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Wikidata is a Giant Crosswalk File.
Drew Breunig shows how to take the 140GB Wikidata JSON export, use sed 's/,$//' to convert it to newline-delimited JSON, then use DuckDB to run queries and extract external identifiers, including a query that pulls out 500MB of latitude and longitude points.
Database Remote-Copy Tool For SQLite (draft)
(via)
Neat new SQLite utilities often show up in branches of the SQLite repository. Here's a new one from last month: sqlite3-rsync, providing tools for efficiently creating and updating copies of WAL-mode SQLite databases on either the same machine or across remote machines via SSH.
The way it works is neat, inspired by rsync (hence the tool's name):
The protocol is for the replica to send a cryptographic hash of each of its pages over to the origin side, then the origin sends back the complete content of any page for which the hash does not match.
SQLite's default page size is 4096 bytes and a hash is 20 bytes, so if nothing has changed then the client will transmit 0.5% of the database size in hashes and get nothing back in return.
The tool takes full advantage of SQLite's WAL mode - when you run it you'll get an exact snapshot of the database state as it existed at the moment the copy was initiated, even if the source database continues to apply changes.
I wrote up a TIL on how to compile it - short version:
cd /tmp
git clone https://github.com/sqlite/sqlite.git
cd sqlite
git checkout sqlite3-rsync
./configure
make sqlite3.c
cd tool
gcc -o sqlite3-rsync sqlite3-rsync.c ../sqlite3.c -DSQLITE_ENABLE_DBPAGE_VTAB
./sqlite3-rsync --help
Update: It turns out you can now just run ./configure && make sqlite_rsync in the root checkout.
Something I’ve worried about in the past is that if I want to make a snapshot backup of a SQLite database I need enough additional free disk space to entirely duplicate the current database first (using the backup mechanism or VACUUM INTO). This tool fixes that - I don’t need any extra disk space at all, since the pages that have been updated will be transmitted directly over the wire in 4096 byte chunks.
I tried feeding the 1800 lines of C through OpenAI’s o1-preview with the prompt “Explain the protocol over SSH part of this” and got a pretty great high level explanation - markdown copy here.
Hybrid full-text search and vector search with SQLite. As part of Alex’s work on his sqlite-vec SQLite extension - adding fast vector lookups to SQLite - he’s been investigating hybrid search, where search results from both vector similarity and traditional full-text search are combined together.
The most promising approach looks to be Reciprocal Rank Fusion, which combines the top ranked items from both approaches. Here’s Alex’s SQL query:
-- the sqlite-vec KNN vector search results
with vec_matches as (
select
article_id,
row_number() over (order by distance) as rank_number,
distance
from vec_articles
where
headline_embedding match lembed(:query)
and k = :k
),
-- the FTS5 search results
fts_matches as (
select
rowid,
row_number() over (order by rank) as rank_number,
rank as score
from fts_articles
where headline match :query
limit :k
),
-- combine FTS5 + vector search results with RRF
final as (
select
articles.id,
articles.headline,
vec_matches.rank_number as vec_rank,
fts_matches.rank_number as fts_rank,
-- RRF algorithm
(
coalesce(1.0 / (:rrf_k + fts_matches.rank_number), 0.0) * :weight_fts +
coalesce(1.0 / (:rrf_k + vec_matches.rank_number), 0.0) * :weight_vec
) as combined_rank,
vec_matches.distance as vec_distance,
fts_matches.score as fts_score
from fts_matches
full outer join vec_matches on vec_matches.article_id = fts_matches.rowid
join articles on articles.rowid = coalesce(fts_matches.rowid, vec_matches.article_id)
order by combined_rank desc
)
select * from final;I’ve been puzzled in the past over how to best do that because the distance scores from vector similarity and the relevance scores from FTS are meaningless in comparison to each other. RRF doesn’t even attempt to compare them - it uses them purely for row_number() ranking within each set and combines the results based on that.
Gemini 1.5 Flash-8B is now production ready (via) Gemini 1.5 Flash-8B is "a smaller and faster variant of 1.5 Flash" - and is now released to production, at half the price of the 1.5 Flash model.
