5 ms·

the choice is to live 2 years behind (e.g. integrate the open source stuff and ride that wave of improvement). for businesses in a competitive space, that’s per

the choice is to live 2 years behind (e.g. integrate the open source stuff and ride that wave of improvement). for businesses in a competitive space, that’s perhaps untenable. but for individuals and anywhere else where this stuff is just a “nice to have”, that’s really just the long-term sustainable approach. it reminds me of a choice like “do i host my website on a Windows Server, or a Linux box” at a time when both of these things are new.

hfjjbf 3y ago
[dead]
BiteCode_dev 3y ago
2 years behind in terms of timeline, but what factor in terms of productivity and quality of life? Not to mention openai's lead compounds, so 2 years now and 4 years in 2025 may be 10 times the original prod/qol gain.
- verdverm 3y ago
  The gap seems to be shrinking, not growing. The OSS models have reach new capabilities faster than most thought
TeMPOraL 3y ago
For individuals, this is a very short window of time where we have cheap access to an actually useful, and relatively unshackled SOTA model[0]. This is the rare time individuals can empower themselves, become briefly better at whatever it is they're doing, expand their skills, cut through tedium, let their creativity bloom. It's only a matter of time before many a corporation and startup parcel it all between themselves, enshittify the living shit out of AI, disenfranchise individuals again and sell them as services what they just took away. No, it's exactly the individuals who can't afford to live "2 years behind". Benefits are too great, and worst that can happen is... going back to where one is now. -- [0] - I'm not talking the political bias and using the idea of alignment to give undue weigh to corporate reputation management issues. I'm talking about gutting the functionality to establish revenue channels. Like, imagine ChatGPT telling you it won't help you with your programming question, until you subscribe to Premium Dev Package for $language, or All Seasons Pass for all languages.
- colinsane 3y ago
  > Benefits are too great, and worst that can happen is... going back to where one is now. true only if there's no form of lock-in. OpenAI is partnered with people who have decades of tech + business experience now: if they're not actively increasing that lock-in as we speak then frankly, they suck at their jobs (and i don't think they suck at their jobs).
  - TeMPOraL 3y ago
    That's my point - right now there is no lock-in for an individual. You'd have to try really, really hard to become dependent on ChatGPT. So right now is the time to use it.
    - colinsane 3y ago
      dependencies have a way of sneaking up on a person. if there was a clear demarcation at which you'd say "they're locking us in: i'm leaving now while i still can!", then that's not the route by which you'll be locked in. yet, even those of us who keep an eye out for these things, we all probably observe ourselves to be locked into one or more things in our life right now: how did each of those happen?
zeven7 3y ago
> it reminds me of a choice like “do i host my website on a Windows Server, or a Linux box” at a time when both of these things are new. Oof, you reminded me of when I chose to use Flow and then TypeScript won.
- mplanchard 3y ago
  Haha this puts me in mind of when I designed a whole deployment strategy for an org based on docker swarm, only to have k8s eat its lunch and swarm to wind up discontinued
  - mst 3y ago
    A lot of people don't really need to go Full k8s, but I think swarm died in part because for many users there was -some- part of k8s that swarm didn't have, and the 'some' varied wildly between users so k8s was something they could converge on. (note "died in part" because there's the obvious hype cycle and resume driven development aspects but I think arguably those kicked in -after- the above effect)
Closi 3y ago
> the choice is to live 2 years behind... That's one world - there is another where the time gap grows a lot more as the compute and training requirements continue to rise. Microsoft will probably be willing to spend multiple billions in compute to help train GPT5, so it depends how much investment open source projects can get to compete. Seems like it's down to Meta, but it depends if they can continue to justify releasing future models as Open Source considering the investment required, or what licensing looks like.
seanhunter 3y ago
That's definitely what a lot of people think the choice is but learned helplessness is not the only option. It ignores the fact that for many many use cases small special-purpose models will perform as well as massive models. For most of your business use cases you don't need a model that can tell you a joke, write a poem, recommend a recipe involving a specific list of ingredients and also describe trig identities in the style of Eminem. You need specific performance for a specific set of user stories and a small model could well do that. These small models are not expensive to train and are (crucially) much cheaper to run on an ongoing basis. Opensource really is a viable choice.
- mst 3y ago
  I suspect small specific purpose models are actually a better idea for quite a lot of use cases. However you need a bunch more understanding to train and run one. So I expect OpenAI will continue to be seen as the default for "how to do LLM things" and some people and/or companies who actually know what they're doing will use small models as a competitive advantage. Or: OpenAI is going to be 'premium mediocre at lots of things but easy to get started with' ... and hopefully that'll be a gateway drug to people who dislike 'throw stuff at an opaque API' doing the learning. But I don't have -that- much understanding myself, so while this isn't exactly uninformed guesswork, it certainly isn't as well informed as I'd like and people should take my ability to have an opinion only somewhat seriously.
  - seanhunter 3y ago
    I have a slightly different take. Not all use cases are narrow use cases. OpenAI crushes the broad and/or poorly defined use cases. On those if you tried to train your own inhouse model it would be very expensive and you would produce a significantly inferior model.
    - mst 3y ago
      I'm not sure how my "quite a lot of use cases" and your "not all use cases are narrow use cases" are meaningfully different (slightly) to you. This isn't a snipe, mind, it's me being unsure if we even disagree, especially given the latter part of your comment seems entirely correct (so far as my limited understanding goes ;).

