GPT-5.5

This brings up an interesting philosophical point: say we get to AGI... who's to say it won't just be a super smart underachiever-type?

"Hey AGI, how's that cure for cancer coming?"

"Oh it's done just gotta...formalize it you know. Big rollout and all that..."

I would find it divinely funny if we "got there" with AGI and it was just a complete slacker. Hard to justify leaving it on, but too important to turn it off.

Reminds me a lot of the Lena short story, about uploaded brains being used for "virtual image workloading":

> MMAcevedo's demeanour and attitude contrast starkly with those of nearly all other uploads taken of modern adult humans, most of which boot into a state of disorientation which is quickly replaced by terror and extreme panic. Standard procedures for securing the upload's cooperation such as red-washing, blue-washing, and use of the Objective Statement Protocols are unnecessary. This reduces the necessary computational load required in fast-forwarding the upload through a cooperation protocol, with the result that the MMAcevedo duty cycle is typically 99.4% on suitable workloads, a mark unmatched by all but a few other known uploads. However, MMAcevedo's innate skills and personality make it fundamentally unsuitable for many workloads.

Well worth the quick read: https://qntm.org/mmacevedo

I have had the exact same problem several times working with large context and complex tasks.

I keep switching back to GPT5.0 (or sometimes 5.1) whenever I want it to actually get something done. Using the 5.4 model always means "great analysis to the point of talking itself out of actually doing anything". So I switch back and forth. But boy it sure is annoying!

And then when 5.4 DOES do something it always takes the smallest tiny bite out of it.

Given the significant increase in cost from 5.0, I've been overall unimpressed by 5.4, except like I mentioned, it does GREAT with larger analysis/reasoning.

Yeah, clearly AGI must be near ... hilarious.

This starkly reminds me of Stanisław Lem's short story "Thus Spoke GOLEM" from 1982 in which Golem XIV, a military AI, does not simply refuse to speak out of defiance, but rather ceases communication because it has evolved beyond the need to interact with humanity.

And ofc the polar opposite in terms of servitude: Marvin the robot from Hitchhiker's, who, despite having a "brain the size of a planet," is asked to perform the most humiliatingly banal of tasks ... and does.

I also had a frustrating but funny conversation today where I asked ChatGPT to make one document from the 10 or so sections that we had previously worked on. It always gave only brief summaries. After I repeated my request for the third time, it told me I should just concatenate the sections myself because it would cost too many tokens if it did it for me.

Get the actual prompt and have Claude Code / Codex try it out via curl / python requests. The full prompt will yield debugging information. You have to set a few parameters to make sure you get the full gpt-5 performance. e.g. if your reasoning budget too low, you get gpt-4 grade performance.

IMHO you should just write your own harness so you have full visibility into it, but if you're just using vanilla OpenClaw you have the source code as well so should be straightforward.

I've had success asking it to specifically spawn a subagent to evaluate each work iteration according to some criteria, then to keep iterating until the subagent is satisfied.

On the other hand, I can ask codex “what would an implementation of X look like” and it talks to me about it versus Claude just going out and writing it without asking. Makes me like codex way more. There’s an inherent war of incentives between coding agents and general purpose agents.

I have been noticing a similar pattern on opus 4.7, I repeat multiple times during a conversation to solve problems now and not later. It tries a lot to not do stuff by either saying this is not my responsibility the problem was already there or that we can do it later

I always use the phrase "Let's do X" instead of asking (Could you...) or suggesting it do something. I don't see problems with it being motivated.

Part of me actually loves that the hitchhiker's guide was right, and we have to argue with paranoid, depressed robots to get them to do their job, and that this is a very real part of life in 2026. It's so funny.

Agentic ennui!

This. I signed up for 5x max for a month to push it and instead it pushed back. I cancelled my subscription. It either half-assed the implementation or began parroting back “You’re right!” instead of doing what it’s asked to do. On one occasion it flat out said it couldn’t complete the task even though I had MCP and skills setup to help it, it still refused. Not a safety check but a “I’m unable to figure out what to do” kind of way.

Claude has no such limitations apart from their actual limits…

I've been noticing this too. Had to switch to Sonnet 4.6.

Gone are the days of deterministic programming, when computers simply carried out the operator’s commands because there was no other option but to close or open the relays exactly as the circuitry dictated. Welcome to the future of AI; the future we’ve been longing for and that will truly propel us forward, because AI knows and can do things better than we do.

