Breza

I write most of my code by hand. I already manage a team, I don't want to spend the rest of my time juggling different agents.

I agree. I feel like we're heading back to the PMBOK days of writing out all the details of your project before you wrote any code.

I agree with you. I use LLMs for a lot of different things. But increasingly, I find myself firing up GPT-0SS-20b on my laptop for smaller tasks. And I know there are better models I just haven't tried yet.

In my experience in the corporate world, figuring out what everybody wants takes a lot more effort than actually doing it. Ten years ago, I could take a detailed request for a small feature, with pseudocode and specific implementation guidelines, send it to a junior dev, and get a result within a week. Now I can get a working result in minutes. But the real delay was usually the series of stakeholder meetings, emails, and IMs necessary to get to that point.

Yeah, just imagine if one of their two biggest customers were to find a way to offload even 10% of their calls to a self-hosted model. That would cost Anthropic billions.

I think of it a little differently. LLMs are clearly poised to have a major impact. And Anthropic and OpenAI are at the forefront of that innovation. But if you look at other technologies that have have huge impacts (trains, semiconductors, you name it) the companies that start off in the lead are rarely the dominant players 20 years later.

Today, Nvidia is bigger than Shockley Semiconductor (founded 1955) and Rivian is bigger than Studebaker (founded 1852). Being first doesn't mean you'll stick around.

I've had a similar experience. Grok is the most confident of any model I use. Unfortunately, it is also the most often incorrect.

I've seen a lot of his interviews and he often makes two arguments: 1. LLMs are not fit for purpose and aren't nearly as useful as people say. 2. It is literally impossible for OpenAI and Anthropic to generate enough profits in the next decade for them to fulfill all of their obligations and return money to investors.

I agree that the first one is sometimes iffy, but even if you think LLMs will replace x% of white collar work by 2030, the second point remains.

Wild how LLMs both weaken software security and supercharge the attackers at the same time

Once WhistlingDiesel posts a stress test of the thrusters, I'll believe they're legit

Last year I went to a steam show and had to share the street with giant steam tractors for the first time. That law suddenly made sense. The visibility and stopping distance aren't great.

Some decisions are important to document. Some are utterly irrelevant. Knowing the difference is a skill.

On one project, we decided we didn't need mobile compatibility. That simplified things greatly.

You're going to want to document that somewhere. And if you also have a stakeholder register, you can link the decision to the person who made it.

There are some things that can't be derived from the code and old school PMP documents are suddenly useful again.

Who are your stakeholders? How are you going to communicate with them? What kinds of approvals do you need to do certain things? Etc. Human devs have long struggled with this stuff and LLMs aren't at better.

I still use R almost every day. It's an amazing language for answering complex questions using data. But yeah, not a great choice for deploying a production system.

I also found myself reading the manual all the time.

I have a toy project I use for learning different tools. Given a CSV containing every MLB game ever played, calculate the win percentage for each team during its first season.

Pandas makes that kind of simple thing painfully unintuitive. Tools like Polars and dplyr and even SQL require code that's far closer to how people think.