This is pretty exciting. I'm a copilot user at work, but also have access to Claude. I'm more inclined to use Claude for difficult coding problems or to review my work as I've just grown more confident in its abilities over the last several months.
I too use Claude more frequently than OpenAi GPT4o. I think this is a two fold move for MS and I like it. Claude being more accurate / efficient for me says it's likely they see the same thing, win number 1. The second is with all the OpenAI drama MS has started to distance themselves over a souring relationship (allegedly). If so, this could be a smart move away tactfully.
Either way, Claude is great so this is a net win for everyone.
Conversely I feel that the experience of searching has been degraded by a lot since 2016/17. My these is that, at this time, online spam increased by an order of magnitude
I think it was the switch from desktop search traffic being dominant to mobile traffic being dominant, that switch happened around the end of 2016.
Google used to prioritise big comprehensive articles on subjects for desktop users but mobile users just wanted quick answers, so that's what google prioritised as they became the biggest users.
But also, per your point, I think those smaller simpler less comprehensive posts are easier to fake/spam than the larger more compreshensible posts that came before.
Old style Google search is dead, folks just haven’t closed the casket yet. My index queries are down ~90%. In the future, we’ll look back at LLMs as a major turning point in how people retrieve and consume information.
It's getting ridiculous. Half of the time now when I ask AI to search some information for me, it finds and summarizes some very long article obviously written by AI, and lacking any useful information.
I recently tried to ask these tools for help with using a popular library, and both GPT-4o and Claude 3.5 Sonnet gave highly misleading and unusable suggestions. They consistently hallucinated APIs that didn't exist, and would repeat the same wrong answers, ignoring my previous instructions. I spent upwards of 30 minutes repeating "now I get this error" to try to coax them in the right direction, but always ending up in a loop that got me nowhere. Some of the errors were really basic too, like referencing a variable that was never declared, etc. Finally, Claude made a tangential suggestion that made me look into using a different approach, but it was still faster to look into the official documentation than to keep asking it questions. GPT-4o was noticeably worse, and I quickly abandoned it.
If this is the state of the art of coding LLMs, I really don't see why I should waste my time evaluating their confident sounding, but wrong, answers. It doesn't seem like much has improved in the past year or so, and at this point this seems like an inherent limitation of the architecture.
Well it is volume business. <1% of advanced skill developers will find AI helper useless but for 99% of IT CRUD peddlers these tools are quite sufficient. All in all if employers cut down 15-20% of net development costs by reducing head counts, it will be very worthwhile for companies.
It's worse than that. Now the balls of mud are distributed. We get incredibly complex interactions between services which need a lot of infrastructure to enable them, that requires more observability, which requires more infrastructure...
Yeah. You can fit a lot of business logic into a 100kloc monolith written by skilled developers.
Once you start shifting it to micro services the business logic gets spread out and duplicated.
At the same time each micro-service now has its own code to handle rest, graphql, grpc endpoints.
And each downstream call needs error handling and retry logic.
And of course now you need distributed tracing.
And of course now your auth becomes much more complex.
And of course now each service might be called multiple times for the one request - better make them idempotent.
And each service will drift in terms of underlying libraries.
And so on.
Now we have been adding in LLM solutions so there is no consistency in any of the above services.
Each dev rather than look at the existing approaches instead asks Claude and it provides a slightly different way each time - often pulling in additional libraries we have to support.
These days I see so much bad code like a single microservice with 3 different approaches to making a http request.
Agree. But we are already in that loop. A 50KLOC properly written "Monolith, hence outdated" app is now 30 micro services of 20KLOC surface + 100KLOC of submerged in terms of convenience libraries with kubernetes, grafana, datadog, servicemesh and so on. From what I am seeing companies are increasingly using off the shelf components so KLOC will keep rising but developer count would not.
Sure, but my specific question was fairly trivial, using a mainstream language and a popular library. Most of my work qualifies as CRUD peddling. And yet these tools are still wasting my time.
