I've found this generally with AI summaries...usually their writing style is terrible, and I feel like I cannot really trust them to get the facts right, and reading the original text is often faster and better.
## Instructions
* Be concise
* Use simple sentences. But feel free to use technical jargon.
* Do NOT overexplain basic concepts. Assume the user is technically proficient.
* AVOID flattering, corporate-ish or marketing language. Maintain a neutral viewpoint.
* AVOID vague and / or generic claims which may seem correct but are not substantiated by the the context.
Assuming it is fact checked, why?
The only argument is that it improves the style of writing.
But I am in an ESL environment and no one cares about that.
Even otherwise why would anyone want to read a decompressed version instead of the "prompt" itself?
As I say, keep your slop in your own trough.
Im so over this timeline.
if this is all ultimately java but with even more steps, its a sign im definitely getting old. it’s just the same pattern of non technical people deceiving themselves into believing they dont need to be technical to build tech and then ultimately resulting in again 10-20 years of re-learning the painful lessons of that.
let me off this train too im tired already
See also 'no-code', 4GLs, 5GLs, etc etc etc. Every decade or so, the marketers find a new thing that will destroy programming forever.
https://en.wikipedia.org/wiki/Fourth-generation_programming_...
Put another way, I am certain that Unity has done more to get non-programmers to develop software than ChatGPT ever will.
UML may be ugly and in need of streamlining, but the idea of building software by creating and manipulating artifacts at the same conceptual level we are thinking at any given moment, is sound. Alas, we've long ago hit a wall in how much cross-cutting complexity we can stuff into the same piece of plaintext code, and we've been painfully scraping along the Pareto frontier ever since, vacillating between large and small functions and wasting time debating merits of sum types in lieu of exception handling, hoping that if we throw more CS PhDs into category theory blender, they'll eventually come up with some heavy duty super-mapping super monad that'll save us all.
(I wrote a lot on it in in the past here; c.f. "pareto frontier" and "plaintext single source of truth codebase".)
Unfortunately, it may be too late to fix it properly. Yes, LLMs are getting good enough to just translate between different perspectives/concerns on the fly, and doing the dirty work on the raw codebase for us. But they're also getting good enough that managers and non-technical people may finally get what they always wanted: building tech without being technical. For the first time ever, that goal is absolutely becoming realistic, and already possible in the small - that's what the whole "vibe coding" thing heralds.
1) Plaintext representation, that is
2) a single source of truth,
3) which we always work on directly.
We're hitting hard against limits of 1), but that's because we insist on 2) and 3).
Limits of plaintext stop being a problem if we relax either 2) or 3). We need to be able to operate on the same underlying code ("single source of truth") indirectly through task-specific view, that hide the irrelevant and emphasize the important for the task at hand, which is something that typically changes multiple times a day, sometimes multiple times an hour, for each programmer. The views/perspectives themselves can be plaintext or not, depending on what makes most sense; the underlying "single source of truth" does not have to be, because you're not supposed to be looking at it in the first place (beyond exceptional situations, similar to when you'd be looking at the object code produced by the compiler).
Expressiveness is a feature, but the more you try to express in fixed space, the harder it becomes to comprehend it. The solution is to stop trying to express everything all at once!
N.b. makes me think of a recent exchange I had on HN; people point out that code is like a blueprint in civil engineering/construction - but then, in those fields there is never a single common blueprint being worked on. You have different documents for overall structure, different for material composition, hydrological studies, load analysis, plumbing, HVAC, electrical routing, etc. etd. Multiple perspectives on the same artifacts. You don't see them merge all that into a single "uber blueprint", which would be the equivalent of how software engineers work with code.
As the sibling comment says, sequence diagrams are often useful too. I've used them a few times for illustrating messages between threads, and for showing the relationship between async tasks in structured concurrency. Again, maybe there are murky corners to UML sequence diagrams that are rarely needed, but the broad idea is very helpful.
I'm not sure what you mean by "unified system". If you mean some sort of giant data store of design/architecture where different diagrams are linked to each other, then I'm certainly NOT advocating that. "Archimate experience" is basically a red flag against both a person and the organisation they work for IMO.
(I once briefly contracted for a large company and bumped into a "software architect" in a kitchenette one day. What's your software development background, I asked him. He said: oh no, I can't code. D-: He spent all day fussing with diagrams that surely would be ignored by anyone doing the actual work.)
Sounds a lot like RegEx to me: if you use something often then obviously learn it but if you need it maybe a dozen or two dozen times per year, then perhaps there’s less need to do a deep dive outside of personal interest.
(Also, I'm _fairly_ sure that sequence diagrams didn't originate with UML; it just adopted them.)
no they don't. some people do. Some people think best in sentences, paragraphs, and sections of structured text. Diagrams mean next to nothing to me.
Some graphs, as in representations of actual mathematical graphs, do have meaning though. If a graph is really the best data structure to describe a particular problem space.
on edit: added in "representations of" as I worried people might misunderstand.
Still, what both you and GP should be able to agree on, is that code - not pseudocode, simplified code, draft code, but actual code of a program - is one of the worst possible representations to be thinking and working in.
It's dumb that we're still stuck with this paradigm; it's a great lead anchor chained to our ankles, preventing us from being able to handle complexity better.
It depends on the language. In my experience, well-written Lisp with judicious macros can come close to fitting the way I think of a problem. But some language with tons of boilerplate? No, not at all.
