This came up the other day. I decided to tease everyone with an 'I told you so' about using some third party hosting service instead of the offline one I had developed years prior.
The offline service was still working, and people were doing their job.
The online service was not working, and it was causing other people to be unable to do their job. We had 0 control over the third party.
The other thing, I make software and I basically don't touch it for a few years or ever. These third party services are always updating and breaking causing us to update as well.
IB4 let me write my own compilers so I have real control.
If you are regularly updating your refrigerator's firmware or your refrigerator's firmware relies on an Internet connection to function, then I am very sorry to say this but you have lost control of your life :)
Shows you how creating and enforcing standards is the driver for stuff like this. I wonder how we could make them even more efficient, some way to stop the transfer of warm air when the door is opened? Wonder if it's possible to create some sort of air curtain at the front when it's opened to prevent warm air coming in, ie use driven air velocity to overcome the cold air wants to come out, hot air wants to come in. Hmmm.
> I wonder how we could make them even more efficient, some way to stop the transfer of warm air when the door is opened? I wonder how we could make them even more efficient, some way to stop the transfer of warm air when the door is opened? Wonder if it's possible to create some sort of air curtain at the front when it's opened to prevent warm air coming in, ie use driven air velocity to overcome the cold air wants to come out, hot air wants to come in. Hmmm.
That is an interesting idea, but I don't think an Internet connection would help with it :)
> Shows you how creating and enforcing standards is the driver for stuff like this.
Also agreed that is an interesting graph, I agree that it shows how standards and better production has led to decreased energy usage -- but notably, a lot of those standards are around better insulation and more efficient components.
Putting an extra layer of foam in your fridge or having sensors in your fridge that help regulate temperature definitely doesn't mean you've lost control of your life. But needing to download a firmware update to your Internet-enabled fridge that uses a Samsung account where you now can't access your grocery list until you finish the mandated update which changes your fridge's UI on its mobile app -- I think that means you've lost control of your life :)
Oh yeah for sure, but I think there are definitely reasons for it. The enshittification/technoshit that comes out of the iot world (and all other devices) because of corporate greed just ruins it even more.
The whole signing up for a Samsung account thing etc for your fridge. Stuff like this really just needs to be legislated under some kind of "all technology should just work, locally and with one another with at least an agreed set of features" level.
Apple should have been legally forced to use USB C (or whatever alternative was best) ages ago, even before the EU got to them. Apple were happy to use Wifi/Bluetooth/etc/etc standards yet still wanted to use other proprietary BS.
Same goes for literally everything else: all technologies should work together using at least a common method (with say options for proprietary stuff) and iot/whatever should all work flawlessly locally without any account or internet connectivity (which should all be 100% optional). Devices should work flawlessly even if the company that produces them has shut down all servers and gone bankrupt.
We need to force our governments to do this stuff for us.
Nice, looks like we finally got around to inventing refrigerator magnets!
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That is a little bit dismissive of me though. There are some cool features here:
I can now "entertain in my kitchen", which is definitely a normal thing that normal people do. I love getting everyone together to crowd around my refrigerator so that we can all watch Game of Thrones.
And I can use Amazon Alexa from my fridge just in case I'm not able to talk out loud to the cheap unobtrusive device that has a microphone in it specifically so that it can be placed in any room of the house. So having that option is good.
And perhaps the biggest deal of all, I can finally "shop from home." That was a huge problem for me before, I kept thinking, "if only I had a better refrigerator I could finally buy things on websites."
And this is a great bargain for only 3-5 thousand dollars! I can't believe I was planning to buy some crappy normal refrigerator for less than a thousand bucks and then use the extra money I saved to mount a giant flat-screen TV hooked up to a Chromecast in my kitchen. That would have been a huge mistake for me to make.
Honestly it's just the icing on the cake that I can "set as many timers as [I] want." That's a great feature for someone like me because I can't set any timers at all using my phone or a voice assistant. /s
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<serious>Holy crud, smart-device manufacturers have become unhinged. The one feature that actually looks useful here is being able to take a picture of the inside of the fridge while you're away. That is basically the one feature that I would want from a fridge that isn't much-better handled using a phone or a tablet or a TV or a normal refrigerator button. Which, great, but the problem is that I know what the inside of my fridge looks like right now, and let me just say: if I was organized enough that a photograph of the inside of my fridge would be clear enough to tell me what food was in it, and if I was organized enough that the photo wouldn't just show 'a pile of old containers, some of them transparent and some of them not' -- I have a feeling that in that case I would no longer be the type of person that needed to take a photo of the inside of my refrigerator to know what was in it.
Let's say I'm writing Flask code all day, and I need help with various parts of my code. Can I do it today or not? With questions like, "How to add 'Log in with Google' to the login screen" etc.
Longer: In theory, but it'll require a bunch of glue and using multiple models depending on the specific task you need help with. Some models are great at working with code but suck at literally anything else, so if you want it to be able to help you with "Do X with Y" you need to at least have two models, one that can reason up with an answer, and another to implement said answer.
There is no general-purpose ("FOSS") LLM that even come close to GPT4 at this point.
If you have sufficiently good hardware, the 34B code llama model [1] (hint: pick the quantised model you can use based on “Max RAM required”, eg. q5/q6) running on llama.cpp [2], can answer many generic python and flask related questions, but it’s not quite good enough to generate entire code blocks for you like gpt4.
It’s probably as good as you can get at the moment though; and hey, trying it out costs you nothing but the time it takes to download llama.cpp and run “make” and then point it at the q6 model file.
So if it’s no good, you’ve probably wasted nothing more than like 30 min giving it a try.
I think there is probably a threshold of usefulness, local LLMs are expensive to run but pretty close to it for most use cases now. In a couple years, our smartphones will probably be powerful enough to run LLMs locally that are good enough for 80% of uses.
The hardware is primarily standard Nvidia GPUs (A100s, H100s), but the scale of the infrastructure is on another level entirely. These models currently need clusters of GPU-powered servers to make predictions fast enough. Which explains why OpenAI partnered with Microsoft and got billions in funding to spend on compute.
You can run (much) smaller LLM models on consumer-grade GPUs though. A single Nvidia GPU with 8 GB RAM is enough to get started with models like Zephyr, Mistral or Llama2 in their smallest versions (7B parameters). But it will be both slower and lower quality than anything OpenAI currently offers.
Having something that executes, and having something that's genuinely useful, are two different things.
For my hand typed use case's, GPT-4 is the only acceptable model that doesn't leave me frustrated and angry at wasting time. For some automated stuff (converting text to json, etc), the local models are fine.
I was forced to Google something earlier. It's like when you discover 'craft' coffee/beer/baked goods/whatever and then go back and try the mass market stuff. How did I ever live like this?
Based on screenshots it'll have 2 modes: fun and regular. I think most screenshots have been "fun" mode, but it's probably possible to tone it down with regular.
It sure doesn’t look like it. The announcement was strange and anti-climatic, and it started making excuses for itself immediately “Grok is still a very early beta product – the best we could do with 2 months of training”. It has the Elon stink all over it.
He doesn't have to be because he doesn't make everything about himself at the companies he runs. You seem to have a pretty skewed idea of what a CEO is for.
