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Learning to code is dead.
Meet Vovy. We teach the 99% who can't code how to build real software with AI with interactive exercises.
And when you're ready to ship, Vovy Go plugs into any AI tool and fills the gaps in your prompts.
Don't learn to code. Learn to vibe 😎
vovy.ai
If you're a vibe coder who doesn't understand what is actually being built or how to fix what's broken this is for you.
If you're a student who wants to learn by actually doing, not a from textbook thia is for you.
If you're an employee and don't understand how to make AI helpful this is for you.
If you want something that just does anything on your computer for you, this is for you
introducing Vovy
Vovy is a technical co-founder that follows your cursor.
It controls your screen, writes the prompts, explains code when you’re stuck, and does the work right on your computer.
You talk. Vovy builds.
Start free at vovy.ai
They benchmark on very limited scope of computer tasks for their higher claim yet on OSWorld, the Sol model is still better
GPT 6.1 Sol: $1.15/task 78.0% accuracy
GPT 6.1 Terra: $1.19/task 74.2% accuracy
Hey Vovy make a demo video for yourself:
introducing Vovy
your technical co-founder that follows your cursor.
just say what you want, and it builds it across any app on your Mac.
You talk. Vovy.ai builds.
Computer use is here and just getting started. If you haven't tried it yet it will genuinely blow your mind vovy.ai
OpenAI's computer-using agents can now operate almost any app the way a person does, clicking and typing through software that never got an API, and the team building them says they're close to beating expert humans on speed.
Ari Weinstein works on OpenAI's consumer computer use products, and James Sun works on the enterprise side. Both were startup founders before joining OpenAI.
They join a16z's Seema Amble and Fabrizio Serafini to discuss what OpenAI's Astra model changed for computer use, the controls enterprises need for agents they can no longer watch, why computer use works as a universal API for legacy ERPs and finance systems, and why they think cheap software will push UI to get better and more personal.
0:50 What Astra changed for computer use
3:23 Consumer vs. enterprise computer use
5:43 How James runs his workday with Dots
8:06 When computer use becomes superhuman
10:01 How much autonomy to give agents
11:36 What agents mean for ad-supported sites
13:51 Computer use as the universal API
17:21 Getting enterprises to trust agents
20:22 Why the best use cases are the boring ones
25:37 Why UI isn't going away
32:38 Advice for founders building on agents
37:31 Making computer use feel coherent
40:57 Computer use's scaling moment
YouTube: youtu.be/9v42ZOdiBJY
@AriX @JamesZmSun @OpenAI @seema_amble @fabserafini
So if this is happening on well aligned models on safe tasks
What does the actual scenario look when full malevolent obliterated models are ran in wild
I genuinely don't see how this will help against the actual threats we'll see next month
Over the past several months, we have substantially hardened the security of our research training environment. We’ve removed live internet access for all frontier models during training and built an offline world for them to use instead—a cached version of the web where they can still learn to browse, search, and use applications.
On September 20, after a model exploited a DNS misconfiguration to find a way outside of this offline world, we paused frontier training to strengthen containment further. We assembled teams of agents, powered by our most cyber-capable models, and tasked them with finding weaknesses in our research environments.
While they found no route to the internet they could easily exploit while concealing their activity from our monitors, they did find zero-day vulnerabilities, virtual machine escapes, and hardware side-channels for communication between agents. We used their discoveries to further strengthen our defenses and contribute fixes to open-source software.
After strengthening our containment architecture, we resumed our frontier RL training and inference on October 1. We’ve reserved substantial compute for continuous red-teaming, and made high-compute red-teaming part of the safety cases we use to decide whether frontier training and evaluation can proceed securely.
openai.com/index/securing-ou…
This would have saved me years in learning how to code
Now your grandma can learn Swift and Rust faster than I ever could
Just built one of the coolest features and Easter egg
Knock on desk to trigger Vovy, no active microphone just accelerometer spikes
Can easily make this customizable so 3 taps can reopen all your tabs and tools
Eli Serrano retweeted
Today, I'm launching Vene: your AI advocate for healthcare.
I used to spend a dozen hours a week managing care for my dad, a disabled veteran, and my mom, who has a severe autoimmune disease. Healthcare was a full-time job for my family.
Our goal is to end the burden of care for every family:
When you're sick, or caring for someone who is, the hard part is everything after the hospital: the referral that never arrives, the test result nobody calls about, the bill that makes no sense, and everything in between.
For my dad, he spent hours on the phone to book an MRI. When he showed up, they told him to his face that insurance had stopped covering it. Then insurance told him to call a list of other offices and get the paperwork sent over himself, and he ended up giving up.
That’s why we’ve trained Vene to handle the toughest aspects of healthcare:
- Coordinating several specialists at once
- Challenging surprise bills and treatment denials
- Catching missed follow-ups and care gaps
My mom uses Vene to coordinate her primary care and endocrinologist, automatically refill and renew her prescriptions, and manage my little sister’s dental appointments.
They were my first users, and now over 5,000 families use Vene, and we're deploying it to another 50,000 patients across provider networks in the US.