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Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

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Re: Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

#131

Post by byzantinegene »

monk_grilla wrote: post #104
killingtime74 wrote: post #90

Earlier quoted context omitted.

All enterprises users (people using them for work and not side projects) can't get the subsidized plans. I would say subsidized plans are a minority of usage?

I imagine that most small to medium sized businesses are on either individual plans or Teams plans. The vast majority of firms do not need more than 150 seats, and API rates are not sustainable for most.

if this is true, the frontier labs are not able to justify their trillion dollar valuations, they are barely making anything on subsidized plans.

Re: Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

#132

Post by bensyverson »

byzantinegene wrote: post #131
monk_grilla wrote: post #104

Earlier quoted context omitted.

I imagine that most small to medium sized businesses are on either individual plans or Teams plans. The vast majority of firms do not need more than 150 seats, and API rates are not sustainable for most.

if this is true, the frontier labs are not able to justify their trillion dollar valuations, they are barely making anything on subsidized plans.

Hey, are you trying to imply that the Emperor has no clothes or something? /s

Re: Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

#134

Post by good8675309 »

stilesja wrote: post #121
cheema33 wrote: post #80

> Fable-level results at 1/3 the cost I am guessing this is not targeting those of us on the heavily subsidized $200/mo plans. Sure, these plans may be temporary, but none of us really know how temporary they are. Until then, 1/3rd of the published API pricing is not very appealing.

I burned though my weekly fable usage last night on the $200 plan. I had $200 in promotional usage credits and was in the middle of executing a moderate sized coding plan. Ran on usage credits for about 1h 15m and burned $120 in usage credits. I was astounded to see how fast the $ usage added up. One problem was that I was using sub-agent execution so multiple agents were running simultaneously and I realized at the…

I stopped using sub agents after they caused me to hit my limit to quickly. Had to create a separate account and pay for another max plan to unblock myself temporarily

Re: Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

#135

Post by suby »

byzantinegene wrote: post #130
cheema33 wrote: post #80

> Fable-level results at 1/3 the cost I am guessing this is not targeting those of us on the heavily subsidized $200/mo plans. Sure, these plans may be temporary, but none of us really know how temporary they are. Until then, 1/3rd of the published API pricing is not very appealing.

I believe they will last until they IPO, and not long after that. $200/mo plans are not good for their P&L when their users using $10000 worth api credits. That's -98% margin loss per user.

People point to the equivalent API costs to show that they are getting a great deal on the subscription, 10,000 dollars worth of tokens for 200 dollars. I do wonder if it's the other way around though - are the API users simply getting ripped off? I have seen Dario say in multiple interviews that they are profitable on inference, which maybe he was only meaning to refer to API usage, but that's not the impression I got.

It's not a 98% margin loss if your users are unwilling to pay 50 times the cost that they were previously paying, and if they have other options like open source providers. The calculus isn't so simple because some portion of users would switch to API, and so it's about how many would continue using the service rather than leaving for a competitor.

I'm aware they need to recoup the enormous cost of training and data centers, but on a purely inference cost level I'm not convinced that the 200 dollar plans are unprofitable.

Re: Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

#136

Post by orsorna »

suby wrote: post #135
byzantinegene wrote: post #130

Earlier quoted context omitted.

I believe they will last until they IPO, and not long after that. $200/mo plans are not good for their P&L when their users using $10000 worth api credits. That's -98% margin loss per user.

People point to the equivalent API costs to show that they are getting a great deal on the subscription, 10,000 dollars worth of tokens for 200 dollars. I do wonder if it's the other way around though - are the API users simply getting ripped off? I have seen Dario say in multiple interviews that they are profitable on inference, which maybe he was only meaning to refer to API usage, but that's not the impression I g…

I figured the subsidization is to entice people to give training data.

Are your thought patterns worth 9800 dollars a month?

What's the RoR on analyzing those thought patterns?

Re: Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

#137

Post by jmspring »

dijit wrote: post #69
jmspring wrote: post #66

So the word security or any topic related to it is mentioned and it flips to an older gen model? Fable is nearly useless now it you do anything around auth.

I can get it to write win32 unsafe rust code. I can't get it to review win32 unsafe rust code. Make it make sense.

need bigger check to guy in DC

Re: Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

#138

Post by victorbjorklund »

suby wrote: post #135
byzantinegene wrote: post #130

Earlier quoted context omitted.

I believe they will last until they IPO, and not long after that. $200/mo plans are not good for their P&L when their users using $10000 worth api credits. That's -98% margin loss per user.

People point to the equivalent API costs to show that they are getting a great deal on the subscription, 10,000 dollars worth of tokens for 200 dollars. I do wonder if it's the other way around though - are the API users simply getting ripped off? I have seen Dario say in multiple interviews that they are profitable on inference, which maybe he was only meaning to refer to API usage, but that's not the impression I g…

Yea, it’s like pointing at the cost of renting all individual movies and TV-series at Netflix and concluding that Netflix subsidizes the subscription with tens of thousands of dollars.

Re: Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

#139

Post by dang »

recursivegirth wrote: post #40
jacobgold wrote: post #33

> Fable-level results at 1/3 the cost using open-weight models But we get ~$2500/mo worth of Fable credits for $200/mo on Anthropic pan? I'm still confused why people (who don't have to use API billing) are chasing open weight models based on cost.

[flagged]

> The good ole American way.

Could you please stop posting unsubstantive comments and flamebait? You've unfortunately been doing it repeatedly. It's not what this site is for, and destroys what it is for.

If you wouldn't mind reviewing https://news.ycombinator.com/newsguidelines.html and taking the intended spirit of the site more to heart, we'd be grateful.

Re: Show HN: Echo – Fable-level results at 1/3 the cost using open-weight models

#140

Post by user_7832 »

Terretta wrote: post #87
onlyrealcuzzo wrote: post #82

Earlier quoted context omitted.

Dogpile was only a good idea while Search Engines were mostly trash. You needed to search all of them to find something decent. That's roughly analogous to today. Ignoring cost, you'd be way better off asking all the LLMs to solve a problem (like coding) where you can verify the answer. So the question is, for things like that -> can a group of models perform better than frontier models, especially at a reasonable co…

> Dogpile was only a good idea while Search Engines were mostly trash. Precisely.

LLMs fail in such bizzare, obscure methods (to the average observer) at times. Sometimes even simple questions ("who was that x person who was super famous I'm thinking of") type questions fail terribly.

The more vague and non committal and hand-wavey and subjective the field for AI to answer, the better the results (imo).

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