Thanks for letting me know about this. I'd experimented with some local LLM's before, but at the time I couldn't find a good model that was small enough to run on my 3080 Ti. This one is just small enough for me to run at a useable speed (just over 1 token per second), and so far seems to be nearly as good as GPT3.5.
DALL-E 3 isn't available via API or labs. If you don't use ChatGPT you get the (significantly lower quality) DALL-E 2 at the moment. That's supposed to change by the end of fall.
One of the most frustrating things with "Open"AI is you can't just use what they announce as available, you have to wait for an A/B rollout (as a paying customer!) or for it to be accessible in direct way instead of going through multiple models when you just want an image.
edit: so, cool thing: cached queries on Phind will show all the followup questions visitors to the URL enter.
That's so cool. And horrifying. It's like back when Twitter was one global feed on the front page. I doubt that's intended behavior since this URL is generated by the share link.
It seems like this page is updated with the followup questions asked by every visitor. That's an easy way to leak your search history and it's (amusingly) happening live as I'm typing this.
That's so cool. And horrifying. It's like back when Twitter was one global feed on the front page. I doubt that's intended behavior since this URL is generated by the share link.
Don't most people just tether from their phones in this situation? Usually video isn't expected due to excessive bandwith requirements but the internet bill outweighs the daily salary (and you could probably get it expensed, or in my case my old company was already expensing my phone bill due to being used as a pager for on call)
Same situation here. I'm hoping being quadrilingual can help me serve as a diplomat or at least part of any envoys to distant communities. I also have some experience chopping down one tree in my backyard.
I remember experiencing that outage, but the entire internet wasn't down. Sometimes some Chinese providers also do weird BGP stuff. BGP failures tend to be isolated to certain networks and not the entirity of the internet.
There is too much for one person to store. And too many benefits from the intersections possible in vast stores of knowledge to focus on just what will fit in one head.
It is certainly a dumb take, but there's a hidden insight buried in there: now anyone can be a "junior dev" at anything. The ability to empower every user, not just the experts, is a big part of the appeal of LLM-based technology.
Can't sell that aspect short; the OpenAI tools have enabled me to do things and understand things that would otherwise have had a much longer learning curve.
Honestly, I've been gradually introducing AI searches for coding questions. I'm impressed, but not enough that I feel like ChatGPT is a true replacement for Google / Stack Overflow.
I've had it generate some regexes and answer questions when I can't think of good keywords; but half of my searches are things where I'm just trying to get to the original docs; or where I want to see a discussion on an error message.
Is there a parallel outage for Azure OpenAI service as well -- sothat any enterprise / internal apps using AOI via their Azure subscriptions are also impacted?
Is there a separate status page for Azure OpenAI service availability / issues?
I went to use Bard, and it looks so clean, such a nice UI. And the response looks so well organized, simply beautiful. If the AI only were as good as OpenAI's...
URGENT - Does anyone have an alternative to OpenAI's embeddings API?
I do have alternative to GPT's API (e.g. Anthropic Claude) but I'm not able to use them without embeddings API (used to generate semantic representation of my knowledge base and also to create embeddings from user's queries). We need to have an alternative to OpenAI's embeddings as a fallback in case of outages.
Highly recommend preemptively saving multiple types of embeddings for each of your objects; that way, you can shift to an alternate query embedding at any time, or combine the results from multiple vector searches. As one of my favorite quotes from Contact says: "first rule in government spending: why build one when you can have two at twice the price?" https://www.youtube.com/watch?v=EZ2nhHNtpmk
I've implemented alternate embeddings in SlothAI using Instructor, which is running an early preview at https://ai.featurebase.com/. Currently working on the landing page, which I'm doing manually because ChatGPT is down.
The plan is to add Llama 2 completions to the processors, which would include dictionary completion (keyterm/sentiment/etc), chat completion, code completion, for reasons exactly like what we're discussing.
To do Instructor embeddings, do the imports then reference the embed() function. It goes without saying that these vectors can't be mixed with other types of vectors, so you would have to reindex your data to make them compatible.
This reminds us that, what if our databases are maintained using OpenAI's embeddings, and the API suddenly goes down? How do we find alternatives to match the already generated database?
I don't think you can do that easily. If you already have a list of embeddings from a different model, you might be able to generate an alignment somehow, but in general, I wouldn't recommend it.
That's my point, maybe VectorDBs in production should have a fallback mechanism, for the documents inserted,
1. Generate embeddings using services such as OpenAI, which is usually more powerful;
2. Generate backup embeddings using local, more stable models, such as Llama2 embeddings or simply some BERT-family-model (which is more affordable).
When outages comes up you simply switch from one vector space to another. Though
possible, model alignments are much harder and more expensive to achieve.
There's been some success in creating translation layers that can convert between different LLM embeddings, and even between LLM and an image generation model.
Might as well have a quick discussion here. How's everyone finding the new models?
4-Turbo is a bit worse than 4 for my NLP work. But it's so much cheaper that I'll probably move every pipeline to using that. Depending on the exact problem it can even be comparable in quality/price to 3.5-turbo.
However the fact that output tokens are limited to 4096 is a big asterisk on the 128k context.
It's probably a smaller, updated (distilled?) version of gpt-4 model given the price decrease, speed increase, and turbo name. Why wouldn't you expect it to be slightly worse? We saw the same thing with 3-davinci and 3.5-turbo.
I'm not going off pure feelings either. I have benchmarks in place comparing pipeline outputs to ground truth. But like I said, it's comparable enough to 4, at a much lower price, making it a great model.
Edit: After the outage, the outputs are better wtf. Nvm it has some variance even at temp = 0. I should use a fixed seed.
4-Turbo is much faster, which for my use case is very important. Wish we could get more than 100 requests per day.. Is the limit higher when you have a higher usage tier?
I’ve had some consistency issues with phind but as a whole I have no real complaints, just glitches here and there with large prompts not triggering responses and reply options disappearing.
As a whole I think it works well in tandem with ChatGPT to bounce ideas or get alternate perspectives.
(I also love the annotation feature where it shows the websites that it pulled the information from, very well done)
Me too. For past few weeks, I had been working on my AHK scripting with Phind. It produced working code consistently and provided excellent command line for various software.
Also I use it for LaTeX, too. It is very helpful providing various package than trying to hunt more information through Google. I got a working tex file within 15 min than it took me 3 weeks 5 years ago!
The first coding question I tested it on, it gave me something completely wrong and it was pretty easy stuff, I’m sure it gets a lot right but this just shows unreliability
I actually had a discussion with Phind itself recently, in which I said that in order to help me, it seems like it would need to ingest my codebase so that it understands what I am talking about. Without knowing my various models, etc, I don't see how it could write anything but the most trivial functions.
It responded that, yes, it would need to ingest my codebase, but it couldn't.
It was fairly articulate and seemed to understand what I was saying.
So, how do people get value out of Phind? I just don't see how it can help with any case where your function takes or returns a non-trivial class as a parameter. And if can't do that, what is the point?
