A flat pricing subscription for Claude Code

I see worrying trends in my office.

Developers (often juniors) use LLM code without taking time to verify it. This leads to bugs and they can't fix it because they don't understand the code. Some senior developers also trust the tool to generate a function, and don't take the time to review it and catch the edge cases that the tool missed.

They rely on ChatGPT to answer their questions instead of taking time to read the documentation or a simple web search to see discussions on stack overflow or blogs about the subject. This may give results in the short term, but they don't actually learn to solve problems themselves. I am afraid that this will have huge negative effects on their career if the tools improve significantly.

Learning how to solve problems is an important skill. They also lose access to the deeper knowledge that enable you to see connections, complexities and flows that the current generation of tools are unable to do. By reading the documentation, blogs or discussions you are often exposed to a wider view of the subject than the laser focused answer of ChatGPT

There will be less room for "vibe coders" in the future, as these tools increasingly solve the simple things without requiring as much management. Until we reach AGI (I doubt it will happen within the next 10 years) the tools will require experienced developers to guide them for the more complex issues. Older experienced developers, and younger developers who have learned how to solve problems and have deep knowledge, will be in demand.

    1. Talk with the LLM about what you're building and have it generate a detailed technical specification. Iterate on this until you have a good, human-readable explanation of the entire application or feature.
    2. Start a completely new chat/context. If you're using something like Gemini, turn temperature down and enable external search.
    3. Have instructions¹ guiding the LLM, this might be the most important step, even moreso than #1.
    4. Create the base/blank project as its own step. Zero features or config.
    5. Copy features one at a time from the spec to the chat context OR have them as separate documents and say things like "we're creating Feature 3A.1" or whatever.
    6. Iterate on each feature until you're happy then repeat.

I have a hypothesis for this.

1. Developers are building these tools/applications because it's far faster and easier for them to build and iterate on something that they can use and provide feedback on directly without putting a marketer, designer, process engineer in the loop.

2. The level of 'finish' required to ship these kinds of tools to devs is lower. If you're shipping an early beta of something like 'Cursor for SEO Managers' the product would need to be much more user friendly. Look at all the hacking people are doing to make MCP servers and get them to work with Cursor. Non-technical folks aren't going to make that work.

So then, once there is a convergence on 'how' to build this kind of stuff for devs. There will be a huge amount of work to go and smooth out the UX and spread equivalents out across other industries. Claude releasing remote MCPs as 'integrations' in their web ui is the first step of this IMO.

When this wave crashes across the broader SaaS/FAANG world I could imagine more demand for devs again, but you're unlikely going to ever see anything like the early 2020s ever again.

Shift feels real. LLMs don't replace devs, but they do compress the value curve. The top 10% get even more leverage, and the bottom 50% become harder to justify.

What worries me isn't layoffs but that entry-level roles become rare, and juniors stop building real intuition because the LLM handles all the hard thinking.

You get surface-level productivity but long-term skill rot.

> All in all, I expect salaries for non FAANG devs to decrease while salaries for FAANG devs to increase slightly (given the increased value they can now make).

I find it interesting how these sort of things are often viewed as a function of technological advancement. I would think that AI development tools would have a marginal effect on wages as opposed to things like interest rates or the ability to raise capital.

Back to the topic at hand however, assuming these tools do get better, it would seemingly greatly increase competition. Assuming these tools get better, a highly skilled team with such tools could prove to be formidable competition to longstanding companies. This would require all companies to up the ante to avoid being outcompeted, requiring even more software to be written.

A company could rest on their laurels, laying off a good portion of their employees, and leaving the rest to maintain the same work, but they run the risk of being disrupted themselves.

Alas, at the job I'm at now my team can't seem to release a rather basic feature, despite everyone being enhanced with AI: nobody seems to understand the code, all the changes seem to break something else, the code's a mess... maybe next year AI will be able to fix this.

LLMs are a solution in search of a problem.

The first problem they have gained traction on is programming auto complete, and it is useful.

