> That's $2.4 trillion of value created for other shareholders plus trillions more for employees, customers, suppliers, governments, and other stakeholders.
That's not Bezos' doing alone, that's Bezos plus over a million workers that did that. If Bezos never existed in history, someone else would have filled in that market. We need to stop this myth that a few men alone create all this value and that without them we'd still be dragging plows through the mud for our farms.
The difficulty lies in the fact that most software is quite cheap to generate very complex designs compared to hardware. For software designs treated similarly to hardware (such as in medical devices or at NASA), you do gain back those benefits at great expense.
Golang's reflection is severely limited by design, so that there is nothing to restrict during compilation. On the other hand, you lose out on powerful tools such as C#'s System.Reflection.Emit. To note, the biggest library limited by AOT compilation, ASP.NET (which does still work with it if you design around the limitations), is being updated to work better with it (and source generators play a big part of that).
The novelty of LLMs are wearing off, people are beginning to understand them for what they are and what they are capable of, and performance has been plateauing. I think that's why people are starting to worry that the AI bubble is a repeat of the dotcom bubble, which was a similar technological revolution.
These same restrictions exist for Go, the Go team just decided that it was easier to never support these features to begin with which has its pros and cons.
Just an FYI, Go still bundles a runtime in its native binaries. C#'s AOT has restrictions on what works (largely reflection), but these same restrictions apply to Go (although Go applies these restrictions into how it's designed for the entire thing).
I'm not sure how that applies in this situation, since you're choosing to buy into a proprietary platform when plenty of open-source competitive options exist. That's the case for every language/tech stack.
That's surprising. For the past two companies, all our library dependencies have been open-source. All our persistences, aws sdk, consul, kubernetes, github/bitbucket libraries, pdf generation, selenium testing, etc etc. I'd recommend giving C# another look, the proprietary stuff is mostly an artifact of back in 2015 when everyone was still on .NET Framework.
That's fine if you can compensate them accordingly to retain them, but if you're going to pay them senior level in a couple of years, why not just hire a senior level to begin with?
A company paying half a million annually to ensure this employee is retained. It's not meant for joe sixpack making $100k/yr as an underpaid consultant.
I imagine for the "best of the best" making $500k+ annually, this is just the cost of business and they're not going anywhere, while for the h1b workers making closer to $100k annually, this is a show stopper.
I bought a uv a+b meter and unfortunately shade, while helping a lot, is still way above levels that cause sun damage. The sun jacket though is a very good idea. I use one myself.
The problem is, in a capitalist society, who is going to be the company that will donate their time and money to teaching a junior developer who will simply go to another company for double the pay after 2 years?
RNN worked that way too, the difference is that Transformers are parallelized, which is what made next-word prediction work so good, you could have an input thousands of tokens in length without needing your training to be thousands of times longer.
This is why I decided to go with PRK. Recovery time is rough, but after a week I was able to go back to work as a programmer. Well worth it to avoid the complications of severing the nerves in my cornea.
>On the other hand, if you're telling your investors that AGI is about two years away, then you can only do that for a few years.
Musk has been doing this with autonomous driving since 2015. Machine learning has enough hype surrounding it that you have to embellish to keep up with every other company's ridiculous claims.
What's funny is that the points you raised are the exact things that work for me on Slack, and I've never found it to be an issue. And yes, Slack supports looping people into conversations they didn't previously have access to.
I imagine this is more of a company culture issue than an e-mail vs IM issue.
It's fine to expect it to not know things, but the complaint is that it makes zero indication that it's just making up nonsense, which is the biggest issue with LLMs. They do the same thing when creating code.
The .NET team said they're working on VS Code integration. LinqPad still has some unique features (mostly database related), but at least for me, VS Code + dotnet run will be sufficient for my needs. Worst case, I can just throw a breakpoint on my database IQueryable result.
The only breaking change for us was how .NET 7 introduced [FromServices] which causes controller methods to use dependency injection for attributeless parameters. Confused me at first since it was a rather subtle update that didn't break anything until runtime.
I'm curious how distantly related the information has to be before it is eligible for this effect. For example, if someone sees a WARNING sign on the container (with no specific information about the SCP on the warning itself), do people remember that warning sign?
BTC has occasionally obtained community driven patches by distributed consensus rather than a centralized approach (as recently as 2021 with the Taproot soft fork). When Quantum Computing finally becomes a threat to BTC, there will almost certainly be a distributed consensus to update the protocol again. Now what happened with Ethereum could be argued as not so decentralized since the organization (Ethereum Foundation) has extremely strong political influence over the corporations that support it.
Unfortunately LLMs fail even basic logic tests given to children so definitely not high praise. I'm just highlighting the absurd amount of data they need versus humans to highlight that they're just spitting out regressions on the training data. We're talking data that would take a human countless thousands of lifetimes to ingest. Yet a human can accomplish infinitely more with a basic grade school education.