rspeele

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18 ชั่วโมงที่ผ่านมาdiscuss

Despite having long valued statically typed languages, and IDEs with excellent refactoring tools (VS + ReSharper was a godsend back in the day before MS implemented most of the must-have features themselves)... I sort of disagree.

The problem is the IDE refactor->rename updates the code but the agent's "rename" will also catch developer-facing documentation in text files, comments, etc. that referenced the old name. It will often even catch reflection code that referred to the old name in a string. And it will do the mental work of disambiguating "this reference is something else that shouldn't be updated, that one is really pointing at the thing that got renamed and should be updated". If asked to, it can catch things like "var postGresClient = new PostgreSQLClient()" and change them to "var dbClient = new DatabaseClient()".

My preconception was: the IDE feature is deterministic and works every time. The LLM may hallucinate and fail to correctly do the rename, so it's both slower and worse.

My actual experience has been: So far, I've never actually seen Opus or GPT5.5 hallucinate and fail at a simple refactoring task like this, but I have had numerous instances where it caught extra stuff that a deterministic rename never could, and therefore did the task slower and much better.

I hate it because it feels lazy and stupid to type "do this trivial thing for me" into a prompt box. But dammit, it works too well.

24 วันที่ผ่านมาdiscuss

"Which is why the Matrix was redesigned to this: the peak of your civilization. I say your civilization, because as soon as we started thinking for you it really became our civilization, which is of course what this is all about."

30 วันที่ผ่านมาdiscuss

Yep. "Developer" was already an inflation over "programmer".

Sometime in the late 2000s/early 2010s people started saying, oh, a programmer means you just type in the code to implement requirements, a developer actually understands and can create the requirements to meet the business needs! Like that wasn't what people were already doing for decades and calling "programming".

edit: I guess it was offshoring that encouraged the self-marketing as a "developer" (not one of those programmers you can get anywhere for cheap) just as AI is doing the same thing with "engineer"

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> "What have you got against machines?" said Buck.

> "They're slaves."

> "Well, what the heck," said Buck. "I mean, they aren't people. They don't suffer. They don't mind working."

> "No. But they compete with people."

> "That's a pretty good thing, isn't it--considering what a sloppy job most people do of anything?"

> "Anybody that competes with slaves becomes a slave," said Harrison thickly, and he left.

Kurt Vonnegut, Player Piano

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It's afraid!

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> Every 1 prompt of building tends to require 1-5 prompts of clean up. Simple, fast, clean good code.

I have found this to be very effective as well. However, it's so easy to do, I can't imagine they won't build it in.

The harnesses will improve and the loop of "self-review, judge what needs clean up, do the refactoring, repeat until clean" will get included in the one-shot. They are already doing this somewhat, they'll just get a lot better at it and as the models get faster and cheaper to run, the refactoring churn at the end of each task won't even create a noticeable delay.

I do not think the high-level "taste" knowledge that I've built up -- when to break something off into its own service, what to put in the DB vs cache vs queues vs blob storage, how to isolate important logic in pure functional layers so it can be tested and validated independently -- is any more "unlearnable" to AI than the stuff I previously considered impressive that's now one-shottable like "write a Prolog implementation from scratch".

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> I'd say SQL is a very high level language.

Yes indeed. When I learned SQL in college, the professor made a HUGE deal about how it was a "4th generation language" so it was so abstract you didn't have to think about how the computer would answer your query.

Even at that time I thought that was massive overstatement of what using SQL was like. It didn't deliver on that promise very well. But it's very funny to see plain SQL now sometimes called "low level"!

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One thing something like AutoCodeBenchmark cannot demonstrate is what happens when you have human-written type definitions defining the domain before the LLM writes a line of code.

That is something I have found very effective in F#, that I model the domain with types, I know what the type signatures of the functions I need are, and the LLM does the work of actually implementing those functions.

Here is a concrete example:

I have been playing around with a program to assist me with projects I make at home on my hobby-grade CNC router, which does not have an automatic toolchanger. I use a mix of Vectric VCarve and some older handwritten programs to generate GCode files. I end up with a USB drive with maybe 6 to 12 GCode files on it and a model in my head of "to make this product, I start with a board here, gotta install this square nose end mill and zero on this corner of the board, run files A and B. Then install a ball nose end mill and run file C. Then flip the board over lengthwise, switch to a smaller square nose end mill, zero here, run file D. etc. etc."

