LLMs require the sum total of human knowledge to ape what you can find on google, meanwhile Ramanujan achieved brilliant discoveries in mathematics using nothing but a grade school education and a few math books.
Mind you this simply meant that, if important enough to temporarily block, an appeal would be needed to ensure that what the President was doing was legal, which is entirely reasonable. This was simply an extra legal check on the president to keep the president inline with the law.
There are approximately 390,000 H1B tech workers in the US. H1B is meant for roles where American workers are not available to fill the job (which is obviously being abused). The tax deduction issue that this post is about is specifically about exacerbating the loss of jobs. Fixing H1B would solve this job issue many times over.
On VS Code's front page it explicitly lists 12 languages and that it "supports almost every major programming language. Several ship in the box, like JavaScript, TypeScript, CSS, and HTML, but extensions for others can be found in the VS Code Marketplace."
Looks like they will be adding support for VS Code, including for debugging.
"In upcoming .NET 10 previews we’re aiming to improve the experience of working with file-based apps in VS Code, with enhnanced IntelliSense for the new file-based directives, improved performance, and support for debugging."
I don't believe this is a factor for the distances that inter-chip transmission has. From what I can find, this is at most an issue for communication spanning tens to hundreds of meters in a datacenter.
Similar to other compression methods, it uses keyframes (which are saved frames that exist throughout the video) as the starting point, after which the next frame is a diff of that until you reach the next keyframe. It seems to generate keyframes when the compression drops below a certain level (at which point you might as well just store the compressed frame instead of the bloom bitmap).
Why is async code harder to reason about? I've been using it in C# and the entire point is that it lets you write callbacks in a way that appears nearly identical to synchronous code. If you dive into concurrency (which is a separate thing but can be utilized with async code, such as joining multiple futures at the same time), that parts hard whether you're doing it with async or with explicit threads.
That's what we ended up doing for our migrations. We actually had a bunch of old Elasticsearch 2.3 databases (ancient), so we stood up an OpenSearch database in parallel for each and on service startup did a one-time automatic index and bulk copy over of all the data. So far very happy with OpenSearch.
It's a matter of resource allocation. Lowering your design requirements for performance can save significant developer cost. Also, Word in 2025 is doing a lot more under the hood than 97.
Is it really a rug pull? It's a closed source extension with terms specific to use with vs code. Nothing has changed in that regard. All they did was close a workaround for a competing company's product.
Ignoring that Microsoft isn't following the MIT licensing requirements, this is my same approach with using the MIT license. I create open source software for the benefit of everyone, for profit or not for profit. The only thing I do wish in return is acknowledgement. That's why in this case, I'd reach out to Microsoft to fix that issue, and nothing more.
Mind you this only helps to tell you if the signal is authentic, you're still without that navigation system at that point, hence the search for a localized alternative.
Technically both techniques can be used for gps hacking. It also seems you can fake a gps signal altogether, because the public signal is not cryptographically signed, which surprises me (the only thing that makes sense to me is that the gps protocol doesn't have room for adding a signature, so it'd be a breaking change to the protocol).
The article does a poor job explaining, but seems to imply that they are working on replacing GPS altogether with a local system that relies on an atomic clock and quantum engineering. From what I can find, there are many approaches to this, including quantum gravimetry, quantum accelerometers, etc.
Yeah people don't realize how tiny federal employees are as a part of our budget. Even firing half of all federal employees only amounts to a paltry 2% savings on our budget. The gop's increased military spending in the latest proposed budget dwarfs those savings.
We use an event database (think Kafka) as our source of truth and we've largely shifted away from redis and elasticsearch in favor of local in-memory singletons. These get pretty big too, up to 6GB in some cases for a single mapping. Since it's all event based data, we can serialize the entire thing to json asynchronously along with the stream event numbers specific to that state and save the file to s3. On startup we can restore the state for all instances and catchup on the remaining few events. The best part is that the devs love being able to just use LINQ on all their "database" queries. We do however have to sometimes write these mappings to be lean to fit in memory for tens of millions of entries, such as only one property we use for a query, then we do a GET on the full object in elasticsearch.
An LLM is one very big nonlinear regression used to pick a token with a clearly defined input, output, and the corresponding weights. It's still far too straight-forward and non-dynamic (the weights aren't constantly changing even during a single inference) compared to the human brain.
As far as the latest "thinking" techniques, it's all about providing the correct input to get the desired output. If you look at the training data (the internet), the hardest and most ambiguous problems don't have a simple question input and answer response, they instead have a lot of back-and-forth before arriving at the answer, so you need to simulate that same back-and-forth to arrive at the desired answer. Unfortunately model architecture is still too simple to implicitly do this within the model itself, at least reliably.
The entire point is that LLMs don't think, they're just regressions of information you'd find on the internet. And automation is a good thing. 90% of Americans were farmers in the 1700s; automation completely revolutionized that. Your final point is still a concern in general, specifically what will happen when menial labor no longer exists. At that point, you have to hope that all the wealth generated from automation will be redistributed back in the form of a basic income, otherwise yes, we'll have a lot of people with no means to survive.