Posted by davidbarker 8 hours ago
I'm burning through so many tokens on Cursor that I've had to upgrade to Ultra recently - and i'm convinced they're tweaking the burn rate behind the scenes - usage allowance doesn't seem proportional.
Thank god the open source/local LLM world isn't far behind.
I guarantee you that price will double by 2027. Then it’ll be a new car payment!
I’m really not saying this to be snarky, I’m saying this to point out that we’re really already in the enshittification phase before the rapid growth phase has even ended. You’re paying $200 and acting like that’s a cheap SaaS product for an individual.
I pay less for Autocad products!
This whole product release is about maximizing your bill, not maximizing your productivity.
I don’t need agents to talk to each other. I need one agent to do the job right.
I know this was last year but...
https://metr.org/blog/2025-07-10-early-2025-ai-experienced-o...
If I pay $3k/month to a developer and a $200/month tool makes them 10% more productive I will pay it without thinking.
If you’re not able to get US$thousands out of these models right now either your expectations are too high or your usage is too low, but as a small business owner and part/most-time SWE, the pricing is a rounding error on value delivered.
But as an individual with no profit motive, no way.
I use these products at work, but not as much personally because of the bill. And even if I decided I wanted to pursue a for profit side project I’d have to validate it’s viability before even considering a 200$ monthly subscription
This did require some amount of effort on my part, to test and iterate and so on, but much less than if I needed to write all the code myself. And, because these programs are for personal use, I don't need to review all the code, I don't have security concerns and so on.
$100 every month for a service that writes me custom applications... I don't know, maybe I'm being stupid with my money, but at the moment it feels well worth the price.
- $20 for Claude Pro (Claude Code) - $20 for ChatGPT Plus (Codex) - Amp Free Plan (with ads and you get about $10 of daily value)
So you get to use 3 of the top coding agents for $40 month.
with the US salaries for SWEs $1000/month is not a rounding error for all but definitely for some. say you make $100/hr and CC saves you say 30hrs / month? not rounding error but no brainer. if you make $200+/hr it starts to become a rounding error. I have multiple max accounts at my disposal and at this point would for sure pay $1000/month for max plan. it comes down to simple math
Traditional SaaS products don't write code for me. They also cost much less to run.
I'm having a lot of trouble seeing this as enshittification. I'm not saying it won't happen some day, but I don't think we're there. $200 per month is a lot, but it depends on what you're getting. In this case, I'm getting a service that writes code for me on demand.
1. 1-3 LLM vendors are substantially higher quality than other vendors and none of those are open source. This is an oligarchy and the scenario you described will play out.
2. >3 LLM vendors are all high quality and suitable for the tasks. At least one of these is open source. This is the "commodity" scenario, and we'll end up paying roughly the cost of inference. This still might be hundreds per month, though.
3. Somewhere in between. We've got >3 vendors, but 1-3 of them are somewhat better than the others, so the leaders can charge more. But not as much more than they can in scenario #1.
Are you spending more than $150k per year on AI?
(Also, you're talking about the cost of your Cursor subscription, when the article is about Claude Code. Maybe try Claude Max instead?)
But it continually, wildly performs slower and falls short every time I’ve tried.
A. You're working on some really deep thing that only world-class expects can do, like optimizing graphics engines for AAA games.
B. You're using a language that isn't in the top ~10 most popular in AI models' training sets.
C. You have an opportunity to improve your ability to use the tools effectively.
How many hours have you spent using Claude Code?
This is a relatively common skill. One thing I always notice about the video game industry is it's much more globally distributed than the rest of the software industry.
Being bad at writing software is Japan's whole thing but they still make optimized video games.
The issues I ran into are primarily “tail-chasing” ones - it gets into some attractor that doesn’t suit the test case and fails to find its way out. I re-benchmark every few months, but so far none of the frontier models have been able to make changes that have solved the issue without bloating the codebase and failing the perf tests.
It’s fine for some boilerplate dedup or spinning up some web api or whatever, but it’s still not suitable for serious work.
Claude would be worse than an expert at this, but this is a benchmarkable task. Claude can do experiments a lot quicker than a human can. The hard part would be ensure that the results aren't just gaming your benchmark.
It's insulting that criticism is often met with superficial excuses and insinuation that the user lacks the required skills.
