AI changes the economics of software rewrites(thetruthasiseeitnow.com) |
AI changes the economics of software rewrites(thetruthasiseeitnow.com) |
For example, the code base contains a physical controller. It’s closed loop in that it can react in realtime to changes. But it’s a slightly untypical implementation because this one can even look into the future through simulations. But Fable does not understand that. Instead, I need to remind it every 30 minutes that this is closed loop. It keeps wrongly claiming that the controller was open loop and then based upon that it will make up constraints that don’t actually exist.
I mean it is a tool and you need to understand how the tool works. When there is too little context, where there is so much context so that you are poisoning it, when you are allowing the tool to do patch-on-patch and etc.
AI won’t.
A rewrite being a good idea often hinges on the ability to simplify. After a decade or more, it's now apparent what the application should and shouldn't do, so one can build it with those learnings and shed all tech debt from how it grew organically.
Aka preserving all behavior is not what I would want from a rewrite. The point would be to make decisions on what behavior should be kept and what complexity can be removed. An AI can't do that. It can help with execution if the decisions are made, but they're made by being very intimate with the codebase and floating all cases and then talking with stakeholders.
And in my experience, these are _dangerous_. People go into "while we're at it..." mode, and it quickly turns into a big 2.0 kind of thing that takes forever.
I would argue that LLMs can speed this kind of thing up, but not by an order of magnitude or anything, just a bit. Unless there's high risk appetite.
https://www.joelonsoftware.com/2000/04/06/things-you-should-...
Maybe the LLM will catch and reproduce all corner cases... maybe not...
Nowadays, a good AI harness can fairly reliably rewrite a medium complexity piece of software to an appropriate modern tech stack with pretty strong confidence of exactly preserving its behavior. The AI can pick up legacy details and keep them exactly the same as before in ways that a human rewriter would usually not bother with. After rewriting each feature it can then exhaustively smoke test all the happy paths and edge cases and ensure the code behaves exactly the same as before, which is another thing that human rewrites basically never do.
Between context collapse and hallucinations, how likely is it that the end result isn't slightly polished slop that misses lots of crucial details?
Eh maybe not.
Stuff that has a lot of deprecated features is honestly burdensome on AI. It keeps rediscovering the deprecated features as the understanding that they are deprecated fall outside of the context window.
What you need is something that either never deprecates syntax, or is <10 years old with minimal changes over that time.
Doing in days what used to take months, is a bit of a game changer. Like with past cost reductions, people will underestimate the work and get it wrong. It helps if you know what you are doing rather than just vibe coding things.
But for rewrites, the sunk cost fallacy becomes a lot cheaper. So, that changes how you deal with stuff that clearly isn't living up to expectations. Unceremoniously replacing what wasn't that expensive to begin with might be the cheaper option relative to fixing it.
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[1] https://fenwick.media/rewild/magazine/dead-broets-society-be...
Sounds great! Have you tried this? Did you see what went wrong? Otherwise this is just the same nonsense as always.
In that sense, my homepage (https://www.makonea.com/en-US) doesn't even make it to the HN front page—it's mostly in SHOWDEAD. Does that mean it has less value than this post? I'm feeling a sense of doubt about myself.
OP is playing the game. The post literally says "from LinkedIn" so if you look, he has 500+ connections and 1400 followers. That's not nothing. Good for him, all advice points to this new attention economy we live in.
I'm a bit aged out of all this. And I rode the 2010s wave so I can't give any advice in good conscience. I can only say that I see you and there's a whole world of silent majorities out there with no follow count and no broetry with our name on it. (search for that word in this thread, just learned it, it's great!)
(At least the author sprang for a $20 a month subscription.)
But this new "you're holding it wrong" series by people whose grasp of the system gets fuzzy somewhere in the v8 headers is a new land speed record for being vacuously correct and still an attractive nuisance for profit.
Yes, the trend towards encoding hard-won domain knowledge as property and fuzz testing and sometimes even proof system was underway before ChatGPT, and yes, the economics of this approach bend sharply under a post terrawright world.
But no, you haven't added anything except tinsel and chaff and some green css on mixpanel.
Just stop with this shit. If you knew shit about AI you'd be too busy printing cash to teach the rest of us about it.
Since our owners also own an IT consultant agency, I ran the same process through with one of our regular consultants who is an actual awesome data architect. The output was strikingly similar, well except that I/we didn't need to make the slides. I then had him run over the actual slides, and all we changed was adding a { between some arrows to make the source of the arrows more clear.
We're still going to use real human consultants in the loop because they are readily and freely available, and because this is still new. I doubt we'd want to spend 100 consultant hours on something like this in 5 years though. I mean, we'd still do it for decisions where we'd want someone to blame.
Of course it won't quite work, but I can definitely see why some people would want that.
Incidentally, Whenever i've done this in the past it's had a pleasant side effect of improving architecture. You end up forcing something akin to "push for's down and pull if's up" because crossing the ffi boundary is not free. It can be quite magical, as in leading to comically unbelievably speed ups when you also take advantage of vector intrinsics.
Estimates are considerably longer, QA is much harder, integration is full of buckets and rakes, some "senior" devs are afraid to touch stale core code, innovation is stifled, devs are frustrated, hiring is harder, attrition bites. The most frustrating thing is that its very hard to communicate the issues as everyone experiences a fragment of the pain and none of it lines up in a spreadsheet for anyone to appreciate the whole cost. Everything just sucks.
LLMs changing the economy of this sounds great, especially if removes the essential issue with the ground up rewrite, which is the "ground up" part.
I tend to believe that the engineering culture you describe will end up producing similar or, as Joel postulates, an even worse result, just dressed up in a modern stack.
If the technical leadership remains the very same one that enabled such a culture, I don't see them being able to suddenly produce a genuinely better software product only because an LLM is in a picture - especially considering how easy it is to convince an LLM that your idea is the best one.
These two technologies combined greatly simplified this specific product making it far easier to maintain. Performance on these services was not important so native code was carrying a lot of penalties without the benefits.
Having a well documented messenger like service bus with great SLAs removed several tools we had needed in the old implementation.
We were able to leverage the tests form the original product to define success and tmthus were able to solve a lot of the edge cases in the new code w before we even shipped.
However, the old code was perfectly fine code. If new technologies had not provided significant simplification of the service architecture, a rewrite would've been foolish. And without the very good previously existing tests, we would've run into a lot of issues as we released.
Meanwhile, I have a hard time to believe people don't encounter problems with AI solutions on a regular basis (I do).
If you gave junior dev exact tasks what to do where you will get better results.
Just like with LLM.
I’ve been hearing for quite some time now that I should be using an LLM to plan before the build. This is treating the LLM as the architect, not as a junior being handed small tasks here and there.
I haven’t bought into it, so don’t use it this way, but an army of people online and in the media are pushing the fomo hard and telling people this is how it should be used. If the LLM isn’t doing what we want, it simply means we need to use the LLM even more. That’s the prevailing message from the industry.
What is definitely not solved is knowing what you want and what user wants and what the end result is supposed to be. To write the code you need the specs and to write the specs you need to know what you want. And that can only be answered by years of therapy.