Although I try to name the GCode files in a self documenting way like 01_TopSide_25square.ngc, if I come back in 1 year and want to make the same thing again, I pretty much always have to open VCarve and eyeball what the hell all the files did and confirm where to zero, what size board to use, etc. So I'm making a tool where I can define those human-operator steps that go with the G-Code files, save it as a "project file", preview in 3d what each step will look like, and export to a printable PDF with screenshots and step-by-step instructions. Hopefully this will reduce the amount of rot that these projects suffer and the cognitive overhead of picking up an old one.

Modeling the steps as F# types was the very first step, like (small excerpt):

    type WorkpiecePlacement =
        {   Id : WorkpieceId
            /// Corner of the workpiece we'll attach to the machine.
            WorkpieceCorner : WorkpieceSpace.Corner3D
            /// Point in machine-space we'll anchor this corner to.
            MachinePoint : MachineSpace.Point
            /// Which face of the workpiece is on top.
            FaceUp : WorkpieceSpace.Face
            /// Rotation around the up-axis.
            Yaw : WorkpieceSpace.Yaw
        }

    type OperationType =
        | PlaceWorkpiece of placement : Operation.WorkpiecePlacement
        | InstallTool of id : ToolId * slot : int option
        | ZeroAt of point : MachineSpace.Point
        | RunGCode of source : GCode.Source
        | RemoveWorkpiece of id : WorkpieceId

For the GCode simulator I needed a parser for GCode files, which produces a type with 1:1 equivalence to the GCode instruction set:

    type GCodeInstruction =
        // --- Motion ---
        | G0_RapidMove of axisMoves : (Axis * float<gcodeunit>) array
        | G1_Move of feedRate : float<gcodeunit/minute> option * axisMoves : (Axis * float<gcodeunit>) array
        | G2_ClockwiseArc of ArcParams
        | G3_CounterClockwiseArc of ArcParams
        | G4_Dwell of seconds : double

        // --- Plane selection ---
        | G17_SelectXYPlane
        | G18_SelectXZPlane
        | G19_SelectYZPlane

        // --- Unit selection ---
        | G20_Inches
        | G21_Millimeters

        // --- Distance mode ---
        | G90_AbsoluteDistance
        | G91_RelativeDistance
        // ... etc truncated, more instructions in real code

But my tool supports doing transforms on toolpaths, like rotating 90 degrees or offsetting so I can easily define that I want to make tiling copies of the same project. To implement those transforms straight up as GCodeInstruction[] -> GCodeInstruction[] is a bad call. GCode is very stateful and lets you switch units, relative vs. absolute coordinate spaces, etc. in instructions. That makes the transform awkward and tricky to write.

So I have a ToolPath type that makes the transforms clean. It normalizes the many ways of expressing the same toolpath in GCode to a single representation with all absolute coordinates in metric units.

    type ToolPathInstruction =
        | Rapid of From : Point * To : Point
        | Linear of From : Point * To : Point * Feed : FeedRate
        | Arc of
            From : Point *
            To : Point *
            Center : Point *
            Plane : Plane *
            Direction : ArcDirection *
            Feed : FeedRate
        | ... etc truncated

That is the appropriate level for the transforms like offset, rotate, scale, etc. to operate on.

Yet there is still ANOTHER level of toolpath-related operations that deserves its own type. When I'm doing simulation of material removal to check for crashes, or rendering the toolpath in 3d, I don't want to deal with arcs! The rendering/simulation is inherently an approximation. It will break down each arc into line segments. So sim code and rendering code shouldn't take a toolpath, it should take basically a line segment list, or in other words...

    type ApproxMove =
        {   From : Vector3
            To : Vector3
            FeedRate : double<m/minute>
            IsRapid : bool
        }

    type ToolPathApproximation =
        {   StartPosition : Vector3
            Moves : ApproxMove[]
        }

Having defined all these types it's clear that I need operations like:

    parse: string -> GCode
    serialize : GCode -> string
    normalizeToToolPath : GCode -> ToolPath
    denormalizeToGCode : ToolPath -> GCode
    offset : Vector3 -> ToolPath -> ToolPath
    rotate90 : ToolPath -> ToolPath
    scale : Vector3 -> ToolPath -> ToolPath
    approximate : ToolPath -> ToolPathApproximation
    simulate : ToolPathApproximation -> MachineState -> MachineState
    renderToolPathWireframe : ToolPathApproximation -> VBO
    renderMachineState : MachineState -> VBO

And so on. An LLM is absolutely awesome at one-shotting the implementations.

I would find it quite frustrating trying to model the same domain without any types, either having all methods working on a single toolpathy data structure that's not really the right fit for any of the places it's used, or having them work on multiple data structures without any clear delineation of which layer is expecting which toolpathy-thing that are all subtly but importantly different.