It's really, really cheap:
- $0.0375 per 1 million input tokens on prompts <128K
- $0.15 per 1 million output tokens on prompts <128K
- $0.01 per 1 million input tokens on cached prompts <128K
Prices are doubled for prompts longer than 128K.
I believe images are still charged at a flat rate of 258 tokens, which I think means a single non-cached image with Flash should cost 0.00097 cents - a number so tiny I'm doubting if I got the calculation right.
OpenAI's cheapest model remains GPT-4o mini, at $0.15/1M input - though that drops to half of that for reused prompt prefixes thanks to their new prompt caching feature (or by half if you use batches, though those can’t be combined with OpenAI prompt caching. Gemini also offer half-off for batched requests).
Anthropic's cheapest model is still Claude 3 Haiku at $0.25/M, though that drops to $0.03/M for cached tokens (if you configure them correctly).
I've released llm-gemini 0.2 with support for the new model:
llm install -U llm-gemini
llm keys set gemini
# Paste API key here
llm -m gemini-1.5-flash-8b-latest "say hi"
Announcing FLUX1.1 [pro] and the BFL API (via) FLUX is the image generation model family from Black Forest Labs, a startup founded by members of the team that previously created Stable Diffusion.
Released today, FLUX1.1 [pro] continues the general trend of AI models getting both better and more efficient:
FLUX1.1 [pro] provides six times faster generation than its predecessor FLUX.1 [pro] while also improving image quality, prompt adherence, and diversity.
Black Forest Labs appear to have settled on a potentially workable business model: their smallest, fastest model FLUX.1 [schnell] is Apache 2 licensed. The next step up is FLUX.1 [dev] which is open weights for non-commercial use only. The [pro] models are closed weights, made available exclusively through their API or partnerships with other API providers.
I tried the new 1.1 model out using black-forest-labs/flux-1.1-pro on Replicate just now. Here's my prompt:
Photograph of a Faberge egg representing the California coast. It should be decorated with ornate pelicans and sea lions and a humpback whale.
The FLUX models have a reputation for being really good at following complex prompts. In this case I wanted the sea lions to appear in the egg design rather than looking at the egg from the beach, but I imagine I could get better results if I continued to iterate on my prompt.
The FLUX models are also better at applying text than any other image models I've tried myself.
Ask HN: What happens to “.io” TLD after UK gives back the Chagos Islands? This morning on the BBC: UK will give sovereignty of Chagos Islands to Mauritius. The Chagos Islands include the area that the UK calls the British Indian Ocean Territory. The .io ccTLD uses the ISO-3166 two-letter country code for that designation.
As the owner of datasette.io the question of what happens to that ccTLD is suddenly very relevant to me.
This Hacker News conversation has some useful information. It sounds like there's a very real possibility that .io could be deleted after a few years notice - it's happened before, for ccTLDs such as .zr for Zaire (which renamed to Democratic Republic of the Congo in 1997, with .zr withdrawn in 2001) and .cs for Czechoslovakia, withdrawn in 1995.
Could .io change status to the same kind of TLD as .museum, unaffiliated with any particular geography? The convention is for two letter TLDs to exactly match ISO country codes, so that may not be an option.
Ethical Applications of AI to Public Sector Problems. Jacob Kaplan-Moss developed this model a few years ago (before the generative AI rush) while working with public-sector startups and is publishing it now. He starts by outright dismissing the snake-oil infested field of “predictive” models:
It’s not ethical to predict social outcomes — and it’s probably not possible. Nearly everyone claiming to be able to do this is lying: their algorithms do not, in fact, make predictions that are any better than guesswork. […] Organizations acting in the public good should avoid this area like the plague, and call bullshit on anyone making claims of an ability to predict social behavior.
Jacob then differentiates assistive AI and automated AI. Assistive AI helps human operators process and consume information, while leaving the human to take action on it. Automated AI acts upon that information without human oversight.
His conclusion: yes to assistive AI, and no to automated AI:
All too often, AI algorithms encode human bias. And in the public sector, failure carries real life or death consequences. In the private sector, companies can decide that a certain failure rate is OK and let the algorithm do its thing. But when citizens interact with their governments, they have an expectation of fairness, which, because AI judgement will always be available, it cannot offer.