That's what I mean by Pareto frontier: the choices made by various current-generation languages and coding methodologies (including choices you as a macro author makes, too), are all promoting readability for some tasks, at the expense of readability for other tasks. We're just shifting the difficulty around the time of day, not actually eliminating it.
To break through that and actually make progress, we need to embrace working in different, problem-specific views, instead of on the underlying shared single-source-of-truth plaintext code directly.
Diagrams and pseudocode allow to push those inconveniences into the background and focus on flows that matter.
Now, I claim that the main thing that's stopping advancement in our field is that we're making a choice up front on what is relevant and what's not.
The "actual problem" changes from programmer to programmer, and from hour to the next. In the morning, I might be tweaking the business logic; at noon, I might be debugging some bug across the abstraction layers; in the afternoon, I might be reworking the error handling across the module, and just as I leave for the day, I might need to spend 30 minutes discussing architecture issue with the team. All those things demand completely different perspectives; for each, different things are relevant and different are just noise. But right now, we're stuck looking at the same artifact (the plaintext code base), and trying to make every possible thing readable simultaneously to at least some degree.
I claim this is a wrong approach that's been keeping us stuck for too long now.
It's just hell of an expensive way to get around doing it. But then maybe at least a real demonstration will convince people of the utility and need of doing it properly.
But then, by that time, LLMs will take over all software development anyway, making this topic moot.
But I just couldn't handle it when I got into like COMP102 and in the first lecture, the lecturer is all "has anybody not used the internet before?"
I spent my childhood doing the stuff so I just had to bail. I'm sure others would find it rewarding (particularly those that were in my classes because 'a computer job is a good job for money').
I.e. the very species we try to limit our contact with, which is why we chose this particular field of work? Or are you from the generation that joined software for easy money? :).
/s, but only partially.
There are aspects of this work where to "engage with my coworkers" is to be doing the exact opposite of productive work.
The AI already generated comprehensive README.md files and detailed module/function/variable (as needed) doc comments, which you could read but end up mostly being consumed by another AI, so you can just tell it what you're trying to do and ask it how you might accomplish that in the codebase, first at a conceptual level, then in code once you feel comfortable enough with the system to be able to validate the work.
All the while you're sitting next to another coworker who's also doing the same thing, while you talk about high level architecture stuff, make jokes, and generally have a good time. Shit, I don't even mind open offices as much as I used to, because you don't need that intense focus to get into a groove to produce code quickly like you did when manually writing it, so you can actually have conversations with an entire table of coworkers and still be super productive.
No comment on the political/climate side of this timeline, but the AI part is pretty good when you master it.
Another approach is Ill dictate how an api SHOULD work, or even go nuclear and write code i want to work, and tell the they must make the test pass and cant change what i wrote. They take these constraints well ime.
It would be helpful if I had a long rambling dialogue with a chat model and it distilled that.
IME this can work pretty well with Gemini in the web UI. If it misinterprets you at any stage you can edit your last comment until it gets on the same page, so to speak. Then once you're to a point in the conversation where you're satisfied it seems to "get it", you can drop in some more directly relevant context like example code if needed and ask for what you want.
Even Gemini/gpt4o/etc are all guilty of this. Maybe they'll tighten things up at some point - if I ask an assistant a simple question like "is it possible to put apples into a pie?" what I want is "Yes, it is possible to put apples into a pie. Would you like to know more?"
But not "Yes, absolutely — putting apples into a pie is not only possible, it's classic! Apple pie is one of the most well-known and traditional fruit pies. Typically, sliced apples are mixed with sugar, cinnamon, nutmeg, and sometimes lemon juice or flour, then baked inside a buttery crust. You can use various types of apples depending on the flavor and texture you want (like Granny Smith for tartness or Honeycrisp for sweetness). Would you like a recipe or tips on which apples work best?" (from gpt4).
document.body.style.backgroundColor = "black";
Wow yeah what a waste. That is exactly the opposite of saving time.
1) This was supposed to be piped through TTS and listened to in the background, and...
2) People like podcasts.
Your typical podcast is much worse than this. It's "blah blah" and "hahaha <interaction>" and "ooh <emoting>" and "<irrelevant anecdote>" and "<turning facts upside down and injecting a lie for humorous effect>", and maybe some of the actual topic mixed in between, and yet for some reason, people love it.
I honestly doubt this specific thing would be useful for me, but I'm not going to assume it's plain dumb, because again, podcasts are worse, and people love it.
They aren't all Joe Rogan.
Unless of course people talking in any capacity is human interaction sounds, in which case, yes, every podcast is > 90% human interaction sounds.
> Unless of course people talking in any capacity is human interaction sounds, in which case, yes, every podcast is > 90% human interaction sounds.
No, I specifically mean all the thing that is not content - hellos, jokes, emoting, interrupting, exchanging filler commentary, etc. It may add character to the show, but from the POV of efficiently summarizing a topic, it's fundamentally even worse than the enterprisey BS fluff in the example in question.
It praised so many things that I would just consider table steaks and made simple tweaks or features sound like massive projects.
I’m sure it could be improved by tweaking the prompt and there were parts of it that I found impressive that it had picked out (specifically things not in commit messages) but I found it unusable in its current form.
I suppose preferences differ, but really, does anyone _like_ this sort of writing style?
1. I shouldn't have used a newly created repo that had no real work over the course of the last week.
2. I should have put more time into the prompt to make it sound less nails on chalkboard.