Software is mostly all you need

https://softwarefordays.com/post/software-is-mostly-all-you-need/

by jbmilgrom • 6 months ago

60 45 6 months ago

wrs• 6 months ago

In other words, a higher-level JIT compiler, meaning it still dynamically generates code based on runtime observations, but the code is in a higher-level language than assembly, and the observations are of a higher-level context than just runtime data types.

daxfohl3• 6 months ago

I agree this is what the article says, but it's a pretty bad premise. That would only be the case if the primary user interaction with coding agents was "feed in requirements, get a finished product". But we all know it's a more iterative process than that.

jbmilgrom6• 6 months ago

Author here

We are building this at docflowlabs ie a self-healing system that can respond to customer feedback automatically. And youre right that not all customers know what they want or even how to express it when they do, which is why the agent loop we have facing them is way more discovery-focused than the internal one.

And we currently still have humans in the loop for everything (for now!) - e.g, the agent does not move onto implementation until the root cause has been approved

verdverm4• 6 months ago

Lost me at the claim AI is good at judgement making, this is the exact opposite of my experience, they make both good and bad decisions with reliability

mvc• 6 months ago

I think it makes better decisions than me provided I give it enough high-level direction and context.

Sometimes I give it __too much__ direction and it finds the solution I had in mind but not the best.

I'm not into it enough that I'm formally running different personas against each other in a co-operative system but I kind of informally do that.

verdverm4• 6 months ago

The type of decision very much matters, coding is one thing. I met a chap at the bar who ChatGPT had verified his crazy theories and he now outsourced all of his major life decisions to it, very proud and enthusiastic about it all. First IRL case of AI psychosis I have encountered. He was keen for my thoughts, as though I was the first person he met IRL that knew more than the layman about Ai. Hope the questions (contradictions) I left him with helped bring him back a bit.

It's going to get a lot worse

rybosworld2• 6 months ago

I think that's also true of people but we are kinder to each other and ourselves when judgement is bad.

How many times have you been in a conversation where you asked the wrong question or stated the wrong thing because you either weren't 100% listening (no one is), or you forgot, or you didn't connect the same dots that others did?

Terr_2• 6 months ago

Treating humans differently makes sense because the "badness" of a judgement isn't just the correctness of an outcome, but also the nature of the process that created it, and humans are a different process.

For example, if two otherwise-identical humans yield the same equally-correct answer, we probably will favor the one that reached it through facts and reasoning, as opposed to the one who literally flipped a coin.

krainboltgreene• 6 months ago

I think that's also true of people

Reductionist positions seem to always pop up in these threads.

2001zhaozhao• 6 months ago

Code is the policy, deployment is the episode, and the bug report is the reward signal

This is a great quote. I think it makes a ton of sense to view a sufficiently-cheap-and-automated agentic SWE system as a machine learning system rather than traditional coding.

* Perhaps the key to transparent/interpretable ML is to just replace the ML model with AI-coded traditional software and decision trees. This way it's still fully autonomously trained but you can easily look at the code to see what is going on.

* I also wonder whether you can use fully-automated agentic SWE/data science in adversarial use-cases where you traditionally have to use ML, such as online moderation. You could set a clear goal to cut down on any undesired content while minimizing false-positives, and the agent would be able to create a self-updating implementation that dynamically responds to adversarial changes. I'm most familiar with video game anti-cheat where I think something like this is very likely possible.

* Perhaps you can use a fully-automated SWE loop, constrained in some way, to develop game enemies and AI opponents which currently requires gruesome amounts of manual work to implement. Those are typically too complex to tackle using traditional ML and you can't naively use RL because the enemies are supposed to be immersive rather than being the best at playing the game by gaming the mechanics. Maybe with a player controller SDK and enough instructions (and live player feedback?), you can get an agent to make a programmatic game AI for you and automatically refine it to be better.

jbmilgrom6• 6 months ago

Perhaps the key to transparent/interpretable ML is to just replace the ML model with AI-coded traditional software and decision trees. This way it's still fully autonomously trained but you can easily look at the code to see what is going on.

For certain problems I think thats completely right. We still are not going to want that of course for classic ML domains like vision and now coding, etc. But for those domains where software substrate is appropriate, software has a huge interpretability and operability advantage over ML

fooker2• 6 months ago

We still are not going to want that of course for classic ML domains like vision

It could make sense to decompose one large opaque model into code with decision trees calling out to smaller models having very specific purposes. This is more or less science fiction right now, 'mixture of experts' notwithstanding.

You could potentially get a Turing award by making this work for real ;)

Terr_2• 6 months ago

Perhaps the key to transparent/interpretable ML is to just replace the ML model with AI-coded traditional software and decision trees. This way it's still fully autonomously trained but you can easily look at the code to see what is going on.

Just yesterday I came across a something a sci-fi webcomic author wrote as backstory back in ~2017, where all future AI has auditable logic-chains, due to a disaster in 2061 involving an American AI defense system.

