Comment by jiggawatts
4 hours ago
People keep forgetting that programming is not just about knowing the target programming language, but also an enormous volume of tacit knowledge:
- Understanding of protocols like HTTP.
- HTML, JS, CSS, SVG, and everything "web".
- Understanding of databases, SQL, etc.
- Abstract code architecture patterns.
- Understanding the users' requests in English.
- Responding in English.
- Command line tool usage (agents/harnesses)
- Industry-specific knowledge that can be applied.
- Frameworks, SDKs, applicable libraries.
- Relevant legal requirements.
- Etc...
I.e.: If I tell a frontier AI that this project is for a "local council in XYZ location" it can immediately figure out that a scalable, globally distributed architecture is not required. It can also figure out that using local time instead of UTC is not only "fine", but even desired. Or that globalization/localization is not required... or.... required if the council is in some place like Belgium or Canada where multiple languages are officially recognised and supported by the government.
Those assumptions are just that - assumptions. "Local council in XYZ location" implies a bunch of things, and each one might be wrong for my specific circumstances. What better way to guide expectations than importing specific knowledge? I.e. if I import the english and catalan modules, then I probably want to localize my site in english and catalan.
It would be trivial to have a pre-flight convo with an llm to guide the user thru module choices. "Build a site" -> "ok, describe the purpose" -> "local council in XYZ location" -> "that implies you won't need localization since XYZ has a monolingual government" -> "english and catalan localization please".
Right now, you prompt and it builds using assumptions, and we prompt to adjust. I think it would be great to be able to pre-load a set of assumptions.
See: https://en.wikipedia.org/wiki/Bitter_lesson
Everyone assumes that carefully crafting a specific AI architecture with bits and pieces bolted together based on their human intuition is necessarily superior to simply using a bigger monolithic AI model. It turns out that the opposite is true, and has been demonstrated over and over again.
The bitter lesson is this: You can simply ask a frontier model to do the things you suggested, in a few terse lines of English. Dump a few lines in AGENTS.md and you are good to go.
Your approach is to "fiddle with inadequate tools" for weeks or months until you can finally attain a pale imitation of what the frontier models can do effortlessly.
It's the classic "But I can customise EMACS endlessly, why would I use an actual IDE?" argument all over.
I get it. You don't feel ownership over someone else's AI. You don't feel involved, you don't feel like you have agency.
It's like LEGO or IKEA furniture: study after study has shown that people enjoy things more if they "put it together themselves", even if fundamentally the thing is worse and/or still essentially nothing more than plastic made in a factory.
> It's the classic "But I can customise EMACS endlessly, why would I use an actual IDE?" argument all over.
That paragraph sets me off. I’ll take Vim and Emacs over VSCode and Eclipse any day.
>I get it. You don't feel ownership over someone else's AI. You don't feel involved, you don't feel like you have agency.
You don't _have_ ownership of someone else's ai, and that comes with real risks.
Security risks, privacy risks, business risk.
They might rug pull you, they might charge you more, or like atrophic, silently corrupt the answers, or code...
The labs are happy to jump on any emergent capability the scaling and training impart: generate prose, teach you things, cyber security, design, code, etc.
Do you really think that the frontier labs won't turn a popular capability, or trend they notice, into a first party tool if the ROI seems there? If it's your own private ai in your datacenter, you can keep it all secret, and not lose your business.
On the bitter lesson you're right of course:), wish I had a super computer to just scale that instead.
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Wouldn't this imply that in terms of AI usage you should take a "Wait and see" approach? I.e. just wait until the models can easily do whatever it is you want?
Tbf the majority of harnesses for frontier models do not allow the agent to do this (gut instinct) and instead get it to search, and for good reason.