I've just started a new role as a senior SWE after 5 months off. I've been using Claude a bit in my time off; it works really well. But now that I've started using it professionally, I keep running into a specific problem: I have nothing to hold onto in my own mind. How this plays out: I use Claude to write some moderately complex code and raise a PR. Someone asks me to change something. I look at the review and thin…
For me it seems more or less similar to reviewing others' changes to a codebase. In any large organization codebase, most of the changes won't be our own.
> Aren't they currently propped up by investor money? Are Chinese model shops propped up by investor money? Is Google? Open weights models are only 6 months behind SOTA. If new model development suddenly stopped, and today's SOTA models suddenly disappeared, we would still have access to capable agents.
>we would still have access to capable agents. But they would be outdated, right? Would an agent that can only code in COBOL would be as useful today?
By six months. Surely, non-SOTA models can eventually get not outdated. And your argument ignores 'new model development suddenly stopped' aspect. If it stops, there is nothing be to be outdated to..
The thing is, agents aren’t going away. So if Bob can do things with agents, he can do things. I mourn the loss of working on intellectually stimulating programming problems, but that’s a part of my job that’s fading. I need to decide if the remaining work - understanding requirements, managing teams, what have you - is still enjoyable enough to continue. To be honest, I’m looking at leaving software because the job…
> So if Bob can do things with agents, he can do things. The problem arrises when Bob encounters a problem too complex or unique for agents to solve. To me, it seems a bit like the difference between learning how to cook versus buying microwave dinners. Sure, a good microwave dinner can taste really good, and it will be a lot better than what a beginning cook will make. But imagine aspiring cooks just buying premade…
I held this point of view for a while but I came to the (possibly naive) conclusion that it was just forced self-assurance. Truth is, the issues with sub-par output are just a prompting and supervision deficiency. An agent team can produce better end product if supervised and promoted correctly. The issue is most don’t take the time to do that. I’m not saying I like that this is true, quite the opposite. It is the reality of things now.
And just like modern vehicles rob the user of autonomy, so too for coding agents. Modern tech moves further and further away from empowering normal people and increasingly serves to grow the influence of corporations and governments over our day to day lives. It's not inherent, but it is reality unless folks stop giving up agency for convenience. I'm not holding my breath.
Soon enough we'll have ads playing in our cars at stoplights.
We have that now for most people, the radio. But hopefully we will be in a llm self driving car and can get ads for the entire trip.
The thing is, agents aren’t going away. So if Bob can do things with agents, he can do things. I mourn the loss of working on intellectually stimulating programming problems, but that’s a part of my job that’s fading. I need to decide if the remaining work - understanding requirements, managing teams, what have you - is still enjoyable enough to continue. To be honest, I’m looking at leaving software because the job…
Can you run an industry level LLM at home? If not, you're changing learning to cook for Uber only meals. And since the alternative is starving, Uber will boil the pot. Don't give up your self sufficiency.
> Can you run an industry level LLM at home?
Assuming that by "at home" you mean using ordinary hardware, not something that costs as much as a car. Yes, very slowly, for simple tests. (Not proprietary models obviously, but quite capable ones nonetheless.) Not exactly viable for agentic coding that needs boatloads of tokens for the simplest things. But then you can run smaller local models that are still quite capable for many things.
> So if Bob can do things with agents, he can do things. The problem arrises when Bob encounters a problem too complex or unique for agents to solve. To me, it seems a bit like the difference between learning how to cook versus buying microwave dinners. Sure, a good microwave dinner can taste really good, and it will be a lot better than what a beginning cook will make. But imagine aspiring cooks just buying premade…
I held this point of view for a while but I came to the (possibly naive) conclusion that it was just forced self-assurance. Truth is, the issues with sub-par output are just a prompting and supervision deficiency. An agent team can produce better end product if supervised and promoted correctly. The issue is most don’t take the time to do that. I’m not saying I like that this is true, quite the opposite. It is the re…
At some point the herding of idiot savants becomes more work than just doing the damn thing yourself in the first place.
> So if Bob can do things with agents, he can do things. I think the key issue is whether Bob develops the ability to choose valuable things to do with agents and to judge whether the output is actually right. That’s the open question to me: how people develop the judgment needed to direct and evaluate that output.
There's a long, detailed, often repeated answer to your open question in the article. Namely, if you can't do it without the AI, you can't tell when it's given you plausible sounding bullshit. So Bob just wasted everyone's time and money.
You can verify by running the code and see if it works.
> So if Bob can do things with agents, he can do things. The problem arrises when Bob encounters a problem too complex or unique for agents to solve. To me, it seems a bit like the difference between learning how to cook versus buying microwave dinners. Sure, a good microwave dinner can taste really good, and it will be a lot better than what a beginning cook will make. But imagine aspiring cooks just buying premade…
Just because Bob doesn't know e.g. Rust syntax and library modules well, doesn't mean that Bob can't learn an algorithm to solve a difficult problem. The AI might suggest classes of algorithms that could be applicable given the real world constraints, and help Bob set up an experimental plan to test different algorithms for efficacy in the situation, but Bob's intuition is still in the drivers's seat. Of course, that…
But if Bob doesn't know rust syntax and library modules well, how can he be expected to evaluate the output generating Rust code? Bugs can be very subtle and not obvious, and Rust has some constructs that are very uncommon (or don't exist) in other languages.
Human nature says that Bob will skim over and trust the parts that he doesn't understand as long as he gets output that looks like he expects it to look, and that's extremely dangerous.