This year PyCon Spain happened just down the road from me in Vigo, and I was able to attend with a few folk from Codethink. Python conferences are usually great as there’s so much variety in the talks and the bar for quality is also quite high.

I’m not sure when the videos will be out and they’ll anyway be in Spanish, but here are some notes for the talks I found most interesting. (Codethink sponsors our attendence to conferences on the basis that we write an internal report afterwards, which is where most of these notes came from).

Mapping illegal tourist apartments with Python

pretalx.com/pycones-2024/talk/…

Presented by Juan Luis Cano Rodríguez, who is a co-founder of Python España and PyCon ES. This talk was activism with some data engineering, in the context of the housing crisis that’s affecting most Spanish cities and tourist destinations.

This talk is the story of how he accidentally got involved as an activist after he noticed someone in his own appt building in Madrid was doing short-term rents to tourists, and wondered whether they had a license.

He started by asking the audience to guess how many long-term rental properties are available on Idealista for Madrid. Is it 7,000, 21,000 or 43,000? Then he asked to guess how many short-term rentals are available on AirBnB.

Tourist apartments in Madrid are licensed only after a professional inspection. The council publish data on how many licenses they’ve issued, and there are 1008 properties listed. Meanwhile the property register, which doesn’t require any inspection, lists 11,000 short-term rental properties. AirBnB meanwhile has 26,000 offers in Madrid. He realized that illegal apartment rentals are far worse than he had imagined.

He used Python (of course) to compare the register of properties against the license data to identify which properties were unlicensed, and participated in a group effort to send 10,000 individual reports to the council in one day. This made the national news back in June. The mayor of Madrid even praised this “citizens effort”.

AirBnB has a “license” field for short-term rentals in Spain, but they don’t do any checking of whether it’s valid. He experimented with their “Report” button with an apartment he knew was unlicensed, and AirBnB’s internal “investigation team” reported back a couple of days later that they had found nothing wrong.

Towards the end of the talk he offered the audience a choice, he could explain the data wrangling with Python in more detail, or give his thoughts on the housing crisis. The vote was around 90% for the latter, and his message was that (unsurprisingly) a lot more pressure is needed before anything is going to change. He finished by announcing a protest happening on the 13th October in Madrid.

The talk was very well received and could probably have filled 3 hours instead of 35 minutes.

How we are removing the GIL in Python

pretalx.com/pycones-2024/talk/…

Presented by Pablo Galindo Salgado, a Python core developer and Bloomberg engineer, this was a summary of the incredible work going on to remove CPython’s “Global Interpeter Lock” or GIL. The full technical details of this are in PEP 703, this is my short summary.

Again this talk could have been a full-day workshop but Pablo packed a lot of details into the time, using the simple of trick of talking incredibly fast.

CPython’s global lock means that writing multithreaded Python code is a waste of time, as the threads just get stuck waiting for the global lock. Today people use horrible workarounds such as the ‘multiprocessing’ library.

However, the GIL has advantages too, mainly its simplicitly. Deadlocks are a common problem in multithreaded code but currently CPython is immune to all that — a deadlock is caused when two mutexes block each other, but of course CPython only has the one lock.

So the big challenge is replacing the GIL with many separate locks, without making CPython slower, and avoiding deadlocks. CPython uses reference counting to manage memory, and this is the hardest piece of the puzzle as sharing objects between threads means every refcount change must be atomic and this can require locking, which limits throughput.

People have been trying to solve this since the mid 1990s and noone has yet succeeded, but the work here is actually being released this week in Python 3.13. It’s behind a compile-time configure flag, and the expectation is to spent around 5 years testing this before it’s enabled by default for everyone.

A minimal overview of how they’ve done it:

• switch to biased ref counting for Python objects: this means there are now two refcounts per object, a thread-local refcount and a global refcount. The global one can be slow to access, but most of the time a local refcount is good enough and this is a fast path.
• make common objects immortal: remember Python dates from the golden age of object-oriented programming, and everything is an object including True, False, None, the number 4 and so on. By setting the highest 2 bits of the refcount, these are now marked as immortal.
• fast locking: using spinlocks from WebKit (the excellently named WTF::Lock) where possible
• new memory allocator: use mimalloc, which allows linked list segments to be allocated contiguously instead of all over the place.