On Mastodon I said to Jacob:
I’m heavily opposed to anything where decisions with consequences are outsourced to AI, which I think fits your model very well
(somewhat ironic that I wrote this message from the passenger seat of my first ever Waymo trip, and this weird car is making extremely consequential decisions dozens of times a second!)
Which sparked an interesting conversation about why life-or-death decisions made by self-driving cars feel different from decisions about social services. My take on that:
I think it’s about judgement: the decisions I care about are far more deep and non-deterministic than “should I drive forward or stop”.
Where there’s moral ambiguity, I want a human to own the decision both so there’s a chance for empathy, and also for someone to own the accountability for the choice.
That idea of ownership and accountability for decision making feels critical to me. A giant black box of matrix multiplication cannot take accountability for “decisions” that it makes.
Building an automatically updating live blog in Django. Here's an extended write-up of how I implemented the live blog feature I used for my coverage of OpenAI DevDay yesterday. I built the first version using Claude while waiting for the keynote to start, then upgraded it during the lunch break with the help of GPT-4o to add sort options and incremental fetching of new updates.
Whisper large-v3-turbo model. It’s OpenAI DevDay today. Last year they released a whole stack of new features, including GPT-4 vision and GPTs and their text-to-speech API, so I’m intrigued to see what they release today (I’ll be at the San Francisco event).
Looks like they got an early start on the releases, with the first new Whisper model since November 2023.
Whisper Turbo is a new speech-to-text model that fits the continued trend of distilled models getting smaller and faster while maintaining the same quality as larger models.
large-v3-turbo is 809M parameters - slightly larger than the 769M medium but significantly smaller than the 1550M large. OpenAI claim its 8x faster than large and requires 6GB of VRAM compared to 10GB for the larger model.
The model file is a 1.6GB download. OpenAI continue to make Whisper (both code and model weights) available under the MIT license.
It’s already supported in both Hugging Face transformers - live demo here - and in mlx-whisper on Apple Silicon, via Awni Hannun:
import mlx_whisper
print(mlx_whisper.transcribe(
"path/to/audio",
path_or_hf_repo="mlx-community/whisper-turbo"
)["text"])
Awni reports:
Transcribes 12 minutes in 14 seconds on an M2 Ultra (~50X faster than real time).
Gergely Orosz’s edited clip of me talking about Open Source. Gergely Orosz released this clip to help promote our podcast conversation AI tools for software engineers, but without the hype - it's a neat bite-sized version of my argument for why Open Source has provided the single biggest enhancement to developer productivity I've seen in my entire career.
One of the big challenges everyone talked about was software reusability. Like, why are we writing the same software over and over again?
And at the time, people thought OOP was the answer. They were like, oh, if we do everything as classes in Java, then we can subclass those classes, and that's how we'll solve reusable software.
That wasn't the fix. The fix was open source. The fix was having a diverse and vibrant open source community releasing software that's documented and you can package and install and all of those kinds of things.
That's been incredible. The cost of building software today is a fraction of what it was 20 years ago, purely thanks to open source.
Bop Spotter (via) Riley Walz: "I installed a box high up on a pole somewhere in the Mission of San Francisco. Inside is a crappy Android phone, set to Shazam constantly, 24 hours a day, 7 days a week. It's solar powered, and the mic is pointed down at the street below."
Some details on how it works from Riley on Twitter:
The phone has a Tasker script running on loop (even if the battery dies, it’ll restart when it boots again)
Script records 10 min of audio in airplane mode, then comes out of airplane mode and connects to nearby free WiFi.
Then uploads the audio file to my server, which splits it into 15 sec chunks that slightly overlap. Passes each to Shazam’s API (not public, but someone reverse engineered it and made a great Python package). Phone only uses 2% of power every hour when it’s not charging!
Conflating Overture Places Using DuckDB, Ollama, Embeddings, and More.
Drew Breunig's detailed tutorial on "conflation" - combining different geospatial data sources by de-duplicating address strings such as RESTAURANT LOS ARCOS,3359 FOOTHILL BLVD,OAKLAND,94601 and LOS ARCOS TAQUERIA,3359 FOOTHILL BLVD,OAKLAND,94601.
