jmiskovic

Nintendo remains a treasured memory of many 90s childhoods. They took great risks to innovate both on hardware and software side for ~2 decades, I'm not defending what they did since.

I'm also not arguing for intellectual property or copyright enforcement. I'm asking what comes next, I'm genuinely curious here.

"Wouldn’t it be nice if Nintendo were more like Microsoft?"

That's... completely unhinged. But, putting aside the mention of the biggest rent seeker in tech world as a role model. What's everyone's take on the gamers cheering against the Nintendo? Who's going to create this generation's own franchises like Nintendo did? What will be their incentives to invest time and effort in any new IP?

"seems to be the best" weasel wording from author with vested interest.

Inference vs training. It's training that takes enormous amount of power, both electrical and the compute. I suspect many many models get simply thrown away because they end up being too low on benchmarks by the time they are done. And some are not released to the public. So we learn only about tiny percentage of trained models and their environmental impact.

When you pile them up they burn my eyes. I seem to have skimmed through them.

Be careful with this, you'll get burned sooner or later. Short sentences and breaking up the rhythm are often taught as good writing style. The people will also pick up some LLMisms, especially the non-native speakers. I enjoyed this prose, I felt like author had a very personal perspective to share. My AI BS detector didn't spike and I think your post is the over-confident one.

Did you know you can get certification from Anthropic? And it actually costs real money.

Good point. And ambient occlusion, as sort of many micro-shadows. A bit more advanced to implement.

Yes, they are dishonest and only loyal to stakeholders, that's capitalism 101. The other examples you mentioned are around for more time and they have safety regulations, inspections and fines. Another thing that changed is media landscape. Tesla can get caught of altering the logs in cases of autopilot crash and it just gets buried in news instead of being brought up and repeated over and over each time their stock is discussed.

The GP is right, the correct mental model is just frequency of accidents. There's a high chance the accident might not be caused by you, it could be that you're a well behaved pedestrian and you still get hit. Your own cognitive abilities and skills matter, but only so much.

Would you rather that the other driver is a person or an algorithm? What if automated driving causes few % less accidents than humans but there's no liability or accountability when it happens? What if algorithms cause more accidents but far less fatal ones? When is it the best time to deploy them at scale, to enable rapid improvements and a significantly safer roads one year down the road? Difficult questions.

3D on 2D screen is all about giving brain enough depth cues. Having size diminish with depth is a big one. The 2nd biggest thing is occlusion, which is altogether skipped in these wireframe renderings. Another big one is fog, or impact of medium the light is traveling through, very easy to do for the basic effect. It's basically just fading the colors to gray when more distant from camera.

You can have a convincing 3D with just the occlusion and fog (or even just the fog), without simulating the perspective. It's just another artistic direction.

In my case the bad internet also tips the scale.

Incredibly important research. We've reached the point where local LLMs are good enough! It takes less time for local model to take the first action on your task than it does for Claude to validate your login, put you into queue and start issuing the commands. Local models are persistent and 100% predictable unlike any cloud offering. It's better for the power system for the demand to be distributed. During the winter time the GPU also doubles as a 300W in-house heater. Not to mention avoiding personal data collection and re-selling.