Generating summaries, pretty marginal benefit (personally I find it useless). Writing emails, quicker just to type "FYI" and press send than instruct the ai. More problems that needed solving will emerge, but it will take time.

I've been using LLMs as learning tools rather than simply answer generators. LLMs can teach you a lot by guiding your thinking, not replacing it.

It's been valuable to engage with the suggestions and understand how they work—much like using a search engine, but more efficient and interactive.

LLMs have also been helpful in deepening my understanding of math topics. For example, I’ve been wanting to build intuition around linear algebra which for me is a slow process. By asking questions to LLM I find explanations make the underlying concepts more accessible.

For me it's about using these tools to learn more effectively.

I think it's an enabler for everyone.

So many people benefit from basic things like sorting tables, searching and filtering data etc.

Things were I might just use excel or a small script, they can now use an LLM for it.

And for now, we are still in dire need for more developers and not less. But yes I can imagine that after a golden phase of 5-15 years it will start to go down to the bottom when automaisation and ai got too good / better than the avg joe.

Nonetheless a good news is also that coding LLMs enable researchee too. People who often struggle learning to code.

You'll commonly see new technologies utilized by people that have the ability to make use of that technology for their own gain. Programmers are (for the most part) the only ones that can unlock LLMs to solve very specific personal problems. There are workflow automation tools allowing non-programmers the ability to do workflows but that's only one way to utilize them and it will always be constrained by the already developed integrations and the constraints of the workflow platform.

In regards to jobs and job losses I have no idea how this is going to impact individual salaries over time in different positions, but I honestly doubt its going to do much. Language models are still pretty bad at working with large projects in a clean and effective way. Maybe that will get better, but I think this generational breakthrough of technology is slowing down a lot.

Even if they do get better, they still need direction and validation. Both of which still require some understanding of what is going on (even vibe coding works better with a skilled engineer).

I suspect there is going to be more "programmers" in the world as a result, but most of them will be producing small boutique single webpage tools and designs that are higher quality than "made by my cousin's kid" that a lot of small businesses have now. Companies > ~30 people with software engineers on staff seem to be using it as a performance enhancer rather than a work replacement tool.

There will always be shitty managers and short-sighted executives that are looking to replace their human staff with some tool, and there will be layoffs but I don't think the overall pool of jobs is going to reduce. For the same reason I don't think there is going to be significant pay adjustments but a dramatic increase in the long-tail of cheap projects that don't make much money on their own.

I don't get why making engineers more productive would decrease their salaries. It should be the reverse.

You could argue that it makes the bar lower to be productive so the candidate pool is much greater, but you're arguing the opposite, increasing the barrier to entry.

I'm open to arguments either way and I'm undecided, but you have to have a coherent economic model.

> Does anyone feel like the biggest selling point of LLMs so far is basically for programmers? Feels like most of the products that look like could generate revenue are for programmers.

No, you're in a tech bubble. I'm in healthcare, and you'd think that AI note takers and summary generators were the reason LLMs were invented and the lion's share of use. I get a new pitch every day, "this product will save your providers hours every day!" They're great products, and our providers love ours, but it's not saving hours.

There's also a huge push for LLMs to work in search and data-retrieval chatbots. The push there is huge, and now Mistal just released Le Chat Enterprise for that exact market.

LLMs for code are so common because they're really easy to create. It's notepad plus chatGPT. Sure, it's actually VS Code and CoPilot, but you get the idea, it's actually not more complicated than regular chatbots.

People forget that software engineers are already speculated to come in 10x and 100x variants, so the impact that one smart dedicated person could make is almost certainly not the problem and not changed at all by AI.

The fact is you could be one is the most insanely valuable and productive engineers in the planet might only write a few lines of code most days, but you'll be writing them in a programming language, OS, or kernel. Value is created by understanding direction and by theory-building, and LLMs do neither.

I built a genuinely new product by working hard as a single human while all my competitors tried to be really productive with LLMs. I'm sure their metrics are great, but at the end of the day I, a human working with my hands and brain and sharpening my OWN intelligence have created what productivity metrics cannot buy: real innovation

My father pays for ChatGPT and it’s his personal consultant/assistant for everything - from troubleshooting appliance repair, to finding the correct part to buy, to guiding him step by step to track down lost luggage and drafting the email to airline asking for compensation (and got it).