Drew uses an entirely offline stack based around Python, DuckDB and Ollama and finds that a combination of H3 geospatial tiles and mxbai-embed-large embeddings (though other embedding models should work equally well) gets really good results.
llama-3.2-webgpu (via) Llama 3.2 1B is a really interesting models, given its 128,000 token input and its tiny size (barely more than a GB).
This page loads a 1.24GB q4f16 ONNX build of the Llama-3.2-1B-Instruct model and runs it with a React-powered chat interface directly in the browser, using Transformers.js and WebGPU. Source code for the demo is here.
It worked for me just now in Chrome; in Firefox and Safari I got a “WebGPU is not supported by this browser” error message.
mlx-vlm (via) The MLX ecosystem of libraries for running machine learning models on Apple Silicon continues to expand. Prince Canuma is actively developing this library for running vision models such as Qwen-2 VL and Pixtral and LLaVA using Python running on a Mac.
I used uv to run it against this image with this shell one-liner:
uv run --with mlx-vlm \
python -m mlx_vlm.generate \
--model Qwen/Qwen2-VL-2B-Instruct \
--max-tokens 1000 \
--temp 0.0 \
--image https://static.simonwillison.net/static/2024/django-roadmap.png \
--prompt "Describe image in detail, include all text"
The --image option works equally well with a URL or a path to a local file on disk.
This first downloaded 4.1GB to my ~/.cache/huggingface/hub/models--Qwen--Qwen2-VL-2B-Instruct folder and then output this result, which starts:
The image is a horizontal timeline chart that represents the release dates of various software versions. The timeline is divided into years from 2023 to 2029, with each year represented by a vertical line. The chart includes a legend at the bottom, which distinguishes between different types of software versions.
Legend
Mainstream Support:
- 4.2 (2023)
- 5.0 (2024)
- 5.1 (2025)
- 5.2 (2026)
- 6.0 (2027) [...]
Ensuring a block is overridden in a Django template (via) Neat Django trick by Tom Carrick: implement a Django template tag that raises a custom exception, then you can use this pattern in your templates:
{% block title %}{% ensure_overridden %}{% endblock %}
To ensure you don't accidentally extend a base template but forget to fill out a critical block.
OpenFreeMap (via) New free map tile hosting service from Zsolt Ero:
OpenFreeMap lets you display custom maps on your website and apps for free. […] Using our public instance is completely free: there are no limits on the number of map views or requests. There’s no registration, no user database, no API keys, and no cookies. We aim to cover the running costs of our public instance through donations.
The site serves static vector tiles that work with MapLibre GL. It deliberately doesn’t offer any other services such as search or routing.
From the project README looks like it’s hosted on two Hetzner machines. I don’t think the public server is behind a CDN.
Part of the trick to serving the tiles efficiently is the way it takes advantage of Btrfs:
Production-quality hosting of 300 million tiny files is hard. The average file size is just 450 byte. Dozens of tile servers have been written to tackle this problem, but they all have their limitations.
The original idea of this project is to avoid using tile servers altogether. Instead, the tiles are directly served from Btrfs partition images + hard links using an optimised nginx config.
The self-hosting guide describes the scripts that are provided for downloading their pre-built tiles (needing a fresh Ubuntu server with 300GB of SSD and 4GB of RAM) or building the tiles yourself using Planetiler (needs 500GB of disk and 64GB of RAM).
Getting started is delightfully straightforward:
const map = new maplibregl.Map({
style: 'https://tiles.openfreemap.org/styles/liberty',
center: [13.388, 52.517],
zoom: 9.5,
container: 'map',
})
I got Claude to help build this demo showing a thousand random markers dotted around San Francisco. The 3D tiles even include building shapes!
Zsolt built OpenFreeMap based on his experience running MapHub over the last 9 years. Here’s a 2018 interview about that project.
It’s pretty incredible that the OpenStreetMap and open geospatial stack has evolved to the point now where it’s economically feasible for an individual to offer a service like this. I hope this turns out to be sustainable. Hetzner charge just €1 per TB for bandwidth (S3 can cost $90/TB) which should help a lot.
DjangoTV (via) Brand new site by Jeff Triplett gathering together videos from Django conferences around the world. Here's Jeff's blog post introducing the project.