Maybe I'll have better luck next time, or maybe I need to improve my prompting skills, or use a different model, etc. I was just expecting more from state of the art LLMs in 2024.
Yeah there is a big disconnect between the devs caught up in the hype and the devs who aren't.
A lot of the devs in my office using Claude/gpt are convinced they are so much more productive but they aren't actually producing features or bug fixes any faster.
I think they are just excited about a novel new way to write code.
To be clear, I didn't ask it to write something complex. The prompt was "how do I do X with library Y?", with a bit more detail. The library is fairly popular and in a mainstream language.
I had a suspicion that what I was trying to do was simply not possible with that library, but since LLMs are incapable of saying "that's not possible" or "I don't know", they will rephrase your prompt and hallucinate whatever might plausibly make sense. They have no way to gauge whether what they're outputting is actually correct.
So I can imagine that you sometimes might get something useful from this, but if you want a specific answer about something, you will always have to double-check their work. In the specific case of programming, this could be improved with a simple engineering task: integrate the output with a real programming environment, and evaluate the result of actually running the code. I think there are coding assistant services that do this already, but frankly, I was expecting more from simple chat services.
Specific is the specific thing that statistical models are not good at :(
> how do I do X with library Y?
Recent research and anecdotal experience has shown that LLMs perform quite poorly with short prompts. Attention just has more data to work with when there are more tokens. Try extending that question like “I am using this programming language and am trying to do this task with this library. How do I do this thing with this other library”
I realize prompt engineering like this is fuzzy and “magic,” but short prompts have a consistent lower performance.
> In the specific case of programming, this could be improved with a simple engineering task: integrate the output with a real programming environment, and evaluate the result of actually running the code.
Not as simple as you’d think. You’re letting something run arbitrary code.
Tho you should give aider.chat a try if you want to test out that workflow. I found it very very slow.
> Recent research and anecdotal experience has shown that LLMs perform quite poorly with short prompts.
I'm aware of that. The actual prompt was more elaborate. I was just mentioning the gist of it here.
Besides, you would think that after 30 minutes of prompting and corrections it would arrive at the correct answer. I'm aware that subsequent output is based on the session history, but I would also expect this to be less of an issue if the human response was negative. It just seems like sloppy engineering otherwise.
> Specific is the specific thing that statistical models are not good at
Some models are good at needle-in-a-haystack problems. If the information exists, they're able to find it. What I don't need is for it to hallucinate wrong answers if the information doesn't exist.
This is a core problem of this tech, but I also expected it to improve over time.
> Tho you should give aider.chat a try
Thanks, I'll do that eventually. If it's slow, it can get faster. I'd rather the tool be slow but give correct answers, than it slowing me down by wasting my time error correcting it.
Thankfully, these approaches can work for programming tasks. There is not much that can be done to verify the output of any other subject.
I'm the same, but had a lot of issues getting structured output from Anthropic. Ended up always writing response processors. Frustrated by how fragile that was, decided to try OpenAI structured outputs and it just worked and since they also have prompt caching now, it worked out very well for my use case.
Anthropic's seems to have addressed the issue using pydantic but I haven't had a chance to test it yet.
>The second is with all the OpenAI drama MS has started to distance themselves over a souring relationship (allegedly). If so, this could be a smart move away tactfully.
I agree, this was a tactical move designed to give them leverage over OpenAI.
The speed with which AI models are improving blows my mind. Humans quickly normalize technological progress, but it's staggering to reflect on our progress over just these two years.
Yes! I'm much more inclined to write one-off scripts for short manual tasks as I can usually get AI to get something useful very fast. For example, last week I worked with Claude to write a script to get a sense of how many PRs my company had that included comprehensive testing. This was borderline best done as a manual task previously, now I just ask Claude to write a short bash script that uses the GitHub CLI to do it and I've got a repeatable reliable process for pulling this information.
