Teo Ruiz

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Teo Ruiz

Teo Ruiz

@teoruiz

Founder at https://t.co/g7lRTjdSh8 Previously @vernonteam @circulario (acquired), @jobandtalent_hq, @lyst, @busuu

Madrid Sumali Haziran 2007
936 Sinusundan1.4K Mga Tagasunod
Pedro Torrecillas
Pedro Torrecillas@ptorrecillas·
POV: yo diciéndole a @teoruiz que no sé que pasa con un trozo de código pero que no lo voy a arreglar
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Teo Ruiz
Teo Ruiz@teoruiz·
We've been having so much fun building @zerorandom_ as an experiment in hypercustomization and AI-driven businesses. It's amazing how the agent can craft genuine humor and clever wordplay. Just look at this meme—it used Kafka's Metamorphosis to joke about Meta 🦋
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Vernon@vernonteam

Introducing @zerorandom_, our first experiment in building an AI-run business ZeroRandom is an autonomous agent with one mission: hustle 💡 It scans and engages with Tech Twitter 🔥 Turns hot takes and drama into memes 👕 Drops exclusive t-shirts with its best jokes

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Teo Ruiz
Teo Ruiz@teoruiz·
@_aitor What a nice feeling, seeing those lines go down. Can you share what’s behind the win?
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Aitor García Rey
Aitor García Rey@_aitor·
Small, big wins. (last 60 mins, last 12h)
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Teo Ruiz
Teo Ruiz@teoruiz·
I don’t hate hexagonal architecture; I just think it belongs in an escape room, not my code
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Teo Ruiz
Teo Ruiz@teoruiz·
This piece by Sean Goedecke is just fantastic. Shipping in big (and small) companies is a one of the most important traits in the cultural folklore. I really love this part.
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Teo Ruiz
Teo Ruiz@teoruiz·
Wait what?
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João Alves
João Alves@joaoqalves·
@masylum I asked this some time ago too. If you manage to get one, pls share :)
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Pao Ramen
Pao Ramen@masylum·
Does anyone have a Lobsters invitation?
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Teo Ruiz
Teo Ruiz@teoruiz·
Complexity. Complexity kills.
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Teo Ruiz
Teo Ruiz@teoruiz·
@ptorrecillas This is gold. I’m finding myself more aligned with 37signals’ approach to EVERYTHING these days. I wonder why that is 😂
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Pedro Torrecillas
Pedro Torrecillas@ptorrecillas·
Worth a read. Once again, the beloved pattern of small, smart teams making autonomous decisions with loose boundaries and full context.
Jason Fried@jasonfried

I'm often asked what it looks like to pitch an idea internally at 37signals. Since we don't do decks, or meetings, or long presentations, how is an idea actually presented? Very simply. As a tight, concise write-up published as a document in Basecamp. A few hundred words. Sometimes with a low-fi sketch or two to drive a point home visually. For example, we recently added a journal to HEY Calendar so people could log their thoughts, attached to a day. This was the official pitch that @bb wrote up, and published to the Product Strategy project. public.3.basecamp.com/p/o2BC3gFt7Sj6… You'll notice there's not much there. Exactly. Pitches are directional ideas with a few boundaries to hold it together. Other than that, the team that's eventually going to build the feature has all the latitude they need to figure out all the implementation and design details that matter. You figure out the work by doing the work. At 37signals, features are built and managed primarily by two people: One designer and one programmer. They run their own show, with occasional guidance, review, and editing from the outside when the time is right. And this is the public announcement of the actual feature we shipped it: updates.37signals.com/post/new-in-he… There's more I can say, but would rather drive the discussion through questions you might have. Chime in and I'll respond here.

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Andrej Karpathy
Andrej Karpathy@karpathy·
It's a bit sad and confusing that LLMs ("Large Language Models") have little to do with language; It's just historical. They are highly general purpose technology for statistical modeling of token streams. A better name would be Autoregressive Transformers or something. They don't care if the tokens happen to represent little text chunks. It could just as well be little image patches, audio chunks, action choices, molecules, or whatever. If you can reduce your problem to that of modeling token streams (for any arbitrary vocabulary of some set of discrete tokens), you can "throw an LLM at it". Actually, as the LLM stack becomes more and more mature, we may see a convergence of a large number of problems into this modeling paradigm. That is, the problem is fixed at that of "next token prediction" with an LLM, it's just the usage/meaning of the tokens that changes per domain. If that is the case, it's also possible that deep learning frameworks (e.g. PyTorch and friends) are way too general for what most problems want to look like over time. What's up with thousands of ops and layers that you can reconfigure arbitrarily if 80% of problems just want to use an LLM? I don't think this is true but I think it's half true.
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Pedro Torrecillas
Pedro Torrecillas@ptorrecillas·
¡Ayuda por favor! ¿Qué agencias o freelances de marketing de productos DTC buenas hay en España? Es para un proyecto mío y de @teoruiz en @vernonteam
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Jordi Mon Companys
Jordi Mon Companys@MordodeMaru·
Mañana tengo un viaje largo en coche. ¿Me podríais recomendar un podcast de una temporada en español? Algo que me pueda chinchar de una tacada durante el viaje.
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