hfjjbf 3y ago

[dead]

BiteCode_dev 3y ago

2 years behind in terms of timeline, but what factor in terms of productivity and quality of life? Not to mention openai's lead compounds, so 2 years now and 4 years in 2025 may be 10 times the original prod/qol gain.

verdverm 3y ago
The gap seems to be shrinking, not growing. The OSS models have reach new capabilities faster than most thought

TeMPOraL 3y ago

For individuals, this is a very short window of time where we have cheap access to an actually useful, and relatively unshackled SOTA model[0]. This is the rare time individuals can empower themselves, become briefly better at whatever it is they're doing, expand their skills, cut through tedium, let their creativity bloom. It's only a matter of time before many a corporation and startup parcel it all between themselves, enshittify the living shit out of AI, disenfranchise individuals again and sell them as services what they just took away. No, it's exactly the individuals who can't afford to live "2 years behind". Benefits are too great, and worst that can happen is... going back to where one is now. -- [0] - I'm not talking the political bias and using the idea of alignment to give undue weigh to corporate reputation management issues. I'm talking about gutting the functionality to establish revenue channels. Like, imagine ChatGPT telling you it won't help you with your programming question, until you subscribe to Premium Dev Package for $language, or All Seasons Pass for all languages.

colinsane 3y ago
> Benefits are too great, and worst that can happen is... going back to where one is now. true only if there's no form of lock-in. OpenAI is partnered with people who have decades of tech + business experience now: if they're not actively increasing that lock-in as we speak then frankly, they suck at their jobs (and i don't think they suck at their jobs).
- TeMPOraL 3y ago
  That's my point - right now there is no lock-in for an individual. You'd have to try really, really hard to become dependent on ChatGPT. So right now is the time to use it.
  - colinsane 3y ago
    dependencies have a way of sneaking up on a person. if there was a clear demarcation at which you'd say "they're locking us in: i'm leaving now while i still can!", then that's not the route by which you'll be locked in. yet, even those of us who keep an eye out for these things, we all probably observe ourselves to be locked into one or more things in our life right now: how did each of those happen?

zeven7 3y ago

> it reminds me of a choice like “do i host my website on a Windows Server, or a Linux box” at a time when both of these things are new. Oof, you reminded me of when I chose to use Flow and then TypeScript won.

mplanchard 3y ago
Haha this puts me in mind of when I designed a whole deployment strategy for an org based on docker swarm, only to have k8s eat its lunch and swarm to wind up discontinued
- mst 3y ago
  A lot of people don't really need to go Full k8s, but I think swarm died in part because for many users there was -some- part of k8s that swarm didn't have, and the 'some' varied wildly between users so k8s was something they could converge on. (note "died in part" because there's the obvious hype cycle and resume driven development aspects but I think arguably those kicked in -after- the above effect)

Closi 3y ago

> the choice is to live 2 years behind... That's one world - there is another where the time gap grows a lot more as the compute and training requirements continue to rise. Microsoft will probably be willing to spend multiple billions in compute to help train GPT5, so it depends how much investment open source projects can get to compete. Seems like it's down to Meta, but it depends if they can continue to justify releasing future models as Open Source considering the investment required, or what licensing looks like.

seanhunter 3y ago

That's definitely what a lot of people think the choice is but learned helplessness is not the only option. It ignores the fact that for many many use cases small special-purpose models will perform as well as massive models. For most of your business use cases you don't need a model that can tell you a joke, write a poem, recommend a recipe involving a specific list of ingredients and also describe trig identities in the style of Eminem. You need specific performance for a specific set of user stories and a small model could well do that. These small models are not expensive to train and are (crucially) much cheaper to run on an ongoing basis. Opensource really is a viable choice.

mst 3y ago
I suspect small specific purpose models are actually a better idea for quite a lot of use cases. However you need a bunch more understanding to train and run one. So I expect OpenAI will continue to be seen as the default for "how to do LLM things" and some people and/or companies who actually know what they're doing will use small models as a competitive advantage. Or: OpenAI is going to be 'premium mediocre at lots of things but easy to get started with' ... and hopefully that'll be a gateway drug to people who dislike 'throw stuff at an opaque API' doing the learning. But I don't have -that- much understanding myself, so while this isn't exactly uninformed guesswork, it certainly isn't as well informed as I'd like and people should take my ability to have an opinion only somewhat seriously.
- seanhunter 3y ago
  I have a slightly different take. Not all use cases are narrow use cases. OpenAI crushes the broad and/or poorly defined use cases. On those if you tried to train your own inhouse model it would be very expensive and you would produce a significantly inferior model.
  - mst 3y ago
    I'm not sure how my "quite a lot of use cases" and your "not all use cases are narrow use cases" are meaningfully different (slightly) to you. This isn't a snipe, mind, it's me being unsure if we even disagree, especially given the latter part of your comment seems entirely correct (so far as my limited understanding goes ;).