It does everything for him and it gives him results.

So no, I don’t think it’s most useful for programmers, in fact I feel people who are not very techy and not good at Googling for solutions benefit the most as chatGPT (and LLM in general) will hand hold them through every problem they have in life, and is always patient and understanding.

I learned to program as a child in the 1960s (thanks Dad!) so I have some biases:

Right now there seem to be two extremely valuable LLM use cases:

1. sidekick/assistant for software developers

2. a tool to let people rapidly explore new knowledge and new ideas; unlike an encyclopedia, being able to ask questions, suggest references and get summaries, etc.

I suspect that the next $$$ valuable use case will be scientific research assistants.

EDIT: I would add that AI in k-12 education will be huge, freeing human teachers to spend more 1 on 1 time with kids while AIs will be patient teaching kids, providing extra time and material as needed.

It is a bit like incandescent light was early selling point of electricity.

Stable odourless on-demand light was in short supply, so it helped to jump-start a new industry and network.

The real range of possible uses is near endless, for tech available today. It is just a coincidence that coding is in short supply today.

It can backfire though.

There is some mental overhead switching projects. Meaning even if a developer is more efficient per project he wont get more money (usually less actually) while increasing mental load (more projects, more managers, more requirements, etc).

Will be interesting to watch

> I expect salaries for non FAANG devs to decrease while salaries for FAANG devs to increase slightly (given the increased value they can now make).

Are you implying that non-FAANG devs aren't able to do more with LLMs?

> Feels like most of the products that look like could generate revenue are for programmers.

Don’t discount scamming and spreading misinformation. There’s a lot of money to be made there, specially in mass manipulation to destroy trust in governments and journalists. LLMs and image generators are a treasure trove. Even if they’re imperfect, the overwhelmingly majority of people can’t even distinguish a real image from a blatantly false one, let alone biased text.

LLM's don't increase programmer productivity. In fact, they actively harm it.

Programmers aren't paid for coding, they're paid for following a formal spec in a particular problem domain. (Something that LLM's can't do at all.)

Improving coding speed is a red herring and a scam.

Doesn't per-call pricing reduce your usage? When I see the price of a session go to >$3, for a handful of interactions I self-limit my usage.

I'd love to have an all-you-can-eat, but $100 p/m isn't compelling enough compared to copy/paste for $20 p/m via chat.

That's not to say the value doesn't exceed $100, I just don't want to pay it.

Back when I used dial-up, I experienced a lot of stress when I was connected. I felt I had to be as effective as possible, because we had to pay for every minute spent.

When I switched to DSL the stress went away, and I found myself using internet in different ways than before, because I could explore freely without time pressure.

I think this applies to Claude as well. I will probably feel more free to experiment if I don't have to worry about costs. I might do things I would never think of if I'm only focused on using it as little as possible to save money.

It's great that there's a choice, but for me the Max plan is likely to save me money already, and I suspect my usage would increase significantly if the top-up (I have intentionally not set it to auto-top-up) didn't regularly remind me not to go nuts.

I don’t know why people expect unlimited usage for limited cost. Copilot hasn’t been good for a long time. They had the first mover advantage but they were too slow to improve the product. It’s still not caught up to cursor or windsurf. Cline leaves it so far in the dust it’s like a decade behind in AI years. So you get what you pay for.

Claude is still the gold standard for AI assisted coding. All your Geminis and o3s of the world still don’t match up to Claude.

I started using Claude code once it became a fixed price with my Claude max subscription. And it’s taken a little getting used to vs Cline, but I think it’s closer to Cline in performance rather than cursor (Cline being my personal gold standard). $100 is something most people on this forum could make back in 1 day of work.

$100 per month for the value is nothing and for what it’s worth I have tried to hit the usage limit and the only thing that got me close was using their deep research feature. I’ve maxed out Claude code without hitting limits.