It seems a lot of this work has been funded by corporations, mainly Meta as noted in the Python 3.13 release notes, and we can assume Bloomberg are somewhat involved as well.

I hope Pablo does the same talk in English at some point because it was excellent. In the meantime you can hear him on the Changelog podcast:

The tsunami of disinformation: how Generative AI can help us in the fight against Fake News.

pretalx.com/pycones-2024/talk/…

Presented by Rubén Míguez and Agustín Cañas from Newtral, a fact-checking organization in Spain.

There are many bullshit talks around about how generative AI will magically solve some or other intractable problem, as if a text synthesis machine built from Reddit comments is somehow going to succeed where our best scientists and leaders have so far failed. I recommend reading pivot-to-ai.com/ on that topic, but let me get back to
the talk.

The talk began with an overview of fake news, which I’m sure I don’t need to repeat here, except the excellent statistic taken from Washington Post fact-checkers that during 2016-2020, the then US president made around 30,000 provably false statements, which works out at 21 lies a day, every day for four years including Sundays and bank holidays. So it’s been an interesting time in the world of fact-checkers.

Newtral exists since 2013 and began in a very analogue way: human fact checkers watching news programs with big notebooks and writing down things to be verified. The goal of the small technology team was not to replace the human fact checkers but to build tools that could make them work 30x as fast.

(So if you were wondering how generative AI tools could possibly detect fake news, when Google’s “state of the art” AI is telling people to throw car batteries into the ocean and put glue on pizza, the answer is of course: it’s not going to, there will still be a human fact
checker in the loop).

The fact checking process is roughly as follows:

1. Monitor media
2. Spot facts
3. Verify them
4. Publish (“exploit”) the result

The talk detailed some tools to automate the first two parts: using speech-to-text models to transcribe political speeches and so on as they happen, and then text analysis tools to identify potentially “verifiable” statements. As well as deciding whether a statement can be
verified, they have to analyse whether it matters, whether the misinformation could be dangerous, whether it’s a joke, and so on. There wasn’t much detail how they do this in the talk, unfortunately.

Then they mentioned a multimodal model named ClaimCheck which has run as a WhatsApp bot since 2020 where people can send forwarded messages to see if they are true. This works using RAG (Retrieval Augmented Generation) to search an existing database of human-verified facts. Again I’d have loved more details on how this works.

Finally they mentioned that Newtral will be launching a technology-focused spinoff named Trueflag, aiming to build the existing tech into a software-as-a-service tool for fact checking that can be used by news organisations, social media platforms and so on.

I found this article online which gives some more info:

• wired.com/story/fact-checkers-…

I’m still very curious about how far they can get with this tech and will be following closely.

Other note

There was some talks I didn’t enjoy so much. One about using Pyomo to model the game Age of Empires 2, delivered by some mathmeticians who unfortunately didn’t actually hook up their model to the real game, and the second half the talk was just graphs when what we obviously wanted was footage from actual Age of Empires 2 games. Pyomo looks interesting though.

I also saw a talk on the Robot Framework for testing. The talk was well delivered but I fundamentally disagree with the premise that by hiding Python code behind a bunch of free form text, “non-programmers” will be able to write and adapt test suites. The tool itself looks fine, but I wonder when we’ll learn that there’s no tool that can magically make testing easy, people just have to learn to write excellent test suites using code, and that’s the only way anyone will be able to produce a good test suite. (I’m considering doing a talk myself called “How to write a good test suite using regular programming tools”.)

Vigo was of course fantastic despite the intermittent rain, minor travel issues and increasingly strange procedures for checking in to tourist apartments. (This is the first time I’ve stayed in a place with a “Virtual key”, a web page linked to a smart lock on the apartment door which means you need working internet to enter the place). At least the rental apartment did have a license.

I’d recommend Python community events to everyone. People use Python for all kinds of different things – science, journalism, art, activism, and so on – you always get an interesting mix of talks and not just dry technical presentations of screenshots of code. Even the very technical talk on the GIL was not dry at all. Wherever you live you probably have a local Python community and they may well organize an annual conference, worth investigating.Those of us outside the UK no doubt also have a national Python
community who probably hold a conference from time to time. Get
involved!

Thanks as always to Codethink for sponsoring accommodation, food and sponsoring the event itself.

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