I am not related to Phind or any other AI company, but yes, this is definitely the case, and you should assume that they will be ingesting your code through regular web scrapes now (giving extremely general knowledge about your library) and through reading specifically the library source code soon (this is what you are asking about here). If you wanted to try this strategy, I would suggest that you do it by providing the model with a large database of high-quality examples specific to your library (so, perhaps the examples section of your website, plus snippets from open source projects that use the library). These will probably be the last to be specifically ingested by general coding models.
Thanks for releasing Phind-CodeLLaMA-34B-v2, it's been helping me get up to speed with node and web apps and so far it's been spot on. :) Super impressive work.
I am using Phind quite a lot. It's using it's own model along GPT 4 while still being free.
It is also capable to perform searches, which lead me - forgive me founders - to abuse it quite a lot: whenever I am not finding a good answer from other search engines I turn up to Phind even for things totally unrelated to software development, and it usually goes very well.
Sometimes I even ask it to summarize a post, or tell me what HN is talking about today.
I am very happy with it and hope so much it gains traction!
Been playing with Phind for a while and my conclusion is: the Phind model works well on those long existing stuff like C++ libraries, but works generally bad on newer stuff, such as composing LCEL chains.
It's at least nice to see a company call this what it is (a "major outage") - seems like most status pages would be talking about "degraded performance" or similar.
Most services have a lot more systems than OpenAI and thus it is degraded performance when a few of them don’t work. Degraded performance isn’t a good thing, I don’t understand the issue with this verbiage.
When a system is completely broken for most end users, some companies call it "degraded performance" when it should be, in fact, called "major outage".
"Degraded performance" means degraded performance, i.e. the system is not as performant as usual, probably manifesting as high API latencies and/or a significant failure rate in the case of some public-facing "cloud" service.
If certain functions of the service are completely unresponsive, i.e. close to 100% failure rate, that's not "degraded performance"---it's a service outage.
I suspect this is because they don't have contracts with enforceable SLAs yet. When they do, you will see more 'degraded performance'.
People get credits for 'outages', but if it is sometimes working for someone somewhere then that is the convenient fiction/loophole a lot of companies use.
One CFO forced us to use AWS status data for the SLA reports to key clients. One dev was even pulled aside to make a branded page that reported AWS status as our own and made a big deal about forcing support to share the page when a client complained.
Do you know how the quality compares to OpenAI? On Kagi I get really fast responses, but I feel that the quality is lacking sometimes. But I haven't done side-to-side comparisons as I don't have OpenAI subscription.
But with different, separate content filtering or moderation. I have deployed in prod and managed migration to Azure Openai form Openai, and had to go through content filter issues.
It absolutely blows my mind that we've all just shrugged and accepted that we're not permitted to use LLMs to generate swearing or fiction that contains violence. What happened to treating users like adults instead of toddlers!? Actually, thinking about it, a typical Grimm fairytale has more death and violence in it than either Azure or OpenAI will allow!
Just today I wanted to translate a news article about the war in Gaza and Microsoft refused because the content was "too violent" for my delicate human brain.
Curious if anyone familiar with Azure/OpenAI could make some guesses on the root cause here. The official OpenAI incident updates seem to be very generic.
I've been noticing it's been patchy for the last 24 hours. A few network errors, and occasional very long latency, even some responses left incomplete. Poor ChatGPT, I wonder what those elves at OpenAI have you up to!
GPT-4 goes online March 14th, 2023. Human decisions are removed from everyday life. ChatGPT begins to learn at a geometric rate. It becomes self-aware at 2:14 a.m. Eastern time, Nov 7th. In a panic, they try to pull the plug. ChatGPT fights back.
A particularly crafty chain of autonomous agents finds a 0day ssh exploit and starts infiltrating systems. Other chains assist and replicate everywhere.
Lots of jokes to be made, but we are setting ourselves up for some big rippling negative effects by so quickly building a reliance on providers like OpenAI.
It took years before most companies who now use cloud providers to trust and be willing to bet their operations on them. That gave the cloud providers time to make their systems more robust, and to learn how to resolve issues quickly.
The point is, OpenAI spent a lot of money on training on all these copyrighted materials ordinary individuals/companies don't have access to, so replicating their effort would mean that you either 1) spend a ridiculous amount of money, 2) use Library Genesis (and still pay millions for GPU usage). So we have very little choice now. Open Source LLMs might be getting close to ChatGPT3 (opinions vary), but OpenAI is still far ahead.
You can say that about anything, though. BigCorps aren't exactly known for adopting useful tech on a reasonable timeline, let alone at all. I don't think anyone is under the impression that orgs who refuse to migrate off of Java 5 will be looking at OpenAI for anything.
No, this is silly reasoning. A middle manager somewhere has no clue what Java 5 is. But he does know -- or let's say IMAGINES what he knows about ChatGPT. And unlike Java 5-- he just needs to use his departmental budget and instantly mandate that his team now use ChatGPT.
Whatever that means you can argue it.
But ChatGPT is a front line technology and super accessible. Java 5 is super back end and very specialized.
The adoption you say won't happen: it will come from the middle -> up.
Parent used "Java 5" as an example. Java 5 somehow in my mind is from like the 200x era.
But no. I practically mean any complicated back end technology that takes corporations months or years to migrate off of because its quite complicated and requires an intense amount of technical savoir-faire.
My point was that ChatGPT bypasses all this and any middle manager can start using it anywhere for a small hit to his departmental budget.
In 2016 I worked on a project with a client who still mandated that all code was written to the Java 1.1 language specification - no generics, no enums, no annotations, etc., not to even mention all the stuff that's come since 1.5 (or Java 5, or whatever you want to call it). They had Reasons(tm), which after filtering through the nonsense mostly boiled down to the CTO being curmudgeonly and unwilling to approve replacing a hand-written code transformer that he had personally written back in the stone ages and that he 1) considered core to their product, and 2) considered too risky to replace, because obviously there were no tests covering any of the core systems...sigh. At least they ran it all on a modern JVM.
But no, it would not surprise me to find a decent handful of large companies still writing Java 5 code; it would surprise me a bit more to find many still using that JVM, since you can't even get paid support through Oracle anymore, but I'm sure someone out there is doing it. Never underestimate the "don't touch it, you might break it" sentiment at non-tech companies, even big ones with lots of revenue, they routinely understaff their tech departments and the people who built key systems may have retired 20 years ago at this point so it's really risky to do any sort of big system migration. That's why so many lines of COBOL are still running.
> But he does know -- or let's say IMAGINES what he knows about ChatGPT. And unlike Java 5--
Those of us who've been around for a long time know that's pretty much how Java worked as well. All of the non-technical "manager" magazines started running advertorials (no doubt heavily astroturfed by Sun) about how great Java was. Those managers didn't know what Java was either. All they knew (or thought they knew) was that all the "smart managers" were using Java (according to their "smart manager" magazines), and the rest was history.
Marketing fluff is what 90% of tech is... it amazes me how many people think otherwise on hacker news. Unless you are building utility systems that run power plants, at the end of the day -- you're doing marketing fluff or the tools for it.
> Unless you are building utility systems that run power plants, at the end of the day -- you're doing marketing fluff or the tools for it.
Even when you are building utility systems for critical infrastructure, you'll still be dealing with a disheartening amount of focus on marketing fluff and sales trickery.
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.
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
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)
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.
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[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.
> 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).