Have I got bad news for you.... Microsoft announced imposing limits on "premium" models from next week. You get 300 "free" requests a month. If you use agent, you consume about 3-4 requests per action easily, I estimate to burn through 300 in about 3-5 working days.

Basically anything that isnt gpt4o is premium, and I find gpt4o near useless compared to Claude and Gemini in copilot.

Doesn’t resonate with me because I’ve spent over $1,000 on Claude Code at this point and the return is worth it. The spend feels cheap compared to output.

In contrast - I’m not interested in using cheaper, less-than, services for my livelihood.

Whoever is paying for your time should calculate how much time you’d save between the different products. The actual product price comparison isn’t as important as the impact on output quality and time taken. Could be $1000 a month and still pay for itself in a day, if it generated >$1000 extra value.

This might mean the $10/month is the best. Depends entirely on how it works for you.

(Caps obviously impact the total benefit so I agree there.)

I'll add on to this: I don't really use agent modes a lot. In an existing codebase, they waste a lot of my time for mixed results. Maybe Claude Code is so much better at this that it enables a different paradigm of AI editing—but I'd need easy, cheap access to try it.

> The problem is that this is $100/mo with limits

Limits are a given on any plan. It would be too easy for a vibe coder to hammer away 8 hours a day for 20 days a week if there was nothing stopping them.

The real question is whether this is a better value than pay as you go for some people.

> I'm not interested in paying 10x as much and still having limits. It would have to be 10x as useful

I don't think this is the right way to look at it. If CoPilot helps you earn an extra $100 a month (or saves you $100 worth of time), and this one is ~2x better, it still justifies the $100 price tag.

I always imagined that these $10/mo plans are essentially loss leaders and that in the long run, the price should be much higher. I'm not even sure if that $100/mo plan pays for its underlying costs.

I think this thinking is flawed. First, it presupposes a linear value/cost relationship. That is not always true - a bag that costs 100x as much is not 100x more useful.

Additionally, when you’re in a compact distribution, being 5% better might be 100x more valuable to you.

Basically, this assumes that the marginal value is associated with cost. I don’t think most things, economically, seem to match that pattern. I will sometimes pay 10x the cost for a good meal that has fewer calories (nutritional value)

I am glad people like you exist, but I don’t think the proposition you suggest makes sense.

Absolutely stellar for 0-to-1-oriented frontend-related tasks, less so but still quite useful for isolated features in backends. For larger changes or smaller changes in large/more interconnected codebases, refactors, test-run-fix-loops, and similar, it has mostly provided negative value for me unfortunately. I keep wondering if it's a me problem. It would probably do much better if I wrote very lengthy prompts to micromanage little details, but I've found that to be a surprisingly draining activity, so I prefer to give it a shot with a more generic prompt and either let it run or give up, depending on which direction it takes.

Yes. For small apps, as well distributed systems.

You have to puppeteer it and build a meta context/tasking management system. I spend a lot of time setting Claude code up for success. I usually start with Gemini for creating context, development plans, and project tasking outlines (I can feed large portions of codebase to Gemini and rely on its strategy). I’ve even put entire library docsites in my repos for Claude code to use - but today they announced web search.

They also have todos built in which make the above even more powerful.

The end result is insane productivity - I think the only metric I have is something like 15-20k lines of code for a recent distributed processing system from scratch over 5 days.

I use it, on a large Clojure/ClojureScript application. And it's good.

The interactions and results are roughly in line with what I'd expect from a junior intern. E.g. don't expect miracles, the answers will sometimes be wrong, the solutions will be naive, and you have to describe what you need done in detail.

The great thing about Claude code is that (as opposed to most other tools) you can start it in a large code base and it will be able to find its way, without me manually "attaching files to context". This is very important, and overlooked in competing solutions.

I tried using aider and plandex, and none of them worked as well. After lots of fiddling I could get mediocre results. Claude Code just works, I can start it up and start DOING THINGS.

It does best with simple repetitive tasks: add another command line option similar to others, add an API interface to functions similar to other examples, etc.