GP said 'falls short every time I’ve tried'. Note the word 'every'.
I feel like comparison just to a junior developer is also becoming a fairly outdated comparison. Yes, it is worse in some ways, but also VASTLY superior in others.
> I went to senior folks at companies like Temporal and Anthropic, telling them they should build an agent orchestrator, that Claude Code is just a building block, and it’s going to be all about AI workflows and “Kubernetes for agents”. I went up onstage at multiple events and described my vision for the orchestrator. I went everywhere, to everyone. (from "Welcome to Gas Town" https://steve-yegge.medium.com/welcome-to-gas-town-4f25ee16d...)
That Anthropic releases Agent Teams now (as rumored a couple of weeks back), after they've already adopted a tiny bit of beads in form of Tasks) means that either they've been building them already back when Steve pitched orchestrators or they've decided that he's been right and it's time to scale the agents. Or they've arrived at the same conclusions independently -- it won't matter in the larger scale of things. I think Steve greately appreciates it existing; if anything, this is a validation of his vision. We'll probably be herding polecats in a couple of months officially.
The main claude instance is instructed to launch as many ralph loops as it wants, in screen sessions. It is told to sleep for a certain amount of time to periodically keep track of their progress.
It worked reasonably well, but I don't prefer this way of working... yet. Right now I can't write spec (or meta-spec) files quick enough to saturate the agent loops, and I can't QA their output well enough... mostly a me thing, i guess?
Same for me, however, the velocity of the whole field is astonishing and things change as we get used to them. We are not talking that much about hallucinating anymore, just 4-5 months ago you couldn't trust coding agents with extracting functionality to a separate file without typos, now splitting Git commits works almost without a hinch. The more we get used to agents getting certain things right 100% of the time, the more we'll trust them. There are many many things that I know I won't get right, but I'm absolutely sure my agent will. As soon as we start trusting e.g. a QA agent to do his job, our "project management" velocity will increase too.
Interestingly enough, the infamous "bowling score card" text on how XP works, has demonstrated inherently agentic behaviour in more way than one (they just didn't know what "extreme" was back then). You were supposed to implement a failing test and then implement just enough functionality for this test to not fail anymore, even if the intended functionality was broader -- which is exactly what agents reliably do in a loop. Also, you were supposed to be pair-driving a single machine, which has been incomprehensible to me for almost decades -- after all, every person has their own shortcuts, hardware, IDEs, window managers and what not. Turns out, all you need is a centralized server running a "team manager agent" and multiple developers talking to him to craft software fast (see tmux requirement in Gas Town).
The fact that Anthropic and OpenAI have been going on this long without such orchestration, considering the unavoidable issues of context windows and unreliable self-validation, without matching the basic system maturity you get from a default Akka installation shows us that these leading LLM providers (with more money, tokens, deals, access, and better employees than any of us), are learning in real time. Big chunks of the next gen hype machine wunder-agents are fully realizable with cron and basic actor based scripting. Deterministically, write once run forever, no subscription needed.
Kubernetes for agents is, speaking as a krappy kubernetes admin, not some leap, it’s how I’ve been wiring my local doom-coding agents together. I have a hypothesis that people at Google (who are pretty ok with kubernetes and maybe some LLM stuff), have been there for a minute too.
Good to see them building this out, excited to see whether LLM cluster failures multiply (like repeating bad photocopies), or nullify (“sorry Dave, but we’re not going to help build another Facebook, we’re not supposed to harm humanity and also PHP, so… no.”).
The truth is that people are doing experiments on most of this stuff, and a lot of them are even writing about it, but most of the time you don't see that writing (or the projects that get made) unless someone with an audience already (like Steve Yegge) makes it.
As usual, the hard part is the actual doing and producing a usable product.
Also, because they are stuck in a language and an ecosystem that cannot reliably build supervisors, hierarchies of processes etc. You need Erlang/Elixir for that. Or similar implementations like Akka that they mention.
[1] Yes, they claim their AI-written slop in Claude Code is "a tiny game engine" that takes 16ms to output a couple of hundred of characters on screen: https://x.com/trq212/status/2014051501786931427
I remember having conversations about this when the first ChatGPT launched and I don’t work at an AI company.