I rarely use LLMs for tasks but i love it for exploring spaces i would otherwise just ignore. Like writing some random bash script isn't difficult at all, but it's also so fiddly that i just don't care to do it. It's nice to just throw a bot at it and come back later. Loosely speaking.
Still i find very little use from LLMs in this front, but they do come in handy randomly.
I wonder how long people will still protest in these threads that "It doesn't know anything! It's just an autocomplete parrot!"
Because.. yea, it is. However.. it keeps expanding, it keeps getting more useful. Yea people and especially companies are using it for things which it has no business being involved in.. and despite that it keeps growing, it keeps progressing.
I do find the "stochastic parrot" comments slowly dwindle in number and volume with each significant release, though.
Still, i find it weirdly interesting to see a bunch of people be both right and "wrong" at the same time. They're completely right, and yet it's like they're also being proven wrong in the ways that matter.
The "statistical parrot" parrots have been demonstrably wrong for years (see e.g. LeCun et al[1]). It's just harder to ignore reality with hundreds of millions of people now using incredible new AI tools. We're approaching "don't believe your lying eyes" territory. Deniers will continue pretending that LLMs are just an NFT-level fad or bubble or whatever. The AI revolution will continue to pass them by. More's the pity.
I don't know how you can say they lack understanding of the world when in pretty much any standardised test designed to measure human intelligence they perform better than the average human. They only thing that don't understand is touch because they're not trained on that, but they can already understand audio and video.
You said it, those tests are designed to measure human intelligence, because we know that there is a correspondence between test results and other, more general tasks - in humans. We do not know that such a correspondence exists with language models. I would actually argue that they demonstrably do not, since even an LLM that passes every IQ test you put in front of it can still trip up on trivial exceptions that wouldn't fool a child.
No you don’t understand, if i put a billion billion trillion monkeys on typewriters, they’re actually now one super intelligent monkey because they’re useful now!
We just need more monkeys and it will be the same as a human brain.
> Deniers will continue pretending that LLMs are just an NFT-level fad or bubble or whatever. The AI revolution will continue to pass them by. More's the pity.
You should re-read that very slowly and carefully and really think about it. Calling anyone that's skeptical a 'denier' is a red flag.
We have been through these AI cycles before. In every case, the tools were impressive for their time. Their limitations were always brushed aside and we would get a hype cycle. There was nothing wrong with the technology, but humans always like to try to extrapolate their capabilities and we usually get that wrong. When hype caught up to reality, investments dried up and nobody wanted to touch "AI" for a while.
Rinse, repeat.
LLMs are again impressive, for our time. When the dust settles, we'll get some useful tools but I'm pretty sure we will experience another – severe – AI winter.
If we had some optimistic but also realistic discussions on their limitations, I'd be less skeptical. As it is, we are talking about 'revolution', and developers being out of jobs, and superintelligence and whatnot. That's not the level the technology is at today and it is not clear we are going to do anything else other than get stuck in a local maxima.
There's the question, "is an LLM just autocomplete"? The answer to that question is obviously no, but the question is also a strawman - people who actually use LLM's regularly do recognize that there is more to their capabilities than randomized pattern matching.
Separately, there's the question of "will LLM's become AGI and/or become super intelligent." Most people recognize that LLM's are not currently super intelligent, and that there currently isn't a clear path toward making them so. Still, many people seem to feel that we're on the verge of progress here, and feel very strongly that anyone who disagrees is an AI "doomer".
Then there's the question of "are we in an AI bubble"? This is more a matter of debate. Some would argue that if LLM reasoning capabilities plateau, people will stop investing in the technology. I actually don't agree with that view - I think there is a lot of economic value still yet to be realized in AI advancements - I don't think we're on the verge of some sort of AI winter, even if LLM's never become super intelligent.
