SimianSci

Google doesn't have Venture Capital it needs to keep pleased while it delays its IPO.

All of the above,

Microsoft overhired due to the spike in global usage during the pandemic. Some jobs are being automated but it’s more a question of "How many." And Microsoft was one of the first to start dumping its cash reserves into data centers. There is a good chance these will remain profitable even after any sort of bubble pop because they still remain a hyperscaler that can provide services beyond just AI compute.

overall, given where the economy is at and how much of their war chest is gone, im not surprised to see this happening.

better yet,in the age of AI why not do more?

Ive been motivated recently to start making my appliances mine by using Ai to reverse engineer and replace their firmware. So long as the appliance has been slow to update its security channels, the recent eMudrha certificate leak was the perfect opportunity to place yourself in the middle of the relationship between device and vendor. Its allowed me to make some trivial updates to my television's firmware and start to have AI build me firmware that better serves my purposes. Hoping to lobotomize it and replace everything with a simple linux system that executes a startup script to launch a Jellyfin client.

I bought it and its mine, this is the freedom we were originally robbed of.

The idea was always that Facebook or Google were listening to us and whenever we would talk about something we would shortly thereafter see an ad related to it.

Now we can basically say that its not Facebook and Google doing this directly, its our televisions and appliances that are listening and just selling that data back to the ad-networks.

DOGE was impressively criminal. The team tried to remove the CVE reporting system entirely and only failed to do so because of intevention from the military and several agencies.

Ai labs look to be desperate to try and stave off the cooling hype that looks to be snowballing in the industry right now. These next few months are going to be crucial to their potential survival and future.

Its not surprising to me that this press release looks half-baked.

Everyone wants to be the one with the power to remake the world according to their own designs. Everyone also doesn't want to be the one being remade according to the designs of others.

The AMD hardware is x86_64, which means lower battery life on average for normal users, and as an architecture, it is very well supported across the Windows ecosystem. The RTX Spark is ARM64, which means more efficient power management at the cost of a small hit to sustained performance loads. But comes at the cost of being less supported when it comes to available software packages.

Aside from this, I’m led to believe that the AMD stack when it comes to local models has more overhead related to its need to translate more NVIDIA native instructions and therefore can be less performant than Nvidia. Not sure how true this is anymore, but ive yet to hear otherwise.

getting a local account is simple.

Windows Pro > Work or School Account > Join a Domain instead > Set password & recovery questions

The one component here that is not present on most machines straight from the manufacturer is the presence of the Pro version. The home version of windows requires the account with increasingly limited alternatives.

In my experience you're just replicating the problem. Slop code doesn't stop being slop code when you add in slop tests to account for the sloppiness. If you generate code at a certain velocity, you eventually get to a point where you generate tests the same way you generate code. The model does its best effort in creating tests, but you'll inevitably get to a point in your work where the tests themselves are faulty and unreliable, the same sloppiness you were trying to control for.

There have been multiple times where my software dev team has gone back to prior implemented tests they had the model generate and found that after some time eventually the model got frustrated with the test no longer passing eventually modifying it to fit current needs around a feature push. You can probably anticipate how we came to discover this, as it ended up introducing a regression around multiple edge cases which the test was meant to control for.

The only answer is to inject actual engineering into the process as much as possible without giving it all up to the models to generate more slop in response to slop. tests are a great answer, but only if you treat them with serious intentionality.

So, in other words, we save time and money when it came to creating stuff, but we've made a whole bunch of new work around verification and testing of said stuff?

What have we gained aside from shifting where time and effort is spent in the design loop?

Anyone who trusts the output of LLMs uncritically, even after the impressive level models and harnesses have gotten to, is borrowing against their future.

Models and harnesses have gotten really good, but they still are hard-capped by the operators that use them. The sycophancy of models is perhaps their greatest weakness, as when a user who might be experienced in one area attempts to use AI to build within an area they are less familiar with, the model does not take a step back and examine the "why" of what is being built from an architectural standpoint, it simply performs the expected task in a direct manner, ultimately creating sloppy work that may be easy to ship but difficult to maintain.

A good example of this was a recent interaction I had with a Dev who was very familiar with how to develop embedded and system level applications. He and every other dev are being asked for increasingly more due to AI, and he had to bring something he had built as a single-user desktop application into a distributed cloud environment. He ended up rsyncing a copy of his working directories including multiple credentials and sensitive internal project documents to a virtual machine binding it to a public ip address. Thankfully our tooling detected this before he could get any farther but it illustrates how the AI model isn't doing any real thinking or engineering at the end of the day. Unless the operator knew to instruct the model about proper cloud architecture and understood best practices around deployment of public systems, the model simply did what it was being told to do by a user who didn't know any better.

Another example of this is the sheer amount of unsophisticated users we've been detecting using Javascript injection into their web browser sessions as a form of automation for web services they may use. These users would never know to ask how to request an API token which may allow them to accomplish things in a much easier and approved way, and instead are relying on the frailest possible automations that expose them to risks of credential exposure.

We've got to be careful with how we engineer things with AI models. Just because we've made boilerplate easier to generate doesn't mean that we can avoid the actual engineering of what we do. And I can't help but think that firms which are fully committed to AI code generation, won't just be introducing an avalanche of slop into their product which will explode the costs of maintenance over time.

  "I hate this damn machine"
  "I wish they would just sell"
  "It never does what I want"
  "Only what I tell it."

I've yet to be convinced we will evolve these systems to a point where we can just click the "Magic Button" thoughtlessly and get results we are satisfied with. We may have reduced the loss of data between thought and action, but nothing suggests these systems will be able to intuit our every want and desire.

Two things can be true at once here.

1. The American frontier labs made a gamble that training more capable models would be their best return on investment and invested trillions into an area of research that has yet to prove profitable and capable of returning on this investment.

2. The frontier labs have created models that have reached a point of danger where their functionality has exceeded a point where it is responsible to release the product to the public.

The answer here is NOT to start regulating the space to the point where these frontier labs can entrench themselves into the economy and create a regulatory moat. We instead need to be holding these companies liable for their misuse. They took a gamble that hasn't paid out what they were hoping.

When car manufacturers competed over the size and power of their engines, they eventually found that the incredibly large and dangerous engines had a very limited customer base as many evaluated the increased speed to be of marginal benefit when paired with the cost and danger. We've reached a similar point in AI development. But this time the manufacturers seem to want to regulate the field to a point that will ensure the only thing anyone can sell are bigger and bigger engines.

The notion that ANY of this is outside of OpenAI’s control is unacceptable sane washing of a company which seems to have forgotten basic engineering practices.