331–340 of 675 posts
I found myself agreeing with quite a lot of this article. I'm a pretty huge proponent for AI-assisted development, but I've never found those 10x claims convincing. I've estimated that LLMs make me 2-5x more productive on the parts of my job which involve typing code into a computer, which is itself a small portion of that I do as a software engineer. That's not too far from this article's assumptions. From the artic…
1.2x increase
This is all you have to takeaway from this article. Social media is a cesspool of engagement farmers dropping BS takes to get you to engage out of FOMO or anger. Every time I'm on there, I am instantly reminded why I quit going there. It's not genuine and it's designed to capture your attention away from more important things.
I've been using LLMs on my own for the past few years and we just recently started our own first party model that we can now use for work. I'm starting to get into agentic actions where I can integrate with confluence, github, jira, etc. It's a learning curve for sure but I can see where it will lead to some productivity gains but the road blocks are still real, especially when working with other teams. Whether you're waiting for feedback or a ticket to be worked on, the LLM might speed run you to a solution but you better be ready with the next thing and the next thing while you're waiting.
A lot of senior engineers in the big tech companies spend most of their time in meetings. They're still brilliant. For instance, they read papers and map out the core ideas, but they haven't been in the weeds for a long time. They don't necessarily know all the day-to-day stuff anymore.
Things like: which config service is standard now? What's the right Terraform template to use? How do I write that gnarly PromQL query? How do I spin up a new service that talks to 20 different systems? Or in general, how do I map my idea to deployable and testable code in the company's environment?
They used to have to grab a junior engineer to handle all that boilerplate and operational work. Now, they can just use an AI to bridge that gap and build it themselves.
Earlier quoted context omitted.
Yeah. I just need to babysit it too much. Take copilot, it gives good suggestions and blows me away sometimes with a block of code which is exactly what I'd type. But actively letting it code (at least with gpt4.1 or gpt4o) just doesn't work well enough for me. Half of the time it doesn't even compile, and after fixing that it's just not really correctly working either. I'd expect it to work like a very junior progra…
> But actively letting it code (at least with gpt4.1 or gpt4o) It's funny, Github Copilot puts these models in the 'bargin bin' (they are free in 'ask' mode, whereas the other models count against your monthly limit of premium requests) and it's pretty clear why, they seem downright nerfed. They're tolerable for basic questions but you wouldn't use them if price weren't a concern. Brandwise, I don't think it does Ope…
If your organization is routinely spending 3 months on a code review, it sounds like there's probably a 10 to 100x improvement you can extract from fixing your process before you even start using AI.
This was the best insight in the article: Do 10x engineers actually exist? "This debate isn't something I want to weigh in on but I might have to. My answer is sometimes, kinda. When I have had engineers who were 10x as valuable as others it was primarily due to their ability to prevent unnecessary work. Talking a PM down from a task that was never feasible. Getting another engineer to not build that unnecessary micr…
but every company is going to enshittify everything they can to pidgeonhole ai use to justify the grifters costs
i look forward to years out when these companies trying to save money at any cost have to pay senior developers to rip all this garbage out
Earlier quoted context omitted.
Then tell it to write better comments...
Ah, so it's good enough to write code on its own without time-consuming, excessive hand-holding. But it's not good enough to write comments on its own.
How often do you use coding LLMs?
> What LLMs produce is often broken, hallucinated, or below codebase standards. With enough rules and good prompting this is not true. The code I generate is usually better than what I'd do by hand. The reason the code is better all the extra polish and gold plating is essentially free. Everything I generate comes out commented great error handling, logging, SOLID, and united tested using established patterns in the…
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I was a skeptic until I started using the tool and learning how to get good results.
Now I'm a convert. I like sharing my experiences and getting the cope replies like this one.
I found myself agreeing with quite a lot of this article. I'm a pretty huge proponent for AI-assisted development, but I've never found those 10x claims convincing. I've estimated that LLMs make me 2-5x more productive on the parts of my job which involve typing code into a computer, which is itself a small portion of that I do as a software engineer. That's not too far from this article's assumptions. From the artic…
I guess this is still the "caveat" that can keep the hype hopes going. But I've found at a team velocity level, with our teams, where everyone is actively using agentic coding like Claude Code on the daily, we actually didn't see an increase in team velocity yet.
I'm curious to hear anecdotal from other teams, has your team seen velocity increase since it adopted agentic AI?