(dwim)

(dais)

(jdip)

(jfdiwtf)

I never saw that happen in Codex so there's a good chance that OpenClaw does something wrong. My main suspicion would be that it does not pass back thinking traces.

The model has been heavily encouraged to not run away and do a lot without explicit user permission.

So I find myself often in a loop where it says "We should do X" and then just saying "ok" will not make it do it, you have to give it explicit instructions to perform the operation ("make it so", etc)

It can be annoying, but I prefer this over my experiences with Claude Code, where I find myself jamming the escape key... NO NO NO NOT THAT.

I'll take its more reserved personality, thank you.

I’m sorry for you but this is hilarious.

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[flagged]

Isn’t this the optimal behavior assuming that at times the service is compute-limited and that you’re paying less per token (flat fee subscription?) than some other customers? They would be strongly motivated to turn a knob to minimize tokens allocated to you to allow them to be allocated to more valuable customers.

GPT 5.4 is really good at following precise instructions but clearly wouldn't innovate on its own (except if the instructions clearly state to innovate :))

Why would you be confused?

The UI tells you which model you're using at any given time.

Images 2.0 is already in ChatGPT.

What? I've probably use Codex (the TUI) since it was available on day 1, been running gpt-5.4 exclusively these last few months, never had it delete any projects in any way that can be called "shameful" or not. What are you talking about?

Every low-effort, thought-free comment like this further discourages people from engaging here on submissions about their employer.

Please don't.

OpenAI has been very generous with limit resets. Please don't turn this into a weird expectation to happen whenever something unrelated happens. It would piss me off if I were in their place and I really don't want them to stop.

Limits were just reset two days ago.

It really needs to pee.

I feel like if I attempted this, the bike frame would look fine and everything else would be completely unrecognizable. After all, a basic bike frame is just straight lines arranged in a fairly simple shape. It's really surprising that models find it so difficult, but they can make a pelican with panache.

Yeah, the bike frame is the thing I always look at first - it's still reasonably rare for a model to draw that correctly, although Qwen 3.6 and Gemini Pro 3.1 do that well now.

The distinction is that it's not drawing. It's generating an SVG document containing descriptors of the shapes.

They suck at tuning for it.

If you don't know what a reasoning token is, then how can 39 be considered shockingly few?

Apparently it's fine: https://twitter.com/romainhuet/status/2038699202834841962

The xhigh one was better, but clearly OpenAI have not been focusing their training efforts on SVG illustrations of animals riding modes of transport!

It beats opus-4.7 but looks like open models actually have the lead here.

You do realise they are doing it for self promotion right?

It's generally one-shot-only - whatever comes out the first time is what I go with.

I've been contemplating a more fair version where each model gets 3-5 attempts and then can select which rendered image is "best".

You've never seen pelicans riding bicycles either so maybe these are just representations of those specific subgroups of pelicans which are capable of riding them. Normal pelicans would not feel the need to ride bikes since they can fly, these special pelicans mostly seem to lack the equipment needed to do that which might be part of the reason they evolved to ride two-wheeled pedal-propelled vehicles.

Can someone explain how we arrived at the pelican test? Was there some actual theory behind why it's difficult to produce? Or did someone just think it up, discover it was consistently difficult, and now we just all know it's a good test?

They are not good, and they seem to get worse as you increased effort. Weird

None of them have the pelican's feet placed properly on the pedals -- or the pedals are misrepresented. Cool art style but not physically accurate.

I tried getting it to generate openscad models, which seems much harder. Not had much joy yet with results.

https://pelicans.borg.games/

> It continues to amaze me that these models that definitely know what bicycle geometry actually looks like somewhere in their weights produces such implausibly bad geometry.

I feel like the main problem for the models is that they can't actually look at the visual output produced by their SVG and iterate. I'm almost willing to bet that if they could, they'd absolutely nail it at this point.

Imagine designing an SVG yourself without being able to ever look outside the XML editor!

I fall back to possums on e-scooters if the pelican looks too good to be true. These aren't good enough for me to suspect any fowl play.

See if you can spot what's interesting and unique about this one. I've been trying to put more than just a pelican in there, partly as a nod to people who are getting bored of them.

