@ditorodevi
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building better and fairer interviews at https://nitter.cf/t.co/Eb1NuUwExM
Barcelona
Joined September 2020
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Juan retweeted
We raised $1.2M in pre-seed funding for @TesterArmy!
Coding agents have changed how fast we build software. We're making sure testing keeps up.
TesterArmy's AI agents test web and mobile apps, run through real user flows, and show you what broke with screenshots and recordings. So you can catch bugs before your users do.
Backed by Script Capital, Eight Capital, AIP Seed, and angels including @rauchg, @ccheever, @walden_yan, @theo, @zenorocha, @pie6k, @fernandorojo, and many more incredible builders.
Thank you to everyone backing us and the teams already testing with TesterArmy.
We're just getting started! 🫡
im very impressed by grok bot and cursor's grok 4.6,
im really thinking about not paying claude code's 200 and instead go cursor pro max
People often rely too heavily on flashes of brilliance, only to end up failing because they are unwilling to move them forward
I remember trying to create a 3D game with three.js, with sonnet 3.5, it is crazy that this sort of stuff is pretty much one shotted
GPT-6 Astra recreated Dax Raad in Blender.
This is very meaningful to me because what Dax thinks about my productivity impacts the lives of my wife and son. It is a relationship in which, ordinarily, the balance of power runs almost entirely in one direction.
Today, I feel like some of that balance has been restored, since Astra trapped this simulacrum of him inside Blender. I can scale Dax. Rotate Dax. Move Dax along the Z axis. Make Dax very small.
I hope models like Astra can help restore that feeling and push us toward a more empowered future.
Replying to @liltechnomancer @MichaelArnaldi
It is very easy to make the AI follow the patterns and use the base better with effect than it is w Python (in my experience)
But the base is important, if AI threw this garbage code then it’s likely it needs to be at the base or rethought
Seeing @EnoReyes on @20vcFund
Interesting take on optimizing for local-first, wonder what @FactoryAI is seeing on enterprise deployments and then what is the strategy here, custom weights like fal with h3 max or an exo harness approach?
Hard question to answer because right now, all the buzz there is says that "you will never beat claude code on how to run Anthropic models", but on every harness benchmark to this day there is no change whatsoever
With the way agents works today and how cheap and intelligent some of them are, I’m starting to ask myself: what stops us from having products with very transparent logs, where we have canary models looking at specific stuff, taking notes and creating artifacts