Trust me there are plenty of us using cloud AI to actually ship stuff. We just aren't writing blog posts about it.

My companie spends a few million per month in token.

For me personally, the only reason why i don't use it 24/7 is cost.

Token costs, energy cost, usage limit due to token/subscription cost.

Part of the issue with "shipping" AI-generated code is, what's the value of shipping something that your prospective customers could just make for themselves? Shipping a product used to be the gold standard of value, but I'm not sure it is anymore, the zeitgeist just yet has to catch up.

I think it does have pretty good value when you are your own customer, because you can make exactly what you need. For instance, I'm using it to make a web playground in which I can test the board game I'm designing, instead of asking people to pay for, and install Tabletop Simulator. I'm satisfied. I can tell the code sucks without even looking at it. But I wouldn't have had the time to do this myself, the token cost isn't prohibitive, and it works fine.

People busy building shippable things are generally too busy to blog about it. It is selection bias in its most classical meaning.

The reality however is that unless you're spending tens of thousands on hardware or setting up cloud instances you can't run anything near close to state of the art.

In almost all cases right now, a subscription with a cloud AI wins.

I agree there... I hate the posts that rattle on about non-real-world work. Real world now (for me anyway) is maintaining legacy code, fixing issues, and new development. Now, some of the problems others have with AI are super crappy prompts with no real info other than "do this". I look at myself now more as a software designer and an architect, and the AI as a paid consultant. I give detailed specs and designs. The models are getting better at real deep code and legacy, but there are still holes. BUT give the AI a fighting chance with real detail in the prompts.

Cloud AI is 100% worth it. We've been able to do so much more with so much less.

The audit point is especially interesting. Is your Claude token-audit tool available anywhere?

I can't remember who it was I spoke to recently, but it was some startup for doing code reviews. So I asked how they're handling larger diffs and especially when a code change is related to something requiring knowing a lot of context. Will it hallucinate things that make the review low-quality, etc. I was told "no it's been given guardrails to prevent hallucinations. it doesn't hallucinate." but I was thinking, that's not really how LLMs do or do not hallucinate, is it? (I'm not an AI person so I really don't know).

It's like the frog prompting the scorpion not to sting him as they cross the river. Or like when image models used to routinely draw human hands incorrectly and you'd see these "prompt hacks" that would essentially be stuffed with phrases like NO DEFORMED FINGERS, NO ELDRITCH HORRORS!

You can reduce hallucinations with appropriate grounding checks. You can’t really get rid of them though, so feedback loops with verification are essential.

You can reduce hallucinations with rules and guardrailes for sure.

And Anthropic clearly shows that we can reduce hallucination in models. Progress is made which you can see in benchmarks.

And at the end of the day, a normal human being doesn't 'hallucinate' but 'missunderstand' things or just doesn't know what they are missing.

A LLM doesn't has to be perfect, it just has to be cheaper with the min. same quality as a human.

So why are you screaming into the void? Because LLM leave deterministic world? A lot of people don't even life in that world anyway.

Good point, thanks. Of course I was not able to fully eliminate hallucinations, but significantly reduce

you can by making a different - usually local small - model ask for verification

I hear you! I read the article and thought the same thing because I've actually built a surprisingly similar pipeline myself, but it culminates with Human review. I've got a trivia app (Hedge Calibrated Trivia) whose whole content pipeline is built on a similar architecture of models evaluating models. I didn't want the same trivia that every other app has and I naively imagined that it would be easy to ask an LLM to give me questions. Turns out when you ask an LLM for trivia questions, the FIRST thing it gives you are the same questions every other app asks!

After iterating into many of the same problems OP describes I settled into a similar pipeline. I generate questions with Gemini 3.5 flash and review with Claude. The different vendor generation and validation eliminates training holes in a single model solution. This is True/False trivia so I ask for an equal mix of True/False questions and also ask for alternate phrasing of the question to the opposite variant. Each statement must provide an explanation and sources which back up the claim. I pass in my existing dataset as questions to avoid, but found the model then rewording the questions or category shopping the questions into related categories in an attempt to re-use them. That led to using Claude Sonnet to check for duplicate "themes" and similar topics. Claude also acts as a critic of the question and the explanation. Sometimes Gemini's "thoughts" leak into the explanation. The critic catches these leaks and also catches "weak" questions (basically shapes that I have identified via examples as lazy or projecting the answer in the question). Once the comparative models have stripped out anything obviously bad, my server curls the source URLs and uses Sonnet to verify that the fetched source supports the claim. Any URL that doesn't resolve throws up flags, and I found Gemini was particularly bad at inventing source URLs for sports trivia. Updated prompting directed it to prefer Wiki links over its fabricated deep links into league websites, which helped immensely. Still one of the biggest tells in review is that the URLs don't resolve or don't confirm the statement.