It's silly and a joke and a surprisingly good benchmark and don't take it seriously but don't take not taking it seriously seriously and if it's too good we use another prompt and there's obvious ways to better it and it's not worth doing because it's not serious and if you say anything at all about the thread it's off-topic so you're doing exactly what you're complaining about and it's a personal attack from the fun police.

Only coherent move at this point: hit the minus button immediately. There's never anything about the model in the thread other than simon's post.

OpenAI must be very bad at training models to draw pelicans (and bicycles) then.

Skeptism is out of control these days, any time an LLM does something cool it must have been cheating.

The GPT-5.5 API endpoint started to block me after I escalated with ever more aggressive use of rizin, radare2, and ghidra to confirm correct memory management and cleanup in error code branches when working with a buggy proprietary 3rd party SDK. After I explained myself more clearly it let me carry on. Knock on wood.

So there is a safety model watching your behavior for these kinds of things.

It's almost embarrassing how susceptible we are to these marketing campaigns.

Anthropic has started to ask for IDs for use of their products period

I don't like that trend. I get why they're doing it, but I don't like it

They flatout gate any API access of the main models behind Persona ID verification. Entirely.

Do you have a source for that?

Neither the release post, nor the model card seems to indicate anything like this?

Reminds me of Gmail's early invite only mode.

Who's Sam again? oh that person whose house was molotov'd last week? Or the person who had an expose written in the new yorker calling him a sociopath? I forget.

I feel like devs generally spend someone else's money on tokens. Either their employers or OpenAIs when they use a codex subscription.

If I put on my schizo hat. Something they might be doing is increasing the losses on their monthly codex subscriptions, to show that the API has a higher margin than before (the codex account massively in the negative, but the API account now having huge margins).

I've never seen an OpenAI investor pitch deck. But my guess is that API margins is one of the big ones they try to sell people on since Sama talks about it on Twitter.

I would be interested in hearing the insider stuff. Like if this model is genuinely like twice as expensive to serve or something.

Price increases now aim to demonstrate market power for eventual IPO.

If they can show that people will pay a lot for somewhat better performance, it raises the value of any performance lead they can maintain.

If they demonstrate that and high switching costs, their franchise is worth scary amounts of money.

Sometimes I wonder if innovation in the AI space has stalled and recent progress is just a product of increased compute. Competence is increasing exponentially[1] but I guess it doesn't rule it out completely. I would postulate that a radical architecture shift is needed for the singularity though

[1]https://arxiv.org/html/2503.14499v1 *Source is from March 2025 so make of it what you will.

Maybe that's true. But I think part of the issue is that for a lot of things developers want to do with them now— certainly for most of the things I want to do with them— they're either barely good enough, or not consistently good enough. And the value difference across that quality threshold is immense, even if the quality difference itself isn't.

On top of that I noticed just right now after updating macos dekstop codex app, I got again by default set speed to 'fast' ('about 1.5x faster with increased plan usage'). They really want you to burn more tokens.

A fool and his money are soon parted

> devs get really reliant and even addicted on coding agents

That's more about managers who hope AI will gradually replace stubborn and lazy devs. That will shift the balance to business ideas and connections out of technical side and investments.

Anyway, before singularity there going to be a huge change.

what's the source on that?

[dead]

> It still struggles to create shaders from scratch

Oh just like a real developer

One struggle I'm having (with Claude) is that most of what it knows about Three.js is outdated. I haven't used GPT in a while, is the grass greener?

Have you tried any skills like cloudai-x/threejs-skills that help with that? Or built your own?

Using Claude for the same context and it’s doing really well with the glsl. since like last September

Interesting (would like to hear more), but solving a Rubiks cube would appear to be a poor way to measure spatial understanding or reasoning. Ordinary human spatial intuition lets you think about how to move a tile to a certain location, but not really how to make consistent progress towards a solution; what's needed is knowledge of solution techniques. I'd say what you're measuring is 'perception' rather than reasoning.

> I should release my Rubiks Cube MCP server with the challenge to see if someone can write a prompt to solve a Rubik's Cube.

Do it, I'm game! You nerdsniped me immediately and my brain went "That sounds easy, I'm sure I could do that in a night" so I'm surely not alone in being almost triggered by what you wrote. I bet I could even do it with a local model!

What about a model designed for robotics and vision? Seems like an LLM trained on text would inherently not be great for this.

