Le weeknote 46

What I did?

So yeah, a week off, in one of my favourite places in the world. I've probably been 6 or 7 times now. If you don't know, Fontainebleu is a forest area around 50km south of Paris, but it feels like a different world. It is a meca for bouldering, people travel from all over the world to climb on the sandstone bounders scattered around the forest. There are a huge number of areas, each with a name, each with a number of climbs, often in coloured circuits. People spent years of their lives working projects.

It's all set in a wonderful forest, with lovely walking, places to hang hammocks, chill and chat, with sections for children and the hardcore climbers alike. Font has it's own style of climbing, lots of rounded tops, often requiring the 'swim' technique to top out. Even back when I climbed properly it was hard. You can come here as a 6b climber, or maybe even climb some 7a's in the gym, and be humbled by 4a's or worse. These days, I'm just happy to cruise around with my daughter, occasionally trying something harder. It felt nice to just be moving and climbing and relaxing.

Aside from the forest, Fontainebleau also has a quite magnificent palace, which was home to many kings and later Napolean.

But you didn't come here for culture or climbing talk did you? You came for the pastries. And yes, I had a few. I'd go out for a run every morning, and come back via a boulanger, and reader, there are many, even just in the small town I was in. So I sampled a few, and to keep myself amused I decided that I would also map and rate them. So I used my own https://www.mapmypatch.co.uk/ to create a quick survey and plot them. You can see the ones I plotted below

Toms pastries - Responses

The pastry weeknotes map View public responses, maps, and insights for this survey.

Map my patchData For Action

I used the Tom Pastry scale (™ pending) which ranked the following - crispiness(or flakiness), softness, lamination and overall pastrieness, each marked out of 10. I think these give a good overview of a pastry. Now you may be asking - how do you get a high score of crispiness and softness? How indeed. Ask the french, because sometimes they manage this. You may also ask, what is the correlation between the three dimensions and overall pastrieness? Well, let's explore

Does that answer your question? Ah, maybe you need to really see it in sort of 3d? Well it just so happens...

So I think what you can conclude from this is I like pastries, and that the ratings system needs work. In fact, I self assessed this and decided that actually I had missed two very important variables, both related to time. The first is, what time of the day you purchase your pastry. The early bird gets the best pastry - before 8 am, probably around 7.20 is my best guess. The second important factor is the time between puchasing and eating. Often I would grab the pastry on the way back from a run, and would still probably have a couple of km back to the campsite. This time delay may well have a significant impact. The highest ranking pastry, the top pain au chocolate was purchased early, and eaten immediately. This is perhaps the lesson, if there is one in any of this madness.

Anyway, if you are still with me, here is your pastry payoff...

Am I the only one who take french baked goods into data viz too far you might be asking? No - someone did a tableau dashboard once

Can I stretch the pastry related content any further? Why yes I can!

Meet Croissant - Croissant is an open, community-built, standardized metadata vocabulary for ML datasets, including key attributes and properties of datasets, as well as information required to load them into ML tools. Croissant enables data interoperability across ML frameworks and beyond, making ML easier to reproduce and replicate.

By building the vocabulary as an extension to schema.org, a machine-readable standard to describe structured data, Croissant also makes ML datasets discoverable beyond the scope of the repository where they have been published. Finally, Croissant operationalizes dataset documentation, extending existing approaches and vocabularies to describe a dataset’s contents, provenance, and usage restrictions.

If you are working in the ML/AI/Data space you should be paying atttention

Croissant - MLCommons

The MLCommons Croissant working group standardizes how ML datasets are described to make them easily discoverable and usable across tools and platforms.

MLCommons

So there, that was my week. I'm doing a little bit of work next week, then back to it properly the week after, which after this weeknote, you may be thankful.

Interesting things

• Who Decides When AI Decides? A Cooperative Response | Platform Cooperativism Consortium
• llm-wiki - karpathy doing karpathy things
• Croissant - MLCommons
• Why Ethical Endurance Exists - And What We're Building
• Scaling - The Cynefin Co
• Good news in the fight against Big Tech
• Delivering lasting impact | Lloyds Bank Foundation for England and Wales
  

Techfreedom

• Community Cloud Storage — Shift Collective
• France Launches Government Linux Desktop Plan as Windows Exit Begins

Note - Reminder that our TechFreedom cohort launches in two weeks, come join us!

Programme — TechFreedom

Three online sessions that help social purpose organisations see their technology dependencies, assess hidden risks, and build practical roadmaps towards greater sovereignty.

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