While the overall concept of "turns on its creators" is not new, I still found the "root cause" darkly amusing:

[...] until the millisecond that Gordon Smith put his hand on a Bible and swore to defend the constitution.
Thus, when the POTUS changed from Vanderbilt to Smith, a switch flipped. TIARA [Threat Intel Analysis and Response Algorithm] was now aware of an individual with 1) a common surname, 2) a lot of money and resources, 3) the allegiance of thousands of armed soldiers, 4) many alternate aliases (like "POTUS"), 5) frequent travel, 6) bases of operation around the world, 7) mentioned frequently in terrorist chatter, etc, etc, etc.
And yes, of course, when TIARA launches a drone strike, it notifies a human operator, who can immediately countermand it. This is, unfortunately, not useful when the drone strike mission has a travel time of zero seconds.
Thousands of intelligent weapons, finding themselves right on top of a known terrorist's assets, immediately did their job and detonated. In less than fifteen minutes, over ten thousand people lost their lives, and the damage was estimated in the trillions of dollars.

[0] https://forwardcomic.com/archive.php?num=200

fooker2• 6 months ago

Perhaps the key to transparent/interpretable ML is to just replace the ML model with AI-coded traditional software and decision trees

I like this train of thought. Research shows that decision trees are equivalent to 1-bit model weights + larger model.

But critically, we only know some classes of problems that are effectively solved by this approach.

So, I guess we are stuck waiting for new science to see what works here. I suspect we will see a lot more work on these topics after the we hit some hard LLM scalability limits.

isodev4• 6 months ago

Neural networks excel at judgment

I don’t think they do. I think they excel at outputting echoes of their training data that best fit (rhyme with, contextually) the prompt they were given. If you try using Claude with an obscure language or use case, you will notice that effect even more - it will keep pulling towards things it knows that aren’t at all what’s asked or “the best judgement” for what’s needed.

rybosworld2• 6 months ago

Neural nets have been better at classifying handwriting (MNIST) than the best humans for a long time. This is what the author means by judgement.

They are super-human in their ability to classify.

verdverm4• 6 months ago

Classifiers and LLMs get very different training and objectives, it's a mistake to draw inference from MNIST for coding agents or LLMs more generally.

Even within coding, their capability varies widely between context and even runs with the same context. They are not better at judgement in coding for all cases, def not

PostOnce2• 6 months ago

Tell that to all the OCR fuckups I see in all the ebooks I read.

boogrpants2• 6 months ago

I think they excel at outputting echoes of their training data that best fit (rhyme with, contextually) the prompt they were given.

Just like people who get degrees in economics or engineering and engage in such role-play for decades. They're often pretty bad at anything they are not trained on.

Coincidentally, if you put a single American English speaker on a team of native German language speakers you will notice information transference falls apart.

Very normal physical reality things occurring in two substrates, two mediums. As if there is a shared limitation called the rest of the universe attempting to erode our efforts via entropy.

LLM is a distribution of human generated data sets. Since humans have the same incompleteness problems in society this affords enough statistical wiggle room for LLMs to make shit up; humans do it! Look in their data!

We're massively underestimating realities indifference to human existence.

There is no doing any better until we effectively break physics, by that I really mean come upon a game changing discovery that informs us we had physics all wrong to begin with.

harry8• 6 months ago

The fact there are a lot of people around who don't think (including me at times!) does mean LLMs doing that are thinking.

Much like LLMs writing text like mindless middle managers, it doesn't mean they're intelligent, more that mindless middle managers aren't.

isodev4• 6 months ago

Just like people

I understand that having model related vocabulary borrow similar words we use to describe human brains and cognition gets confusing. We are not the same, we don’t “learn” the same we certainly don’t use the knowledge we posses in the same way.

The major difference between an LLM and a human is that as a human, I can look at your examples (which sound solid at first glance) and choose to truly “reason” about them in a way that allows me to judge if they’re correct or even applicable.

jauntywundrkind2• 6 months ago

Here here. Code has uniquely an incredible volume of data. And incredibly good ways to assess & test it's weights, to immediately find out of its headed the right way on the gradient.

geraneum3• 6 months ago

And incredibly good ways to assess & test it's weights

What weights are you referring to? How does [Claude?] code do that

jbmilgrom6• 6 months ago

Author here

We are building this learned software system at Docflow Labs to solve the integration problem in healthcare at scale ie systems only able to chat with other systems via web portals. RPA historically awful to build and maintain so we've needed to build this to stay above water. Happy to answer any questions!

cadamsdotcom• 6 months ago

There’s something interesting here around when to write code, when to reuse code, and when to replace the code that was written.

Browser use could be improved by being partly done with code and part genetically.. completing tasks on the web is deceptive because it seems easily codifiable (just have the model write some Playwright code!) while actually being gnarly as hell. What if the page has changed completely since last visit?

It’d be interesting to let the agent build up a library of code that it can reuse if it feels confident that will get the job done, while feeding back any error to let the agent debug.. and that might lead to it writing a new routine to stick in the library, possibly replacing the old one.

Seems like something today’s models could be made to do with a bit of work in the harness. Anyone tried anything like this?

Software is mostly all you need

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