SCADAbreach is a browser-based interactive simulation focused on SCADA / ICS security.
The goal was to model industrial systems and attack/defense mechanics inspired by real-world incidents, while keeping it accessible directly in the browser.
I think some wires got crossed. My point wasn’t that LLMs can’t produce useful infra or complex code clearly they can, as many examples here show.
It’s just that neither extreme narrative AI writes everything now vs. you can’t trust it for anything serious reflects how teams actually work.
LLMs are great accelerators for boilerplate, declarative configs, and repetitive logic, but they don’t replace engineering judgement they shift where that judgement is applied.
That’s why I see AI as real, transformative tech inside an overhyped investment cycle, not as magic that removes humans from the loop.
I keep wondering about one thing: maybe Disney isn’t paying for the technology at all — maybe they’re paying for a spot in the future. If generative video becomes as common as social media, AI models will be the new TV channels, and whoever controls the prime shelf space wins. In that sense, this billion isn’t a fee for Sora it’s the price of having Disney’s front row booth in a new world of storytelling. So the real question isn’t why is Disney paying? but who’s going to own the shelves in this new story marketplace?
True, but even with native audio-token models you still need to split the model’s output channels. Reasoning/internal tokens shouldn't go into the audio stream only user-facing content should be emitted as audio. The principle is the same, whether the last step is TTS or audio token generation.
A simple way is to split the model’s output stream before TTS.
Reasoning/structured tokens go into one bucket, actual user-facing text into another. Only the second bucket is synthesized. Most thinking out loud issues come from feeding the whole stream directly into audio.
A lot of the debate here swings between extremes. Claims like “AI writes most of the code now” are obviously exaggerated especially coming from a nontechnical author but acting like any use of AI is a red flag is just as unrealistic. Early stage teams do lean on LLMs for scaffolding, tests and boilerplate, but the hard engineering work is still human. Is there a bubble? Sure, valuations look frothy. But like the dotcom era, a correction doesn’t invalidate the underlying shift it just clears out the noise. The hype is inflated, the technology is real.
Interesting experiment. Using modern LLMs to retroactively grade decade-old HN discussions is a clever way to measure how well our collective predictions age. It’s impressive how little time and compute it now takes to analyze something that would’ve required days of manual reading. My only caution is that hindsight grading can overvalue outcomes instead of reasoning — good reasoning can still lead to wrong predictions. But as a tool for calibrating forecasting and identifying real signal in discussions, this is a very cool direction.
Honestly, this feels like another case where the headline sounds bold, but the real impact will be minimal. Any age-based restriction ends up in the same place: platforms are forced to collect more data just to “prove” someone’s age. When the target group is teenagers, that’s basically a privacy disaster waiting to happen.
From a technical perspective, this is impossible to enforce cleanly. Anyone with even basic internet literacy can bypass it with a VPN + fresh account + throwaway email. And of course, the teens most determined to get around it will be the ones the policy is supposedly protecting. The bigger issue is the false sense of security. Parents and politicians get to feel like something has been “done,” while the actual online risks don’t disappear — they just move somewhere less visible. If the goal is genuinely improving teen mental health, digital literacy and real support systems work far better than regulations that will inevitably leak.
The “9x faster than Unity” line also jumped out at me. Empty-scene benchmarks are basically a measurement of how thin your abstraction layer is, not how the engine behaves under actual game workloads.
What is interesting, though, is that engines like this often reveal how much overhead comes from tooling, scene graph complexity, editor integrations, GC pressure, etc. Sometimes a very lean engine feels “faster” simply because it avoids all the layers that a mature engine needs to support large teams.
I’d love to see a demo that stresses real systems — entity updates, materials, batching, physics, etc. That would say far more about the architecture than raw FPS of drawing nothing.
I always find it interesting how often the simplest hash table layouts end up performing best in real workloads. Once you avoid pointer chasing and keep everything in a compact array, CPU caches do most of the heavy lifting.
It’s also a good reminder that clarity of layout often beats more “clever” designs, especially when the dataset fits comfortably in memory.
The hardware story is interesting, but I’m curious how much of the real-world adoption will depend on the maturity of the compiler stack. Trainium2 already showed that good silicon isn’t enough if the software layer lags behind.
If AWS really delivers on open-sourcing more of the toolchain, that could be a much bigger signal for adoption than raw specs alone.
I’d love feedback.