jkukul

You can use Cursor CLI, it's a terminal agent, just like Claude Code and Codex. I use it sometimes, I haven't opened the actual Cursor IDE in ages.

The potential benefit of Cursor CLI (vs CC and Codex) is that you can easily between all major models (by Anthropic, OpenAI, xAI, as well as Kimi K3 and GLM 5.2). I found it useful when reviewing work - e.g. I implement using Opus then review using Sol, etc. Models by different providers tend to have different perspective on things and they can find different issues with the code.

Quite ironic. The original Robin Hood took from the rich and gave to the poor. Robinhood, the app, seems to do the exact opposite: it helps the rich get richer at the expense of regular folk.

> I ended up saying goodbye to those devops folks,

The irony is that "DevOps" was supposed to be a culture and a set of practices, not a job title. The tools that came with it (=Kubernetes) turned out to be so complex that most developers didn't want to deal with them and the DevOps became a siloed role that the movement was trying to eliminate.

That's why I have an ick when someone uses devops as a job title. Just say "System Admin" or "Infrastrcutre Engineer". Admit that you failed to eliminate the siloes.

Enabling Claude Code's sandbox (as OP suggested) does exactly that. It's a system-level filesystem sandbox that only permits access to specified locations for any process, including the python interpreter.

Yes, I also doubt it'll ever happen considering how hard Anthropic went after Clawdbot to force its renaming.

I hope you're not too invested into GPT-4o because it has been retired so you'll need to use a different model :)

A profitable customer? How would Hetzner know if you're profitable or not?

I've hosted side projects on Hetzner for years and have never experienced anything like that. Do you have any references of projects to which it happened?

I don't understand why your comment is downvoted.

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.

> and other than a bit of open source (PyTorch and React are nice, I guess) as far as I can tell it's never really had any mission other than getting big.

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.

I use iOS's built in Screen Time settings. For "bad" apps (Reddit, TikTok, etc) and "bad" websites ("hackernews", etc) I set a daily time limit of, let's say, 15 minutes.

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.

Yes, you can pre-fill the assistant's response with "```json {" or even "{" and that should increase the likelihood of getting a proper JSON in the response, but it's still not guaranteed. It's not nearly reliable enough for a production use case, even on a bigger (8B) model.

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.

I think in the era of LLMs good docs/FAQ are of an even greater value.

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.