When the temperature is 0.5, both Claude 3.5 and GPT-4o can't properly recognize that GitHub is capitalized. You can see the responses by clicking in the sentence. Each model was asked to validate the sentence 5 times.
If the temperature is set to 0.0, most models will get it right (most of the time), but Claude 3.5 still can't see the sentence in front of it.
> I think calling it intelligent is being extremely generous ... can't properly recognize that GitHub is capitalized.
Wouldn't this make chimpanzees and ravens and dolphins unintelligent too? You're asking it to do a task that's (mostly) easy for humans. It's not a human though. It's an alien intelligence which "thinks" in our language, but not in the same way we do.
If they could, specialized AI might think we're unintelligent based on how often we fail, even with advanced tools, pattern matching tasks that are trivial for them. Would you say they're right to feel that way?
Animals are capable of learning. LLMs can not. LLM uses weights that are defined during the training process to decide what to do next. LLM cannot self evaluate based on what it has said. You have to create a new message for it to create a new probability path.
Animals have the ability to learn and grow by themselves. LLMs are not intelligent and I don't see how they can be since they just follow the most likely path with randomness (temperature) sprinkled in.
Ok so just to be clear, that's an entirely different and unrelated argument from the one I responded to.
Second, it's wrong. LLMs can learn within their context window. The main issue now is the limited size of their context window; animals have a lifetime of compressed context and LLMs only have approximately one conversation.
> Ok so just to be clear, that's an entirely different and unrelated argument from the one I responded to.
It honestly made no sense what you were saying so I didn't respond to that directly as I assumed it would be clear from my explanation as to why animals can be intelligent and LLM are not.
> LLMs can learn within their context window.
They don't learn from the context window as much as they use what is in the context window to define a probabilistic path. If you put something in the context window that it was never trained on, it would spit out BS or say it doesn't know.
Are you confusing frequency of use with usefulness?
If these tools boost tue productivity where is the output spike of all the companies, the spike in revenue and profits?
How often do we lose the benefit auto text generation to the loop of
That’s wrong
Oh yes of course, here is the correct version
Nope, still wrong
Prompt editing?
Lots of progress, but I feel like we've been seeing diminishing returns. I can't help but feel like recent improvements are just refinements and not real advances. The interest in AI may drive investment and research in better models that are game-changers, but we aren't there yet.
I don't know about you, but o1-preview/o1-mini has been able to solve many moderately challenging programming tasks that would've taken me 30 mins to an hour. No other models earlier could've done that.
It's an improvement but...I've asked it to do some really simple tasks and it'll occasionally do them in the most roundabout way you could imagine. Like, let's source a bash file that creates and reads a state file to do something for which the functionality was already built-in. Say I'm a little skeptical of this solution and plug it into a new o1-preview prompt to double check the solution, and it starts by critiquing the bash script and error handling instead of seeing that the functionality is baked in and it's plainly documented. Other errors have been more subtle.
When it works, it's pretty good, and sometimes great. But when failure modes look like the above I'm very wary of accepting its output.
You're proving GP's point about normalization of progress. It's been two years. We're still during the first iteration of applications of this new tech, advancements didn't have time yet to start compounding. This is barely getting started.
I use both Claude and ChatGPT/GPT-4o a lot. Claude, the model, definitely is 'better' than GPT-4o. But OpenAI provides a much more capable app in ChatGPT and an easier development platform.
I would absolutely choose to use Claude as my model with ChatGPT if that happened (yes, I know it won't). ChatGPT as an app is just so far ahead: code interpreter, web search/fetch, fluid voice interaction, Custom GPTs, image generation, and memory. It isn't close. But Claude absolutely produces better code, only being beaten by ChatGPT because it can fetch data from the web to RAG enhance its knowledge of things like APIs.
Claude's implementation of artifacts is very good though, and I'm sure that is what lead OpenAI to push out their buggy canvas feature.
Are there any good 3rd-party native frontend apps for Claude (on MacOS)? I mean something like ChatGPTs app, not an editor. I guess one option would be to just run Claude iPad app on MacOS.
