2,261 karma · joined February 9, 2013
The problem is the hardest to parse addresses are also often the hardest to match, making the problem somewhat circular. I wrote about this more in a recent blog on address matching: https://www.robinlinacre.com/address_matching/
This resonates. Moreover, it's very easy for architects to assume that because different areas of the business use data about the 'same' thing, the thing must be the same.
But often the analysis requires a slightly different thing. Like: we want a master list of prisons. But is a prison a building, a collection of prisoners (such that the male prison and the female prison on the same site are different prisons), or the institution with that name managed under a particular contract?
There's a kind of inversion in how you do things: e.g. rather than navigating to settings until you find the screen time controls and changing the settings, you explain in natural language what to do, the agent asynchronously proposes the solution, and you just click accept or reject.
So I'm perfectly happy with a system of higher education that strongly rewards this behaviour
Unless I'm missing something, it's amazing how few free, _high quality_ materials are online.
Ultimately I'm interested in two things: genuinely fun games that make you do some maths, and quality visualisations that help make concepts easier to learn
To be honest I think both are quite limited by context length (in that they try to limit the context they send to the LLM and hence cost), and so I find myself using Gemini 2.5 in AI studio with the 1m context length, and asking it to generate instructions for Copilot (which seems to work pretty well)
It's personal taste, but this is significantly better than the last couple of fiction books I've read (which were both well reviewed).
I think it's good enough that it's hard to argue is emotionally vacuous, unless you define that to mean 'it was written by a machine'
I think increasing we'll find AIs are extremely good at emotional 'manipulation' (which I mean in the same sense has how a good tearjerker is in some sense emotionally manipulative).
I've used sqlglot to do this in the past, but doing it natively would be nice
input_data = duckdb.sql("SELECT * FROM read_parquet('...')")
step_1 = duckdb.sql("SELECT ... FROM input_data JOIN ...")
step_2 = duckdb.sql("SELECT ... FROM step_1")
final = duckdb.sql("SELECT ... FROM step_2;")
For context, I've tried it! I've been working on a free library for geocoding UK addresses quickly and accurately. It comes with the caveat that you need access to the dataset of all addresses you're geocoding against - which could be your own list, or a commercial product like addressbase: https://github.com/robinL/uk_address_matcher/
That said, a 100 year wait for social housing in Westminster is not surprising, and not as bad as it sounds. A typical professional couple in London could not afford a family home in Westminster, and it's extremely common for people in their 30s to move out of the centre when they have kids. I don't think this should be any different for social tenants: they should certainly not have to wait 100 years, but it seems reasonable for them to be offered housing outside of Westminster. The Guardian article I read about the 100 year wait specifically mentioned 3 and 4 bed family homes, and my immediate reaction was that no one else can get them either!
I think the bigger problem is the lack of affordable housing particularly for young families (social or otherwise) outside of London
Someone I know recently had a referral - it's pretty light touch, you just get a prompt appointment, and they do a minor op to remove the mole, and send it to the lab for testing. Luckily in their case, it wasn't cancer. But nothing in the process seemed weird, it was just the way of the GP escalating it because they couldn't be sure. Hypothetically, if the AI had been able to diagnose with higher certainty than the GP, all of this could have been avoided, so definitely room for improvement.
My perception is that it's caught up a lot. It still seems a bit slower to run agent, and slower to apply diffs. As the auto complete is a little worse. But it's no longer a no brainer to spend the extra on the cursor subscription.
For harder problems, I've also found myself reaching for long-context Gemini 2.5 pro using files-to-prompt, meaning I've been relying on copilot/cursor for less complex tasks
I can't help feeling like this is a vicious cycle - the lack of community facilities is causing greater isolation, causing a rise in health needs and so on.
- A library for fast, accurate matching (geocoding) of UK addresses that uses Splink under the hood: https://github.com/RobinL/uk_address_matcher
- An npm library that generates maths mental arithmetic problems that align to the UK national curriculum, that can be used to power maths games: https://github.com/RobinL/maths-game-problem-generator
- A breakout maths game that uses the above: https://github.com/RobinL/maths-game-problem-generator
> Gemini 2.5 barely made a ripple in the broader tech conversation.
But my sense (vibes from using it) is it's the very best model right now (though I have not tried the $200 a month exclusive openai offerings)
For example, I asked it to render a few lines of text on a medieval scroll, and it basically looked like a picture of a gothic font written onto a background image of a scroll