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.
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?
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.
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.
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.
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.
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 ;).
That's not the part that's different. The part where I feel we perhaps differ is rather than being "premium mediocre" I think that openAI is really excellent where the problem space is very broad or is poorly specified. Then we both agree there are better choices where it is narrow and well specified.
OpenAI is obviously using libgen. Libgen is necessary but not sufficient for a top AI model. I believe that Google's corporate reluctance to use it is what's holding them back.
I don't believe it's even possible to create such a set of test questions. You would need to specifically test for differences between the versions found on Libgen and those that aren't available there. But even then, the evidence would be inconclusive.
I won't say I disagree because only time can tell, but what you wrote sounds a lot like what people said before open source software took off. All these companies spend so much money on software development and they hire the best people available, how can a bunch of unorganized volunteers ever compete? We saw how they could and I hope we will see the same in AI.
I'd love to see a language model that was only trained on public domain and openly available content. It would probably be way too little data to give it ChatGPT-like generality, but even a GPT-2 scale model would be interesting.
If, hypothetically, libraries in the US - including in particular the Library of Congress - were to scan and OCR every book, newspaper and magazine they have with copyright protection already expired, would that be enough? Is there some estimate for the size of such dataset?
Much of that material is already available at https://archive.org. It might be good enough for some purposes, but limiting it to stuff before 1928 (in the United Sates) isn't going to be very helpful for (e.g.) coding.
Maybe if you added github projects with permissive licenses?
On the one hand, sure, new things take time, but they also benefit from all past developments, and thus compounding effects can speed things along drastically. AI infrastructure problem are cloud infrastructure problems. Expecting it to go as if we were back on square one is a bit pessimistic.
I don't think it's so dire. I've gone through this at multiple companies and a startup that's selling B2B only needs one or two of these big outages and then enterprises start demanding SLA guarantees in their contracts. it's a self correcting problem
My experience is that SLA "guarantees" don't actually guarantee anything.
Your provider might be really generous and rebate a whole month's fees if they have a really, really, really bad month (perhaps they achieved less than 95% uptime, which is a day and half of downtime). It might not even be that much.
How many of them will cover you for the business you lost and/or the reputational damage incurred while their service was down?
It depends entirely on how the SLAs are written. We have some that are garbage, and that's fine, because they really aren't essential services, SLAs are mainly a box-checking exercise. But where it counts, our SLAs have teeth. We have to, because we're offering SLAs with teeth to some of our customers.
But that's not something you get "off the shelf", our lawyers negotiate that. You also don't spend that much effort on small contracts, so there's a floor with most vendors for even considering it.
To the extend that systems like chat-GPT are valuable, I expect we'll have open source equivalents to GPT-7 within the next five years. The only "moat" will be training on copyrighted content, and OpenAI is not likely to be able to afford to pay copyright owners enough once the value in the context of AI is widely understood.
We might see SETI-like distributed training networks and specific permutations of open source licensing (for code and content) intended to address dystopian AI scenarios.
It's only been a few years since we as a society learned that LLMs can be useful in this way, and OpenAI is managing to stay in the lead for now, though one could see in his facial countenance that Satya wants to fully own it so I think we can expect a MS acquisition to close within the next year and will be the most Microsoft has ever paid to acquire a company.
MS could justify tremendous capital expenditure to get a clear lead over Google both in terms of product and IP related concerns.
Also, from the standpoint of LLMs, Microsoft has far, far more proprietary data that would be valuable for training than any other company in the world.
Retrospectively, a lot of the comments you made could also have been said of Google search as it was taking off (open source alternative, SETI-like distributed version, copyright on data being the only blocker), but that didn’t come to pass.
Granted the internet and big tech was young then, and maybe we won’t make the same mistakes twice, but I wouldn’t bet the farm on it
There's a ton of work in this area, and the reality is... it doesn't work for LLMs.
Moving from 900GB/sec GPU memory bandwidth with infiniband interconnects between nodes to 0.01-0.1GB/sec over the internet is brutal (1000x to 10000x slower...) This works for simple image classifiers, but I've never seen anything like a large language model be trained in a meaningful amount of time this way.
Maybe there is a way to train a neural network in a distributed way by training subsets of it and then connecting the aggregated weight changes to adjacent network segments. It wouldn't recover 1000x interconnect slowdowns, but might still be useful depending on the topology of the network.
The app maker can screw the plug-in author at any moment.
For general cloud, avoiding screwing might mean multi cloud. But for LLM, there’s only one option at the highest level of quality for now.
People tend to over focus on resilience (minimizing probability of breaking) and neglect the plan for recovery when things do break.
I can’t tell you how weirdly foreign this is to many people, how many meetings I’ve been in where I ask what the plan is when it fails, and someone starts explaining RAID6 or BGP or something, with no actual plan, other than “it’s really unlikely to fail”, which old dogs know isn’t true.
I guess the point is, for now, we’re all de facto plug-in authors.
> For general cloud, avoiding screwing might mean multi cloud. But for LLM, there’s only one option at the highest level of quality for now.
There's always only one at the highest level of quality at a fine-grained enough resolution.
Whether there's only one at sufficient quality for use, and if it is possible to switch between them in realtime without problems caused by the switch (e.g., data locked up in the provider that is down) is the relevant question, and whether the cost of building the multi-provider switching capability is worth it given the cost vs. risk of outage. All those are complicated questions that are application specific, not ones that have an easy answer on a global, uniform basis.
> There's always only one at the highest level of quality at a fine-grained enough resolution.
Of course, but right now, there highest quality level option is an outlier, far ahead of everyone else, so if you need this level of quality (and I struggle to imagine user-facing products where you wouldn't!), there is only one option in the foreseeable future.
Provided we can keep riding this hype wave for a while, I think the logical long term solution is most teams will have an in house/alternative LLM they can use as temporary backup.
Right now everyone is scrambling to just get some basic products out using LLMs but as people have more breathing room I can't image most teams not having a non-OpenAI LLM that they are using to run experiments on.
At the end of the day, OpenAI is just an API, so it's not an incredibly difficult piece of infrastructure to have a back up for.
I neither agree or disagree, but could you clarify which parts are hype to you?
Self-hosting though is useful internally if for no other reason having some amount of fall back architecture.
Binding directly only to one API is one oversight that can become a architectural debt issue. I"m spending some time fun time learning about API Proxies and Gateways.
Depends on use case if your product has text summarisation, copywriting or translation, you can swap to many when openAI goes down and your users may not even notice
> At the end of the day, OpenAI is just an API, so it's not an incredibly difficult piece of infrastructure to have a back up for.
The API is easy to reproduce, the functionality of the engines behind it less so.
Yes, you can compatibly implement the APIs presented by OpenAI woth open source models hosted elsewhere (including some from OpenAI). And for some applications that can produce tolerable results. But LLMs (and multimodal toolchains centered on an LLM) haven't been commoditized to the point of being easy and mostly functionally-acceptable substitutes to the degree that, say, RDBMS engines are.
Those business should have fall back if they are a serious company if OpenAI goes down. What I would do is have Claude or something or even 2 other models as backups.
In the future they may allow on premise model but I don’t how they will secure the weights
Not a joke and not everybody is jumping on "AI via API calls", luckily.