... the "limit" were agents were not as smart then, context window was much smaller and RLVR wasn't a thing so agents were trained for just function calling, but not agent calling/coordination.
we have been doing it since then, the difference really is that the models have gotten really smart and good to handle it.
But this shows how much stuff is still to do in the ai space
Haven't tried Kimi, hear good things.
At least, my M1 Pro seems to struggle and take forever using them via Ollama.
This new orchestration feature makes it much more useful since they share a common task list and the main agent coordinates across them.
[1] https://github.com/pchalasani/claude-code-tools?tab=readme-o...
Wonder how they compare?
Often times if I'm only working on a single project or focus, then I'm not using most of those roles at all and it's as you describe, one agent divvying out tasks to other agents and compiling reports about them. But due to the fact that my velocity with this type of coding is now based on how fast I can tell that agent what I want, I'm often working on 3 or 4 projects simultaneously, and Gas Town provides the perfect orchestration framework for doing this.
No polecats smh
I love that we are in this world where the crazy mad scientists are out there showing the way that the rest of us will end up at, but ahead of time and a bit rough around the edges, because all of this is so new and unprecedented. Watching these wholly new abstractions be discovered and converged upon in real time is the most exciting thing I've seen in my career.
Then, in your prompt you tell it the task you want, then you say, supervise the implementation with a sub agent that follows the architecture skill. Evaluate any proposed changes.
There are people who maximize this, and this is how you get things like teams. You make agents for planning, design, qa, product, engineering, review, release management, etc. and you get them to operate and coordinate to produce an outcome.
That's what this is supposed to be, encoded as a feature instead of a best practice.
So the LLM will do something and not catch at all that it did it badly. But the same LLM asked to review against the same starting requirement will catch the problem almost always
The missing thing in these tools is that automatic feedback loop between the two LLMs: one in review mode, one in implementation mode.
This sounds more like an automation of that idea than just N-times the work.
Just ask claude to write a plan and review/edit it yourself. Add success criteria/tests for better results.
```
Rules:
- Only one disk can be moved at a time.
- Only the top disk from any stack can be moved.
- A larger disk may not be placed on top of a smaller disk.
For all moves, follow the standard Tower of Hanoi procedure: If the previous move did not move disk 1, move disk 1 clockwise one peg (0 -> 1 -> 2 -> 0).
If the previous move did move disk 1, make the only legal move that does not involve moving disk1.
Use these clear steps to find the next move given the previous move and current state.
Previous move: {previous_move} Current State: {current_state} Based on the previous move and current state, find the single next move that follows the procedure and the resulting next state.
```
This is buried down in the appendix while the main paper is full of agentic swarms this and millions of agents that and plenty of fancy math symbols and graphs. Maybe there is more to it, but the fact that they decided to publish with such a trivial task which could be much more easily accomplished by having an llm write a simple python script is concerning.
this does eat up tokens _very_ quickly though :(
You run out of context so quickly and if you don’t have some kind of persistent guidance things go south
This would also be true of Junior Engineers. Do you find them impossible to work with as well?
https://github.com/FredericMN/Coder-Codex-Gemini https://github.com/fengshao1227/ccg-workflow
This one also seems promising, but I haven't tried it yet.
https://github.com/bfly123/claude_code_bridge
All of them are made by Chinese dev. I know some people are hesitant when they see Chinese products, so I'll address that first. But I have tried all of them, and they have all been great.
I don't need anything more complicated than that and it works fine - also run greptile[1] on PR's
1. GPT-5.2 Codex Max for planning
2. Opus 4.5 for implementation
3. Gemini for reviews
It’s easy to swap models or change responsibilities. Doc and steps here: https://github.com/sathish316/pied-piper/blob/main/docs/play...
https://www.augmentcode.com/product/intent
can use the code AUGGIE to skip the queue. Bring your own agent (powered by codex, CC, etc) coming to it next week.
Though I do hope the generated code will end up being better than what we have right now. It mustn't get much worse. Can't afford all that RAM.
It's just HN that's full of "I hate AI" or wrong contrarian types who refuse to acknowledge this. They will fail to reap what they didn't sow and will starve in this brave new world.
And if I think about one 'senior' in my team I would pref an expensive ai subscription over that one person already.