
λx. Co-Founded @iuvia_ and @trackula_. Tweets assorted in EN/ES/GL. Opinions are my own and may change. 🧵https://xitter.cf/t.co/fMG3l71jRa
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Joined January 2009
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Santiago Saavedra retweeted
Claude, plz rewrite wordpress in rust. Yes all the plugins too. No I don't just mean the source code, I mean like, go replace everybody's wordpress install.
You don't have access, well funny thing...
Assuming no ACID guarantees (SoR is Kafka) you need an indempotency guarantee. You can do this with bounded counters (so you only pay the contention on the last units) or per-SKU keying (in Kafka you are guaranteed that for a single key you get FIFO) as an idempotency key.
Recently I interviewed a Senior Engineer.
Strong candidate.
Kafka? ✅
Redis? ✅
Microservices? ✅
Distributed systems? ✅
Then I asked:
"You're building the inventory service for a flash sale.
Only 1 item left in stock."
Two users hit “Buy Now” at the exact same millisecond.
Both requests pass the “is available?” check.
Both create an order.
Both deduct stock.
Now you’ve sold 2 items when only 1 existed.
How do you guarantee you never oversell — even with concurrent requests, client retries, and partial failures?
"Just use a database transaction."
Doesn’t fully solve it if both requests read `stock = 1` before either writes, or if the response is lost and the client retries.
"Check stock before deducting."
What happens when both checks happen at the same time?
"Use Redis DECR and reject if it goes negative."
What happens if Redis is down, or the decrement succeeds but the order is never persisted?
Your turn:
How would you design this inventory reservation API so concurrent buyers and retries never oversell the last item?
There’s a specific distributed-systems concept behind this.
And you’ll encounter it everywhere inventory, tickets, seats, coupons, and limited resources are involved.
No son propuestas, son deseos. Ni siquiera son deseos socialistas. Son deseos bastante de centro, y lo más triste es que van sin propuestas de cómo conseguirlos. Cómo va alguien a poder soñar que todo es posible si no tenéis ni el camino?
Estas son nuestras 5 propuestas para tener paz, casa y trabajo.
1️⃣Las casas son para vivir.
2️⃣Bajar el coste de la vida.
3️⃣Ni un euro para guerras.
4️⃣Subir el SMI a 1.800€ y jornada laboral de 30h.
5️⃣Proteger lo público y políticas contra el cambio climático.
🎥@_PabloFdez_
Santiago Saavedra retweeted
1/ We discovered something totally unexpected about the neural structure of consciousness that overturns decades of thinking on its biological basis and brings us significantly closer to the recipe for generating conscious awareness.
New preprint: biorxiv.org/content/10.64898… (led by @HesseJanis, with @FrankLanfranchi, @_conor_mcgrory, @Yuelin_Shi, and @EngelTatiana)
Santiago Saavedra retweeted
Ben Affleck says AI is simply matrix multiplication, linear algebra and floating point calculations, and however convincingly it sounds like a person, it has nothing to do with thought, humanity or feeling
“Nor do I believe, and I think it’s important to draw the distinction between what AI does, which is simply predict the next token or the next word in a sentence, then add that word on, run it back to the machine, do a lot of computation and pick the next word."
"That is a very different thing than sentience or thought. And I think we need to be really clear about that because it can be convincingly, in an illusory way, sound like thought or like a person."
"But let’s be real clear. This is matrix multiplication. This is linear algebra. These are floating point calculations. It has nothing to do with thought, humanity, or feeling.”
Ben Affleck reveals he writes Python, understands convolutional neural networks, worked extensively with GPUs, and used his celebrity status to get private looks at Google and OpenAI’s video models
“I’ve always been kind of into computers since I was young. Then, when film started to move from analog film to digital, I became more interested in that aspect of it. The visual-effects workflow for many years has included machine learning, so I can write pretty shitty Python scripts and stuff like that.
“With convolutional neural networks, which were the precursors to what the transformer can do, which is much more computation simultaneously, you would do things like look at what’s called a tensor. That’s the numerical translation of a visual image in numbers, like the batch number, the frame number and the red, green and blue values of each pixel in each frame. It’s just that simple. That numeric is called a tensor.
“You’d use a convolutional neural network to identify patterns that reveal what’s called edge detection or feature extraction, which is identifying patterns well enough to know, this is where the window ledge is, so we can more easily take the green-screen image out and replace it with something.
“That was familiar to me early on because, prior to Artists Equity, I had a small visual-effects company. I’ve worked with GPUs a lot too. The visual-effects guys said, ‘Hey, you should see. There are a couple: Google and this other company, OpenAI, are doing really interesting stuff with transformers in video.’
“I’ve learned that I can actually just call up and go, ‘Hey, it’s Ben Affleck. Can I come see what you’re doing?’ Sometimes people say yes, to my astonishment.”
Santiago Saavedra retweeted
Kids making fun of influencers just restored my faith in humanity. Nature is healing🤣
Santiago Saavedra retweeted
Desde hace muchos años desarrollo una especie de diógenes digital que se ha acentuado con la crisis de los 40. Este fin de semana he decidido poner un poco de orden al caos digital, y el resultado era una cosa impensable para mi hace un año.
This is not significant. Tests were performed by the same agent session within the code and the tests.
Predictable quality improvement happens at empty context, not dirty with impl. detail. Use ultracode in Claude or a coordinator agent that will spin different subagents.
ok it's official... AI-written unit tests and integration tests are empirically proven to be unhelpful. you should tell your agents to stop writing tests by themselves
on the deepswe eval set, banning sonnet 5.5 high from writing any tests actually resulted in slightly higher success rate (non stat-sig), with less time and token spent (stat-sig). this is pretty hard empirical evidence
across the tests that were written by the "tests allowed" baseline arm, 65% of them were unit tests, 35% were integration tests, neither bucket resulted in any improvement compared to not writing any tests at all
i also picked a random sample of 44 tasks subset where i completely disabled executing even existing tests - it also did not affect success rate at all
i spot checked many tests written in the baseline arm, and my intuition is that most tests are simply a repetition of the implementation
agent-written tests do not add any value because both the implementation and the tests were simply the agent's interpretation of our intent. the tests aren't any more accurate than the implementation itself
i suspect we can still extract some value from unit tests and integration tests if we describe them ourselves when we believe we can articulate our intent better through test cases than through requirements. but i have not proven this yet
also worth noting, during deepswe eval the agent wrote almost no e2e tests (only 17, compared to 3000+ unit/integration tests written). so this analysis does not prove nor disprove the value of e2e tests - i will do another eval specifically for that