In other words, I'd give it a serious thumbs up: I'd rather work with this than a junior intern, and I have hope for improvement in models in the future.

Here's a very small piece of I code I generated quickly (i.e. <5 min) for a small task (I generated some data and wanted to check the best way to compress it):

https://gist.github.com/rachtsingh/e3d2e2b495d631b736d24b56e...

Is it correct? Sort of; I don't trust the duration benchmark because benchmarking is hard, but the size should be approximately right. It gave me a pretty clear answer to the question I had and did it quickly. I could have done it myself but it would have taken me longer to type it out.

I don't use it in large codebases (all agentic tools for me choke quickly), but part of our skillset is taking large problems and breaking them into smaller testable ones, and I give that to the agents. It's not frequent (~1/wk).

Yes. It costs me a few bucks per feature, which is an absolute no-brainer.

If you don't like what it suggests, undo the changes, tweak your prompt and start over. Don't chat with it to fix problems. It gets confused.

Claude Code is the first AI coding tool that actually worked for me on a small established Laravel codebase in production. It builds full stack features for me requiring only minor tweaks and guidance (and starting all over with new prompts). However, after a while I switched to Cursor Agent just because the IDE integration makes the workflow a little more convenient (especially the ability to roll back to previous checkpoints).

Just to throw my experience in, it's been _wildly_ effective.

Example;

I'm wrapping up, right now, an updated fork of the PHP extension `phpredis` because Redis 8 recently was released with support for a new data type, Vector Set but the phpredis extension (which is far more performant that non-extension redis libraries for PHP) doesn't support the new vector-related commands. I forked the extension repo, which is in C (I'm a PHP developer, I had to install CLion for the first time just to work along with CC) and fired up claude code with the initial prompt/task of analyzing the extensions code and documenting the purpose, conventions, and anything that it (claude) felt would benefit the bootstrapping process of future sessions such that whole files wouldn't need to be read into a CLAUDE.md file.

This initially, depending on the size of the codebase, could be "expensive". Being that this is merely a PHP extension and isn't a huge codebase, I was fine letting it just rip through the whole thing however it saw fit - were this a larger codebase I'd take a more measured approach to this initial "indexing" of the codebase.

This results in a file that claude uses like we do a readme.

Next I end this session, start a new one and tell it to review that CLAUDE.md file (I specifically tell it to do this, every single new session start moving forward) and then generate a general overview/plan of what needs to be done in order to implement the new Vector Set related commands so that I can use this custom phpredis extension in my PHP environments. I indicated that I wanted to generate a suite of tests focused on ensuring each command works with all of it's various required and optional parameters and that I wanted to use docker containers for the testing rather than mess up my local dev environment.

$22 in API costs and ~6 hours spent and I have the extension, working, in my local environment with support for all of the commands I want/need to use. (there's still 5 commands that I don't intend to use that I haven't implemented)

Not only would I have certainly never embarked upon trying to extend a C PHP extension, I wouldn't have done so over the course of an evening and morning.

Another example:

Before this redis vector sets thing I used CC to build a python image and text embedding pipeline backed by Redis streams and Celery that consumes tasks pushed to the stream by my Laravel application that currently manages ~120 million unique strings and ~65 million unique images that I've been generating embeddings for. Prior to this I'd spent very little time with Python and zero with anything related to ML. Now I have a performant python service that's portable that I run from my Macbook (M2 Pro) or various GPU-having Windows machines in my home that generate the embeddings on an 'as available' basis, pushing the results back to a redis stream that my Laravel app then consumes and processes.

The results of these embeddings and the similarity-related features that they've brought to the Laravel application are honestly staggering. And while I'm sure I could have spent months stumbling through all of this on my own - I wouldn't have, I don't have that much time for side project curiosities.

Somewhat related - these similarity features have directly resulted in this side project becoming a service people now pay me to use.

On a day to do - the effectiveness is a learned skill. You really need to learn how to work with it in the same way you, as a layperson, wouldn't stroll up to a highly specialized piece of aviation technology and just infer how to use it optimally. I hate to keep parroting "skill issue" but - it's just wild to me how effective these tools are and how there's so many people who don't seem to be able to find any use.

