back
75 comments
I have a MA system setup for personal use.

You give it a problem, you then refine that problem where a fast, cheaper model asks you questions which you answer to get a better input prompt. You then choose a MA strategy for example take problem break up to sections then final judge concludes or you do multi turn where agents debate then judge summarises debate.

The best approach is what I call 'all angles' where all these strategies run in parallel the final meta-judge synthesise the response - the most useful part of this which I recently added is a view to see the variance in each strategy.

Been using this for life stuff - housing search, schools, family challenges!

Perhaps I should make a video of it in action if people in HN community interested let me know.

Right here is the video demo of what I built - https://streamable.com/e49cgt
I have also developed a similar system not focused on the exploratory refinement of prompt(s). But more focused on feedback loops cybernetic style, so focused on the maintaining of stability of the prompt outputs by a growing library of deterministic checks and autofixes. Anything that is a "problem" which isn't covered by that library is surfaced to the human driving the process.
You mention cost in one of the replies. Can you elaborate on the cost profile (ballpark) for various problem types? I would also be curious to understand the strategies employed and what the costs look like across each.
Definitely interested, would love to see a video :)
The cheap models may ask subpar questions leading to subpar solutions
So what harness are you using? And what LLM’s
The problem with these kinds of systems (they have been well studied), is that that the overall output is ultimately anchored to the dumbest models used.

I.e. you cannot end up having a more intelligent output by using more dumber models (that is: dumber than the most intelligent model used).

It's generally always best to refine your prompt and send it (at most) to the two smartest frontier models possible. And then have the smartest model review the output from the second smartest.

amusing side note:

Was in a meeting reviewing a potential new product, it was going well until they showed us that they had added AI to it (of course they have). It was pretty obviously just shoehorned in, and one part of that obviousness was that they had a column that showed how many tokens it took to make each query.

I asked who is paying for the tokens, they said its included in the license. I said, so is there a budget or is it all you can eat. they said good question they didnt know and would get back to me. I said the reason i asked was just one query there had a 250k token burn on it. and it was a fairly simple query about one device.

then, one of the execs on their side was heard saying out loud "Why are we even showing this to the customers?"

it have us quite a chuckle. But lesson learned... the cost of adding AI to anything isnt really being accounted for let alone the true cost of actually running the AI.

all things AI are going to get more expensive. even if you dont want the AI aspect.

AIshittification
One month I could use Github Copilot fully with no disruptions. The next month, after pricing changes, I’ve run out of tokens in two days.

Such drastic changes tell me that pricing of tokens is arbitrary, and AI business is running out of money fast.

I think it's more a consequence of pushing for the biggest valuation/IPO. Rumoured profits on inference are north of 70%.

Taking SpaceX as an example, they have increased prices across all their consumer products over the past six months. But they definitely aren't short on money with Alphabet and Anthropic combined paying them over $2 billion per month.

Microsoft/GitHub lost out here as they were just repacking other people's products.

The github example is also a bit of an outlier because they made a recent change to their pricing so that's why its such a drastic jump.

Also I mean prices in generally for all things are based on underlying factors, that doesn't make them arbitary (i.e. github executives using a random number generator for token pricing would be arbitary)

> Furthermore, we observe that input tokens consistently constitute the largest share of consumption for an average of 53.9%

I'm seeing a ratio of around 10:1 in my usage. A vast majority of the tokens consumed are on the input side. The agent will often read a million tokens just to patch one line of code.

I think if you are seeing something closer to 1:1 or more on the output side, there is either a problem with the agent or the codebase is new / empty.

Did you experiment with giving agent better tools to navigate and document the codebase? Asts, language servers and so on?

A million tokens (not cached) sounds like a lot.

If input tokens dominate the cost to that extent, this implies that major gains are possible by making better use of caching. You could basically ask the model to do a one-time "compaction" step including a dump of the relevant portions of the code, and use that as the cached prefix for a large amount of "swarm" subagent calls.
One thing I've noticed using agents for coding is that they really like to write thousands of unit tests but not dynamically test.
And they like to burn a ton of tokens writing and debugging tests that are semantically corrupt.
And AWS heavily pushes a complex lambda solution stringing together as many chargeable AWS services as possible for a simple requirement

Their interests are often not your interests. In this case they want you to unnecessary money on useless work (let's stop the euphemism of "tokens" btw)

Unit tests are a type of dynamic testing. As opposed to static testing which is linting/typechecking etc.

If you want a difference kind of dynamic testing besides unit tests, have you tried writing it in as a requirement during the planning/PRD phase?

you can just tell them to do more dynamic testing. I think dynamic testing is partly frowned upon because it slows things down & can take down software where you wouldn't expect
Reminded me of this paper from last year trying to optimize efficient token usage providing budget guidance information. [1]

[1] https://scholar.google.com/scholar?hl=en&as_sdt=0%2C5&q=Stee...

It’s just like Airline reward miles and offers no benefit to companies over just renting bare metal GPU time
I hope this horrible time will soon be over when cheaper NPUs come available from more hardware companies, and also when model size get optimized down further.

I wonder what hyperscaled compute farms and models will be good for at that running cost when most AI needs can be fulfilled by on-prem and on-device hardware and models. Probably only customer left are big governments. So in the end the tax payer has to pay for those billions of investments by the AI cartel.

At its current iteration the AI tech market is not economically sustainable, not for the other markets outside the AI economy, and most deadly not even for the main target customers or AI tech companies themselves. There have been several news of companies having overspent their token budget month after month. The hardware monopolist and his network of buddy companies can determine the token price as freely as they want, there are no competitors, their only "competitor" is when people stop using AI alltogether.
I don't think business is interested in any sustainability of anything. There's zero incentives for that for anyone.
In the past Google et al would hire engineers based on how well they could optimize the infrastructure.

Maybe soon companies will look at how engineers can optimize the token efficiency of AI.

That assumes Tokens will remain a meaningful expense. I’m not sure developers will find uses for ever more tokens nearly as quickly as the prices fall.
I know how to drop a company’s token costs to zero: treat tokens as a utility same as internet and make engineers pay for it.
I wrote a Subsack post on this topic back in December https://open.substack.com/pub/zacharywhitley/p/the-coming-ag...
Tokenomics is already a word used to describe cryptocurrency economics, not sure why they'd try to redefine it for AI even if a different sort of token is used.
Tokenomics had been already used by marijuana enthusiasts for a long time.
New fad. Forget about the old fad. This one will be old soon, you better get on board before its too late!
First thought was "only 30 tasks" however the findings map to what I've seen personally: code review consumes majority of tokens