The EU is also great at creating a heavy regulatory environment. Which entrenches existing incumbents. So the EU creates barriers that favor big companies, then tries to fix it with grants that... also favor big companies.

And then everyone's surprised that there's no innovation in Europe.

From all the world's companies worth over 100B$ there's only one European company - SAP, founded 50 years ago. [1]

[1] https://www.economist.com/briefing/2021/06/05/once-a-corpora...

Would you rather have an economy of SAPs or an economy of Teslas?

In part probably because it's harder to become a monopoly in the EU.

This x2. A close friend of mine works at a major EU HW tech company and his job is wearing a suit, going to dinner parties and rubbing shoulders with high level local and national bureaucrats to convince them to fund X, Y, Z projects, none of which result in any major commercial success or ROI for the governments because the money they get is not enough to make new successful products, but hey, he's loving his job which gives him amazing job security against the waves of layoffs the company went through due to falling of sales, plus the networking he gets out of that is invaluable.

So at least some people are enjoying the gravy train while it lasts. But that's why a lot of EU tech companies immediately go to the US first before opening up to the EU market. US VSs are more generous with their cheque books than EU governments and investors, plus the 300 million people single consumer market speaking English as the common language and all that.

The comment you're replying to is tainted with the survivorship bias. We see successful companies that got government funding, but not the opposite. Maybe we'd have more innovation and competition without government picking these specific winners.

Ironically, one of the companies you mentioned (Apple) now operates in an environment with very little competition and regularly faces antitrust claims.

Government picking winners may actually reduce competition in the long run. The key difference: when private money picks wrong, it's their loss. When government picks wrong, it's taxpayer money.

[1] https://workspace.google.com/resources/esignature/

It probably comes down to the fact that code is not that crucial but all the other non technical aspects like distribution, supplier relations and marketing that makes a product.

Maybe LLM wrappers turn out to be that way. The model may not matter but the distribution and customer relation etc would matter more.

I sometimes wonder what motivations these orgs have in contributing to open source.

My cynical side refuses to believe that the reasons are altruistic (although I'm sure there are altruistic individuals in those orgs!).

I think that the decisions to contribute to open source are calculated business decisions made to benefit the organization by:

* Getting outside contributions to the software that's widely used inside an organization

* Getting more people familiar with the software so that when they're hired they are already up to speed

* Attracting talent

* Improving PR

* Undermining competition (Llama?)

Regardless of the reasons, I think that there's a huge net benefit to society from large companies open-sourcing their software. I just don't think that's an argument to view these companies more favorably.

In other words, wiping out your competitor's business moats. If their cashflow is dependent on selling phones, open source your phone operating system to lower the value of the proprietary system.

It can also be used to quickly gain market share where you previously had none and wants to catch up on your competitors. You're bleeding money any way to try to pry open an established market, and open source might be the cheaper route. Most famous examples are perhaps Apple (webkit, cups) and Facebook (AI).

[1] https://github.com/settings/copilot

I configure a random password for Screen Time so that it's a real hassle to circumvent the daily limit when I get over it.

I could recommend using ollama or VLLm inference servers. They support a `response_format="json"` parameter (by implementing grammars on top of the base model). It makes it reliable for a production use, but in my experience the quality of the response decreases slightly when a grammar is applied.

You can write a support bot that sends a user's question + docs/FAQ to an LLM to automatically deal with the basic questions and only involve a human in the loop once a question goes beyond what's in the docs.

Not even the big giants manage to create LLM bots that work.

I've done tests like this for some companies. It felt a lot fairer and more closely resembling the actual work environment than live leet-code interviews, with biased interviewer(s) and a stress factor that's not a part of the actual job.

They will also be able to use ChatGPT on the job. And StackOverflow. And Google. If they know how to use tools available to solve a problem, that will benefit them on the job.

If you're testing them for what ChatGPT can already solve, then are the skills being tested worth anything, in this day and age?

Take-home LeetCode, even with cheating will still filter out a good chunk of candidates. Those who are not motivated enough or those who don't even know how to use the available help. You'll still be able to rank those who solved the task. You'll still see the produced code and be able to judge it.

Like other commenter points out, you can always follow up the take-home LeetCode. Usually, it becomes apparent really quickly if a candidate solved it on their own.

I wonder if either of the following could be cost-effective:

(a) Fly the candidate to a company office, where their compute usage could be casually monitored by an employee.

(b) Use high-quality proctoring services that are nearby to the candidate. E.g., give them 1-2 days in a coworking space, and hire a proctor to verify that thy're not egregiously using tools like ChatGPT.

Or alternatively, would it suffice to just have a long conversation with the candidate about their solution? E.g. what design trade-offs they considered, or how might they adapt their solution to certain changes in the requirements.

In practice there are constraints that limit job markets geographically, e.g. time zone differences or legal obstacles to hire foreigners.