> What's Anthropic's optimization target??? Getting you the right answer as fast as possible!

What makes you believe this? The current trend in all major providers seem to be: get you to spin up as many agents as possible so that you can get billed more and their number of requests goes up.

> Slot machines have variable reward schedules by design

LLMs by all major providers are optimized used RLHF where they are optimized in ways we don't entirely understand to keep you engaged.

These are incredibly naive assumptions. Anthropic/OpenAI/etc don't care if you get your "answer solved quickly", they care that you keep paying and that all their numbers go up. They aren't doing this as a favor to you and there's no reason to believe that these systems are optimized in your interest.

> I built things obsessively before LLMs. I'll build things obsessively after.

The core argument of the "gambling hypothesis" is that many of these people aren't really building things. To be clear, I certainly don't know if this is true of you in particular, it probably isn't. But just because this doesn't apply to you specifically doesn't mean it's not a solid argument.

> The gambling analogy completely falls apart on inspection. Slot machines have variable reward schedules by design — every element is optimized to maximize time on device. Social media optimizes for engagement, and compulsive behavior is the predictable output. The optimization target produces the addiction.

Intermittent variable rewards, whether produced by design or merely as a byproduct, will induce compulsive behavior, no matter the optimization target. This applies to Claude

Right. A platform who makes money the more you have to use it is definitely optimizing to get you the right answer in as few tokens as possible.

There is absolutely no incentive to do that, for any of these companies. The incentive is to make the model just bad enough you keep coming back, but not so bad you go to a competitor.

We've already seen this play out. We know Google made their search results worse to drive up and revenue. Exact same incentives are at play here, only worse.

Anthropic themselves have described CC as a slot machine:

https://www-cdn.anthropic.com/58284b19e702b49db9302d5b6f135a...

(cmd-f "slot machine")

> What's Anthropic's optimization target??? Getting you the right answer as fast as possible!

Are you totally sure they are not measuring/optimizing engagement metrics? Because at least I can bet OpenAI is doing that with every product they have to offer.

Thank you! I don't get how so many people want to see dark patterns everywhere. All arguments miss the big counterargument: in a world where you have competitors, even free ones, you can't fuck around. You need to get it working. it's not a slot machine for me. How on earth are people using it? And if it would be I'd take my money elsewhere (kimi for example, openrouter or whatever). It needs to do my work as correct as possible. That's the business they are in. Tech folks talking about economics is so cringe. It's always just "corporations bad". As if they exist in a vacuum.

> What's Anthropic's optimization target??? Getting you the right answer as fast as possible!

That is a generous interpretation. Mighr be correct. But they dont make as much money if you quickly get the right answer. They make more money if you spend as many tokens as possible being on that "maybe next time" hook.

Im not saying theyre actually optimizng for that. But charlie munger said "show me the incentives, and ill show you the outcome"

> The gambling analogy completely falls apart on inspection.

The analogy was too strained to make sense.

Despite being framed as a helpful plea to gambling addicts, I think it’s clear this post was actually targeted at an anti-LLM audience. It’s supposed to make the reader feel good for choosing not to use them by portraying LLM users as poor gambling addicts.

At one point, people said Google's optimization target was giving you the right search results as soon as possible. What will prevent Anthropic from falling into the same pattern of enshittification as its predecessors, optimizing for profit like all other businesses?

Doesn't the alignment sort of depend on who is paying for all the tokens?

If Dave the developer is paying, Dave is incentivized to optimize token use along with Anthropic (for the different reasons mentioned).

If the Dave's employer, Earl, is paying and is mostly interested in getting Dave to work more, then what incentive does Dave have to minimize tokens? He's mostly incentivized by Earl to produce more code, and now also by Anthropic's accidentally variable-reward coding system, to code more... ?

>The pathologizing of "person who likes making things chooses making things over Netflix" requires you to treat passive consumption as the healthy baseline, which is obviously a claim nobody in this conversation is bothering to defend

I think their greater argument was to highlight how agentic coding is eroding work life balance, and that companies are beginning to make that the norm.