Open-WebUI doesn't support Claude natively (only through a series of hacks) but it is absolutely "THE" go-to for a ChatGPT Pro like experience (it is slightly better).
If you're willing to settle for a client-side only web frontend (i.e. talks directly with APIs of the models you use), TypingMind would work. It's paid, but it's good (see [0]), and I guess you could always go for the self-hosted version and wrap it in an Electron app - it's what most "native" apps are these days anyway (and LLM frontends in particular).
Jan [0] is MacOS native, open source, similar feel to the ChatGPT frontend, very polished, and offers Anthropic integration (all Claude models).
It also features one-click installation, OpenAI integration, a hub for downloading and running local models, a spec-compatible API server, global "quick answer" shortcut, and more. Really can't recommend it enough!
It's not native, but I've been pretty happy with big-AGI. It's just an `npm run` away. I don't use it for coding tasks, though.
Its most unique feature is its "beam" facility, which allows you to send a query to multiple APIs simultaneously (if you want to cross-check) and even combine the answer.
It’s all a dice game with these things, you have to watch them closely or they start running you (with bad outcomes). Disclaimers aside:
Sonnet is better in the small, by a lot. It’s sharply up from idk, three months ago or something when it was still an attractive nuisance. It still tops out at “Best SO Answer”, but it hits that like 90%+. If it involves more than copy paste, sorry folks, it’s still just really fucking good copy paste.
But for sheer “doesn’t stutter every interaction at the worst moment”? You’ve got to hand it to the ops people: 4o can give you second best in industrial quantity on demand. I’m finding that if AI is good enough, then OpenAI is good enough.
>If it involves more than copy paste, sorry folks, it’s still just really fucking good copy paste.
Are you sure you're using Claude 3.5 Sonnet? In my experience it's absolutely capable of writing entire small applications based off a detailed spec I give it, which don't exist on GitHub or Stack Overflow. It makes some mistakes, especially for underspecified things, but generally it can fix them with further prompting.
FWIW, I was able to get a decent way into making my own client for ChatGPT by asking the free 3.5 version to do JS for me* before it was made redundant by the real app, so this shouldn't be too hard if you want a specific experience/workflow?
* I'm iOS by experience; my main professional JS experience was something like a year before jQuery came out, so I kinda need an LLM to catch me up for anything HTML
Have you tried using Cursor with Claude embedded? I can't go back to anything else, it's very nice having the AI embedded in the IDE and it just knows all the files i am working with. Cursor can use GPT-4o too if you want
> ChatGPT as an app is just so far ahead: code interpreter, web search/fetch, fluid voice interaction, Custom GPTs, image generation, and memory. It isn't close.
Funny thing, TypingMind was ahead of them for over a year, implementing those features on top of the API, without trying to mix business model with engineering[0]. It's only recently that ChatGPT webapp got more polished and streamlined, but TypingMind's been giving you all those features for every LLM that can handle it. So, if you're looking for ChatGPT-level frontend to Anthropic models, this is it.
ChatGPT shines on mobile[1] and I still keep my subscription for that reason. On desktop, I stick to TypingMind and being able to run the same plugins on GPT-4o and Claude 3.5 Sonnet, and if I need a new tool, I can make myself one in five minutes with passing knowledge of JavaScript[2]; no need to subscribe to some Gee Pee Tee.
Now, I know I sound like a shill, I'm not. I'm just a satisfied user with no affiliation to the app or the guy that made it. It's just that TypingMind did the bloodingly stupid obvious thing to do with the API and tool support (even before the latter was released), and continues to do the obvious things with it, and I'm completely confused as to why others don't, or why people find "GPTs" novel. They're not. They're a simple idea, wrapped in tons of marketing bullshit that makes it less useful and delayed its release by half a year.