If it's burning through cash, you're not being focused enough with it. If it's writing code that's always slightly wrong, stop it and make adjustments. Those adjustments likely/potentially need to be documented in something like I described above in a long-running document used similarly to a prompt.

From my own experience, I watch the "/settings/logs" route on anthropics website while CC is working once I know that we're getting rather heavy with the context. Once it gets into the 50-60,000 tokens range I either aim to wrap up whatever the current task is, or I understand that things are going to start getting a little wonky into the 80k+ range. It'll keep on working up into the 120-140k tokens or more - but you're likely going to end up with lots of "dumb" stuff happening. You really don't want to be here unless you're _sooooo close_ to getting done what you're trying to. When the context gets too high and you need/want to reset but you're mid task - /compact [add notes here about next steps] and it'll generate a summary that will then be used to bootstrap the next session. (Don't do this more than once, really, as it starts losing a lot of context - just reset the session fully after the first /compact)

If you're constantly running into huge contexts you're not being focused enough. If you can't even work on anything without reading files with thousands of lines - either break up those files somehow or you're going to have to be _really_ specific with the initial prompt and context - which I've done lots of. Say I have a model that belongs to a 10+ year old project that is 6000 lines long and I want to work on a specific method in that model - I'll just tell claude in the initial message/prompt which line that method starts on, ends on and what number of lines from the start of the model it should read (so it can get the namespace, class name, properties, etc) and then let it do it's thing. I'll tell it specifically not to read more than 50 lines of that file at a time when looking for something or reviewing something, or even to stop and ask me to locate a method/usages of things, etc rather than reading whole files into context.

So, again, if it's burning through money - focus your efforts. If you think you can just fire it up and give it a generic task - you're going to burn money and get either complete junk, or something that might technically work but is hideous, at least to you. But, if you're disciplined and try to set or create boundaries and systems that it can adhere to - it does, for the most part.

I faced a related dilemma when I finished my CS degree: to work as a full-stack dev or to work on more foundational technology (and actually use what I learned in my degree). My experience is that the "foundational technology" area is more "research-oriented", which means you get to work on projects where LLM's don't help that much: writing code in languages that have little data in the LLM's training corpus, coming up with performance benchmarking approaches unique for your application, improving a workload's throughput with insights derived from your benchmarking results and your ingenuity, etc. Had I gone down the full-stack path, I think I'd be worried now.

> I don't want to use LLMs for writing code because it's one of the things I enjoy the most in life

I think LLM's are really good for the "drudge work" when you're coding. I always say it's excellent for things where the actual task is easy but the bottleneck is how fast you can type.

As an example I had a project where I was previously extracting all strings in the UI into an object. For a number of reasons I wanted to move away from this but this codebase is well over 50k LOC and I have probably 5k lines of strings. Doing this manually would have been very tedious and would have taken me quite some time so I leveraged AI to help me and managed to refactor all the strings in my app in a little over an hour.

Are you using it for other things? I think you can write code without it but it’s so good for research and stack overflow replacement.

Last night I used it to look through some project in an open source code base in a language I’m not familiar with to get a report on how that project works. I wanted to know what are its capabilities and integrations with these other specialized tools, because the documentation is so limited. It saved me time and didn’t help me write code. Beyond that it’s good for asking really stupid questions about complex topics that you’d get roasted on for stack overflow.

> Does anyone have advice for maintaining this feeling but also going with the flow and using LLMs to be more productive

Coding with LLMs brought me so much more joy coding. Not always, but it is getting better. Sometimes is quite frustrating, but when you have some good idea, explain it well and get the model to generate the code the way you would code or even better and you can use it to build new things faster, that's magical. Many devs are having this experience, some earlier, some now, some later. But for sure I would not say that using LLMs to code made it less enjoyable.

I think there will always be jobs out there that don't demand you write code with an LLM, just the same that most jobs don't demand you use vim or emacs or LSP-based autocomplete as part of your workflow.