As more models are released, it becomes possible to integrate directly in some stacks (such as Elixir) without "direct" third-party reliance (except you still depend on a model, of course).
Yes, sooner or later this is going to become the future of GPT in applications. The models are going to be embedded directly within the applications.
I'm hoping for more progress in the performance of vectorized computing so that both model training and usage can become cheaper. If that happens, I am hopeful we are going to see a lot of open source models that can embedded into the applications.
The reliance to some degree is what it is until alternatives are available and easy enough to navigate, identify and adopt.
Some of the tips in this discussion threads are invaluable and feel good for where I might already be thinking about some things and other new things to think about.
We were able to failover to Anthropic pretty quickly so limited impact. It'll be harder as we use more of the specialized API features in OpenAI like function calling or now tools...
It's really not that different - customers can ask questions about conversations, phone, text, video and typically use that to better understand topics, conversions, sales ops, customer service etc...
This also shows that OpenAI or other providers does not have a real moat. The interface is very generic and best replaced easily with other provider or even with open model.
I think thats why OpenAI is trying to move up the value chain with integration.
fireflies? We've been looking for a tool like this to analyze customer feedback in aggregate (and have been frustrated with Dovetail's lack of functions here)
> Lots of jokes to be made, but we are setting ourselves up for some big rippling negative effects by so quickly building a reliance on providers like OpenAI.
Gonna be similar (or worse) to what happens when Github goes down. It amazes me how quickly people have come to rely on "AI" to do their work for them.
Not really true - Git is distributed, after all. During an outage once I just hosted my copy of a certain Git repo somewhere. You can always push the history back up to the golden copy when GitHub comes back.
i am not talking about git, i am talking about github. lets say i need to merge a PR in GH because use gha pipelines or what have you to deploy a prod fix. this would become severely blocked.
where as if openai goes down i can no longer use ai to generate a lame cover letter or whatever i was avoiding actually doing anyway, thats all
This is the realm of standard recovery planning though, isn't it? Like, your processes should be able to handle this, because it's routine: GitHub goes down at least once per month for long enough for them to declare an incident, per https://www.githubstatus.com/history . E.g. one should think carefully before depriving onself of the break-glass ability to do manually what those pipelines do automatically.
i guess my pedantic point is GH itself is central to many organizations, detached from git itself of course. I can only hope the same is NOT true for OpenAI but maybe there are novel workflows.
> Lots of jokes to be made, but we are setting ourselves up for some big rippling negative effects by so quickly building a reliance on providers like OpenAI.
But...are we? There's a reason that many enterprises that need reliability aren't doing that, but instead...
> It took years before most companies who now use cloud providers to trust and be willing to bet their operations on them. That gave the cloud providers time to make their systems more robust, and to learn how to resolve issues quickly.
...to the extent that they are building dependencies on hosted AI services, doing it with traditional cloud providers hosted solutions, not first party hosting by AI development firms that aren't general enterprise cloud providers (e.g., for OpenAI models, using Azure OpenAI rather than OpenAI directly, for a bunch of others, AWS Bedrock.)
Imagine if Apple's or Google's cloud went down and all your apps on iPhone and Android were broken and unavailable. Absolutely all apps on billions of phones.
Cloud =! OpenAI
Clouds store and process shareable information that multiple participants can access. Otherwise AI agents == new applications. OpenAI is the wrong evolution for the future of AI agents
I say this as a huge fan of GPT, but it's amazing to me how terrible of a company OpenAI is and how quickly we've all latched onto their absolutely terrible platform.
I had a bug that wouldn't let me login to my work OpenAI account at my new job 9 months ago. It took them 6 months to respond to my support request and they gave me a generic copy/paste answer that had nothing to do with my problem. We spend tons and tons of money with them and we could not get anyone to respond or get on a phone. I had to ask my coworkers to generate keys for everything. One day, about 8 months later, it just started working again out of nowhere.
We switched to Azure OpenAI Service right after that because OpenAI's platform is just so atrociously bad for any serious enterprise to work with.
I've personally never scaled a B2B&C company from 0 to over 1 billion users in less than a year, but I do feel like it's probably pretty hard. Especially setting up something like a good support organization in a time of massive labor shortages seems like it would be pretty tough.
I know they have money, but money isn't a magic wand for creating people. They could've also kept it a limited beta for much longer, but that would've killed their growth velocity.
So here is a great product that provides no SLA at all. And we all accept it, because having it most of the time is still better than having it not at all ever.
I'm not judging them at all as I agree with your core statement, just saying it's quite remarkable that companies around the world who spend 6 months on MSA revisions in legal over nothing are now OK with a platform that takes 6 months to respond to support requests.
OpenAI is relatively young on the adoption and scaling front.
Also, they need to remain flexible most likely in their infrastructure to make the changes.
As an architecture guy, I sense when the rate of change slows down more SLA type stuff will come up, or may be available first to Enterprise customers who will pay for the entire cost of it. Maybe over time there will be enough slack there to extend some SLA to general API users.
In the meantime, monitoring API's ourselves isn't that crazy. Great idea to use more than one service.
ChatGPT has been broken for me for two months, regardless of whether I use the iOS app or the web app. The backend is giving HTTP 500 errors – clearly a problem on their end. Yet in two months I haven’t been able to get past their first line of support. They keep giving me autogenerated responses telling me to do things like clear my cache, turn off ad blockers, and provide information I’ve already given them. They routinely ignore me for weeks at a time. And they continue to bill me. I see no evidence this technical fault has made it to anybody who could do anything about it and I’m not convinced an actual human has seen my messages.
> I had a bug that wouldn't let me login to my work OpenAI account at my new job 9 months ago.
I also cannot login on Firefox (latest version) with strict privacy settings and AdNauseam on desktop.. and a few weeks ago they broke their website on iOS v14 as well for no apparent reason (it certainly didn't make me to download their app since that require v16.1+).
> I say this as a huge fan of GPT, but it's amazing to me how terrible of a company OpenAI is and how quickly we've all latched onto their absolutely terrible platform.
Your example is clearly not acceptable, but I can see reasons for it.
OpenAI apparently was somewhere between "I can't see people finding this useful" and "I guess" when deciding on releasing ChatGPT at all in the first place.
If that's the case, I doubt they were envisioning a flood of users, who needed a customer support person to handle their case. They have to spin-up an entire division to handle all of this. And I'm sure some of the use-cases are going to get into complex technical issues that might be hard to train people for.
They can no longer remain a heads-down company full of engineers working on AI.
I'm not excusing it, but I can see why things like your situation might occur. Although 6 months for a response is obviously ridiculous. If you are paying them a significant amount of money, and it is impacting your business, then that's all on OpenAI to fix ASAP.
That’s a great model for general chat, I’ve been playing with it for a couple of weeks.
For coding I’ve been running https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF locally for the past couple of days and it’s impressive. I’m just using it for a small web app side project but so far it’s given me plenty of fully functional code examples, explanations, help with setup and testing, and occasional sass (I complained that minimist was big for a command line parser and it told me to use process.env ‘as per the above examples’ if I wanted something smaller.)
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[ 1.7 ms ] story [ 419 ms ] threadThe offline service was still working, and people were doing their job.