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It has been about 10 years since Python was a daily driver for me and at that time I wrote it the old fashioned way with no type hints and no static checking, just like grandma used to make. The times I have needed to dig back into it have involved working on old code, so I haven't kept up with modern tooling.

However, in principle any dynamically typed language can be tolerable to me if it can be turned into a statically typed language ;)

But I think I'd still prefer the ergonomics of a language designed that way from the start vs having bolt-ons. My favorite language for the past several years has been F# and I think ML-family languages in general strike a great balance of being able to write terse code when you want to, and being able to model a domain really well with types when you want to.

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> the errors I get at runtime are almost never type-based

That surprises me, but everyone's experiences are different. I've been in the statically typed language space for so long and enjoyed it so much, I find it pretty irritating to go back to Python (my long-ago favorite) but many people are in the exact opposite frame of mind. I'm curious: what kinds of errors do you classify as a type-based error? I think that varies from person to person.

For example, null references. A C programmer would say dereferencing a null is not a type-based error, because it's not feasible to encode non-nullable pointers in the C type system. A Haskell programmer would say it is a type-based error because Haskell makes it difficult not to encode this in the type system, you really have to go out of your way to create a runtime null dereference error.

A C# or TypeScript programmer could answer differently depending on who you ask, because both of those languages make it possible to leverage the typechecker to prevent null-deref at compile time, but neither one makes it required (you can turn those checks off or make them warnings if you like), so it depends on the programmer's build settings and how much typechecking they personally have chosen to use.

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Fascinating quote and good point.

It should also be remembered that while the industrial revolution netted humanity enormous wealth and eventually a higher average standard of living, it also kinda sucked for the generations of working class living through it, prior to labor reform. Millions of people lived entire lives where the industrial revolution was nothing but bad for them and never saw the upside. So anybody opposing a new industrial revolution is not necessarily acting out of irrationality.

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> whats wrong with struggling alcoholics having jobs via a program?

Finally, a job AI will never beat me at.

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The camera analogy is a good one but I have never had a camera that had every great picture somebody else had taken, plus every work of art, baked into it. They only captured what they were aimed at directly by the user. Well, maybe next time I upgrade my phone that will not be the case since they now have built in AI "enhancement" of photos.

I agree with the framing of the AI as a tool not an autonomous entity. The thing is, to me, it is exactly that framing that makes it so the use of that tool means "copying" more than it means "learning and taking inspiration and creating new art", because who is doing the learning and being inspired? The person who types "make me a 3d arena FPS" certainly didn't do any learning from the Quake source code. The AI itself, being just a program, can't take credit.

I think of a trained AI like a lossy, highly compressed copy of its training data set. AI companies charge access to decompress targeted pieces of that copy and the lossiness makes that decompression interesting and "new". But normally I can't charge for access to other people's stuff even if the access is highly lossy, like a camcorder bootleg.

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> A derivative work is a work that itself includes copyrighted content from the original work.

If you put a GPL C program through Emscripten to run in a browser the output doesn't include the original C code but it's surely a derivative work.

> Someone who looks at a dozen code examples in public repos to learn how to do e.g. a quick sort, then upon understanding the logic flow of the quick sort algorithm, writes his own quick sort implementation is not creating a derivative work of the code in the repos he exampled. And the way LLMs work is much more similar to that process than to the "compressed anthology" concept you're describing.

This is undoubtedly the core of the disagreement. Humans can learn from what they have seen, appreciate it, understand it, and draw on that experience in what they create. They do this without being considered ripoff artists, so why not machines that simulate the "same" thing automatically?

To me the answer is simply that humans are special. Human thought and human effort makes it creativity when a human does it, copying when a machine does it. It's a double standard I am perfectly willing to accept. I am unabashedly biased in this regard.

That may seem remarkably unfair to the machines, or like a cop-out. I just carved out a hardcoded special case for humans, and my whole philosophical reasoning is "because I said so". But how fair do we want to be? After all, if you want to treat a machine exactly like a human who learns from prior art to create new art, then the ownership of the new art would also belong to the machine. Not to the person who prompts it.

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> Perhaps this illustrates a fissure that was always lurking under the surface, then(...)

Yes, I do think there has always been such a fissure. People publish OSS code for many reasons, often a blend of multiple reasons. There are selfish reasons such as the desire for one's work to be recognized, or even the hope of getting better employment through showing ones' skill or making something companies will pay for support on. There are social reasons like the desire to collaborate with others. There are altruistic benefit-of-all-mankind reasons like Richard Stallman said "...restrictions reduce the amount and the ways that the program can be used. This reduces the amount of wealth that humanity derives from the program."

