AI thought follower @ramplabs
New York, NY
Joined January 2025
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Ramp inspect is the best internal tool I have ever used, and now we are sharing it with the world! Please use it, you will not regret 📈
Cyber whitepill:
1. Economics favor defenders. Coding costs money now, and the quality/security of your code is a function of how much money you are willing to spend on it. Attackers must also invest large amounts of money on inference, and attack success rate scales with compute spend. Large software corporations & the US govt have much more money to burn than the vast majority of attackers.
2. Critical infrastructure has always been vulnerable to physical and digital threats. Powerful nation-state adversaries avoid large cyberattacks against critical US infra not because they are unable to penetrate, but because of fear of retaliation. AI does not remove this deterrent.
3. For closed-source software, defenders have privileged source code access. White-box vuln hunting is much easier & more compute-efficient than black-box.
4. Labs are giving their most powerful models to defenders. Many of the most complex & destructive issues are only discoverable by frontier intelligence which is exclusively accessible to defenders.
5. The blue team has AI too. Capable defenders will use agents to patch vulns, harden systems, and respond to incidents faster.
There's a lot of worry and sensationalism about cyber right now, some of it justified. Important to remember that defenders have an asymmetric advantage, and can maintain it with consistent & ambitious action.
Despite what haters on this website are saying, my mid-50s parents are using instinct to book their flights and absolutely love it
This is a skill issue from devs. Have to stay ambitious & keep pushing every new model to its limit
OpenAI, Anthropic and other leading AI labs face a growing challenge: Better models aren't necessarily attracting more usage. A 10-point intelligence advantage now earns just 20% more developer traffic, down from nearly 69% in late 2025.
Full story: thein.fo/4hGJ9jY
Biggest Ramp news of the day
We had early access to @NVIDIAAI Nemotron 3.5 Super VL and post-trained it for one of our highest-volume workflows: receipt extraction.
One epoch of supervised fine-tuning on 10,000 examples made it competitive with leading open and closed-source models on our receipt benchmark.
Who is @tryramp?
in math: my solution ➗
in history: my king 👑
in art: my muse 🎨
in science: my oxygen 💨
in geography: my world 🌎
It's actually crazy how sampling more tokens with a bigger model is the solution to 99% of applied ai problems
Favorite week of the year is when @TheRestHistory resumes their French Revolution series 🇫🇷⚔️👑
Starting next week on @TheRestHistory: THE TERROR.
All 6 episodes will be available here: therestishistory.com
Alex L retweeted
Introducing Ramp Accounting Bench.
We partnered with accounting professionals to create 137 tasks and grading rubrics grounded in everyday accounting workflows.
Even with three attempts, the best model achieved only 21% accuracy. Reliable agentic accounting remains an open challenge.
My intern is building killer robot simulation software dawg we are all gonna die 😭😭
Maybe I’m just washed, but Jev is beating me at my own game. Built a Krunker-inspired FPS where you 1v1 Jev. Try it out yourself: jev-arcade.vercel.app/duel
There's a new version of this post
While you (emotional, reactive, impulsive) are freaking out about the scaling discourse, I (prescient, confident, visionary) am unperturbed.
NS is a stunning development but I've already forecasted model progress over several years and mentally priced in future breakthroughs.
Ramp is an ROI lab now
AI spend is measured in tokens, model calls, and dollars. However, none of these fields describe the actual work being done.
At Ramp, we built a semantic layer that attributes agent spend to objectives and outcomes. This allowed us to go from monitoring AI spend to understanding AI ROI.
Here's how we built it 🧵