I dug into general AI deskilling a couple days back[1] - no good signs anywhere, but there's a few things we can do like make concerted efforts to do programming (like a coding hour every week), do a Leet code question, and getting the agent to quiz before merging something (stolen from Notion).
I've been having this same gnawing feeling. I just started reading the book "The Collision" by Eric C So which promises to cover this subject. I haven't finished it but seems decent so far.
It's so depressing a thought that if I continue on this path, in a few years, let alone a decade, I'll be utterly unable to produce or even understand code. I realize the skill may not be very economically useful, but losing it is still a depressing outcome that's preventable in theory. At the same time I'm feeling a deep apathy towards building anything. Playing a Zachtronics game is the farthest my internal motivation can push me towards...
I’m personally using it to augment my coding more than replace it. I find joy in it.
I use my agent to prototype a rough version that mostly works, and then I iterate through (with or without my LLM) to clean it up. Then back to the top with a new feature until I’m satisfied with the project and code.
I do tend to like it. My real selling point is that I’m not “locked in” to any choices until I’m happy with how it looks. I used to have to build the thing to see how it looks and acts, and going back on those choices was painful.
I do think there will be value in it, but I’ve been wrong before. My agent occasionally makes stupid choices that make every prompt after that 4x as hard to fix. Like I screwed up a pydantic model in an unreleased library and it supported a “legacy” path no one would ever use for like the next 5k LoC. By the time I noticed, legacy handlers were like 25% of the codebase.
That’s gonna burn a looot of tokens if you scale it up to a million line of code app.
I don't know if it's true. I didn't program at all between 2000 and 2015 and I've been able to restart without too much effort. I forgot many things, but I found it relatively easy to recover...
I'm more worried about people that did never program and started directly with LLM support...
Nice video, but I have said it before, the difference between just “vibe coding” and reliably using AI is the specs you need to write first and the tests after. If you start prompting the end goal/s the AI will basically guide you on what needs to be done and you just accept it as long as it runs, and this is following what most software developers were doing before LLM, the agile way, the quick ship way, the daily standup and ship first discus later way, the code pairing way, which worked mostly (even though I hated personally) in teams, so these developers are replacing what they were used to do with an agent and that’s the mistake. However, if you go with traditional engineering ways, waterfall, specs based, blueprints, and then tests, you control the agents and guide them not the other way around, in your specs you define the tiniest possible thing to what library to be used or excluded, the methods and approaches, among others, the agent here will produce the syntax and help you in the tests, just like in other engineering aspects how machines didn’t take over, they follow your specs and frameworks. The syntax is the past, AI can produce better syntax than any human alive or all combined, if you think otherwise you are delusional and still didn’t move on, it’s time to stop the “developer” mindset and do the engineering one.
I personally have a project that have been spending 2 months now making the specs and blueprint, more than 1000 files defining every bit including the future features too, and not a single code has been written yet, later, an agent will use this to produce the syntax and all the needed tests to have exactly what’s been needed.
Let's not rush to such judgment - our desires change as we age, the field has changed (including what nose-to-the-grindstone coding looks like) and burnout affects many.
Plus, some people enjoy the rigors of solving in-the-weeds code problems and others enjoy building systems-level designs. This was always true.
You guys act like the React component nonsense from the past 10 years is the same as the code I was learning at age 16. It's by far and away completely different. And none of it has been audited by Daybreak.
I'm very happy to let the robot handle any incidental React or other extraneous tools that end up adjacent to the core work I'm doing. It avoids getting blocked without interrupting teammates or spending time and cognitive capacity on stuff that has no lasting value in my work.
The use of quotes indicates that that's the US perception of her under the current administration. You can read the relevant executive order/public statements if you need insight into why Trump and his appointees feel that way.
This is cool, but I've never had an issue with agents working on Cloudflare by just making REST calls. With the rest docs, it knows pretty much everything about what kind of operations it can do. Is the CLI a subset of that or does in encompass everything?
Yes I am doing that too. I have a separate folder called ai/cloudflare. the AGENTS.md references the cf docs and instructs the agent to leave a change log + write down learnings whenever it does something
It’s now so good that I cd into this folder and say things like “add this and that to this configuration” and the agent immediately knows how to do it
I’m guessing cf just wraps the API into a CLI that’s easier to traverse and explore
Bug is fixed in the playground. Thanks again for catching it!
It gave me some ideas to make routing better, too. For example, maybe you don't want the edge to curve above, but rather below. Or, in more complex examples, maybe you want the edge to curve above one thing and to the right of another.
This is just for me, but I don't think I want to get into the business of specifying placement in that much detail. It would too cumbersome and I'd rather focus on what I want the diagram to convey, rather than how.
This is awesome OP - very impressive. I'm definitely going to be using it to classify customer support tickets and log error classification. Thank you!
I use this to produce the task list which I then feed into a Ralph loop using a bash script. I save a lot of tokens since each tasks context is so small.
[1] https://www.youtube.com/watch?v=EWuf2EekRSM
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