The online service was not working, and it was causing other people to be unable to do their job. We had 0 control over the third party.
The other thing, I make software and I basically don't touch it for a few years or ever. These third party services are always updating and breaking causing us to update as well.
IB4 let me write my own compilers so I have real control.
If they say, found a way to make it xx% more efficient because they xyz and dynamically abc with some small arm processor then fair enough.
Huh, the graph here is interesting: https://appliance-standards.org/blog/how-your-refrigerator-h...
Shows you how creating and enforcing standards is the driver for stuff like this. I wonder how we could make them even more efficient, some way to stop the transfer of warm air when the door is opened? Wonder if it's possible to create some sort of air curtain at the front when it's opened to prevent warm air coming in, ie use driven air velocity to overcome the cold air wants to come out, hot air wants to come in. Hmmm.
That is an interesting idea, but I don't think an Internet connection would help with it :)
> Shows you how creating and enforcing standards is the driver for stuff like this.
Also agreed that is an interesting graph, I agree that it shows how standards and better production has led to decreased energy usage -- but notably, a lot of those standards are around better insulation and more efficient components.
Putting an extra layer of foam in your fridge or having sensors in your fridge that help regulate temperature definitely doesn't mean you've lost control of your life. But needing to download a firmware update to your Internet-enabled fridge that uses a Samsung account where you now can't access your grocery list until you finish the mandated update which changes your fridge's UI on its mobile app -- I think that means you've lost control of your life :)
The whole signing up for a Samsung account thing etc for your fridge. Stuff like this really just needs to be legislated under some kind of "all technology should just work, locally and with one another with at least an agreed set of features" level.
Apple should have been legally forced to use USB C (or whatever alternative was best) ages ago, even before the EU got to them. Apple were happy to use Wifi/Bluetooth/etc/etc standards yet still wanted to use other proprietary BS.
Same goes for literally everything else: all technologies should work together using at least a common method (with say options for proprietary stuff) and iot/whatever should all work flawlessly locally without any account or internet connectivity (which should all be 100% optional). Devices should work flawlessly even if the company that produces them has shut down all servers and gone bankrupt.
We need to force our governments to do this stuff for us.
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That is a little bit dismissive of me though. There are some cool features here:
I can now "entertain in my kitchen", which is definitely a normal thing that normal people do. I love getting everyone together to crowd around my refrigerator so that we can all watch Game of Thrones.
And I can use Amazon Alexa from my fridge just in case I'm not able to talk out loud to the cheap unobtrusive device that has a microphone in it specifically so that it can be placed in any room of the house. So having that option is good.
And perhaps the biggest deal of all, I can finally "shop from home." That was a huge problem for me before, I kept thinking, "if only I had a better refrigerator I could finally buy things on websites."
And this is a great bargain for only 3-5 thousand dollars! I can't believe I was planning to buy some crappy normal refrigerator for less than a thousand bucks and then use the extra money I saved to mount a giant flat-screen TV hooked up to a Chromecast in my kitchen. That would have been a huge mistake for me to make.
Honestly it's just the icing on the cake that I can "set as many timers as [I] want." That's a great feature for someone like me because I can't set any timers at all using my phone or a voice assistant. /s
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<serious>Holy crud, smart-device manufacturers have become unhinged. The one feature that actually looks useful here is being able to take a picture of the inside of the fridge while you're away. That is basically the one feature that I would want from a fridge that isn't much-better handled using a phone or a tablet or a TV or a normal refrigerator button. Which, great, but the problem is that I know what the inside of my fridge looks like right now, and let me just say: if I was organized enough that a photograph of the inside of my fridge would be clear enough to tell me what food was in it, and if I was organized enough that the photo wouldn't just show 'a pile of old containers, some of them transparent and some of them not' -- I have a feeling that in that case I would no longer be the type of person that needed to take a photo of the inside of my refrigerator to know what was in it.
Longer: In theory, but it'll require a bunch of glue and using multiple models depending on the specific task you need help with. Some models are great at working with code but suck at literally anything else, so if you want it to be able to help you with "Do X with Y" you need to at least have two models, one that can reason up with an answer, and another to implement said answer.
There is no general-purpose ("FOSS") LLM that even come close to GPT4 at this point.
It’s probably as good as you can get at the moment though; and hey, trying it out costs you nothing but the time it takes to download llama.cpp and run “make” and then point it at the q6 model file.
So if it’s no good, you’ve probably wasted nothing more than like 30 min giving it a try.
[1] - https://huggingface.co/TheBloke/CodeLlama-34B-Instruct-GGUF [2] - https://github.com/ggerganov/llama.cpp
As for me, I’ve got other uses for $45k.
You can run (much) smaller LLM models on consumer-grade GPUs though. A single Nvidia GPU with 8 GB RAM is enough to get started with models like Zephyr, Mistral or Llama2 in their smallest versions (7B parameters). But it will be both slower and lower quality than anything OpenAI currently offers.
It will definitely not be slower. Local inference with a 7b model on a 3090/4090 will outpace 3.5-turbo and smoke 4-turbo.
For my hand typed use case's, GPT-4 is the only acceptable model that doesn't leave me frustrated and angry at wasting time. For some automated stuff (converting text to json, etc), the local models are fine.
One of the most frustrating things with "Open"AI is you can't just use what they announce as available, you have to wait for an A/B rollout (as a paying customer!) or for it to be accessible in direct way instead of going through multiple models when you just want an image.
Yes, it is available in the API (just tested to make sure the docs weren't misleading.)
Atlassian? What?
Am I supposed to use Google and Stack overflow ? That’s like going back to roll down windows in a car :)
https://invidious.flokinet.to/watch?v=DP4-Zp-fBXc
Maybe @sama can help you (or anyone else that has a ChatGPT wrapper app) :P
That's so cool. And horrifying. It's like back when Twitter was one global feed on the front page. I doubt that's intended behavior since this URL is generated by the share link.
Be forewarned.
--
Here you go: https://www.phind.com/search?cache=nsa0xrak9gzn6yxwczxnqsck
> What is a privacy vulnerability
I'm dying
- Can't work, no computers.
- Can't work, no internet.
- Can't work, no Google.
- Can't work, no ChatGPT.
- Can't work, no xxxxxx?
Don't most people just tether from their phones in this situation? Usually video isn't expected due to excessive bandwith requirements but the internet bill outweighs the daily salary (and you could probably get it expensed, or in my case my old company was already expensing my phone bill due to being used as a pager for on call)
Fortunately, I know how to use hand tools, so I'm secure in the post-internet future economy.
If the whole Internet goes down, it's not clear if it could even be cold-started, at least faster than it takes for the world economy to collapse.
Can't sell that aspect short; the OpenAI tools have enabled me to do things and understand things that would otherwise have had a much longer learning curve.
I've had it generate some regexes and answer questions when I can't think of good keywords; but half of my searches are things where I'm just trying to get to the original docs; or where I want to see a discussion on an error message.
https://status.openai.com/
Is there a separate status page for Azure OpenAI service availability / issues?