Disagree. Unreliability is intractable because of the human, not the tool.

Even a perfect LLM will not be able to produce perfect outputs because humans will never put in all the context necessary to zero-shot any non-trivial query. LLMs can't read your mind and will always make distasteful assumptions unless driven by users without any unique preferences or a lot of time on their hands to ruminate on exactly how they want something done.

I think it will always be mostly boring back-and-forth until the jackpot comes. Maybe future generations will align their preferences with the default LLM output instead of human preferences in that domain, though.

> The gambling analogy completely falls apart on inspection.

yeah I think the bluesky embed is much more along the lines of what I'm experiencing than the OP itself.

> "person who likes making things chooses making things over Netflix"

This is subtly different. It's not clear that the people depicted like making things, in the sense of enjoying the process. The narrative is about LLMs fitting into the already-existing startup culture. There's already a blurry boundary between "risky investment" and "gambling", given that most businesses (of all types, not just startups) have a high failure rate. The socially destructive characteristic identified here is: given more opportunity to pull the handle on the gambling machine, people are choosing to do that at the expense of other parts of their life.

But yes, this relies on a subjective distinction between "building, but with unpredictable results" and "gambling, with its associated self-delusions".

Claude RARELY get it right on the fifth time. Usually i write the damn thing when my account is on "cooldown".

> What's Anthropic's optimization target???

It is a business that sells monthly subscriptions

> What's Anthropic's optimization target??? Getting you the right answer as fast as possible!

Wait, what? Anthropic makes money by getting you to buy and expend tokens. The last thing they want is for you to get the right answer as fast as possible. They want you to sometimes get the right answer unpredictably, but with enough likelihood that this time will work that you keep hitting Enter.

The LLM is not the slot machine. The LLM is the lever of the slot machine, and the slot machine itself is capitalism. Pull the lever, see if it generates a marketable product or moment of virality, get rich if you hit the jackpot. If not, pull again.

You may have a point but either way: immediately taking it personally like this and creating a whole semi-rant that includes something to the effect "I've been doing this since before you were born" really makes you sound like a person with a gambling problem.

Trust me, we all feel like the house is our friend until its isn't!

Another interesting thing here is that the gap between "burned out but just producing subpar work" and "so crispy I literally cannot work" is even wider with AI. The bar for just firing off prompts is low, but the mental effort required to know the right prompts to ask and then validate is much higher so you just skip that part. You can work for months doing terrible work and then eventually the entire codebase collapses.

> With the tech job market being rough and AI tools making it so frictionless to produce real output, the line between work time and personal time is basically disappearing.

This isn't generally true at all. The "all tech companies are going to 996" meme comes up a lot here but all of the links and anecdotes go back to the same few sources.

It is very true that the tech job market is competitive again after the post-COVID period where virtually nobody was getting fired and jobs were easy to find.

I do not think it's true that the median or even 90th percentile tech job is becoming so overbearing that personal time is disappearing. If you're at a job where they're trying to normalize overwork as something everyone is doing, they're just lying to you to extract more work.

My impression so far is that the parallel agent story is a fabrication of "ai influencers" and the labs themselves.

I might run 3-4 claude sessions because that's the only way to have "multiple chats" to e.g. ask unrelated things. Occasionally a task takes long enough to keep multiple sessions busy, but that's rather rare and if it happens its because the agent runs a long running task like the whole test suite.

The story of running multiple agents to build full features in parallel... doesn't really add up in my experience. It kinda works for a bit if you have a green field project where the complexity is still extremely low.

However once you have a feature interaction matrix that is larger than say 3x3 you have to hand hold the system to not make stupid assumptions. Or you prompt very precisely but this also takes time and prevents you from ever running into the parallel situation.

The feature interaction matrix size is my current proxy "pseudo-metric" for when agentic coding might work well and at which abstraction level.

I might be able to shine a little light on this.

I came from embedded, where I wasn't able to use agents very effectively for anything other than quick round trip iterative stuff. They were still really useful, but I definitely could never envision just letting an agent run unattended.