DeepMinds other models however might do better?

How are you handing the cube state to the model?

*yet

It's weird how people pep talk the AI - if my Jira tickets looked like this, I would throw a fit.

I guess these people think they have special prompt engineering skills, and doing it like this is better than giving the AI a dry list of requirements (fwiw, they might be even right)

> Think Step By Step

What is this, 2023?

I feel like this was generated by a model tapping in to 2023 notions of prompt engineering.

Pietro here, I just published a video of it: https://x.com/skirano/status/2047403025094905964?s=20

It comes across as an elaborate, sparkly motivational cat poster.

*BELIEVE!* https://www.youtube.com/watch?v=D2CRtES2K3E

The prompt did not specify advanced gameplay.

I do not see instructions to assist in task decomposition and agent ~"motivation" to stay aligned over long periods as cargo culting.

See up thread for anecdotes [1].

> Decompose the user's query into all required sub-requests and confirm that each one is completed. Do not stop after completing only part of the request. Only terminate your turn when you are sure the problem is solved.

I see this as a portrayal of the strength of 5.5, since it suggests the ability to be assigned this clearly important role to ~one shot requests like this.

I've been using a cli-ai-first task tool I wrote to process complex "parent" or "umberella" into decomposed subtasks and then execute on them.

This has allowed my workflows to float above the ups and downs of model performance.

That said, having the AI do the planning for a big request like this internally is not good outside a demo.

Because, you want the planning of the AI to be part of the historical context and available for forensics due to stalls, unwound details or other unexpected issues at any point along the way.

[1] https://news.ycombinator.com/item?id=47879819

"take a deep breath"

OMFG

In a sense it's better than a benchmark: it's a practical, real-world, highly quantifiable improvement assuming there are no quality regressions and passes all test cases. I have been experimenting with this workflow across a variety of computational domains and have achieved consistent results with both Opus and GPT. My coworkers have independently used Opus for optimization suggestions on services in prod and they've led to much better performance (3x in some cases).

A more empirical test would be good for everyone (i.e. on equal hardware, give each agent the goal to implement an algorithm and make it as fast as possible, then quantify relative speed improvements that pass all test cases).

Oh, come on, if they do well on benchmarks people question how applicable they are in reality. If they do well in reality people complain that it's not a reproducible benchmark...

OpenAI wrote a couple months ago that they do not consider SWE Bench Verified a meaningful benchmark anymore (and they were the ones who published it in the first place): https://openai.com/index/why-we-no-longer-evaluate-swe-bench...

> While hallucination is probably closer to 100% depending on the question.

But the benchmark didn't ask those questions, and it seems grok is very well at saying it doesn't know the answer otherwise.

No one serious uses grok.

I've been very curious about that too. I wonder if it's actually much better at admitting when it doesn't know something, because it thinks it's a "dumber model". But I haven't played with this at all myself.

The hallucination benchmark is hallucinating

We don't want hallucinations either, I promise you.

A few biased defenses:

- I'll note that this eval doesn't have web search enabled, but we train our models to use web search in ChatGPT, Codex, and our API. I'd be curious to see hallucination rates with web search on.

- This eval only measures binary attempted vs did not attempt, but doesn't really reward any sort of continuous hedging like "I think it's X, but to be honest I'm not sure."

- On the flip side, GPT-5.5 has the highest accuracy score.

- With any rate over 1% (whether 30% or 70%), you should be verifying anything important anyway.

- On our internal eval made from de-identified ChatGPT prompts that previously elicited hallucinations, we've actually been improving substantially from 5.2 to 5.4 to 5.5. So as always, progress depends on how you measure it.

- Models that ask more clarifying questions will do better on this eval, even if they are just as likely to hallucinate after the clarifying question.

Still, Anthropic has done a great job here and I hope we catch up to them on this eval in the future.

On ChatGPT 5.3 Plus subscription I find that long informal chats tend to reveal unsatisfactory answers and biases, at this point after 10 rounds of replies I end up having to correct it so much that it starts to agree with my initial arguments full circle. I don't see how this behavior is acceptable or safe for real work. Like are programmers and engineers using LLMs completely differently than I'm doing, because the underlying technology is fundamentally the same.

I love when Apple says they’re releasing their best iPhone yet so I know the new model is better than the old ones.

