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
Details and repo post on ShowHN here - https://github.com/monkeydust/rightmind
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 :)
Sure let me do that. Can I post this as a ShowHN if its just video? The rules say people need to try out but that will cost me a small fortune :) ...could perhaps post on Github and people can setup the repo themselves with their own Openrouter key if that works. Have never done a ShowHN but would be fun to try it.
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, Iâve 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.
Inference can only happen after having invested in training and datacenter construction. Arguing about "inference profitability" sounds a lot to me like ignoring large cost centers of these comanies.
> Rumoured profits on inference are north of 70%.
Rumors are worth squat when theyâre most likely put in motion by the people with a vested interest in this industry.
Letâs talk about profits when thereâs real data from the IPO documentation.
> Rumors are worth squat
You can make some educated guesses and find out some limits on inferencing cost by looking at 3rd party providers on platforms like openrouter. You can get some median cost /tok for a given model size. Then make some educated guesses on SotA model sizes, and you can get an estimate on pure cost of serving a model. Error bars and all that, of course. But still a range, with some limits.
How is spacex not short on money when no one will pay them to use their models and they lose money every quarter? Sure theyâre now transitioning to a data center provider away from actually being an AI company because theyâre losing less money that way but it doesnât sound like a strategic success
SpaceX is increasing prices because they're trying really hard to get into the S&P 500.
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)
What I mean by arbitrary is that like raising bread prices from $5 to $50 kind of thing. Thatâs not a sign of cost-based pricing. Itâs arbitrary.
> 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.
The target codebase is very large. A million tokens is a drop in the proverbial bucket.
I still don't understand how caching helps me very much. I must be misunderstanding it because I thought the user's prompt (which is the biggest variable) necessarily sits prior to all of these token intensive tool calls. How can we cache the reading of codebase if the prefix is always moving?
If an agent makes a tool call, the LLM provider will receive the full context again after the result of the tool call becomes available in order to decide the next move. Everything up to the point of the tool call being made will no longer change and could thus in theory be cached. If the agent makes a ton of tool calls, then for every tool call one should be hitting the cache an equal amount of times.
A new instruction by the user will be appended at the end if it done in the same conversation. Thus only has influence on the cacheability of the original agent prompt, but not of subsequent tool calls.
Often to me it seams like using MA is like letting a million monkeys lose.
Has ai forgotten about high level design? Surely all it needs to know is what the methods, objects or functions in the code base actually does and the actual code it is meant to be fixing?
I wonder if half the issues is that the LLM try to change too much?
> The target codebase is very large.
But, does every prompt need the entire codebase?
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)
This kind of cute conspiracy theories donât actually hold true in real life. The companies want to make useful products.
They're all just in it for the love of making customers happy, for sure. Amazon actually donates all their profit to charity. Bez os is currently building a foundation to shift all his wealth to, and he's going to become an alpaca farmer
/s
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...
Itâs 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.
The typical NPU is only marginally helpful for on-prem inference. A GPU can read quantized data from main memory and dequantize/pad it locally (making effective use of memory throughput); a NPU often needs to read padded data directly from memory, which is wasteful. So it only helps a little bit wrt. prefill.
Also, smaller models can obviously be used but a smaller model will be a lot weaker in real-world knowledge and this tends to limit their smarts in a way that can't be compensated by more thinking.
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.
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