--
[0] - "GPTs", seriously. That's not a feature, that's just system prompt and model config, put in an opaque box and distributed on a marketplace for no good reason.
[1] - Voice story has been better for a while, but that's a matter of integration - OpenAI putting together their own LLM and (unreleased) voice model in a mobile app, in a manner hardly possible with the API their offered, vs. TypingMind being a webapp that uses third party TTS and STT models via "bring your own API key" approach.
One service is not really enough -- you need a few to triangulate more often than not, especially when it comes to code using latest versions of public APIs
Phind is useful as you can switch between them -- but only get a handful of o1 and Opus a day which I burn through quick at moment on deeper things -- Phind-405b and 3.5 Sonnet are decent for general use
Is Microsoft Copilot even a single product? It seems to me they just shove AI in random places throughout their products and call it Copilot. Which would make Github Copilot essentially another one of these places the branding shows up (even if it started there)
I wonder what the rationale for this was internally. More OpenAI issues? competitiveness with Cursor? It seems good for the user to increase competition across LLM providers.
Also ambiguous title. I thought GitHub canceled deals they had in the work. The article is clearly about making a deal, but it's unclear from the article's title.
Could be a fight against Llama, which excludes MS and Google in its open license (though I think has done separate pay deals with one or both of them). Meta are notably absent from this announcement.
I was at the keynote, Llama was featured in the Copilot models section and called out specifically, as was Mistral.
I assume they just aren't at the point where they have the ability or want to host the compute to offer up Llama as an option as opposed to OpenAI, Anthropic and Google who are all offering the model as a service.
Ah ok, I thought the deal was giving them access to Github data too without the anti scraping stuff. And had this article in mind: https://archive.ph/FvI9G
Obviously! However, Google being an American company, that was surprising. I'm in Europe and am used to seeing newest posts "from yesterday" when they are from the USA. This one is weird.
This is a standard “commoditize your complement” play. It’s in GitHub / Microsoft’s best interest to make sure none of the LLMs become dominant.
As long as that happens, their competitors light money on fire to build the model while GitHub continues to build / defend its monopoly position.
Also, given that there are already multiple companies building decent models, it’s a pretty safe bet Microsoft could build their own in a year or two if the market starts settling on one that’s a strategic threat.
See also: “embrace, extend, extinguish” from the 1990’s Microsoft antitrust days.
Yes, because transfer learning works. A specialized model for X will be subsumed by a general model for X/Y/Z as it becomes better at Y/Z. This is why models which learn other languages become better at English.
Custom models still have use cases, e.g. situations requiring cheaper or faster inference. But ultimately The Bitter Lesson holds -- your specialized thing will always be overtaken by throwing more compute at a general thing. We'll be following around foundation models for the foreseeable future, with distilled offshoots bubbling up/dying along the way.
Evaluating cross-lingual transfer learning approaches in multilingual conversational agent models[1]
Cross-lingual transfer learning for multilingual voice agents[2]
Large Language Models Are Cross-Lingual Knowledge-Free Reasoners[3]
An Empirical Study of Cross-Lingual Transfer Learning in Programming Languages[4]
That should get you started on transfer learning re. languages, but you'll have more fun personally picking interesting papers over reading a random yahoo's choices. The fire hose of papers is nuts, so you'll never be left wanting.
It may not be a model quality issue. It may be that GitHub wants to sell a lot more of Copilot, including to companies who refuse to use anything from OpenAI. Now GitHub can say "Oh that's fine, we have these two other lovely providers to choose from."
Also, after Anthropic and Google sold massive amounts of pre-paid usage credits to companies, those companies want to draw down that usage and get their money's worth. GitHub might allow them to do that through Copilot, and therefore get their business.
I think that the credit scenario is more true for OpenAI than others . Existing Azure commits can be used to buy OpenAI via the marketplace. It will never be as simple for any non Azure partner (Only Github is tying up with Anthropic here not Azure)
GitHub doesn’t even support using those azure managed APIs for copilot today, it is just a license you can buy currently and add to a user license. The best you can do is pay for copilot with existing azure commits .
