Readit NewsThese awful three sentence abominations:
"Each subagent runs in complete isolation. The primary agent handles all the orchestration. Simple." "No conversation. No “remember what we talked about.” Just task in, result out." "No ambiguity. No interpretation. Just data."
AI is good at some things, but copywriting certainly isn't one of them. Whatever the author put into the model to get this output would have been better than what the AI shat out.
(I find there's a growing push-back against being fed AI-anything, so when this is suspected it seems like it generates outsized reactions)
For context, I'm a solo developer building UserJot. I've been recently looking deeper into integrating AI into the product but I've been wanting to go a lot deeper than just wrapping a single API call and calling it a day.
So this blog post is mostly my experience trying to reverse engineer other AI agents and experimenting with different approaches for a bit.
Happy to answer any questions.
- Did you build your own or are you farming out to say Opencode? - If you built your own, did you roll from scratch or use a framework? Any comments either way on this? - How "agentic" (or constrained as the case may be) are your agents in terms of the tools you've provided them?
For context, I'm a solo developer building UserJot. I've been recently looking deeper into integrating AI into the product but I've been wanting to go a lot deeper than just wrapping a single API call and calling it a day.
So this blog post is mostly my experience trying to reverse engineer other AI agents and experimenting with different approaches for a bit.
Happy to answer any questions.
[0]: https://docs.anthropic.com/en/docs/build-with-claude/prompt-...
What a silly premise. Markets don't care. All markets do is express the collective opinion; in the short term as a voting machine, in the long term as a weighing machine.
Seeing a real uptick of socio-policital prognostication from extremely smart, soaked-in-AI, tech people (like you Salvatore!), casting heavy doom-laden gestures towards the future. You're not even wrong! But this "I see something you all clearly don't" narrative, wafer thin on real analysis, packed with "the feels", coated with what-ifs.. it's sloppy thinking and I hold you to a higher standard antirez.
This is not an analogous situation.
Inference APIs aren’t subsidised, and I’m not sure the monthly plans are any more either. AI startups burn a huge amount of money on providing free service to drive growth. That’s something they can reduce at any time without raising costs for their customers at all. Not to mention the fact that the cost of providing inference is plummeting by several orders of magnitude.
Uber weren’t providing free service to huge numbers of people, so when they wanted to turn a profit they couldn’t reduce there and had to raise prices for their customers. And the fees they pay to drivers didn’t drop a thousandfold so it wasn’t getting vastly cheaper to provide service.
> Inference APIs aren’t subsidised
This is hard to pin down. There are plenty of metal companies providing hosted inference at market rates (i.e. assumed profitably if heading towards some commodity price floor). The premise that every single one of these companies is operating at a loss is unlikely. The open question is about the "off-book" training costs for the models running on these servers: are your unit economics positive when factoring training costs. And if those training costs are truly off-book, it's not a meritless argument to say the model providers are "subsidizing" the inference industry. But it's not a clear cut argument either.
Anthropic and OpenAI are their own beasts. Are their unit economics negative? Depends on the time frame you're considering. In the mid-longer run, they're staking everything on "most decidedly not negative". But what are the rest of us paying on the day OpenAI posts 50% operating margins?
> LLMs get endlessly confused: they assume the code they wrote actually works; when test fail, they are left guessing as to whether to fix the code or the tests; and when it gets frustrating, they just delete the whole lot and start over. This is exactly the opposite of what I am looking for. Software engineers test their work as they go. When tests fail, they can check in with their mental model to decide whether to fix the code or the tests, or just to gather more data before making a decision. When they get frustrated, they can reach for help by talking things through. And although sometimes they do delete it all and start over, they do so with a clearer understanding of the problem.
My experiences are based on using Cline with Anthropic Sonnet 3.7 doing TDD on Rails, and have a very different experience. I instruct the model to write tests before any code and it does. It works in small enough chunks that I can review each one. When tests fail, it tends to reason very well about why and fixes the appropriate place. It is very common for the LLM to consult more code as it goes to learn more.
It's certainly not perfect but it works about as well, if not better, than a human junior engineer. Sometimes it can't solve a bug, but human junior engineers get in the same situation too.
Yup.
> I ... review each one
Yup.
These two practices are core to your success. GenAI hangs reliably hangs itself given longer rope.