The choice of C++ is bold.

Despite the security concerns often highlighted, modern C++ with smart pointers, and RAII patterns can be just as safe as Rust when done right. Vaev’s security model should focus on process isolation, sandboxing techniques, and leveraging modern C++ features to minimize vulnerabilities.

Super excited to see such raw innovation and courage in tackling a colossal task often monopolized by juggernauts like Chromium.

The current human-in-the-loop model exists largely because our technology hasn't been good enough yet, not because there's something inherently special about human judgment in this context. Weather prediction is fundamentally a pattern recognition problem. Pattern analysis at scale is exactly what computers do better than us.

Perhaps someone could apply to YC with this idea. There is one YC startup doing this already: https://www.ycombinator.com/companies/atmo

The "super tutor" isn't some distant fantasy - millions already use ChatGPT, Claude and similar tools daily for personalized learning. They're imperfect but genuinely helpful for programming, languages, math, and countless other topics.

Look at what happened with YouTube: millions of people transformed themselves into programmers, musicians, mechanics, and countless other professions through free video tutorials. Khan Academy revolutionized math education. Coursera and edX brought university courses to anyone with internet. This wasn't utopian thinking - it was practical technology solving real educational problems at scale.

What's different now is that LLMs enable the missing piece: personalization. The one-on-one adaptive experience that was previously limited to those who could afford human tutors at $50-100/hour is now available to anyone at negligible marginal cost.

Your skepticism about cancer applications too ignores the technological trajectory we've been on for decades. Just as YouTube and online platforms democratized education, technology has been steadily dismantling bottlenecks in medical research.

The human genome project initially cost $3 billion and took 13 years. Today you can sequence a genome for under $1,000 in days. This wasn't utopian thinking; it was technological progress following its natural course.

Think what LLMs will do here.

But guess what? Now, finally, we can co-opt LLMs for things humans fumble: e.g., real-time conversational tutoring, adaptive negotiation agents, or even scalable personal 'bullshit detectors' as countermeasures. I hope conversation doesn't go into AI-Safeteyism and restricting LLMs and more about building stuff. Let's build, not block.

It's worth being specific: the National Weather Service operates some of the most robust automation and radar ingest pipelines on Earth, but the final go/no-go warning call is almost always human—often a single overnight forecaster on a console, monitoring a swath of counties. Automation (e.g., Warn-on-Forecast guidance) can surface threats, but the NWS intentionally doesn't have an 'auto-warn' button for tornadoes, because of the asymmetry of false positives (blow credibility, cost lives in the long run).

Budget cuts reduce redundancy and experience in those overnight shifts. When you have only one person monitoring instead of a team of two or three, you get decision fatigue and coverage holes, especially during clustered, multi-cell outbreaks. We've seen near-misses in the past, and every pro-meteorologist I know says they're playing defense against process errors, not just technology failures.

Before we point fingers or blame 'technology/automation' shortfalls, let's quantify the concrete bottleneck: skilled human decision-makers are the limiting reagent; machine learning warning aids are still years away from majority trust.

ANY critical pipeline that can be broken by one missing seat is overdue for technical reinforcement

If you architect your infrastructure around non-retention, even a temporarily defeated law signals it’s time to future-proof elsewhere.

If law passes, if Proton leaves, what matters most isn't their press release—it's the engineers voting with their code and hardware locales.