Product Architect and Design Engineer interested in Cybersecurity 🔑, Blockchain 🔗, Machine Learning 🤖 and Human-Computer Interaction 🧮.
Austin → Cambridge → Oslo
Joined September 2008
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Daniel Arevalo retweeted
Cursor tip: if you're doing web dev, there's an integrated browser!
The agent can read console logs, network requests, and even control the browser to do automated testing for you.
You don't have to install any third-party MCP servers, just works 😄
Daniel Arevalo retweeted
insane alpha in this video for PMs & founders building AI products 🤯
a must-watch, as with anything Andrej Karpathy puts out. thanks YC 🙏
RT @david_perell: The Ghibli-style memes are the beginning of something bigger. The age of minimalism is over. No longer is the Internet st…
Daniel Arevalo retweeted
After advising 50+ consumer companies over the last year, the one thing that separates those who can execute and those who can't:
Having a full-time designer in the room at all times
I've met with countless companies that have raised millions—and even one that has raised billions—that do not even have a designer on payroll.
This makes product development broken:
1/ You simply cannot have constructive conversations about ideas without visualizing them in real-time
2/ Your experiments will frequently have inconclusive results because users cannot discover features or they misunderstand how they work
3/ There is no one who can galvanize the team with a vision of what the product could look and feel like
And to be abundantly clear: I'm not referring to visual UI or graphics. I'm talking about someone who can think through the fundamental building blocks of product comprehension—like navigation, interaction and copywriting—and is technically savvy enough to visualize those components in high resolution.
There can certainly be exceptions to not having a designer, like where the CEO is an exceptional visual thinker, but that does not scale beyond a small team.
At the end of day, products live and die in the pixels: it's what the users see and tap. And without someone shepherding that process, you are effectively wandering the desert blind.
Daniel Arevalo retweeted
finally someone with a large enough platform says this. it baffles me to see product teams who share a designer (part time) or have no designer. and ideally they should be a senior to be able to talk on the level. if i was starting today i’d have a founding engineer and designer
Daniel Arevalo retweeted
Uses shadcn/ui for components 😎
With Canvas in Gemini, you can:
⌨️ Write, iterate & preview React/HTML code
📝 Draft & edit comprehensive documents
🎨 Build interactive prototypes, games & visualization
…and more.
Simply select ‘Canvas’ in your prompt bar and you can write and edit documents or code, with changes appearing in real-time: gemini.google.com/canvas
Try it today and share your Canvas creations by tagging us here!
Daniel Arevalo retweeted
This level of performance should be the baseline for any application in 2025
Daniel Arevalo retweeted
"The tools we use have a bigger compounding effect on our own quality and craft than we realize."
Tools matter. Your team likely spends many hours each week using a given tool. Friction or a poor experience can add up, ultimately affecting work quality and velocity.
Why we use Linear at Knock
The tools we use shape our standards for quality, our belief in the power of software, and our excitement to make our contribution to it.
Our issue tracker is where we live every day. We screen share it at standup. It’s where we go to file bugs and improvements.
Linear is a great product and it does all of those things supremely well.
It's also our daily reminder that software can be a magical experience.
If I was at my prior company and still using a legacy issue tracker, I’d wipe the slate clean and start with Linear asap.
The tools we use have a bigger compounding effect on our own quality and craft than we realize.
Daniel Arevalo retweeted
Sorry, but as an AI Agent builder, I’m completely disappointed ☹️!
Sam Altman from OpenAI tells us o3 smashed all the evals, but at what cost?
Every improvement on popular evals made the model less attentive to the details in the prompts. Making it unreliable for unattended use.
What matter the most for the dream AI agentic future is the ability of LLMs to follow the commands with high precision.
All chat models have been getting poorer at this since March 2023. The original gpt4 was better than all the models after it.
The completion models were way better than chat models too.
So far, LLMs are all obsessed by evals and Chatbot usage only.
We need completely different trajectory, approach and evals to move towards autonomous agentic future.
The number one eval to optimize for: attention to the prompt and all little details of the prompt by LLMs.
So that it doesn’t behave like an old deaf grandmother who has been asked to bring salt but she went and closed the window.
Daniel Arevalo retweeted
this is actually insane wtf
Everything you love about generative models — now powered by real physics!
Announcing the Genesis project — after a 24-month large-scale research collaboration involving over 20 research labs — a generative physics engine able to generate 4D dynamical worlds powered by a physics simulation platform designed for general-purpose robotics and physical AI applications.
Genesis's physics engine is developed in pure Python, while being 10-80x faster than existing GPU-accelerated stacks like Isaac Gym and MJX. It delivers a simulation speed ~430,000 faster than in real-time, and takes only 26 seconds to train a robotic locomotion policy transferrable to the real world on a single RTX4090 (see tutorial: genesis-world.readthedocs.io…).
The Genesis physics engine and simulation platform is fully open source at github.com/Genesis-Embodied-…. We'll gradually roll out access to our generative framework in the near future.
Genesis implements a unified simulation framework all from scratch, integrating a wide spectrum of state-of-the-art physics solvers, allowing simulation of the whole physical world in a virtual realm with the highest realism.
We aim to build a universal data engine that leverages an upper-level generative framework to autonomously create physical worlds, together with various modes of data, including environments, camera motions, robotic task proposals, reward functions, robot policies, character motions, fully interactive 3D scenes, open-world articulated assets, and more, aiming towards fully automated data generation for robotics, physical AI and other applications.
Open Source Code: github.com/Genesis-Embodied-…
Project webpage: genesis-embodied-ai.github.i…
Documentation: genesis-world.readthedocs.io…
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