 00:00:00 UTC (exclusive): 1 mentions; 0 submissions, 1 comments20172018-01-01 00:00:00 UTC to 2019-01-01 00:00:00 UTC (exclusive): 6 mentions; 0 submissions, 6 comments2019-01-01 00:00:00 UTC to 2020-01-01 00:00:00 UTC (exclusive): 5 mentions; 1 submissions, 4 comments2020-01-01 00:00:00 UTC to 2021-01-01 00:00:00 UTC (exclusive): 21 mentions; 1 submissions, 20 comments2021-01-01 00:00:00 UTC to 2022-01-01 00:00:00 UTC (exclusive): 11 mentions; 2 submissions, 9 comments20212022-01-01 00:00:00 UTC to 2023-01-01 00:00:00 UTC (exclusive): 12 mentions; 0 submissions, 12 comments2023-01-01 00:00:00 UTC to 2024-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2024-01-01 00:00:00 UTC to 2025-01-01 00:00:00 UTC (exclusive): 5 mentions; 0 submissions, 5 comments2025-01-01 00:00:00 UTC to 2026-01-01 00:00:00 UTC (exclusive): 3 mentions; 0 submissions, 3 comments2026-01-01 00:00:00 UTC to 2027-01-01 00:00:00 UTC (exclusive): 8 mentions; 1 submissions, 7 comments. Incomplete calendar period.2026*

* An incomplete calendar period is marked with an outlined bar. Its count is not directly comparable with a full period.

Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.

View mention counts and date ranges
UTC ranges include their start and exclude their end. Select a period to filter the source evidence.
PeriodStart (UTC)End (exclusive, UTC)MentionsSubmissionsComments
20172017-01-01 00:00:00 UTC2018-01-01 00:00:00 UTC101
20182018-01-01 00:00:00 UTC2019-01-01 00:00:00 UTC606
20192019-01-01 00:00:00 UTC2020-01-01 00:00:00 UTC514
20202020-01-01 00:00:00 UTC2021-01-01 00:00:00 UTC21120
20212021-01-01 00:00:00 UTC2022-01-01 00:00:00 UTC1129
20222022-01-01 00:00:00 UTC2023-01-01 00:00:00 UTC12012
20232023-01-01 00:00:00 UTC2024-01-01 00:00:00 UTC505
20242024-01-01 00:00:00 UTC2025-01-01 00:00:00 UTC505
20252025-01-01 00:00:00 UTC2026-01-01 00:00:00 UTC303
2026 (partial)2026-01-01 00:00:00 UTC2027-01-01 00:00:00 UTC817

From the project README

An excerpt from the repository, fetched Nov 13, 2026.

Read the project’s introduction
An open source multi-tool for exploring and publishing data Datasette is a tool for exploring and publishing data. It helps people take data of any shape or size and publish that as an interactive, explorable website and accompanying API. Datasette is aimed at data journalists, museum curators, archivists, local governments, scientists, researchers and anyone else who has data that they wish to share with the world. Explore a demo, watch a video about the project or try it out on GitHub Codespaces. datasette.io is the official project website Latest Datasette News Comprehensive documentation: https://docs.datasette.io/ Examples: https://datasette.io/examples Live demo of current main branch: https://latest.datasette.io/ Questions, feedback or want to talk about the project? Join our Discord Want to stay up-to-date with the project? Subscribe to the Datasette newsletter for tips, tricks and news on what's new in the Datasette ecosystem. Installation If you are on a Mac, Homebrew is the easiest way to install Datasette: brew install datasette You can also install it using pip or pipx: pip install datasette Datasette requires Python 3.11 or higher. We also have detailed installation instructions covering other options such as Docker. Basic usage datasette serve path/to/database.db This will start a web server on port 8001 - visit http://localhost:8001/ to access the web interface. serve is the default subcommand, you can omit it if you like. Use Chrome on OS X? You can run datasette against your browser history like so: datasette ~/Library/Application\ Support/Google/Chrome/Default/History --nolock Now visiting http://localhost:8001/History/downloads will show you a web interface to browse your downloads data: metadata.json If you want to include licensing and source information in the generated datasette website you can do so using a JSON file that looks something like this: { "title": "Five Thirty Eight", "license": "CC Attribution 4.0 License", "license_url": "http://creativecommons.org/licenses/by/4.0/", "source": "fivethirtyeight/data on GitHub", "source_url": "https://github.com/fivethirtyeight/data" } Save this in metadata.json and run Datasette like so: datasette serve fivethirtyeight.db -m metadata.json The license and source information will be displayed on the index page and in the footer. They will also be included in the JSON produced by the API. datasette publish If you have Heroku or Google Cloud Run configured, Datasette can deploy one or more SQLite databases to the internet with a single command: datasette publish heroku database.db Or: datasette publish cloudrun database.db This will create a docker image containing both the datasette application and the specified SQLite database files. It will then deploy that image to Heroku or Cloud Run and give you a URL to access the resulting website and API. See Publishing data in the documentation for more details. Datasette Lite Datasette Lite is Datasette packaged using WebAssembly so that it runs entirely in your browser, no Python web application server required. Read more about that in the Datasette Lite documentation.
Read the full README on GitHub

