TrainedMonkey

My understanding is that, if confirmed, this demonstrates that AI can find novel solutions. This is a strong counterpoint to generative-AI-is-strictly-limited-to-training-data.

It's almost like it is based on the training data and regimen that is largely the same between versions.

I really enjoyed fantasy part of many small farmers. It felt rustic. However based on my understanding the modern world is moving towards megacorps and economies of scale.

Why is that?

> it doesn't dramatically reduce screen brightness or image quality.

AFAIK it significantly decreases the brightness. Jerry Rig Everything demonstrates this here - https://youtu.be/TRW4W7KkJXs?t=32

Yes

Servicing is not done by Apple, it's 3rd party contractors. They have a rubric of possible issues from Apple and their profit margins are thin. I suspect contacting Apple support about Apple support issues would have resulted in a swift replacement of the item.

Will need to wait for real benchmarks, but based on OpenAI marketing Instant is their latency optimized offering. For voice interface, you don't actually need high tok/s because speech is slow, time to first token matters much more.

Apple seems to be pushing for accessibility and volume. Cheaper phones, mac minis, and entry point mac that will be introduced on Wednesday.

Exceptionalism says we have best of everything, including idiots.

What if, and hear me out here, "You don't have to"

No, what he is saying is that benchmarks are static and there is tremendous reputational and financial pressure to make benchmark number go up. So you add specific problems to training data... The result is that the model is smarter, but the benchmarks overstate the progress. Sure there are problem sets designed to be secret, but keeping secrets is hard given the fraction of planetary resources we are dedicating to making the AI numbers go up.

I have two of my own comments to add to that. First one is that there is problem alignment at play. Specifically - the benchmarks are mostly self-contained problems with well defined solutions and specific prompt language, humans tasks are open ended with messy prompts and much steerage. Second is that it would be interesting to test older models on brand new benchmarks to see how those compare.

You can chain normalized quaternions to combine or diff transformations. For example you can subtract desired attitude quaternion from predicted attitude quaternion to get attitude error quaternion which you can then feed to control algs designed for driving that error to zero. This is even more important when multiple frames of reference are involved as quaternions can be used to transform between them.

Sure, and there are a ton of ways to shifting income around. For example selling a subsidiary in lower tax jurisdiction patents and then paying for their usage. Another example is Hollywood accounting where productions pay exorbitant rates for equipment and catering to affiliated companies. This inflates the costs so the movies end up unprofitable despite smashing box office.

Huh, that is kind of amazing. It turns out the problem is that we got way too good at scaling semiconductor density.

It could be as simple as links. People drop links in the slack discussions, other people from Geolocated IP addresses (or same) click on them. Google analytics et. al. hovers a lot of data.

Or perchance it is the other way around. The word started as official term and over time got shady connotation because can't trust Big Government.

Good intuition, that is generally how radiators work in space.

I can see that you are committed to your current level of understanding.

No. Apple PE ~33 implies ~14% EPS growth over the next 10 years, seems achievable. YOY Apple is ~10% and silver is over 270%.

Aha, make it drab, soviet, and raining filter. Peak hipster, I love it.

> But an "asset" to lose 30% of its value in a day... Wow!

When prices are determined by speculation it do be do like that.

If I was cynical I would assume that manufacturing emergencies was the whole point.

> The repo still compiles. No one knows why.

Penultimate commit refactored dispatch to bootstrap reality when accessed any means. Comment on the commit is "made things deterministic by sidestepping heisenberg principle".

In litigious countries confirmations and alerts also serve as a mitigation against lawsuits.

> CPU: Dual-core 300MHz ARM Cortex-M4F

It's absolute bonkers amount of hardware scaling that happened since Doom was released. Yes, this is a tremendous overkill here, but the crazy part here is that this fits into an earpiece.

It's software engineering crack. Starting a project feels amazing, features are shipping, a complex feature in the afternoon - ezpz. But AI lacks permanence, for every feature you start over from scratch, except there is more of codebase now, but the context window is still the same. So there is drift, codebase randomizes, edge cases proliferate, and the implementation velocity slows down.

Full disclosure - I am a heavy codex user and I review and understand every line of code. I manually fight spurious tests it tries to add by pointing a similar one already exists and we can get coverage with +1 LOC vs +50. It's exhausting, but personal productivity is still way up.

I think the future is bright because training / fine-tuning taste, dialing down agentic frameworks, introducing adversarial agents, and increasing model context windows all seem attainable and stackable.

All other governments is a stretch here, but likelihood of at least one another government getting same privileges is extremely high.

Humans also respond differently when prompted in different ways. For example, politeness often begets politeness. I would expect that to be reflected in training data.

The problem is, people who make that decision can either spend 0.1% to support open source and get return on investment in terms of better business performance in 2-3 business years. Or they could pay themselves 0.1% in bonuses right now and get an immediate return.