kolja005

This is an interesting perspective I haven't heard before. Do you have links to anyone who has articulated this further?

Think about what the implication here is for people who answered no to that question. If I were to go up to my boss and say "I'm not interested using AI because I think it's bad for society" I would essentially be saying that I'm not interested in becoming more productive and thus making more money for the company. That's a very poor reputation to be carrying around and most people are going to avoid it. I believe that this, more than any specific actions by AI companies, has contributed to the sense of inevitability that this technology is taking over whether we want it or not.

I would be inclined to agree with this except that my "most common needs" keeps expanding and increasing in difficulty each year. In 2023 and 2024, most of my needs were asking models simple questions and getting a response. They were a drop-in replacement for Stack Overflow. I think the best open source models today that I can run on my laptop serve that need.

Now that coding agents are a thing my frame of reference has shifted to where I now consider a model that can be that my most common need. And unfortunately open models today cannot do that reliably. They might, like you said, be able to in a year or two, but by then the cloud models will have a new capability that I will come to regard as a basic necessity for doing software development.

All that said this looks like a great release and I'm looking forward to playing around with it.

I just refactored a bunch of our microservices into a monolith. Fortunately the business justification was pretty straightforward because it was clear to all of us that the service architecture was weighing us down.

Since one of microservice's benefits is solving a coordination problem, now that teams are getting smaller due to AI, I wonder if we will see monoliths make a resurgence in some cases.

>Since the database is used by everyone, it becomes cared for by no one. Startups don’t have the luxury of a DBA, and everything owned by no one is owned by infrastructure eventually.

This post was a great read.

Tangent to this, I've always found "best practices" to be a bit of a misnomer. In most cases in software and especially devops I have found it means "pay for this product that constrains the way that you do things so you don't shoot yourself in the foot". It's not really a "practice" if you're using a product that gives you one way to do something. That said my company uses a very similar tech stack and I would choose the same one if I was starting a company tomorrow, despite the fact that, as others have mentioned, it's a ton to keep in your head all at once.

My company has the github page for it blocked. They block lots of AI-related things but that's the only one I've seen where they straight up blocked viewing the source code for it at work.

Hey pretty cool! I recently added a similar feature to my neovim setup. I can press a keymap and cycle through all of the unstaged git hunks with each shown in a preview window. I can also change the base branch to one that, say, I'm trying to merge into so that I can have the same workflow when doing code reviews.

People like to make fun of these models for sounding like a broken record, being over complementary, etc, but I'm actually starting to think that models having a very recognizable style is a good thing because it makes identifying AI-generated content in the wild really easy. Sure, the verbosity is annoying when I'm just trying to get a straightforward, simple answer from it. But I like that I can have a pretty good sense of when content on the Internet is low-effort AI spam. If models become too good at emulating the personality of a real human, then that gets lost.

What are folks doing who were just using it for CI/test/dev environments? Just build the image yourself? Use Garage as some have suggested? I'm curious what people see as the pros and cons.

This is pretty neat. Similarly, I feel like we are really close to smartwatches being able to replace smartphones for all essential tasks. I try to rely on my apple watch with cellular as much as I can and leave my phone at home. I can't wait until agents get a bit better at navigating the web and someone makes a killer UI for the watch. I'll be able to do everything I need to do with a much more ambient device that doesn't suck attention the way a smartphone does.

Funny to see this today.

I'm a rank and file dev at a non-big tech company and I got a call from a Windsurf sales rep this week who I had connected with on LinkedIn the day before (I never gave them my number). They told me my company was in talks with Windsurf about a licensing deal but that they would give me a 30 day trial of an enterprise account for use on personal projects to let me try it in advance. I guess the idea for them is to build enthusiasm among devs in the company?

Is this a standard sales strategy for products like this? It seems pretty aggressive to me but I'm just an engineer so I wouldn't know.

As it pertains to Python in particular I think OOP is great for libraries but of limited usefulness at the application layer. Things like pytorch's nn.module IMO is a great abstraction, but every time I've tried to map some concrete business concept to an OOP construct I've regretted it.

Do we work at the same company?

You put it much better than I could have. Do you know if polars at all solves the problem of having opaque, mutable objects everywhere? I feel like there's a good market for having a dataframe library that's easier to reason about in your editor. It could even be a wrapper around pandas that adds rich typing sort of the way FastAPI does with Pydantic for Starlette.

Unrelated but does anyone have any recommendations for good resources on learning how to write tests/testable software?