But I recently switched domains into vaguely "fullstack web" using very popular frameworks. If I spend a good portion of my day going back and forth with an agent, working on a detailed implementation plan that spawns multiple agents, there is seemingly no limit* to the scope of the work they are able to accurately produce. This is because I'm reading through the whole plan and checking for silly gotchyas and larger implementation mistakes before I let them run. It's also great because I can see how the work can be parallelized at certain parts, but blocked at others, and see how much work can be parallelized at once.

Once I'm ready, I can usually let it start with not even the latest models, because the actual implementation is so straightforwardly prompted that it gets it close to perfectly right. I usually sit next to it and validate it while it's working, but I could easily imagine someone letting it run overnight to wake up to a fresh PR in the morning.

Don't get me wrong, it's still more work that just "vibing" the whole thing, but it's _so_ much more efficient than actually implementing it, especially when it's a lot of repetitive patterns and boilerplate.

* I think the limit is how much I can actually keep in my brain and spec out in a well thought out manner that doesn't let any corner cases through, which is still a limit, but not necessarily one coming from the agents. Once I have one document implemented, I can move on to the next with my own fresh mental context which makes it a lot easier to work.

I had a few useful examples of this. In order to make it work you need to define your quality gates, and rather complex spec. I personally use https://github.com/probelabs/visor for creating the gates. It can be a code-review gate, or how well implementation align with the spec and etc. And basically it makes agent loop until it pass it. One of the tips, especially when using Claude Code, is explictly ask to create a "tasks", and also use subagents. For example I want to validate and re-structure all my documentation - I would ask it to create a task to research state of my docs, then after create a task per specific detail, then create a task to re-validate quality after it has finished task. You can also play around with the gates with a more simple tooling, for example https://probelabs.com/vow/

Hope it helps!

Not a dev but doing some side projects.

As i build with agents, i frequently run into new issues that arent in scope for the task im on and would cause context drift. I have the agent create a github issue with a short problem description and keep going on the current task. In another terminal i spin up a new agent and just tell it “investigate GH issue 123” and it starts diving in, finds the root cause, and proposes a fix. Depending on what parts of the code the issue fix touches and what other agents ive got going, i can have 3-4 agents more or less independently closing out issues/creating PRs for review at a time. The agents log their work in a work log- what they did, what worked what didnt, problems they encountered using tools - and about once a day i have an agent review the worklog and update the AGENTS.md with lessons learned.

With 5.3 Codex, the execplans skill and a well specified implementation task, you can get a good couple of hours work in a single turn. That's already in the scope of "set it up before bed and review it in the morning".

If you have a loop set up, e.g., using OpenClaw or a Ralph loop, you can stretch that out further.

I would suggest that when you get to that point really, you want some kind of adversarial system set up with code reviews (e.g., provided by CodeRabbit or Sourcery) and automation to feed that back into the coding agent.

> what are those agents actually doing?

Providing material for attention-grabbing headlines and blog posts, primarily. Can't (in good conscience, at least) claim you had an agent running all night if you didn't actually run an agent all night.

Maybe it's the programmer equivalent of rolling coal.

If you visualize it as AI Agents throwing a rope to wrangle a problem, and then visualize a dozen of these agents throwing their ropes around a room, and at each other -- very quickly you'll also visualize the mess of code that a collections of agents creates without oversight. It might even run, some might say that's the only true point but... at what cost in code complexity, performance waste, cascading bugs, etc.

Is it possible? Yes, I've had success with having a model output a 100 step plan that tried to deconflict among multiple agents. Without re-creating 'Gas town', I could not get the agents to operate without stepping on toes. With _me_ as the grand coordinator, I was able to execute and replicate a SaaS product (at a surface level) in about 24hrs. Output was around 100k lines of code (without counting css/js).

Who can prove that it works correctly though? An AI enthusiasts will say "as long as you've got test coverage blah blah blah". Those who have worked large scale products know that tests passing is basically "bare minimum". So you smoke test it, hope you've got all the paths, and toss it up and try to collect money from people? I don't know. If _this_ is the future, this will collapse under the weight of garbage code, security and privacy breaches, and who knows what else.

