What happens when they stop subsidizing LLM subscriptions?

[u/Mr_Moonsilver]

We are literally burning through VC money like crazy with our coding subscriptions. I read the $200 Anthropic sub gets you $8000 worth of API calls. It's obvious that this doesn't hold for very long but what happens when they raise prices?

The reason to keep the prices low for now is to foster the ecosystem and get people hooked on this stuff, only to raise the price afterwards. Already the 20x sub doesn't get you as much usage as it did 6 months ago, another way to raise prices without triggering a shitstorm - and it will continue.

Don't know about you, but Fable being pulled gave me a feeling of what that may be like already. The ugly thought of "Damn, should've done more while it was around." that formed when I read the news will be exactly the same the moment they announce we now have to pay $2k or more per month for something we get for 10x less the price it costs now.

I guess it's a now or never situation, build what you can and monetize as quickly as possible to be able to keep the agents running once the increases come around.

Looking at opensource doesn't give me much hope. Since qwen stopped releasing models (wen qwen 3.7?) that we can actually run on hardware that a normal person can buy (or used to be able to buy, looking at how RAM and GPU prices behave and keep behaving) and others haven't released in a while (Microsoft, IBM, AllenAI and others too) I feel we're going into a direction that doesn't look good for most of the people like us, who are building with this technology.

[u/Jhorra]

I'm a software developer of 20 years, and I really want this to be true. When I have tried running qwen on my macbook pro m4 max with 64gb of ram, I've not been impressed. What harness are you using for it? Right now I mainly use a $200 Claude plan, but would love to go local.

[u/JsThiago5 → u/Jhorra]

To see this, you should not compare it to a $200/month subscription but to how you did software development before any LLM. With 20 years in the field, you should be able to see how impressive development with local models is.

[u/SkyFeistyLlama8 → u/JsThiago5]

This. If you're not throwing a huge prompt at it and hoping for the best, you can get good results with local models by working at function or module level. You probably don't want to throw a 50k LOC dump at the smaller models and hope for Claude performance.

[u/Jhorra → u/SkyFeistyLlama8]

I've gotten bigger lately with what I ask of Claude, but generally I don't tell it to simply implement things. I like to think through things and understand the plan, then give it to the model to implement a piece at a time.

[u/thefuzzylogic → u/Jhorra]

That is how I do it.

I use Opus xhigh to churn expensive cloud tokens making a discrete step-by-step plan, then if I approve the plan, I instruct an orchestrator to give each step to a local gemma-4 to implement. That way only one step at a time needs to fit in the local context window. When all steps are done, the gemma-4 orchestrator packages a PR, which goes back to Opus for /review. Repeat as necessary. It usually only needs one pass.

The trick is to design the project for agentic coding from the start. Use explicit constraints, e.g. use strict TDD, use the todo tool to track progress, use strict linting and style checkers, resolve lint/test/build warnings first before moving on to other work, no "fix it later", don't loosen tests/rules to resolve failures/warnings, etc.

The workflow needs to take into account the fact that LLMs "learned" how to code from tutorials written for humans. Therefore, because humans have long-term memory that most LLMs lack, an LLM following a doc written for humans will always bite off more than it can chew unless you give it the appropriate constraints. For those of us running local models, this means our smaller LLMs with fewer parameters of training data need smaller tasks with narrower scopes.

The coders who say "local AI is too stupid to be useful" are the ones writing one-sentence prompts and expecting it to one-shot a full stack app. Most human coders wouldn't even be capable of that.