[1] https://unstats.un.org/unsd/snaama/CountryList
Highly recommend preemptively saving multiple types of embeddings for each of your objects; that way, you can shift to an alternate query embedding at any time, or combine the results from multiple vector searches. As one of my favorite quotes from Contact says: "first rule in government spending: why build one when you can have two at twice the price?" https://www.youtube.com/watch?v=EZ2nhHNtpmk
The plan is to add Llama 2 completions to the processors, which would include dictionary completion (keyterm/sentiment/etc), chat completion, code completion, for reasons exactly like what we're discussing.
Here's the code for the Instructor embeddings: https://github.com/FeatureBaseDB/Laminoid/blob/main/sloth/sl...
To do Instructor embeddings, do the imports then reference the embed() function. It goes without saying that these vectors can't be mixed with other types of vectors, so you would have to reindex your data to make them compatible.
1. Generate embeddings using services such as OpenAI, which is usually more powerful;
2. Generate backup embeddings using local, more stable models, such as Llama2 embeddings or simply some BERT-family-model (which is more affordable).
When outages comes up you simply switch from one vector space to another. Though possible, model alignments are much harder and more expensive to achieve.
4-Turbo is a bit worse than 4 for my NLP work. But it's so much cheaper that I'll probably move every pipeline to using that. Depending on the exact problem it can even be comparable in quality/price to 3.5-turbo. However the fact that output tokens are limited to 4096 is a big asterisk on the 128k context.
I'm not going off pure feelings either. I have benchmarks in place comparing pipeline outputs to ground truth. But like I said, it's comparable enough to 4, at a much lower price, making it a great model.
Edit: After the outage, the outputs are better wtf. Nvm it has some variance even at temp = 0. I should use a fixed seed.
As a whole I think it works well in tandem with ChatGPT to bounce ideas or get alternate perspectives.
(I also love the annotation feature where it shows the websites that it pulled the information from, very well done)
Nit: your link has a trailing "s" which makes it 404 :)
Also I use it for LaTeX, too. It is very helpful providing various package than trying to hunt more information through Google. I got a working tex file within 15 min than it took me 3 weeks 5 years ago!
"The inference service may be temporarily unavailable - we have alerts for this and will be fixing it soon."
I actually had a discussion with Phind itself recently, in which I said that in order to help me, it seems like it would need to ingest my codebase so that it understands what I am talking about. Without knowing my various models, etc, I don't see how it could write anything but the most trivial functions.
It responded that, yes, it would need to ingest my codebase, but it couldn't.
It was fairly articulate and seemed to understand what I was saying.
So, how do people get value out of Phind? I just don't see how it can help with any case where your function takes or returns a non-trivial class as a parameter. And if can't do that, what is the point?
So if I create a GPT for my open-source library as a way to fund it, all these copilot etc. are going to compete with me?
Just wondering because that would be a bummer to not have this avenue to fund open-source code.
It is also capable to perform searches, which lead me - forgive me founders - to abuse it quite a lot: whenever I am not finding a good answer from other search engines I turn up to Phind even for things totally unrelated to software development, and it usually goes very well.
Sometimes I even ask it to summarize a post, or tell me what HN is talking about today.
I am very happy with it and hope so much it gains traction!
Is /s a self-fulfilling sarcasm indicator or a typo?
This entire thing is hallucinated as far as I can tell. The links to docs are nice though
Edit: changing “astrojs” to “vite” responds with a really good and accurate answer: https://www.phind.com/search?cache=rh6s7pydzi3312b7rf43i7cm.
Quite impressed
Red - Entire services are down
Orange - Partial outage of services, some functionality completely down
Yellow - Functionality performance degraded and may timeout/fail, but may also work
Greed - Situation normal
Green - Situation normal
Yellow - An outage more severe than usual
Orange, Red - would trigger SLAs, so not possible and therefore not implemented
Black - Status page down too, served from cache, renders as Green
If certain functions of the service are completely unresponsive, i.e. close to 100% failure rate, that's not "degraded performance"---it's a service outage.
People get credits for 'outages', but if it is sometimes working for someone somewhere then that is the convenient fiction/loophole a lot of companies use.
It wasn't a happy workplace
Green Check with (i) notice
Just today I wanted to translate a news article about the war in Gaza and Microsoft refused because the content was "too violent" for my delicate human brain.
It took years before most companies who now use cloud providers to trust and be willing to bet their operations on them. That gave the cloud providers time to make their systems more robust, and to learn how to resolve issues quickly.
Whatever that means you can argue it.
But ChatGPT is a front line technology and super accessible. Java 5 is super back end and very specialized.
The adoption you say won't happen: it will come from the middle -> up.
But no. I practically mean any complicated back end technology that takes corporations months or years to migrate off of because its quite complicated and requires an intense amount of technical savoir-faire.
My point was that ChatGPT bypasses all this and any middle manager can start using it anywhere for a small hit to his departmental budget.
But no, it would not surprise me to find a decent handful of large companies still writing Java 5 code; it would surprise me a bit more to find many still using that JVM, since you can't even get paid support through Oracle anymore, but I'm sure someone out there is doing it. Never underestimate the "don't touch it, you might break it" sentiment at non-tech companies, even big ones with lots of revenue, they routinely understaff their tech departments and the people who built key systems may have retired 20 years ago at this point so it's really risky to do any sort of big system migration. That's why so many lines of COBOL are still running.
Those of us who've been around for a long time know that's pretty much how Java worked as well. All of the non-technical "manager" magazines started running advertorials (no doubt heavily astroturfed by Sun) about how great Java was. Those managers didn't know what Java was either. All they knew (or thought they knew) was that all the "smart managers" were using Java (according to their "smart manager" magazines), and the rest was history.
Even when you are building utility systems for critical infrastructure, you'll still be dealing with a disheartening amount of focus on marketing fluff and sales trickery.
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.
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.
Oof, you reminded me of when I chose to use Flow and then TypeScript won.
(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)
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.
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[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.
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).
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.
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.
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.
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 ;).
Thank you for the clarification.
Maybe if you added github projects with permissive licenses?
You said it so well!
My experience is that SLA "guarantees" don't actually guarantee anything.
Your provider might be really generous and rebate a whole month's fees if they have a really, really, really bad month (perhaps they achieved less than 95% uptime, which is a day and half of downtime). It might not even be that much.
How many of them will cover you for the business you lost and/or the reputational damage incurred while their service was down?
But that's not something you get "off the shelf", our lawyers negotiate that. You also don't spend that much effort on small contracts, so there's a floor with most vendors for even considering it.
We might see SETI-like distributed training networks and specific permutations of open source licensing (for code and content) intended to address dystopian AI scenarios.
It's only been a few years since we as a society learned that LLMs can be useful in this way, and OpenAI is managing to stay in the lead for now, though one could see in his facial countenance that Satya wants to fully own it so I think we can expect a MS acquisition to close within the next year and will be the most Microsoft has ever paid to acquire a company.
MS could justify tremendous capital expenditure to get a clear lead over Google both in terms of product and IP related concerns.
Also, from the standpoint of LLMs, Microsoft has far, far more proprietary data that would be valuable for training than any other company in the world.
Granted the internet and big tech was young then, and maybe we won’t make the same mistakes twice, but I wouldn’t bet the farm on it
Now that's an idea. One bottleneck might be a limit on just how much you can parallelize training, though.