What Hacker News says

77 sources
Right, the transcript is linked from the issue: https://github.com/simonw/sqlite-utils/issues/787#issuecomme... As far as I can tell I've shared more prompt transcripts than anyone else. Happy to be proven wrong about that. Look through my commit history on https://github.com/simonw/tools/commits and https://github.com/simonw/sqlite-utils/commits and https://github.com/simonw/datasette/commits and you'll see that commits that were AI-assisted almost all link to a transcript or include a prompt or both.
In Prefer strict tables in SQLite
Sqlite is good for lots of stuff, but you're probably focusing your days on high-scale webapps that want sharding with networked DBs. That's one domain, and an interesting one, but there are lots of others. I'm a big fan of re-evaluating prior "best practices" in light of technology changes, especially in ways that improve simplicity. Running my family's social media site off a single sqlite DB on a VPS is great. ~15 users, almost zero maintenance. I run my FreshRSS instance off of sqlite, as well as my "now" page. At work, I used sqlite for all kinds of things over the past decades: as an ad hoc job queue, as a quick way to ingest and query lots of logs locally, and present/filter in realtime with simonw's excellent https://github.com/simonw/datasette. I don't think it's every "sqlite for everything" as much as it is "sqlite in lots of
Read the rest
places you probably didn't think to apply it." kentonv/Cloudflare's work on sqlite at the edge might have made this thinking a bit more popular, but it was always around. https://blog.cloudflare.com/sqlite-in-durable-objects/ I suspect being aware of all those little neat cases and wanting to leverage sqlite for them may be an indicator of experience, rather than the opposite.
In SQLite is all you need for durable workflows
I'm working mostly in a web framework that's used by me and almost nobody else (the weird little ASGI wrapper buried in Datasette) and I find the coding agents pick it up pretty fast. One trick I use that might work for you as well: Clone GitHub.com/simonw/datasette to /tmp then look at /tmp/docs/datasette for documentation and search the code if you need to Try that with your own custom framework and it might unblock things. If your framework is missing documentation tell Claude Code to write itself some documentation based on what it learns from reading the code!
In Opus 4.5 is not the normal AI agent experience that I have had thus far
Funny you should use role/permissions as an example here, I spent the weekend using Claude Code to rewrite my own permissions engine to a new design that uses SQL queries to solve the problem "list all of the resources that this actor can perform this action on". My previous design required looping through all known resources asking "can actor X action Y on this?". The new design gets to generate a very complex by thoroughly tested SQL query instead. Applying that new design and updating the hundred of related tests would have taken me weeks. I got it done in two days. Here's a diff that captures most of the work: https://github.com/simonw/datasette/compare/e951f7e81f038e43...
In AI can code, but it can't build software
I've been using LLM-assistance for my larger open source projects - https://github.com/simonw/datasette https://github.com/simonw/llm and https://github.com/simonw/sqlite-utils - for a couple of years now. Also literally hundreds of smaller plugins and libraries and CLI tools, see https://github.com/simonw?tab=repositories (now at 880 repos, though a few dozen of those are scrapers and shouldn't count) and https://pypi.org/user/simonw/ (340 published packages). Unlike my tools.simonwillison.net stuff the vast majority of those products are covered by automated tests and usually have comprehensive documentation too. What do you mean by my script?
In The current state of LLM-driven development
Simon Willison's github would be a great place to get started imo - https://github.com/simonw/datasette https://github.com/simonw/sqlite-utils So, his code might not be a good place to find best patterns (for ex, I don't think they are fully typed), but his repos are very pragmatic, and his development process is super insightful (well documented PRs for personal repos!). Best part, he blogs about every non-trivial update, so you get all the context!
In Ask HN: High quality Python scripts or small libraries to learn from

Showing the 15 most recent of 77 sources. Enable JavaScript to search and browse every indexed mention.

MAKE YOURSELF AT HOME

A few shortcuts

/
Search the collection
?
Open this guide
Esc
Close dialogs and navigation

Your saved repositories and interests stay in this browser.