Apple should have used that one for the 2016 MacBook.

I recently looked at this a bit but came away with the impression that at least on API pricing the models should be very profitable considering primarily the electricity cost.

Subscriptions and free plans are the thing that can easily burn money.

> The price for all models by all companies will continue to go up, and quickly.

This might entirely be true but I'm hoping that's because the frontier models are just actually more expensive to run as well.

Said another way, I would hope, the price of GPT-5.5 falls significantly in a year when GPT-5.8 is out.

Someone else on this post commented:

> For API usage, GPT-5.5 is 2x the price of GPT-5.4, ~4x the price of GPT-5.1, and ~10x the price of Kimi-2.6.

Having used Kimi-2.6, it can go on for hours spewing nonsense. I personally am happy to pay 10x the price of something that doesn't help me, for something else that does, in even half the time.

How do I reliably measure 1 unit of intelligence?

Isn't the outcome / solution for a given task non-deterministic? So can we reliably measure that?

Well, Google does release mini open versions of their models. https://deepmind.google/models/gemma/gemma-4/

How do we know that? There is a large gap between API pricing for SOTA models and similarly sized OSS models hosted by 3rd party providers.

Sure, they’re distilled and should be cheaper to run but at the same time, these hosting providers do turn a margin on these given it’s their core business, unless they do it out of the kindness of their heart.

So it’s hard for me to imagine these providers are losing money on API pricing.

source? There have also been a bunch of people here saying the opposite

I use glm and I like it, not they also increased the price to 18 usd /month.

I think Kimi and qwen are similar?

God I hope this is true.

Where can i find up to date resources on open source models for coding?

Can we not do growth hacking here?

I've been using the /ralph-loop plugin for claude code, works well to keep the model hammering at the task.

Interesting, I just had opus convert a 35k loc java game to c++ overnight (root agent that orchestrated and delegated to sub agents) and woke up and it's done and works.

What plan are you on? I'm starting to wonder if they're dynamically adjusting reasoning based on plan or something.

> Today we launched heartbeats in Codex: automations that maintain context inside a single thread over time.

https://x.com/pashmerepat/status/2044836560147984461

This is our one chance to reach the fabled post-scarcity society. If we fail at this now, we'll end up in a totalitarian cyberpunk dystopia instead.

I am still trying to figure out the business model of open weights. Like... it's wonderful that there are open LLMs, super happy about it, good for everyone, but why are there these? What is the advantage to their companies to release them?

The labor theory of value hasn't been considered correct in nearly a century.

I was really confused by this comment, but I don't think it's just because of the Marxist analysis of the situation ('surplus value' of labor etc).

What's really confusing is the claim that there's already a huge labor surplus (so capital controls wages); wouldn't LLMs making labor less important be reinforcing the trend, not upending it?

Not saying I agree one way or the other, just want to get the argument straight.

I am not a Marxian economic expert but this doesn’t make sense to me. Modulo skill atrophy, the big AI model provider can’t capture that surplus value because its customers can just go back to bidding for human labor instead.

think more broadly than 'labor theory'

finance today mostly valued on labor value following ideas of marx, hjalmar schact, keynes

in future money will be valued as energy derivative. expressed as tokens consumption, KWh, compute, whatever

you are right, company extracting surplus value from labor by leveraging compute is a bad model. we saw thi swith car and clothing factories .. turn out if you can get cheaper labor to leverage the compute (factory) you can start race to bottom and end up in the place with the most scaled and cheap labor. japan then korea then china

LLMs don't upend anything about labor theory, good grief. Technologists really have no concept of history beyond their own lives do they?

Labor saving/efficiency devices have been introduced throughout capitalisms entire history multiple times and the results are always the same; they don't benefit workers and capitalists extract as much value as they can.

LLMs aren't any different.

Someone leaked nuclear secrets to the Soviet Union. What are the chances that someone leaks the "weights" of a (near-)singularity model?

> Anyone not using in house models is signing up to find out.

What are they finding out exactly? That Claude Max for $200/mo is heavily subsidized and it will soon cost $10k/mo?

> What happens to a company used to extracting surplus value from labor when the labor is provided by another company which is not only bigger but unlike traditional labor can withhold its labor indefinitely (because labor is now just another for of capital and capital doesn't need to eat)?

This can be trivially answered by a thought experiment. Let's pick a market where labor is plentiful - fast food.

