481–490 of 1001 posts
My mind doesn't develop a mental model of that code, I don't know where the relevant parts are, I can't quickly navigate through it and I have to reach the LLM for every small change.
Which is why I like Copilot style editing more than agents as a working model but agents are just so much more powerful and smarter thanks to everything available to them.
I like Thomas, but I find his arguments include the same fundamental mistake I see made elsewhere. He acknowledged that the tools need an expert to use properly, and as he illustrated, he refined his expertise over many years. He is of the first and last generation of experienced programmers who learned without LLM assistance. How is someone just coming out of school going to get the encouragement and space to indepe…
I suspect a large proportion of claims made for productivity increases are skewed by the fact that the speed at which code is produced by AI makes you _feel_ productive, but these gains are largely replaced by the effort to understand, refactor, review and clean up the code. The high that you get when something "works" tends to stick more in your memory than the time when you had to spend a day cleaning up dead code,…
This is incorrect. The library was part of the MCP framework we launched on March 25 -- the same month development began:
https://blog.cloudflare.com/remote-model-context-protocol-se...
Indeed the speed with which we were able to turn this around was critical to us, as it allowed us to have our Remote MCP framework ready immediately when the spec was finalized, which led to quite a few companies building MCP servers on Cloudflare: https://blog.cloudflare.com/mcp-demo-day/
I'm not an AI maximalist. I still write lots of code by hand, because there's a lot AI isn't good at. It's good at boilerplate and straightforward code, it's bad at refactoring deep systems. But AI assistance was undeniably a huge win for the OAuth project. There's no way I could have written that library by hand so quickly. (Maybe when I was 25 and had no responsibilities, but these days I have like 1 solid day a week to actually write code...)
One of the biggest anti LLM arguments for me at the moments is about security. In case you don't know, if you open a file with copilot active or cursor, containing secrets, it might be sent to a server a thus get leaked. The companies say that if that file is in a cursorignore file, it won't be indexed, but it's still a critical security issue IMO. We all know what happened with the "smart home assistants" like Alexa…
One thing that I find truly amazing is just the simple fact that you can now be fuzzy with the input you give a computer, and get something meaningful in return. Like, as someone who grew up learning to code in the 90s it always seemed like science fiction that we'd get to a point where you could give a computer some vague human level instructions and get it more or less do what you want.
This quote did not age well
In 2025 I am about 800-900 hours deep into using Cursor, blowing through 500 credits each 2 days (500 credits = 20$). I also organize a local "vibe code" workshop and meetup, with people from all sorts of experience levels, from no coding experience, to twice my senior. And what I am seeing is that what separates those from those who are good at using AI and those who are not, generally is a level of rigid stubbornness.
I can't wait for time to do its course here and those people who now are "all nuts" to slowly see programming of the past become an old relic.
I find the Konwinski Prize to be very interesting in this context. 1 million dollars to whoever's open source LLM solves >90% of a set of novel Github issues. https://www.kaggle.com/competitions/konwinski-prize/ Currently, the #1 spot sits at a score of 0.09, not 0.9. A far cry from being useful. I know that open source models are not as good as closed source, but still, we're a long way from LLMs being good for code…
The models that are both open source and quantized so that they can fit within that much memory are going to be significantly less capable than full scale frontier closed source models, I wonder how the latter would perform.
Hmm a bit of a tone shift from: https://fly.io/docs/about/open-source/ > Fly.io builds on the work of a tremendous open source infrastructure community. We want open source authors to benefit from their work because we think a healthy, thriving open source ecosystem will help us build better products. To the fine article: > Meanwhile, software developers spot code fragments seemingly lifted from public repositories o…
But this is not new https://fly.io/blog/wrong-about-gpu/
> At one point, we hex-edited the [NVIDIA] closed-source drivers to trick them into thinking our hypervisor was QEMU.