I will give you an example I heard from an acquaintance yesterday - this person is very smart but not strictly “technical”.

He is building a trading automation for personal use. In his design he gets a message on whatsapp/signal/telegram and approves/rejects the trade suggestion.

To define specifications for this, he defined multiple agents (a quant, a data scientist, a principal engineer, and trading experts - “warren buffett”, “ray dalio”) and let the agents run until they reached a consensus on what the design should be. He said this ran for a couple of hours (so not strictly overnight) after he went to sleep; in the morning he read and amended the output (10s of pages equivalent) and let it build.

This is not a strictly-defined coding task, but there are now many examples of emerging patterns where you have multiple agents supporting each other, running tasks in parallel, correcting/criticising/challenging each other, until some definition of “done” has been satisfied.

That said, personally my usage is much like yours - I run agents one at a time and closely monitor output before proceeding, to avoid finding a clusterfuck of bad choices built on top of each other. So you are not alone my friend :-)

This is my experience of it too. Perhaps if it was chunking through a large task like upgrading all of our repos to the latest engine supported by our cloud provider, I could leave it overnight. Even then it would just result in a large daylight backlog of "not quite right" to review and redo.

This is very dependent on what kind of work you're asking the agent to do. For software, I've had quite a bit of success providing detailed API specifications and asking an LLM to build a client library for that. You can leave it running unattended as long as it knows what it's supposed to build and it won't need a lot of correction since you're providing the routes, returned statuses and possible error messages.

Do some people just create complete SaaSlop apps with it overnight? Of course, just put together a plan (by asking the LLM to write the plan) with everything you want the app to do and let it run.

There has only been one instance of coding where I let the agent run for like 7 hours. To generate playwright test. Once the scaffolding is done, it is just matter of writing test for each of the component. But yeah even for that I didn't just fire and forget.

I wrote a program to classify thousands of images but that was using a model running on my gaming PC. Took about 3 days to classify them all. Only cost me the power right?

I have agents run at night to work through complicated TTRPG campaigns. For example I have a script that runs all night simulating NPCs before a session. The NPCs have character sheets + motivations and the LLMs do one prompt per NPC in stages so combat can happen after social interactions. IF you run enough of these and make the prompts well written you can save a lot of time. You can't like... simulate the start of a campaign and then jump in. Its more like you know there is a big event, you already have characters, you can throw them in a folder to see how things would cook all else being equal and then use that to riff off of when you actually write your notes.

I think of my agents like golems from disc world, they are defined by their script. Adding texture to them improves the results so I usually keep a running tally of what they have worked on and add that to the header. They are a prompt in a folder that a script loops over and sends to gemeni(spawning an agent and moving to the next golem script)

I also was curious to see if it could be used it for developing some small games, whenever I would run into a problem I couldn't be bothered to solve or needed a variety of something I would let a few llms work on it so in the morning I had something to bounce off. I had pretty good success with this for RTS games and shooting games where variety is something well documented and creativity is allowed. I imagine there could be a use here, I've been calling it dredging cause I imagine myself casting a net down into the slop to find valuables.

I did have an idea where all my sites and UI would be checked against some UI heuristic like Oregon State's inclusivity heuristic but results have been mixed so far. The initial reports are fine, the implementation plans are ok but it seems like the loop of examine, fix, examine... has too much drift? That does seem solvable but I have a concern that this is like two lines that never touch but get closer as you approach infinity.

There is some usefulness in running these guys all night but I'm still figuring out when its useful and when its a waste of resources.

Spin up a mid sized linux vm (or any machine with 8 or 12 cores will do with at least 16GB RAM with nmve). Add 10 users. Install claude 10 times (one per user). Clone repo 10 times (one per user). Have a centralized place to get tasks from (db, trello, txt, etc) - this is the memory. Have a cron wake up every 10 minutes and call your script. Your script calls claude in non-interactive mode + auto accept. It grabs a new task, takes a crack at it and create a pull request. That is 6 tasks per hour per user, times 12 hours. Go from there and refine your harnesses/skills/scripts that claude's can use.

