this post was submitted on 28 Jan 2026
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[–] o_d@lemmygrad.ml 1 points 6 days ago (1 children)

Thanks for sharing! It's easy to take for granted that so much information in the west is easily accessible via the internet. This not being the case in Africa is a really interesting point covered in the article. They need to do a lot of the training themselves, and this is after hiring labour to digitize the data in the first place. Having open source models is so critical to keeping costs down for this to be feasible.

I've been taking a break from the tech industry for the last half year for mental health reasons. This was right around the time where agents became available so my experience with AI as a dev tool is mostly without them. At that time, I generally found it to be more effective and rewarding to spend the time coming up with a solution on my own, rather than trying to find the right prompts to get the resulting code to an acceptable place.

In my experience, productivity of writing code was never a big cost. Especially after teaching myself to touch type correctly and learning to navigate VIM/NeoVIM. The vast majority of my time is spent understanding the problem as deeply as possible and coming up with a solution that takes into account the context and can be easily maintained, enhanced, and scaled. The RFC/TDD phase and testing/iterating.

I don't think this is unique to myself. I've heard and read a lot of the same from other senior devs. Would you agree with this? If so, how has AI been beneficial more specifically in your experience?

Also, just want to add that I agree that a junior with a LLM cannot take the place of a senior at this time. But I do think that's the ultimate goal of the tech oligarchs. I have no idea if this is truly achievable with LLMs since there's still a lot of problems that need to be solved, especially around iterating and enhancing existing products. Progress is being made however, so it could be just a matter of time.

[–] yogthos@lemmygrad.ml 4 points 6 days ago (1 children)

Coding with agents is a completely different experience. You can kind of think of it as doing a pair programming session. Modern agents, are quite good at understanding the project, finding things in it, making changes reliably, and I find it saves an incredible amount of time. Tasks that used to take hours take minutes now. The key difference is that the agent actually sees your specific project, and you can basically get it to do TDD where you set up tests that it has to pass so it can't cheat.

The huge time saving comes from finding the spots that need to change, which is traditionally a pain in a large codebse, figuring out stuff like API calls, library usage, etc. It's a lot better at tracking variables across the code too. A lot of time it would find and clean up things I would've missed. You used to have to look this stuff up constantly, and now the agent just does it for you. It's particularly great for working with stuff like web services where it can figure out API calls from the code. Previously, I'd have to set up and run a bunch of services, try calling the APIs, inspect the payloads. Now the agent can figure this stuff out all by itself just by doing code analysis.

Being able to get something working quickly also means your iteration cycle is a lot faster. You can try different things, and just throw them out if they don't work. Something that might've taken a week of time to try out before can be done in a few hours now. I find this directly helps with understanding the problem too, because as you try different things you learn the problem space and get a better idea of what the right solution is.

It also lets you work with languages you don't have a lot of experience with. I'm currently working on a React/Js project, and I haven't touched either before. I'm able to use my knowledge of how to structure apps, and solve problems and then have the agent deal with the implementation details and all the quirks of Js syntax. I simply would not be able to take on a project of this scope before.

I think the goal to replace seniors might be real, but it's clearly not achievable. Ultimately, LLMs don't have understanding in a human sense. They don't have an internal model of the world like we do to base decisions on. These are just statistical inference engines. Ultimately, the human had to understand the problem being solved and what the correct way to solve it is. The agent can help you find the solution, but you have to be able to evaluate it.

The more realistic scenario going forward is that it's the juniors who end up being replaced with companies keeping a handful of senior devs wrangling agents handling the implementation details. Of course, that's obviously going to backfire in the long run as there's going to be a gap in the talent pool.

I imagine that eventually people will figure out that these tools aren't magic, and how to effectively integrate them into development workflows. Any new tech like this ends up being disruptive in the short term, and the hype results in stupid decisions being made. Over time the hype dies down, and people figure out how to use new technology in a sane way.

[–] o_d@lemmygrad.ml 2 points 5 days ago (1 children)

Thanks for taking the time to share your perspective! I'm actually curious about working with agents myself now.

[–] yogthos@lemmygrad.ml 2 points 5 days ago (1 children)

No problem, and it's definitely worth trying out. I can recommend crush which is a pretty decent tool supporting a bunch of LLM providers as well as local models. I'd start with DeepSeek because their API costs are really cheap and it's fairly competent at a lot of tasks.

[–] Comprehensive49@lemmygrad.ml 1 points 19 hours ago (1 children)

Why so you prefer Crush over OpenCode, Claude Code, or Cursor CLI? I'm exploring this space myself as well.

[–] yogthos@lemmygrad.ml 1 points 17 hours ago

I find they're all fairly similar in practice. What matters most is the model capability. The workflow is really similar across different tools from what I've seen.