Some Go web dev notes. Julia Evans on writing small, self-contained web applications in Go:
In general everything about it feels like it makes projects easy to work on for 5 days, abandon for 2 years, and then get back into writing code without a lot of problems.
Go 1.22 introduced HTTP routing in February of this year, making it even more practical to build a web application using just the Go standard library.
Niche Museums: The Vincent and Ethel Simonetti Historic Tuba Collection. DjangoCon was in Durham, North Carolina this year and thanks to Atlas Obscura I found out about the fabulous Vincent and Ethel Simonetti Historic Tuba Collection. We got together a group of five for a visit and had a wonderful time being shown around the collection by curator Vincent Simonetti. This is my first update to Niche Museums in quite a while, it's nice to get that project rolling again.
django-plugin-datasette. I did some more work on my DJP plugin mechanism for Django at the DjangoCon US sprints today. I added a new plugin hook, asgi_wrapper(), released in DJP 0.3 and inspired by the similar hook in Datasette.
The hook only works for Django apps that are served using ASGI. It allows plugins to add their own wrapping ASGI middleware around the Django app itself, which means they can do things like attach entirely separate ASGI-compatible applications outside of the regular Django request/response cycle.
Datasette is one of those ASGI-compatible applications!
django-plugin-datasette uses that new hook to configure a new URL, /-/datasette/, which serves a full Datasette instance that scans through Django’s settings.DATABASES dictionary and serves an explore interface on top of any SQLite databases it finds there.
It doesn’t support authentication yet, so this will expose your entire database contents - probably best used as a local debugging tool only.
I did borrow some code from the datasette-mask-columns plugin to ensure that the password column in the auth_user column is reliably redacted. That column contains a heavily salted hashed password so exposing it isn’t necessarily a disaster, but I like to default to keeping hashes safe.
Llama 3.2. In further evidence that AI labs are terrible at naming things, Llama 3.2 is a huge upgrade to the Llama 3 series - they've released their first multi-modal vision models!
Today, we’re releasing Llama 3.2, which includes small and medium-sized vision LLMs (11B and 90B), and lightweight, text-only models (1B and 3B) that fit onto edge and mobile devices, including pre-trained and instruction-tuned versions.
The 1B and 3B text-only models are exciting too, with a 128,000 token context length and optimized for edge devices (Qualcomm and MediaTek hardware get called out specifically).
Meta partnered directly with Ollama to help with distribution, here's the Ollama blog post. They only support the two smaller text-only models at the moment - this command will get the 3B model (2GB):
ollama run llama3.2
And for the 1B model (a 1.3GB download):
ollama run llama3.2:1b
I had to first upgrade my Ollama by clicking on the icon in my macOS task tray and selecting "Restart to update".
The two vision models are coming to Ollama "very soon".
Once you have fetched the Ollama model you can access it from my LLM command-line tool like this:
pipx install llm
llm install llm-ollama
llm chat -m llama3.2:1b
I tried running my djp codebase through that tiny 1B model just now and got a surprisingly good result - by no means comprehensive, but way better than I would ever expect from a model of that size:
files-to-prompt **/*.py -c | llm -m llama3.2:1b --system 'describe this code'
Here's a portion of the output:
The first section defines several test functions using the
@djp.hookimpldecorator from the djp library. These hook implementations allow you to intercept and manipulate Django's behavior.
test_middleware_order: This function checks that the middleware order is correct by comparing theMIDDLEWAREsetting with a predefined list.test_middleware: This function tests various aspects of middleware:- It retrieves the response from the URL
/from-plugin/using theClientobject, which simulates a request to this view.- It checks that certain values are present in the response:
X-DJP-Middleware-AfterX-DJP-MiddlewareX-DJP-Middleware-Before[...]
I found the GGUF file that had been downloaded by Ollama in my ~/.ollama/models/blobs directory. The following command let me run that model directly in LLM using the llm-gguf plugin:
llm install llm-gguf
llm gguf register-model ~/.ollama/models/blobs/sha256-74701a8c35f6c8d9a4b91f3f3497643001d63e0c7a84e085bed452548fa88d45 -a llama321b
llm chat -m llama321b
Meta themselves claim impressive performance against other existing models:
Our evaluation suggests that the Llama 3.2 vision models are competitive with leading foundation models, Claude 3 Haiku and GPT4o-mini on image recognition and a range of visual understanding tasks. The 3B model outperforms the Gemma 2 2.6B and Phi 3.5-mini models on tasks such as following instructions, summarization, prompt rewriting, and tool-use, while the 1B is competitive with Gemma.