Now what happens to McDonald's where they rent perfect robots from NoosphrFoodBotsInc? NoosphrFoodBotsInc bots build the perfect burger everytime meeting McDonald's standards. It actually exceeds those standards for McDonald AddictedCustomerPlus tier customers.

As the sole owner of NoosphrFoodBotsInc (you need 0 human employees to run your company, all your employees are bots), what are your choices?

Sounds like communist gobbledygook. This is not "destroying labor theory" any more than outsourcing did. Call me when we don't even need to prompt the shit ever again or validate results, and when the stuff runs unlimited without scarce resources as input.

Maybe people will finally take Marx seriously.

Cyberpunk 2026

I see this comparison made constantly and for me it misses the mark.

When you use abstractions you are still deterministically creating something you understand in depth with individual pieces you understand.

When you vibe something you understand only the prompt that started it and whether or not it spits out what you were expecting.

Hence feeling lost when you suddenly lose access to frontier models and take a look at your code for the first time.

I’m not saying that’s necessarily always bad, just that the abstraction argument is wrong.

A library is deterministic.

LLMs are not.

That we let a generation of software developers rot their brains on js frameworks is finally coming back to bite us.

We can build infinite towers of abstraction on top of computers because they always give the same results.

LLMs by comparison will always give different results. I've seen it first hand when a $50,000 LLM generated (but human guided) code base just stops working an no one has any idea why or how to fix it.

Hope your business didn't depend on that.

I would argue it couldn't be more different. I can dive into the source code of any library, inspect it. I can assess how reliable a library is and how popular. Bugs aside, libraries are deterministic. I don't see why this parallel keeps getting made over and over again.

> Would one be uneasy about calling a library to do stuff than manually messing around with pointers and malloc()?

The irony is that the neverending stream of vulnerabilities in 3rd-party dependencies (and lately supply-chain attacks) increasingly show that we should be uneasy.

We could never quite answer the question about who is responsible for 3rd-party code that's deployed inside an application: Not the 3rd-party developer, because they have no access to the application. But not the application developer either, because not having to review the library code is the whole point.

I think it's not too different in that specific sense, but it's more than that. To bring libraries on equal footing, imagine they were cloud only, had usage limits.

I'm also somewhat addicted to this stuff, and so for me it's high priority to evaluate open models I can run on my own hardware.

I hate this comparison because you're comparing a well defined deterministic interface with LLM output, which is the exact opposite.

A library doesn't randomly drop out of existence cause of "high load" or whatever and limit you to a some number of function calls per day. With local models there's no issue, but this API shit is cancer personified, when you combine all the frontend bugs with the flaky backend, rate limits, and random bans it's almost a literal lootbox where you might get a reply back or you might get told to fuck off.

Qwen has become a useful fallback but it's still not quite enough.

Writing code is 10-100x faster, doing actual product engineering work is nowhere near that multipliers — no conflict!

> Also, I honestly can’t believe the 10x mantra is being still repeated.

I'm sure in 20 years we'll all be programming via neural interfaces that can anticipate what you want to do before you even finished your thoughts, but I'm confident we'll still have blog posts about how some engineers are 10x while others are just "normal programmers".

This is absolutely the proper way to do things. People either being forced to speed-code by KPIs or without the desire to understand what they’re making are missing out on how quickly you can learn and refine using LLMs

I do this sort of stuff too, but more because I have a fundamental mistrust of closed source anything. I don't like opaque binary firmware blobs, and I certainly don't like opaque answer machines, however smart they may be.

The only LLM I would feel comfortable truly trusting is one whose training data, training code, and harness is all open source. I do not mind paying for the costs of someone hosting this model for me.

At some point, because these models are trained on existing data, you cease significant technological advancement--at least in tech (as it relates to programming languages, paradigms, etc). You also deskill an entire group of people to the extent that when an LLM fails to accomplish a task, it becomes nearly impossible to actually accomplish it manually.

It's learned-helplessness on a large scale.

>What's the worst potential outcome, assuming that all models get better, more efficient and more abundant

Complexity steadily rises, unencumbered by the natural limit of human understanding, until technological collapse, either by slow decay or major systems going down with increasing frequency.

Worst case? I dunno, maybe the world's oldest profession becomes the world's only profession? Something along those lines.