It sounds like your view of things is limited mostly to that last version of FOSS, the copyleft style. But even adherents of that style, I think, are not too happy with AI consumption of their code. For one, it allows laundering of the copyleft license so their work goes into closed-source products that are never shared. And for two, if your idea of OSS is that we all put our contributions into the great shared river of human achievements to benefit the world, it is disappointing to see that river funneled into a giant waterwheel of profit for a half dozen trillion dollar companies charging rent for its bounty.

> Given that FOSS licenses were always constructed to function within applicable copyright law, I don't see how they could mean anything else.

I agree from a legal standpoint. I cannot enforce my personal definition of copying nor do I expect that to become possible. It was just conveniently aligned with the reality of how copying software worked in the past, and no longer is and never will be again. That doesn't mean I will be writing OSS software with a new made-up unenforceable license. It just means, like OP, I'll weigh differently whether I want to bother releasing stuff at all.

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The contract behind open source was something like (GPL):

"If you copy my work, you should share your work too."

or at minimum (MIT):

"If you copy my work, you should credit me."

I think it is no longer under dispute that the legal contract is satisfied by LLMs. The AI companies won and will continue to win.

But we are talking about a social contract, which is not quite the same thing. The social contract is what leads some devs who previously enjoyed publishing their work openly to no longer feel the same way. What did the authors mean by "copy"? Did they mean literally CTRL+C, CTRL+V or something broader?

This is a matter of opinion which only each individual creator can answer. For me, copying meant something like:

"To reproduce the function of my work, dependent on my having published it, without effort nor understanding of your own"

Ten years ago this basically required doing a CTRL+C, CTRL+V so there was no need to be more specific. Anybody who did enough work to, say, rewrite in another language (with that language's idioms), met the bar of clause 3. Now AI enables a form of "copying" that matches my definition, without the user even being aware of whose works they are copying. It perfectly launders the origins of its output. It can write an FFmpeg clone in Rust for you that would appear to be a novel work.

Of course, I cannot say that my own little bits and pieces of open source code would make a scratch in AI's capability, were it removed.

But I do strongly believe that if all the code that was published by authors with the same mindset was unavailable, Claude would be a far weaker developer.

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> Static types are, in a real sense, a compensation for the gap I just described - the gap between the description and the running thing. When you can't easily inspect or reshape the live system, you want the compiler to tell you as much as possible before you cross that gap.

I think this underrates static typing. For me the biggest value add of a static type system is that while doing a big, breaking-change refactor, I can near-instantly see all the places I need to update callers. Getting the code to work in the place I was actually working on is easy, I was already focused there. Static types pay off by helping me know when my change broke other parts of the system I wasn't even thinking about.

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> But I'm already responsible for the bugs in my software. Also, who cares if someone else is responsible? And how does that align with OSS's "no warranty provided"?

The original example from Simon Willison referred not to pulling in a 3rd party library, but working "at larger organizations" where "another team hands over something". In other words we area all working on the same product for the same company, they have been assigned another part of it and I'm expected to use their code.

In that scenario of course I care that someone else is responsible! It may affect whether I get fired or not!

It's different if you're a solo founder of a startup and for everything you ship, the buck stops with you. But proportionally many many more devs are in a situation where they are a cog in a machine.

> AI seems radically, insanely more qualified to not write bugs like that. I doubt that if you polled developers 99% would be able to tell you what a CRC32 even is, let alone why it's insufficient as a cache key.

I actually do agree that AI generally writes pretty good code. Doesn't mean I'm not gonna check. Sometimes it is too clever for its own good, such as re-implementing from scratch something that already exists and is well-proven.

The whole example is kind of contrived in the first place (how many environments don't have an excellent "image resizing" solution to reach for off the shelf?), so I hope you don't mind my bug example is also contrived.

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Two points.

1. You can treat software like a black box when other people developed it for you because they can stand behind it. They have their own reputations to uphold. You can't when AI developed it for you because YOU are responsible for 100% of the bugs in it. If you take this trendy stance of "I never read or write code, just specs", you are just rolling the dice on what you stamp your name on.

2. Just because you have unit tests and you've tested the software by clicking through the app doesn't mean you've found every bug. There have always been bug types, like the example checksum collision, that are easier to detect by reading the code than by running the code because it will work most of the time even though the approach is wrong.

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I'm prepared to excise the word "genuinely" from my vocabulary after working with Claude.

One of my biggest fears with using AI at work is that I will subconsciously start talking and writing like a bot, despite making conscious efforts to do the opposite. Just like how when you read a lot of books by one author, their style infects your own writing style.