FiniteIntegral

Who has the political authority and/or social power to "do math"?

The issue with "doing math" is the incentive infrastructure. Modern mathematics research has always been tied with leveraging increasingly potent and numerous computing resources. With LLMs the experts are doing what they do best -- utilizing the latest and greatest. OpenAI leveraged an internal model and investment capital most people couldn't hope to touch. That is a display of strength and privilege.

It's motivated by cold hard cash instead of being driven by it. OpenAI wants more investors, more fuel for the fire, and this is how the company has decided to get it done. Token costs have increased, but so has general usage in the population. This is a PR campaign.

I'm not sure where people are going with using LLMs for research on a topic.

LLMs construct documents like a middle manager trying to get you fired, so it's a bad idea to get a system to try to research something for you. Anytime I've used the ol' panopticon-homunculus for something applied and niche I always follow the source linked to verify it. And the source is vague and generally unverifiable. Humans can actually link other documents that are actually readable to a greater degree.

I think a large part of the blame is the dataset for large scale LLMs being thoroughly and disproportionality poisoned with popular media sites like Reddit.

It's not that things get more expensive as they get smaller. As long as you're within reasonable tolerances it can be more cost effective. There is very little reason in a consumer-grade product, especially a devboard, to push any major fabs' tolerances. SMD components are grain-sized and you can make traces pretty thin.

For example, this project is a 2-layer PCB. Those are bog standard. With this small of a footprint it can be printed on a single surface and cut out. The schematic they posted keeps everything on a single surface for print. This is also an operation any fab can easily perform. If you order from China, even cheaper (even accounting for duty cost + S&H).

I'd be more concerned about the MCU and the components rather than the cost of a custom PCB.

A subtle tell for generated text is just how damn flat it is to read. Not that technical documentation require some form of grand prose, but how unspecific the text can truly get. Reading a high school persuasive essay can have more detail, and those are often just written for a grade.

I can understand someone needing help with writing but getting an agent to do the job for you feels like a personal defeat.

I've used this site for years, I originally found it off their subreddit. When they finally moved to a dedicated site it really improved the whole user experience from whatever reddit CSS was doing.

The admins keep it consistently updated and remove problem sources on a regular basis.

Yet at the same time "towards" does not equate to "nearing". Relative terms for relative statements. Until there's a light at the end of the tunnel, we don't know how far we've got.

I think part of this is due to the AI craze no longer being in the wildest west possible. Investors, or at least heads of companies believe in this as a viable economic engine so they are properly investing in what's there. Or at least, the hype hasn't slapped them in the face just yet.

Agreed. Security is a task that not even a group of humans can perform with upmost scrutiny or perfection. 'Eternal vigilance is the price of liberty' and such. People want to move fast and break things without the backing infrastructure/maintenance (like... actually checking what the AI wrote).

Agreed, it's cherry picked and strange since a lot of western developers already profess a lot of these practices -- especially the "extensive comments" and "descriptive naming" points.

It reminds me a lot about "innovative Japanese management solutions" which consists of MBAs learning what a bottleneck is and that DevOps is just sensible basic business practice.

I think you need to spend some more time testing this service if you are advertising this as a service that inherently interfaces with humans. I see that others in this thread like the applications for scambaiting, but I don't fully understand the use case you have here. If it's AI on both ends of the phone... whats the point of the call in the first place? It's not that hard to get a human on the other line who is able to help me far better than any robotic agent could.

If the agent has trouble solving "complex verification or (providing) documents" I doubt that a monthly fee for simple tasks doesn't sound like a viable and sustainable business model. It sounds like the anti-social bunch would like it but past that it's going to be hard drumming up a lot of support.

I think you underestimate the willingness of people to pay to troll, it may filter out people but an app that was (in theory) meant to be secure shouldn't think of a problem as filtering rather than securing. Admins knowing peoples' identities simply moves the weakest link in the chain to the admins. I think an app like this was doomed from the start and 4chan simply pulled the plug on an already leaking bathtub.

Apple released a paper showing the diminishing returns of "deep learning" specifically when it comes to math. For example, it has a hard time solving the Tower of Hanoi problem past 6-7 discs, and that's not even giving it the restriction of optimal solutions. The agents they tested would hallucinate steps and couldn't follow simple instructions.

On top of that -- rebranding "prompt engineering" as "context engineering" and pretending it's anything different is ignorant at best and destructively dumb at worst.

It really says something when the instability of the dollar is (relatively) as bad as when Nixon took us off the Gold Standard in 1973. Trump's policies certainly have caused a large amount of instability.

It's not surprising that responses are anecdotal. An easy way to communicate a generic sentiment often requires being brief.

A majority of what makes a "better AI" can be condensed to how effective the slope-gradient algorithms are at getting the local maxima we want it to get to. Until a generative model shows actual progress of "making decisions" it will forever be seen as a glorified linear algebra solver. Generative machine learning is all about giving a pleasing answer to the end user, not about creating something that is on the level of human decision making.

SoftBank Group is not always known for the most sound funding, they did invest in the "Stargate" program that hasn't seen a whole lot of action.