Here's the Llama 3.2 collection on Hugging Face. You need to accept the new Llama 3.2 Community License Agreement there in order to download those models.
You can try the four new models out via the Chatbot Arena - navigate to "Direct Chat" there and select them from the dropdown menu. You can upload images directly to the chat there to try out the vision features.
Solving a bug with o1-preview, files-to-prompt and LLM.
I added a new feature to DJP this morning: you can now have plugins specify their middleware in terms of how it should be positioned relative to other middleware - inserted directly before or directly after django.middleware.common.CommonMiddleware for example.
At one point I got stuck with a weird test failure, and after ten minutes of head scratching I decided to pipe the entire thing into OpenAI's o1-preview to see if it could spot the problem. I used files-to-prompt to gather the code and LLM to run the prompt:
files-to-prompt **/*.py -c | llm -m o1-preview "
The middleware test is failing showing all of these - why is MiddlewareAfter repeated so many times?
['MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware5', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware2', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware5', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware4', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware5', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware2', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware5', 'MiddlewareAfter', 'Middleware3', 'MiddlewareAfter', 'Middleware', 'MiddlewareBefore']"The model whirled away for a few seconds and spat out an explanation of the problem - one of my middleware classes was accidentally calling self.get_response(request) in two different places.
I did enjoy how o1 attempted to reference the relevant Django documentation and then half-repeated, half-hallucinated a quote from it:
This took 2,538 input tokens and 4,354 output tokens - by my calculations at $15/million input and $60/million output that prompt cost just under 30 cents.
The Pragmatic Engineer Podcast: AI tools for software engineers, but without the hype – with Simon Willison. Gergely Orosz has a brand new podcast, and I was the guest for the first episode. We covered a bunch of ground, but my favorite topic was an exploration of the (very legitimate) reasons that many engineers are resistant to taking advantage of AI-assisted programming tools.
Updated production-ready Gemini models.
Two new models from Google Gemini today: gemini-1.5-pro-002 and gemini-1.5-flash-002. Their -latest aliases will update to these new models in "the next few days", and new -001 suffixes can be used to stick with the older models. The new models benchmark slightly better in various ways and should respond faster.
Flash continues to have a 1,048,576 input token and 8,192 output token limit. Pro is 2,097,152 input tokens.
Google also announced a significant price reduction for Pro, effective on the 1st of October. Inputs less than 128,000 tokens drop from $3.50/million to $1.25/million (above 128,000 tokens it's dropping from $7 to $5) and output costs drop from $10.50/million to $2.50/million ($21 down to $10 for the >128,000 case).
For comparison, GPT-4o is currently $5/m input and $15/m output and Claude 3.5 Sonnet is $3/m input and $15/m output. Gemini 1.5 Pro was already the cheapest of the frontier models and now it's even cheaper.
Correction: I missed gpt-4o-2024-08-06 which is listed later on the OpenAI pricing page and priced at $2.50/m input and $10/m output. So the new Gemini 1.5 Pro prices are undercutting that.
Gemini has always offered finely grained safety filters - it sounds like those are now turned down to minimum by default, which is a welcome change:
For the models released today, the filters will not be applied by default so that developers can determine the configuration best suited for their use case.
Also interesting: they've tweaked the expected length of default responses:
For use cases like summarization, question answering, and extraction, the default output length of the updated models is ~5-20% shorter than previous models.
nanodjango. Richard Terry demonstrated this in a lightning talk at DjangoCon US today. It's the latest in a long line of attempts to get Django to work with a single file (I had a go at this problem 15 years ago with djng) but this one is really compelling.