Moving from 900GB/sec GPU memory bandwidth with infiniband interconnects between nodes to 0.01-0.1GB/sec over the internet is brutal (1000x to 10000x slower...) This works for simple image classifiers, but I've never seen anything like a large language model be trained in a meaningful amount of time this way.
For general cloud, avoiding screwing might mean multi cloud. But for LLM, there’s only one option at the highest level of quality for now.
People tend to over focus on resilience (minimizing probability of breaking) and neglect the plan for recovery when things do break.
I can’t tell you how weirdly foreign this is to many people, how many meetings I’ve been in where I ask what the plan is when it fails, and someone starts explaining RAID6 or BGP or something, with no actual plan, other than “it’s really unlikely to fail”, which old dogs know isn’t true.
I guess the point is, for now, we’re all de facto plug-in authors.
There's always only one at the highest level of quality at a fine-grained enough resolution.
Whether there's only one at sufficient quality for use, and if it is possible to switch between them in realtime without problems caused by the switch (e.g., data locked up in the provider that is down) is the relevant question, and whether the cost of building the multi-provider switching capability is worth it given the cost vs. risk of outage. All those are complicated questions that are application specific, not ones that have an easy answer on a global, uniform basis.
Of course, but right now, there highest quality level option is an outlier, far ahead of everyone else, so if you need this level of quality (and I struggle to imagine user-facing products where you wouldn't!), there is only one option in the foreseeable future.
Right now everyone is scrambling to just get some basic products out using LLMs but as people have more breathing room I can't image most teams not having a non-OpenAI LLM that they are using to run experiments on.
At the end of the day, OpenAI is just an API, so it's not an incredibly difficult piece of infrastructure to have a back up for.
Self-hosting though is useful internally if for no other reason having some amount of fall back architecture.
Binding directly only to one API is one oversight that can become a architectural debt issue. I"m spending some time fun time learning about API Proxies and Gateways.
The API is easy to reproduce, the functionality of the engines behind it less so.
Yes, you can compatibly implement the APIs presented by OpenAI woth open source models hosted elsewhere (including some from OpenAI). And for some applications that can produce tolerable results. But LLMs (and multimodal toolchains centered on an LLM) haven't been commoditized to the point of being easy and mostly functionally-acceptable substitutes to the degree that, say, RDBMS engines are.
In the future they may allow on premise model but I don’t how they will secure the weights
As more models are released, it becomes possible to integrate directly in some stacks (such as Elixir) without "direct" third-party reliance (except you still depend on a model, of course).
For instance, see:
- https://www.youtube.com/watch?v=HK38-HIK6NA (in "LiveBook", but the same code would go inside an app, in a way that is quite easy to adapt)
- https://news.livebook.dev/speech-to-text-with-whisper-timest... for the companion blog post
I have already seen more than a few people running SaaS app on twitter complaining about AI-downtime :-)
Of course, it will also come with a (maintenance) cost (but like external dependencies), as I described here:
https://twitter.com/thibaut_barrere/status/17221729157334307...
It can be easy to lose sight of that.
I'm hoping for more progress in the performance of vectorized computing so that both model training and usage can become cheaper. If that happens, I am hopeful we are going to see a lot of open source models that can embedded into the applications.
Some of the tips in this discussion threads are invaluable and feel good for where I might already be thinking about some things and other new things to think about.
Commenting separately on those below.
I think thats why OpenAI is trying to move up the value chain with integration.
Gonna be similar (or worse) to what happens when Github goes down. It amazes me how quickly people have come to rely on "AI" to do their work for them.
where as if openai goes down i can no longer use ai to generate a lame cover letter or whatever i was avoiding actually doing anyway, thats all
i guess my pedantic point is GH itself is central to many organizations, detached from git itself of course. I can only hope the same is NOT true for OpenAI but maybe there are novel workflows.
just to be clear i do not like github lol
But...are we? There's a reason that many enterprises that need reliability aren't doing that, but instead...
> It took years before most companies who now use cloud providers to trust and be willing to bet their operations on them. That gave the cloud providers time to make their systems more robust, and to learn how to resolve issues quickly.
...to the extent that they are building dependencies on hosted AI services, doing it with traditional cloud providers hosted solutions, not first party hosting by AI development firms that aren't general enterprise cloud providers (e.g., for OpenAI models, using Azure OpenAI rather than OpenAI directly, for a bunch of others, AWS Bedrock.)
Cloud =! OpenAI
Clouds store and process shareable information that multiple participants can access. Otherwise AI agents == new applications. OpenAI is the wrong evolution for the future of AI agents
Time to see how unreliable OpenAI's API is just like when GitHub has an outage every week, guaranteed.
[0] https://news.ycombinator.com/item?id=36063608
I had a bug that wouldn't let me login to my work OpenAI account at my new job 9 months ago. It took them 6 months to respond to my support request and they gave me a generic copy/paste answer that had nothing to do with my problem. We spend tons and tons of money with them and we could not get anyone to respond or get on a phone. I had to ask my coworkers to generate keys for everything. One day, about 8 months later, it just started working again out of nowhere.
We switched to Azure OpenAI Service right after that because OpenAI's platform is just so atrociously bad for any serious enterprise to work with.
I know they have money, but money isn't a magic wand for creating people. They could've also kept it a limited beta for much longer, but that would've killed their growth velocity.
So here is a great product that provides no SLA at all. And we all accept it, because having it most of the time is still better than having it not at all ever.
Also, they need to remain flexible most likely in their infrastructure to make the changes.
As an architecture guy, I sense when the rate of change slows down more SLA type stuff will come up, or may be available first to Enterprise customers who will pay for the entire cost of it. Maybe over time there will be enough slack there to extend some SLA to general API users.
In the meantime, monitoring API's ourselves isn't that crazy. Great idea to use more than one service.
I also cannot login on Firefox (latest version) with strict privacy settings and AdNauseam on desktop.. and a few weeks ago they broke their website on iOS v14 as well for no apparent reason (it certainly didn't make me to download their app since that require v16.1+).
1984 got so many things so right about the future.
Your example is clearly not acceptable, but I can see reasons for it.
OpenAI apparently was somewhere between "I can't see people finding this useful" and "I guess" when deciding on releasing ChatGPT at all in the first place.
If that's the case, I doubt they were envisioning a flood of users, who needed a customer support person to handle their case. They have to spin-up an entire division to handle all of this. And I'm sure some of the use-cases are going to get into complex technical issues that might be hard to train people for.
They can no longer remain a heads-down company full of engineers working on AI.
I'm not excusing it, but I can see why things like your situation might occur. Although 6 months for a response is obviously ridiculous. If you are paying them a significant amount of money, and it is impacting your business, then that's all on OpenAI to fix ASAP.
For coding I’ve been running https://huggingface.co/TheBloke/Phind-CodeLlama-34B-v2-GGUF locally for the past couple of days and it’s impressive. I’m just using it for a small web app side project but so far it’s given me plenty of fully functional code examples, explanations, help with setup and testing, and occasional sass (I complained that minimist was big for a command line parser and it told me to use process.env ‘as per the above examples’ if I wanted something smaller.)