But review is the entire point here. I have to review them all. A question that lands in the app and is wrong reflects terribly. So while I have a trivia app on the App Store, the real complex App is my private admin app that lets me sit on the couch swiping through each and every question sorted by the model's confidence in its own quality and correctness (weakest first). No question makes it into my bank without me reading, checking all the indicators that came from the models and potentially even rewriting the question to make it better. It all happens in the admin app and exists entirely to prevent hallucinations from reaching real users. Early on I was maybe accepting 1 in 3 questions generated, but I'm probably closer to 2/3rds now. Of course not every dismissed question is wrong. The quality checks I've put in the pipeline are probably giving me 80% questions that are correct and sourced, but sometimes they just aren't what I'm looking for or don't meet my standard.

The best thing I ever did was make an app to review these questions though. Sitting on the couch, kick off a question generation, come back 20 minutes later with 50+ questions in the queue. Sort through them and pull out the 30 best when I have the time. That's the real anti-hallucinations pipeline.

Couldn't someone build an adaptive system, where the llm is frequently judging the difficulty of a task and switching to a more/less powerful model?

Yeah, I do this as well. Some of the last large tools I built have been started with Gemini (I get it with my Google package), and even one with Copilot.

So did you find out how to save tokens yet, or are you still spending tokens on the search for the answer?

I think this is almost a tautology, to the point that I'm surprised more people don't realize it.

If you ask the AI to do more work, it uses more tokens. If you do more of the work instead, fewer tokens are used.

I've been looking at Matt Pocock's skills recently, and they're very useful. But they use tokens. You can do that work yourself without the token usage, and just have the AI do the coding, or you can tell the AI to do everything and pay the cost.

And you can scale it down even further, and reduce the AI to a glorified code completion tool, and use way fewer tokens. But it's not going to speed up your work nearly as much.

For now, finding the right balance is important. In the future, I think things will eventually shift to the point that we just hand most of it to the AI and let it go without worrying about tokens.

I don’t think subagents are the problem. Uncontrolled fan-out is. Separate contexts and independent analysis are extremely useful for larger tasks. In my experience, Pro is far too limited for sustained programming, although that probably depends a lot on the kind of engineering work you do.

Fable is great, but overkill for most tasks. Sonnet and Opus are already good enough for the execution work. I find Fable much more useful for planning, orchestration, advising, and judgment.

Unlees you invest thousands your local ai won't even come close to the cheap cloud llms. Local is only worth it if you care about privacy or have a legitimate usage for the hardware otherwise. Money wise it isn't worth it

This is like the "half of my marketing spend is wasted" quote. The complexity is finding out which half.

To run a local AI that is half decent at research at usable speeds requires hardware that costs thousands of dollars.

Spending thousands of dollars on hardware to save dollars per month on tokens does not make financial sense.

If you run the numbers you'll probably find that using cheaper cloud models makes more financial sense than running those same models locally.

That can work, especially for privacy or repetitive tasks. My experiment focused on subscriptions I already had, but local models are a natural addition to the routing layer.

https://www.reddit.com/media?url=https%3A%2F%2Fi.redd.it%2F9...

Thanks! I thought I saw something like this a month or so back, but could not find it again.

looking promising, will give it a try! thanks for sharing

Checked it out, but C# is forsaken.

Elsewhere in these replies, someone linked to very similar idea by Karpathy https://gist.github.com/karpathy/442a6bf555914893e9891c11519...

Thanks for sharing. That’s a very interesting direction. I will take a look.

Thank you, glad you found the article useful!

Try to mix both subscribtions and take the best from both providers. In my opinion, we should not just one over another but understand the power of both and mix them to reach the better results.

I don't think you made that. It looks for sure like an LLM made it.

And now that you have typed some words into a chat box to produce a thing, you are confused about why other people won't pay money to you for the output of the chatbox, instead of typing the same thing into their own chat boxes.

At the shell level, the agent does not need to know about Bash. The contents of "$OUT" are passed to echo as one argument, then sent to claude-mem-save over stdin. Bash does not re-evaluate them as shell code. printf '%s\n' "$OUT" would be more robust than echo. Whether claude-mem-save can use arbitrary output is a separate question.

Yeah, that was pretty much my experience. The models weren’t lacking knowledge as much as discipline. Without a good workflow, they will most likely spend thousands of tokens exploring dead ends.

What most AI model users don't understand is (most here will know) that they are probabilistic (statistically based) and not deterministic (static/fixed). So answers to the same prompts can vary (wildly sometimes)

Yes, but you could have an uncached dynamic tail, this would not kill the cache though. And you could still periodically kill the cache, still keeping the same prefix every N requests. But it's questionable what all this extra complexity delivers.

I found that now that the models are better, at least Codex compaction with GPT 5.5+ xhigh is almost unnoticible. So the answer is: just let the context fill up, don't prune much, the model is smart enough. When you're out of space, have the model summarize and continue from that point. A smart model will know the information pieces it needs to re-read after compaction; it won't make incorrect guessed when some details got lost.

I have a process generating trivia questions for my app and by passing in my existing dataset each time as a cached exclusion list, the output tokens it save more than pay for it.