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- I heavily use browser based LLM for bugs I care about. Code that is close to business logic like SQL is all 'by hand'.
Frontend stuff for me, which I'm bad at, I let agents do all of and I test the end result.
Other things are in-between, but time wise it's probably mostly me writing code. Lines of code is the LLM for sure.
by ddxv - As others have noted it's obviously more productive to use AI to write most of the code and for you to focus on the areas where you can add value.
However soon you will find have no real advantage over AI in any task. Therefore caring about the skills which are of fading value today makes almost as little sense as caring about the skills that mattered yesterday.
> What is gonna be sought after in the coming years: someone that persevered with manual coding and debugging but is slow and uncomfortable around agents, or someone that has little coding and debugging skills left but knows how to orchestrate dozens of agents effectively?
Neither. If your concern is your career or income then you should have a real plan based on how the world is realistically going to look in the future.
by kypro - Its beneficial for the company for sure at this stage cause most of the people using these LLM tools and agents have built the muscle to be able to review the code so the work gets done faster. These tools are definitely helpful in lot of ways to move faster.
Where it does not helps, software engineering is a field where you have to actively practice it to make sure your mind remembers the trade offs, different paths you have evaluated before, sort of building muscle memory. Using LLMs mean that eventually your debugging skills, ability to understand some one else code would degrade and moving to the next level in terms of software competency may never happen for you.
A concrete example, the ability to remember 10 digit phone numbers was really normal before smart phone, try to push your mind now to see if you are still able to do that?
Lastly, at any given point of time 90% of the engineers were working on managing the existing code base, so career wise with so much code being written the job safety should not be an issue if you have skills to understand and debug some one else code
by sandeepkd - I'm one of those people that forces myself to use AI coding tools purely to stay relevant, and I tend to use them for things that are tedious for me to do manually. I'm also the type of person that will spend all day working on my car, which I could pay someone else to do it that has a lift, and could do it in 1/3 of the time. I enjoy programming, as I enjoy working with my hands, and the sense of accomplishment that it results in. That being said, I hate the current status quo, but I also don't have anyone telling me how to do my job, or how many tokens I'm expected to burn.by platevoltage
- I have the agent grill me/ask me questions about what has been implemented, often on a fairly technical/granular level of the feature is important enough.This is obviously way slower than just vibing the whole thing and ticking off a passing test suite (that the agent also wrote). But I find that doing this has allowed me to retain most of my problem solving and reasoning skills (which have definitely atrophied).
People are kidding themselves thinking they fully understand what is going on at a deep level for 3-4 (or more) parallel tasks constantly context switching. It's also an anxiety-inducing way to work and has led to a proliferation of agent management tools enabling max efficiency.
Focusing on systems thinking/observability/knowing when NOT to build something/using agents to fix/learn new things quickly seem to be where I see devs still bringing the most value compared to any other cowboy.
- I'm not necessarily a software engineer, but I do a lot of coding for my cybersecurity projects. I find it's very helpful to have AI generate the first "batch" of code, but I do it in a very unusual manner. I don't use agents, I just use an LLM, and I review the code before I ever hit "copy" so I ensure I'm not outsourcing my thinking to a bot.
Anyway, when it comes time to make a project, I first figure out what I want to build. Then I research the steps I would need to take to build it. For example, if I wanted to make a webapp that has a deliberate SQL injection for a CTF, I would need a database, a login page, and a dashboard to navigate to after logging in. For larger projects, I might use AI to help me figure out the components required.
Then I save those notes and then focus on a single component. Continuing with this example, I'll start with the login page. I'll get AI to generate the code on that. Sometimes it doesn't cooperate and makes the SQL query secure, so I have to manually go in there and fix it myself, rather than ask it to redo it again. I get serious imposter syndrome whenever I use AI to generate code, so I make sure I can understand some of it before I run it, and I try to manually fix bugs before I ask for help.
Once all the components are built, I tie them all together and start testing the program.
by ToriTech - > if the AI is writing all the code, what value am I bringing?
What wouldn't an average person off the street be able to do?
I think you're mistaking typing characters on a keyboard for coding. Designing a system is coding. Knowing what refactorings to do to ensure things evolve in the right direction is coding.
Being able to recall all the syntax has lost value due to AI, but designing and leading the feature development hasn't and that can't (yet) be trusted to AI.
I'm not too worried about keeping skills sharp - if I'm ever put in a situation where they become relevant again I'm confident I can just relearn them.
by tripleee - You're there to make sure the code works. Refusing to let AI code is like a janitor refusing to use a vacuum. They're not taking your job any more than the robot vacuums.
AI opens up new forms of debugging. You can trace through a bug on the OS level and understand what it's really happening - we traced some bugs to how Android handles wifi based around location access on different versions. We give it a 20k line log to reproduce the exact flow when certain bugs happen. We make it write hypotheses on why it's happening and how to fix it, and break that down into logs.
There's plenty of engineering work to do with production code, especially on front end where you're dealing with different manufacturers, multiple cultures (some using , or . as decimal separators), RTL languages, broken abstraction layers, hardware permissions, etc, etc.
Someone needs to fully understand the big picture around how all these pieces go together. AI is still poor with writing larger things. Your uniqueness is in how quickly you can pick up the big technical picture.
by muzani