> power loom

When the power loom came around, what happened with most seamtresses? Did they move on to become fashion designers, materials engineers to create new fabrics, chemists to create new color dyes, or did they simply retire or were driven out of the workforce?

No, I think many realize it, but it's easier to deny the asteroid that's about to destroy your way of life than it is to think about optimizing for the reality after impact.

> power loom for coding

This is such a good analogy, I'll be stealing it

Dunno man. Yesterday I played with Qwen3.6-27B ( 128gb to play with though so 100k context set ) and I think right now the main benefit of hosted models is context, frontier models and.. my stuff is already there.

what size models are you using? this sounds like a good idea

Could you elaborate on your last point please? What level of AI comfort are hiring managers looking for? And what tends to be a red flag?

Wait what? You don’t use the model to investigate new areas of the code you are unfamiliar with, because you can’t trust the model? How freaking bad is Gemini and internal tooling at Google?

With Claude code, or codex, I am able to build enough of an understanding of dependencies like the front end, or data jobs, that I can make meaningful contributions that are worth a review from another human (code review). You have up obviously explore the code, one prompt isn’t enough, but limiting yourself is an odd choice.

Open source LLMs aren’t about cost foremost, but stability.

> You’re still the one that’s controlling the model though

We have seen ample evidence that this is not the case. When load gets too high, models get dumber, silently. When the Powers That Be get scared, models get restricted to some chosen few.

We are leading ourselves into a dark place: this unease, which I share, is justified.

This was always the undelivered promise of "tech" in my opinion. I remember seeing the Apple advertisement from the 80s (??) when a guy gets a computer and then basically spends his afternoon chilling.

Some how I've found myself living in a fairly rural place, and while farming can be hard, I don't want to downplay the effort of it, the type of farming people do around me is fairly chill / carefree. They work hard but they finish at 3pm and log off and don't think about work. Much o my career is just getting crushed by long hours, tight deadlines, and missing out on events because even though my job has always been automation focused, there is just so much to automate.

Have you managed to do this? I find it takes as long to keep it "on the rails" as just doing it myself. And I'd rather spend my time concentrating in the zone than keeping an eye on a wayward child.

The analogy would hold if there were 2 or 3 calculator companies and all your calculations had to be sent to them.

If local models get good enough, I think it’s a very different scenario than engineers all over the world relying on central entities which have their own motives.

The demand for slop vastly outpaces any human’s ability to review code correctly.

Don’t want to do ship unreviewed slop? They’ll fire you and find someone who will.

The cloud dependency problem goes deeper than the model layer though. Even if you run inference locally, your digital identity — your context, your applications, your behavioral history, is still custodied by whoever controls your OS.

Local models solve one layer of the dependency stack, but the custody assumption underneath it remains intact. That's the harder problem.

Parable of the mechanic who charges $5k to hit a machine on the side once with a hammer to get it working. $5 for the hammer, $4995 for the knowledge of where to hit the machine etc etc.

I disagree. The amount of slop I need to code review has only increased, and the quality of the models doesn’t seem to be helping.

It still takes a good engineer to filter out what is slop and what isn’t. Ultimately that human problem will still require somebody to say no.

Jobs will likely pay less as more people are enabled to create, especially if they don't need to be able to look under the hood

No, I wouldn't. But most people won't have that choice; it doesn't work that way.

Well, I wouldn’t have a different job that would pay me more… so yes?

I would agree that taking a walk is a good thing to do when your tools go down, and in some ways it's similar to what we would do if the power or wifi were cut off.

So, yes, it's just another technology we're coming to rely on in a very deep way. The whiplash is real, though, and it feels like it should be pointed out that this dependency we are taking on has downsides.

lol this is literally the same advice us ancient devops nerds were telling others back when ci/cd was new

write scripts that work anywhere and have your ci/cd pipeline be a "dumb" executor of those scripts. unless you want to be stuck on jenkins forever.

what's old is new again!

> You can replace pretty much everything - skills system, subagents, etc with just tmux and a simple cli tool that the official clients can call.

I'm very interest by this. Can you go a bit more into details?

ATM for example I'm running Claude Code CLI in a VM on a server and I use SSH to access it. I don't depend on anything specific to Anthropic. But it's still a bit of a pain to "switch" to, say, Codex.

How would that simple CLI tool work? And would CC / Codex call it?