401–410 of 744 posts
Let's say it is complicated. But what is the better alternative when dealing with large software? To what point we can simplify it and not lose anything important?
Good write-up. I don't disagree with any of his points, but does anybody here have practical suggestions on how to move forward and think about one's career? I've been a frontend (with a little full stack) for a few years now, and much of the modern landscape concerns me, specifically with how I should be positioning myself. I hear vague suggestions like "get better at the business domain" and other things like that.…
This is normal with all that is going on in the industry and the AI/ML hype. But, one should not allow that to lead to "analysis paralysis".
> specifically with how I should be positioning myself. ... Does anybody have any thoughts or suggestions on this?
You have a stable job; hence your entire focus (for now) should be to "grow" in your job/organization. This means taking more responsibilities both technical/non-technical and demonstrating your long-term commitment to management. On the technical side, start with "full stack development" both frontend and backend so you can contribute end-to-end to the entire product line. Learn/Use all available tools (AI and otherwise) to demonstrate independent initiative. Step up for any tasks which might not have a owner (eg. CI/CD etc.). Keep your boss/higher-ups informed so as to maintain visibility throughout the organization. Learn more about the problem domain, interact more with Marketing/Sales so as to become the liaison between Engineering and rest of the organization/clients.
Generally, all higher management look for initiative and independent drive so that they can delegate work with the assurance that it will be taken care of and that is what you need to provide.
The cost of writing simple code has dropped 90%. If you can reduce a problem to a point where it can be solved by simple code you can get the rest of the solution very quickly. Reducing a problem to a point where it can be solved with simple code takes a lot of skill and experience and is generally still quite a time-consuming process.
Most of software work is maintaining "legacy" code, that is older systems that have been around for a long time and get a lot of use. I find Claude Code in particular is great at grokking old code bases and making changes to it. I work on one of those old code bases and my productivity increased 10x mostly due to Claude Code's ability to research large code bases, make sense of it, answer questions and making careful…
This is the key take right here. LLMs excel at parsing existing content, summarizing it, and use it to explore scenarios and hypotheticals.
Even the best coding agents out there such as Claude Code or Gemini often fail to generate at the first try code that actually compiles, let alone does what it is expected to do.
Apologists come up with excuses such as the legacy software is not architected well enough to be easily parseable by LLMs but that is a cheap excuse. The same reference LLMs often output utter crap in greenfield projects they themselves generated, and do so after a hand full of prompts. The state of a project is not the issue.
The world is coming to the realization that the AI hype is not delivering. The talk of AI bubble is already mainstream. But like IDEs with auto complete, agents might not solve every problem but they are nevertheless useful and are here to stay. They are more a kin to a search engine where a user doesn't need to copy/paste code snippets to apply a code change.
The sooner we realize this, the better.
> I'm sure every organisation has hundreds if not thousands of Excel sheets tracking important business processes that would be far better off as a SaaS app. Far better off for who? People constantly dismiss spreadsheets, but in many cases, they are more powerful, more easily used by the people who have the domain knowledge required to properly implement calculations or workflow, and are more or less universally acce…
Better security. Better availability. Less chance of losing data. Assuming the SaaS is implemented competently, of course. Otherwise there's not much advantage.
Had the cost of building custom software dropped 90%, we would be seeing a flurry of low-cost, decent-quality SaaS offering all over the marketplace, possibly undercutting some established players. From where I sit, right now, this does not seem to be the case. This is as if writing down the code is not the biggest problem, or the biggest time sink, of building software.
"Today we’re introducing Fizzy. Kanban as it should be, not as it has been. [...] we’ll host your account for just $20/month for unlimited cards and unlimited users. [...] And here’s a surprise... Fizzy is open source! If you’d prefer not to pay us, or you want to customize Fizzy for your own use, you can run it yourself for free forever." https://x.com/jasonfried/status/1995886683028685291
Earlier quoted context omitted.
For me, as a remote developer, it means I'm able to finish my work in 1 hour instead of 8 hours. So I'm able to capture "extra value" in the form of time. In our team everyone uses GitHub Copilot and I use Claude Code. My teammates' productivity increased slightly but my productivity increased a lot. This is because 1. Claude Code is just a better coding agent 2. I invested time to get good at agentic coding. Eventua…
I'm really curious on what your role is, and which industry are you in? I'm awed by these productivity gains others report, but I feel like AI helps in such a small part of my job (implementing specific changes as I direct). Agentic workflows for me results in bloated code, which is fine when I'm willing to hand over an subsystem to the agent, such as a frontend on a side project and have it vibe code the entire thin…
Coding this way requires an effort that is equal to both designing, coding, and reviewing except the code i review isnt mine. Strange situation.
Then why is all my software slower, buggier, and with a worse UX?
With the steam engine, though, consumers made a trade-off: You pay less, and get (in most cases, I presume) a worse product. With LLMs and other machine learning technologies, maybe if you're paying for the software there's a trade-off (if the software is actually cheaper anyway), but otherwise it doesn't exist. It costs the same amount of money for you to read an LLM-generated article as to read a real one; your internet bill doesn't go down. Likewise for gratis software. It's just worse, with no benefit.
Hacker News is full of producers, in this sense, who often benefit from cutting corners, and LLMs allow them to cut corners, so obviously there are plenty of evangelists here. I saw someone else in this comment section mention that gamers who are not in the tech industry don't like "AI". That's to be expected; they're not the producers, so they're not the ones who benefit.
It is throwaway software at best.
What AI did was to move the goalpost even further. Now you need to write exceptional software.
Earlier quoted context omitted.
Aren't we having major issues with there being too many small libraries right now and dependency chain that grows exponentially? I have thought LLMs will actually benefit us a lot here, with not having to use a lib for every little thing (leftpad etc?).
Frankly if you were relying on libraries like Leftpad to you probably had no business writing code before AI And if you rely on AI to write that code now you still have no business writing code
The cost of writing simple code has dropped 90%. If you can reduce a problem to a point where it can be solved by simple code you can get the rest of the solution very quickly. Reducing a problem to a point where it can be solved with simple code takes a lot of skill and experience and is generally still quite a time-consuming process.
Most code is simple, the fact that large complex systems are layers of simple code on top of itself, like garbage heaps at the dump, makes it complex. Sticking with the garbage analogy, the LLM is like upgrading from one shovel to an crew of 10 people with excavators to look for a lost Bitcoin hard drive. Your project is still going to fail, but it will fail faster with the 10 excavators.