You don't have to go with the flow. I took a step back from AI tech because a lot of startups in that field come with extra cultural baggage that doesn't sit well with me.

Do you use compilers? Linker loaders? Web bundlers? Linters and formatters? Code gen for or from schema? Image editors? Memory safe or garbage collected languages?

Then you already use levers to build code.

LLMs are a new kind of tool. They’re weird and probabilistic and only sometimes useful. We don’t yet know quite how and when to use them. But they seem like one more lever for us to wield.

Have a side project where you control the entire experience and let your main job bend to the beast.

Treat them as resources for remembering/exploring code libraries and documentation. For example, I needed to import some JSON files as structs into Unreal Engine. Gemini helped me to quickly identify the classes UE has for working with JSON.

To rescue a flailing project that I took over when a senior hire ghosted a customer in the middle of a project, I got the 200$ Pro package from OpenAI (which is much less usable than Claude for our purposes; there were other benefits related to my client's relationship w/ OpenAI)

In the end, I was able to rescue the code part, rebuilding a 3 month long 10 person project in 2 weeks, with another 2 weeks to implement a follow-up series of requirements. The sheer amount of discussion and code creation would have been impossible without AI, and I used the full limits I was afforded.

So to answer your question, I got my money's worth in that specific use case. That said, the previous failing effort also unearthed a ton of unspoken assumptions that I was able to leverage. Without providing those assumptions to the AI, I couldn't have produced the app they wanted. Extracting that information was like extracting teeth so I'm not sure if we would have really had a better situation if we started off with everyone having an OpenAPI Pro account.

* Those who work in enterprise know intuitively what happened next.

If your employer spends $20k a month on you (salary + everything else), $200 a month breaks even at around a 1% boost in productivity.

I've tried every variation of this very thing. Even managed to build a quick and dirty ticketing system that I could assign to the LLM of my choosing. WITH context. Talking Graph Codebase's diagrams, mappings, tree structure of every possibility, simple documentation, complex documentation, a bunch of OSS to do this very thing automatically etcetcetc.

In the codebase I've tried modularity via monorepo, or faux microservices with local apis, monoliths filled with hooks and all the other centralized tricks in the book. Down to the very very simple. Whatever I could do to bring down the context window needed.

Eventually.....your return diminish. And any time you saved is gone.

And by the time you've burned up a context window and you're ready to get out. Now you're expeciting it to output a concise artifact to carry you to the next chat so you don't have to spend more context getting that thread up to speed.

Inevitably the context window and the LLMs eagerness to touch shit that it's not supposed (the likelihood of which increases with context) always gets in the way.

Anything with any kind of complexity ends up in a game of too much bloat or the LLM removing pieces that kill other pieces that it wasn't aware about.

/VENT

Sounds like they are great fans of Numberwang.

Is any of the ~$20/month with unlimited usage tooling actually profitable though? It goes without saying that if all else is equal then the product sold at a greater loss will be more popular, but that only works until the vendor runs out of money to light on fire.

> now happily using Gemini which costs nothing

…and you think this is going to last? :-)

If that's the case you should stop using it, because there's no way you see any ROI when you spend that much to just do some coding stuff.

It would be cheaper to your company to literally pay your salary while you do nothing.

Claude Max is less than a 1/2 percentage point of a Jr. Devs average salary. If you can't make that work then....

Isn't it exactly the same on Bedrock as in the API?

Claude Code runs in the terminal

But at least Claude Desktop runs on my Intel Mac.

    I'm sorry

    - Open AI

It's flat as in flat rate, similar to fixed rate, meaning a set cost per month, instead of pay-as-you-go. Hope that helps.

It's flat if you graph your spend over multiple months :)

Judging by your username you're in Israel?

If so just get yourself an Israeli mobile virtual number (which can receive SMS)

https://www.flynumber.com/cities/israel/mobile/

If you're using Cursor with Claude it's gonna be pretty much the same thing. Personally I use Claude Code because I hate the Cursor interface but if you like it I don't think you're missing much.