CEO @Linguooapp Founder @ CIIAL Comunidad de Inteligencia Artificial LATAM. Social entrepreneur, machine learning enthusiast and proud geek. #AI#Linguoo#NLP
Something I have been thinking about: in the past, the best engineers I knew spent a lot of time automating their work in various ways. Better vim/emacs automations, writing lint rules to catch repeat code issues, building up a suite of e2e tests so they don't need to smoke test the app manually. These kinds of things were the highest leverage activities an engineer could do, because it multiplied their own output, which in turn meant they could build more things.
I think many of these automations have become even more important now. This is true for a number of reasons.
First, infra and DevX automation speeds you up. And if you are running an army of agents, each of those agents will be sped up also. More automation == more output per unit of time.
Second, moving things to code improves efficiency. Your agent could fix an issue every time it sees that issue happen, but that uses tokens and might miss cases. If Claude instead writes a lint rule, CI step, or routine, that class of issue can be fully automated forever. This is really what people are talking about when they talk about loops -- it's about automating entire types of busywork rather than solving them one off. This isn't a new idea at all. Engineers have been doing this for a long time!
Third and most importantly, automation makes it possible for others to contribute to the codebase more easily. Increasingly what I am seeing is engineers are contributing to codebases on day one because Claude can navigate the codebase for them, and that non-engineers are able to contribute to a codebase as effectively as engineers can. What gets in the way of both of these is domain knowledge that lives in peoples' heads rather than in automation -- the stuff you used to have to learn when ramping up. What has changed thanks to agents is the domain knowledge that can be encoded as infrastructure is no longer limited to what is expressible in lint rules and types and tests; it can now capture nearly all domain knowledge, encoded as code comments and skills and CLAUDE.md rules and memories. If I put up a PR for an iOS codebase I don't know and a code reviewer rejects it because it doesn't use the right framework, or if a designer builds a new feature and it gets rejected because it doesn't follow the right architectural patterns, these are failures of automation.
Every team should be writing the CLAUDE.md's, REVIEW.md's, skills, and docs that enable agents to productively work in their codebase with zero additional context from the prompter. This sounds crazy, and at the same time is a natural extension of the stuff engineers have always done: automate, and encode domain knowledge as infrastructure. As the model gets smarter and as the harness matures, this task becomes easier. In the meantime, it is on every team to look for ways to convert their domain knowledge to infra so that Claude can write code better, so that code review catches issues automatically, and so the next person working on your codebase can contribute more easily.
New podcast on AI (full episode). Links below.
A Motorcycle for the Mind
0:00 If you want to learn, do
2:13 Vibe coding is the new product management
6:49 Training models is the new coding
10:13 Is traditional software engineering dead?
13:07 There is no demand for average
14:12 The hottest new programming language is English
18:36 AI is adapting to us faster than we are adapting to it
22:56 No entrepreneur is worried about AI taking their job
26:46 The goal is not to have a job
29:49 AIs are not alive
32:55 AI fails the only true test of intelligence
36:49 Early adopters of AI have an enormous edge
39:37 AI meets you exactly where you are
43:02 Always leverage the best intelligence
44:37 If you can't define it, you can't program it
49:37 The solution to AI anxiety is action
We’re applying to @ycombinator with a clear mission:
Train robots to support humans—not replace them.
Here’s our first message to the world:
🤖❤️ A Heart for Humanity
#RobotsForHumanity#YC#Robotics#DigitalTwins
🔗 https://t.co/QIrBSJ8zww
Meta, in partnership with Microsoft, is introducing an open-source large language model, Llama 2, available for free for research and commercial use.
The new model, which comes with 7B, 13B, and 70B parameter models, is pretrained on 40% more data than its predecessor and is promised to have significant improvements. It can be downloaded directly or accessed through Azure. An optimized version for Windows is also available.
Próximo destino: Adopción masiva ✈️
🤝En #Binance seguimos trabajando para brindarles mayores facilidades y opciones al usuarios. En este caso trabajando junto a @DespegarAr e @inswitch.
Conocé más, acá: https://t.co/FJXhcuYQfc
@airtminc necesito sacar MIS BTC de su plataforma, realizaron una transferencia sin mi autorización a vuestro nombre y no lo autoricé.
Devuelvan mis BTC.
Como los obtengo?
¡Formá parte del evento #binancian del año!
Demostrá tu pasión #cripto y accedé a esta experiencia única:
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🔸 Los anuncios más esperados del año 😎
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🔸 Networking y mucho meetup 🤝
Solo 4 pasos para participar:
Último sorteo del año: 5 becas en carreras completas! Ya saben, pueden participar desde cualquier lugar del mundo y solo necesitan darle RT a este tweet.
Pueden contarme qué carrera les gustaría hacer y por qué. El 23/12 anuncio a los 5 ganadores, éxitos a todos.