Damiano Rodriguez

CTO & Co-Founder @ @naturaAI
AI slop ambassador

[Damiano Rodriguez @Daaamn]

@four44

[Damiano Rodriguez @Daaamn]

agree
shorting SaaS even more at this point

[Nikita Bier @nikitabier]

Technical moats are mostly over at this point. If you’re building something that is meant to be durable, the only moats now are:

• Proprietary data
• Network effects
• A physical component

[Damiano Rodriguez @Daaamn]

@carloAI based

[Damiano Rodriguez @Daaamn]

@jayair for the voice control part
https://t.co/YUbqONLl6I

[Carlo @carloAI]

Introducing Interface, the primary hardware for the agent era.

Interface lets you control all your agents from your hand.

$99 for early adopters. Ships in January.

[Carlo @carloAI]

make tech fun again

[Damiano Rodriguez @Daaamn]

@carloAI @NaturaAI I hope he will not cast our ring into Mordor and keep it

[Damiano Rodriguez @Daaamn]

“lets you summon AI agents”
we just dropped a legendary tier item into the simulation

always wanted to cast spells with my voice while moving my hands

[TechCrunch @TechCrunch]

Natura’s $99 Interface smart ring lets you summon AI agents with the press of a finger to complete tasks, capture thoughts, and control devices — while doubling as a health tracker.
https://t.co/wPQKCwlSGW

[Damiano Rodriguez @Daaamn]

@alexxgrowth don't worry, we'll move fast!
the app already feels magical, we just need a bit more time to make it fully bulletproof
near-instant latency took real work to get right

pre-orders open early next month and production is already in a strong place for scale

[Damiano Rodriguez @Daaamn]

https://t.co/O7HHcYxPZ3
one more thing: we are hiring!

[Damiano Rodriguez @Daaamn]

https://t.co/TTxD9JJKM7

oh
hello @TechCrunch

[Damiano Rodriguez @Daaamn]

@ChrisGPT https://t.co/YUbqONLl6I
finally it's out @ChrisGPT

[Carlo @carloAI]

Introducing Interface, the primary hardware for the agent era.

Interface lets you control all your agents from your hand.

$99 for early adopters. Ships in January.

[arsen @arsenfounder]

lmao i was working on this at Omi last year, is this Omi's killer?

tbh i'm sure there needs to be a new hardware device, gotta try this one

[Damiano Rodriguez @Daaamn]

from biohacking to agents
humans with superpowers just got an upgrade

[Max Marchione @maxmarchione]

The first AI interface I actually want to buy. Legendary

[Damiano Rodriguez @Daaamn]

@adcock_brett Hark looks sick
we already route Grok and Muse from our ring
want to add Hark so users can send it commands from the device, but no connection yet

any plans to open that?

[Carlo @carloAI]

Introducing Interface, the primary hardware for the agent era.

Interface lets you control all your agents from your hand.

$99 for early adopters. Ships in January.

[Damiano Rodriguez @Daaamn]

One of the hardest things we've ever built

Interface is finally here

[Carlo @carloAI]

Introducing Interface, the primary hardware for the agent era.

Interface lets you control all your agents from your hand.

$99 for early adopters. Ships in January.

[Alex Imas @alexolegimas]

New essay for the @GoogleDeepMind Institute with James Manyika on AI x Science, "Bending the Curve of Discovery".

While accelerating existing scientific practices is certainly useful, the real promise of AI is its potential to act as an invention of a method of invention (IMI) a la Griliches--e.g., the microscope or statistical inference--which would unlock questions and whole modes of discovery that were previously beyond human reach.

Today, LLMs and specialized models like AlphaFold act as economic complements. LLMs handle analysis, coding, and writing, while specialized tools handle domain-specific predictions. Most handoffs between them run through the scientist.

What may the future of science look like? LLMs orchestrating those handoffs automatically--prompting specialized models, auditing outputs, and looping until either the question is answered or a non-automated stage is reached (e.g., wet lab testing). The scientist's role shifts from running each step to designing this loop. We saw a glimpse of this workflow with Anthropic’s enzyme discovery a few weeks ago, and we're seeing this in our own work too.

This raises foundational epistemic questions:

1. What will scientific understanding look like when discoveries are made by black-box models whose output is increasingly difficult to interpret?
2. How do we extract underlying mechanisms, not just outputs, as science becomes more automated? What will theory look like?
3. If AI automates the writing and junior lab work, how do we train the next generation of scientists to push the frontier?
4. What will motivate scientists in this epistemic reality?

There's an economics angle too. Hypothesis generation gets cheap, so bottlenecks move downstream to verification and to choosing which questions are worth asking. Realizing AI’s full potential will take investment in infrastructure and institutional reform--making this as much an organizational challenge as a technical one

We don't have the answers yet, and I'd love to hear what others think.

Paper here: https://t.co/kvHCFMIFmL

[OpenAI @OpenAI]

We’re releasing a broad range of new mathematical results produced by an internal frontier model.

We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.

https://t.co/7N6TPlft1P

[Damiano Rodriguez @Daaamn]

@scaling01 didn't think a model this good was even possible at Opus scale with Anthropic's current compute

makes me wonder what the actual ceiling is right now

[Damiano Rodriguez @Daaamn]

@petergostev tbh this is the first time I see a sort of an artistic taste from an llm
insane