Comment by adamddev1
5 hours ago
> But that slow formation led to software that was durable rather than ephemeral, with a strong foundation that could be built upon.
People say that agentic development is great because you can churn out so much so fast. But that doesn't mean that any of it will be truly good and reliable.
The things that are truly insightful and solid end up being used exponentially more, which makes the linear cost of extra development time (asymptotically) insignificant in the cost/benefit equation.
The emphasis on quantity while neglecting quality calls to mind a passage from EWD1175[0]:
> My second warning remark is that I shall refuse to discuss the academic enterprise in financial terms. The first reason is that the habit of trying to understand, explain, or justify in financial terms is unhealthy: it creates the ethics of the best-seller society in which saleability is confused with quality. The other day we had to discuss the professional quality of one of our colleagues, in whose favour it was then mentioned that one of his Ph.D.s had earned lots and lots of money in the computer business, and few people seemed to notice how ridiculous a recommendation this was. We also know that the financial success of a product can be totally independent of its quality (as everyone who remembers for instance the commercially successful IBM360 should know). The second reason for my refusal is that the value of money is a very fuzzy notion, so fuzzy in fact, that efforts to understand in financial terms always lead to greater confusion. [Remember this, for it is quite likely that this afternoon will give you the opportunity to observe the phenomenon. Note that money need not be mentioned explicitly for the nonsense to emerge, a reference to "the taxpayer" can do the job. The role of "the taxpayer" then invariably leads to the conclusion that of State Universities at least the undergraduate curriculum has to be second- or third-rate.] The final reason for my refusal is that the habit appeals to the quantitative mind and I come from a culture in which the primarily quantitative mind does not evoke admiration. [A major reason that we considered Roman Catholics to belong to a lower class was precisely their quantitative bent: they always counted, number of faithful, number of days in purgatory, you name it.....]
[0] https://www.cs.utexas.edu/~EWD/transcriptions/EWD11xx/EWD117...
The aside at the end seems a bit odd given the certainly "quantitative" bent of the Dutch Calvinist merchant class...
The trade-off between quality and speed of development has always been a tenet of software engineering. Agentic coding changes the equation a lot, but the equation is still there.
It may be so but the article does not claim that the strong foundation is the code, rather it seems to be product design, and design of other products, at that (the two predecessors, webapp and desktop app). No reason why you couldn't study existing products now and tell your agent to build something based on that.
This feels like a sleight of hand to me. The hard part of evolving Scribe and Web Scrapbook was discovering that a browser extension manipulating a local SQLite database was _the only_ architecture that could reconcile local offline persistence with live DOM scraping across arbitrary catalogs of academic data.
An agent can synthesize existing solutions but (because I see this failure mode at work constantly) it can't synthesize an architecture to resolve the sorts of tensions that the person prompting it doesn't yet understand (not that that is stopping anyone). You can't prompt it to build something if the operational primitives required to solve the problem haven't been mapped.
"Build a tool based on Scribe and Web Scrapbook" in 2003 would've made a fragile PHP wrapper because that's what the existing landscape looked like.
Yes, exactly. And this is why I don't think that the LLMs can make significant process beyond what humans have done and published.
"But the math proofs," people will say. A lot of those seem to be spam-solving things with a huge swath of existing lemmas, and a some of these are being debunked and retracted.
Just today I was quizzing ChatGPT about a basic grammar question for a language that has huge training data but for which the grammar was not well documented. It kept giving me confidently wrong answers until I drilled and drilled it and then finally it found/gave back an explanation that perfectly fit a pattern given in one particular grammar, citing that as a source. It doesn't appear to have been able to figure out the inner structure on it's own. It appears only able to pattern match and put things together from what humans have already discovered and written.
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