I tried nanodjango out just now and it works exactly as advertised. First install it like this:
pip install nanodjango
Create a counter.py file:
from django.db import models from nanodjango import Django app = Django() @app.admin # Registers with the Django admin class CountLog(models.Model): timestamp = models.DateTimeField(auto_now_add=True) @app.route("/") def count(request): CountLog.objects.create() return f"<p>Number of page loads: {CountLog.objects.count()}</p>"
Then run it like this (it will run migrations and create a superuser as part of that first run):
nanodjango run counter.py
That's it! This gave me a fully configured Django application with models, migrations, the Django Admin configured and a bunch of other goodies such as Django Ninja for API endpoints.
Here's the full documentation.
XKCD 1425 (Tasks) turns ten years old today (via) One of the all-time great XKCDs. It's amazing that "check whether the photo is of a bird" has gone from PhD-level to trivially easy to solve (with a vision LLM, or CLIP, or ResNet+ImageNet among others).
The key idea still very much stands though. Understanding the difference between easy and hard challenges in software development continues to require an enormous depth of experience.
I'd argue that LLMs have made this even worse.
Understanding what kind of tasks LLMs can and cannot reliably solve remains incredibly difficult and unintuitive. They're computer systems that are terrible at maths and that can't reliably lookup facts!
On top of that, the rise of AI-assisted programming tools means more people than ever are beginning to create their own custom software.
These brand new AI-assisted proto-programmers are having a crash course in this easy-v.s.-hard problem.
I saw someone recently complaining that they couldn't build a Claude Artifact that could analyze images, even though they knew Claude itself could do that. Understanding why that's not possible involves understanding how the CSP headers that are used to serve Artifacts prevent the generated code from making its own API calls out to an LLM!
Things I’ve Learned Serving on the Board of The Perl Foundation (via) My post about the PSF board inspired Perl Foundation secretary Makoto Nozaki to publish similar notes about how TPF (also known since 2019 as TPRF, for The Perl and Raku Foundation) operates.
Seeing this level of explanation about other open source foundations is fascinating. I’d love to see more of these.
Along those lines, I found the 2024 Financial Report from the Zig foundation really interesting too.
simonw/docs cookiecutter template. Over the last few years I’ve settled on the combination of Sphinx, the Furo theme and the myst-parser extension (enabling Markdown in place of reStructuredText) as my documentation toolkit of choice, maintained in GitHub and hosted using ReadTheDocs.
My LLM and shot-scraper projects are two examples of that stack in action.
Today I wanted to spin up a new documentation site so I finally took the time to construct a cookiecutter template for my preferred configuration. You can use it like this:
pipx install cookiecutter
cookiecutter gh:simonw/docs
Or with uv:
uv tool run cookiecutter gh:simonw/docs
Answer a few questions:
[1/3] project (): shot-scraper
[2/3] author (): Simon Willison
[3/3] docs_directory (docs):
And it creates a docs/ directory ready for you to start editing docs:
cd docs
pip install -r requirements.txt
make livehtml
Jiter (via) One of the challenges in dealing with LLM streaming APIs is the need to parse partial JSON - until the stream has ended you won't have a complete valid JSON object, but you may want to display components of that JSON as they become available.
I've solved this previously using the ijson streaming JSON library, see my previous TIL.
Today I found out about Jiter, a new option from the team behind Pydantic. It's written in Rust and extracted from pydantic-core, so the Python wrapper for it can be installed using:
pip install jiter
You can feed it an incomplete JSON bytes object and use partial_mode="on" to parse the valid subset:
import jiter partial_json = b'{"name": "John", "age": 30, "city": "New Yor' jiter.from_json(partial_json, partial_mode="on") # {'name': 'John', 'age': 30}
Or use partial_mode="trailing-strings" to include incomplete string fields too:
jiter.from_json(partial_json, partial_mode="trailing-strings") # {'name': 'John', 'age': 30, 'city': 'New Yor'}
The current README was a little thin, so I submiitted a PR with some extra examples. I got some help from files-to-prompt and Claude 3.5 Sonnet):
cd crates/jiter-python/ && files-to-prompt -c README.md tests | llm -m claude-3.5-sonnet --system 'write a new README with comprehensive documentation'
How streaming LLM APIs work.
New TIL. I used curl to explore the streaming APIs provided by OpenAI, Anthropic and Google Gemini and wrote up detailed notes on what I learned.
Also includes example code for receiving streaming events in Python with HTTPX and receiving streaming events in client-side JavaScript using fetch().