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Some of you asked for the link to my Objects Talking to Objects talk:
Objects Talking to Objects
youtu.be/xow9xfa7qlE?si…
YouTube
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Gavin Morrice
3K posts
Gavin Morrice
@morriceGavin
Classically trained rubyist. Leading conversations about OOP. Chow-hound. Professional toddler negotiator.
Scotland Katılım Mayıs 2020
582 Takip Edilen583 Takipçiler
@morriceGavin @ivanburazin I like their hardware so I won't switch until I find something outside of Apple I can tolerate
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Mac search has been broken for 2 years, and nobody at Apple seems to care.
- I open spotlight search
- Type "calc"
- Calculator appears
- I hit return
But in the 0.2 seconds before I hit return, it swaps to a random file with the word "calc" in it somewhere. And I open a file I didn't want.
This happens to me every single day.
Very unbecoming of a $5T company whose customers are still obsessed with its products.
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Me explaining Egypt to a software engineer:
In Scotland, queues are FIFO.
In Egypt, queues are LIFO.
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I caught TDD virus at the start of my career, and this talk by Erik Meijer saved me!
As often the case with snake oil salesmen, they take a good core idea (write usage code first), and turn it into a parody to sell books and courses to people who don't know better.
This talk is 10 years old, but still relevant today. LLMs like writing A LOT of tests, but you must delete most of them and only leave integrational and e2e ones that test real system behavior.
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@cmuratori His next public statement is going to describe GPT as a “cursed monkey paw”
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It consistently baffles me how bad AI CEOs are at communicating. Is Sam Altman really unaware that the "wish-granting genie" trope is associated primarily with stories cautioning against the kind of wish fulfillment he's proposing?
Chief Nerd@TheChiefNerd
SAM ALTMAN: “We are close to creating a genie that can grant any wish.”
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Coding is, until further notice, not solved
Polymarket@Polymarket
BREAKING: Claude is currently experiencing a major outage.
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@RealAdamHunt Turns out intelligence is about more than just language.
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Recently I've flipped from being bullish to being bearish about AI.
I think I'm updating my bearishness to be more solidly bearish. Early thoughts (which I hope to be disproven in the next year or so, I would prefer progress) and my reasoning:
The whole 'it turns out if you keep training and scaling the models more they develop broad new capabilities in lots of domains' thesis is wrong (sorry Demis). The recent batch of models haven't got more general, they've got less general. This is most obvious in the fact that their language outputs have got much worse in comparison to e.g. o3. If they were gaining generalist capacities we would expect them to be describing their work in ever more graceful and comprehensive prose!
The image that was being shared as the AGI thesis (November 2025, Tomas Pueyo) was the spiky bubble that has a current spike or two out past human capabilities (e.g. on coding or math) but below human on other capabilities on the other spikes - the future prediction was that as the models scale/advance, every spike would grow bit by bit until the whole center encompasses the human capabilities, with super-superhuman on some spikes. I think it seems like what's actually happened in the last few models has been that the coding/math spike has grown, but leaving behind or even at the cost of the other spikes. The models are no better at some simple logic, language (and sometimes worse!).
This makes sense from a simple RL perspective; you can't RL something endlessly on one domain of tasks and expect it to improve on the other tasks. The fact that early LLMs did seem to improve generally was a byproduct of the written language corpus covering everything - that corpus is general, so training it on that gave the appearance of something generally intelligent and becoming more generally intelligent as it got better at replicating that corpus. But the actual logic and underlying ground truths behind the language aren't captured efficiently enough and weren't effectively RLd in - they top out at some point (I guess this happened around the time that there was the 'has scaling hit a wall' discussion in late 2024). Chain of thought was then a genuine breakthrough, along with web search, which plugged into that general LLM global-corpus intelligence to lead to post 2024 gains.
The AI companies have since worked out that coding works (and pays) really well (basically this is because the entire job is nearly perfectly recorded and exists as training data, and you can set up clear benchmarks and rewards). The recent models (and benchmarks) have been maxxing that and we've seen degradation on normal English use for that reason. This could still be transformative, leading to extremely powerful (and potentially dangerous, particularly in cyber security) models but it's not a pathway to AGI.
I'm probably at about 40% confidence about this. It fits my current observations of AI progress and has a basic explanatory model. It doesn't account for potential breakthroughs, which is a major reason for discounting.
To make some predictions, I guess if I'm right this will become broadly apparent and more widely acknowledged in the next year or two, as we see how the spikiness of models that keep getting released develops.
Maybe there will be efforts to concentrate on specific spikes e.g. health or law which require going back to earlier models and RLing on a different data set/with different rewards/benchmarks. Maybe those separate models can be linked together to give a more apparently general model. How capital intensive that is/the potential profitability will be a defining question. But I just don't see general abilities emerging atm, and I don't think we will any time soon. Good news - a whole industry of tackling important specific problems/sectors can open up!
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@JamesMelville @zoeharcombe The auto transcription of this is wild 😅
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This Scottish highlander farmer points out a few inconvenient home truths about the wildfires currently in the Cairngorms. He should know. He works on the land.
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@ChShersh 95% of being a parent is simply being asked “why?”.
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Please no one tell Dave Thomas, but I wrote an abstract base class this week. Actually, I forced my agent to write it. 😬
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@jeremysmithco We have it as a sort of convention sort of not. Some people use it but others don’t. About 5,000 models, so quite a lot of Bases which makes it more difficult to find.
I encourage engineers to rage against the convention
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@morriceGavin I could see that…in this case I'm following an existing convention in the codebase. :)
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@ThePrimeagen My top liked comment on Instagram is on one of your old posts where I encourage people to do exactly this
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@karpathy This is my primary use case with ChatGPT. Something I can ramble to while I walk the dogs.
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One pattern I find useful for working with LLMs is a nice long ramble session. Sometimes the LLM needs more bits to understand what you're trying to achieve, but you're too lazy to type them. In these cases I like to lean back, switch to /voice and just ramble for like 10 minutes, total mess, anything goes, full stream of consciousness. Sometimes I declare it up top, something like "switching to speech recognition sorry for any typos...". Sometimes I turn it into a small interview of a few turns. But I find that the LLMs are somehow very good at reconstructing long incoherent rambles and often their echo of your own tangle of thoughts comes out quite a bit cleaner than what you started with. The result is that you improve the mind meld and have to correct things less from that point on.
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