In my case, I built a small api that claude can call to get tasks. I update the tasks on my phone.

The assumption is that you have a semi-well structured codebase already (ours is 1M LOC C#). You have to use languages with strong typing + strict compiler.You have to force claude to frequently build the code (hence the cpu cores + ram + nmve requirement).

If you have multiple machines doing work, have single one as the master and give claude ssh to the others and it can configure them and invoke work on them directly. The usecase for this is when you have a beefy proxmox server with many smaller containers (think .net + debian). Give the main server access to all the "worker servers". Let claude document this infrastructure too and the different roles each machine plays. Soon you will have a small ranch of AI's doing different things, on different branches, making pull requests and putting feedback back into task manager for me to upvote or downvote.

Just try it. It works. Your mind will be blown what is possible.

> what are those agents actually doing

Generate material for yet another retarded twitter hype post.

Yeah I'm finding that there's "clock time" (hours) and "calendar time" (days/weeks/months) and pushing people to work 'more' is based on the fallacy that our productivity is based on clock time (like it is in a factory pumping out widgets) rather than calendar time (like it is in art and other creative endeavors). I'm finding that even if the LLM can crank out my requested code in an hour, I'll still need a few days to process how it feels to use. The temptation is to pull the lever 10 times in a row because it was so easy, but now I'll need a few weeks to process the changes as a human. This is just for my own personal projects, and it makes sense that the business incentives would be even more intense. But you can't get around the fact that, no matter how brilliant your software or interface, customers are not going to start paying in a few hours.

If you are really good and fast validating/fixing code output or you are actually not validating it more than just making sure it runs (no judging), I can see it paying out 95% of the time.

But for what I've seen both validating my and others coding agents outputs I'd estimate a much lower percentage (Data Engineering/Science work). And, oh boy, some colleages are hooked to generating no matter the quality. Workslop is a very real phenomenon.

> It's more like being hooked on a slot machine which pays out 95% of the time because you know how to trick it.

“It’s not like a slot machine, it’s like… a slot machine… that I feel good using”

That aside if a slot machine is doing your job correctly 95% of the time it seems like either you aren’t noticing when it’s doing your job poorly or you’ve shifted the way that you work to only allow yourself to do work that the slot machine is good at.

I was going to mention Yegge's recent blog posts mirroring this phenomena.

There's also this article on hbr.org https://hbr.org/2026/02/ai-doesnt-reduce-work-it-intensifies...

This is a real thing, and it looks like classic addiction.

> It's more like being hooked on a slot machine which pays out 95% of the time because you know how to trick it.

I think you are mistaken on what the "payout" is. There's only one reason someone is working all hours and during a party and whatnot: it's to become rich and powerful. The payout is not "more code", it's a big house, fast cars, beautiful women etc. Nobody can trick it into paying out even 1% of the time, let alone 95%.

It's 95% if you're using it for the stuff it's good at. People inevitably try to push it further than that (which is only natural!), and if you're operating at/beyond the capability frontier then the success rate eventually drops.

Being on a $200 plan is a weird motivator. Seeing the unused weekly limit for codex and the clock ticking down, and knowing I can spam GPT 5.2 Pro "for free" because I already paid for it.

Just need to point out that the payout is often above 95% at online casinos. As long as it's below 100 the house still wins.

> It's more like being hooked on a slot machine which pays out 95% of the time because you know how to trick it

Right but the <100% chance is actually why slot machines are addictive. If it pays out continuously the behaviour does not persist as long. It's called the partial reinforcement extinction effect.

thanks, that steve yegge piece was a very good read.

That 95% payout only works if you already know what good looks like. The sketchy part is when you can't tell the diff between correct and almost-correct. That's where stuff goes sideways.

I’d rather grind Runescape at this point, than become addicted at trying to automate away my job.