36,291 karma · joined March 3, 2007
Data, compilers, software architecture
barry.j.kelly@gmail.com
They are cash flow positive, $3.4 billion in H1.
That means they're making money.
There's a separate account that describes it as the result of four days of consultation: https://www.wildsidejourneys.com/post/a-message-from-the-eld...
Obviously it has been written in collaboration with someone with good knowledge of AI, but it's written to that audience, so why wouldn't they collaborate?
It would be a worse kind of cultural imperialism to deny these people the right to say something by criticizing how they say it.
Minimum wage in Zurich city is 23.90 chf/hour. About 37 minutes of time, at most.
Tailscale is the key ingredient.
All my SIMs I've used in my phone seem to have been added as verified phone numbers to my primary account, but my secondary accounts don't have any phone numbers still.
It's still somewhat obnoxious since it means random travel sims that could get reused after they expire are "verified" for my account.
(It made everything worse if you ask me but they did do it.)
Because Windows is tied together through APIs and databases and not text files (unlike Linux), everything is text is not as useful as something that can talk objects. And a lot of Windows is object oriented, from the window message dispatching system through COM and down to NT's object manager.
Time is "just" another dimension. There's temporal continuity between frames, a video VAE would be learning and representing those temporal shifts, but there's nothing to say that e.g. a recursively applied generative model at the pixel level also doesn't learn and represent those things.
As ever, figuring out how to train the thing is the hard bit I expect.
(Handwaving over "textures" here, VAEs encode more like somewhat macro blocks of image whose content is also conditioned on surrounding blocks, rather than tiny patches of patterned pixels.)
(And yes I'm a total imposter layman here, I just see VAEs as seeming to be a crutch that reduce data size - super super helpful of course - but being strictly speaking redundant and inhibiting correct fine detail.)
I drink a lot of wine, but 1 full bottle is too much to have regularly. A half bottle - two generous glasses - is more reasonable, but you'll still feel it the next day. And 10 litres of alcohol puts you at 8 generous glasses a week.
I reckon this is still quite a lot of alcohol.
If energy prices are too high, industry suffers. If you let the price rise, maybe China just eats your lunch.
What's the option value vs risk of gas price rises? Gas sets the marginal price of electricity in Italy today for 60% of the year [1]
[1] https://www.terna.it/DesktopModules/AdactoBackend/API/direct... "However, the electricity system remains significantly exposed to gas price volatility, which in 2024 determined electricity prices for more than 60% of the hours."
You could build something with storage and composition out of MCP functions, but come on, have you seen how LLMs - particularly budget LLMs - try and invoke functions reliably? The amount of retries you have to hide, feedback you need to send back to the LLM about what it did wrong. Parameters get replaced with synonyms, arrays are passed for singular arguments and vice versa, structured inputs are flattened, etc.
So maybe you fine tune on interactions with your subset of MCPs, to improve reliability. But all you end up doing is reinventing a Unix-like command line, poorly.
Firecracker micro-VMs, gVisor, wasm sandboxes. There are ways to make this work that aren't heavyweight. Giving LLMs tools that they've seen how to use millions of times in training corpora just works better.
These guys are the common factor, the guys running the evals that let all the AI agents out, it looks like.
If you don't have something running somewhere, you don't have an agent, you don't have a harness. You've got a token generator, an LLM from the 2024 era.
Think of an agentic harness as like a kind of body for the LLM. It gives it primitive inputs (read_file, web_search or whatever) and primitive outputs (edit file, respond to user, etc). Give it a command line environment (in a locked down sandbox, with as few or as many tools as you prefer), and you've given it a toolbox. It can do a whole lot more, faster and more efficiently. It can compose tools together. It makes fewer transcription errors manually shifting data around. It can tame verbosity with good protections in the harness and access to grep, sed and awk.
It's really up to you how useful you want your agent to be.
Going without the sandbox means hobbling the LLM. It can do things directly but is less able to construct ad-hoc programs to deal with looping, conditionality, tame verbosity, connect tools together, and so on.
It's a choice to not give the LLM an environment. As you say, it can be necessary if you're using dumb models. I don't find it particularly worth the trade most of the time.
Peter Dicks, David Carruthers
State violence is used to ensure compliance, so that companies self-censor.
Variables can be described by types. That is, a mutable slot referencing a value can be a value itself (a reference to a reference) and we can use subtype relationships to describe it. Variables are covariant when used as inputs (i.e code reads from the variable) and contravariant when used as outputs, and invariant when used as both. You can logically supply a Box<Cat> to a vet(in Box<Animal>) routine, and supply Box<Animal> to catchAndStore(out Box<Cat>). Substitute `ref X` for `Box<X>` when using a language that can pass variables by reference.