This seems about not being left behind as other models outpace what copilot can do with their custom OpenAI model that doesn’t seem to getting updated .
If you want to destroy open source completely, the more models the better. Microsoft's co-opting and infiltration of OSS projects will serve as a textbook example of eliminating competition in MBA programs.
And people still support it by uploading to GitHub.
Migration is on my todo list, but it’s non trivial enough I’m not sure when I’ll ever have cycles to even figure out the best option. Gitlab? Self-hosted Git? Go back to SVN? A totally different platform?
Truth be told, Git is a major pain in the ass anyway and I’m very open to something else.
Me teaching my brain someone’s way of syntactically expressing procedures is analogous to AI developers teaching their model that same mode of expression.
I think in this regard it works just fine. If the laws move to say that "learning from data" while not reproducing it is "stealing", then yes, you reading others code and learning from it is also stealing.
If I can't feed a news article into a classifier to teach it to learn whether or not that I would like that article that's not a world I want to live in. And yes it's exactly the same thing as what you are accusing LLMs of.
They should be subject to laws the same way humans are. If they substantially reproduce code they had access to then it's a copyright violation. Just like it would be for a human doing the same. But highly derived code is not "stolen" code, neither for AI nor for humans.
It's not your reading that would be illegal, but your copying. This is well a documented area of the law and there are concrete answers to your questions.
To me, the argument is a LLM learning from GPL stuff == creating a derivative of the GPL code, just "compressed" within the LLM. The LLM then goes on to create more derivatives, or it's being distributed (with the embedded GPL code).
Yes, I provide it as a service to my employer. It's called a job. Guess what? When I read code I learn from it and my brain doesn't care what license that code is under.
If the product is the result of compiling all the open source code out in the wild into a LLM, it can be argued that the derived product, the LLM itself, must follow the licensing requirements of the used source code.
The AI companies don't care much about this. When the time comes, they will open their models or stop using sources that don't meet the appropriate licensing. Their current concern is learning how to build the best models, and win the race to become the dominant AI provider - who cares if they need to use polluted sources to reach their goal. They will fix it later.
Sure, Let me know when they extinguish kubernetes, helm, vscode, LSP, playwright, powershell, typescript, npm, or the other 6000 projects/repos sitting on GitHub.
You're free to explain how you think it's linked to the issue if you want to, but it just isn't. Yes, Microsoft contributes some open source software. That obviously does not preclude them from being exploitative toward other creators of open source software.
Some? So you're not even aware. They're the largest OSS company in the history of OSS even if you don't like it. That's the link you're obviously missing.
> The social rewards (e.g., credit, respect) that often motivate open source work are undermined.
You mean people making contributions to solve problems and scratch each others' itches got displaced by people seeking social status and/or a do-at-your-own-pace accreditation outside of formal structures, to show to prospective employees? And now that LLMs start letting people solve their own coding problems, sidestepping their whole social game, the credit seekers complain because large corps did something they couldn't possibly have done?
I mean sure, their contributions were a critical piece - in aggregate - individually, any single piece of OSS code contributes approximately 0 value to LLM training. But they're somehow entitled to the reward for a vastly greater value someone is providing, just because they retroactively feel they contributed.
Or, looking from a different angle: what the complainers are saying is, they're sad they can't extract rent now that their past work became valuable for reasons they had no part in, and if they could turn back time, they'd happily rent-seek the shit out of their code, to the point of destroying LLMs as a possibility, and denying the world the value LLMs provided?
I have little sympathy for that argument. We've been calling out "copyright laundering" way before GPT-3 was a thing - those who don't like to contribute without capturing all the value for themselves should've moved off GitHub years ago. It's not like GitHub has any hold over OSS other than plain inertia (and the egos in the community - social signalling games create a network effect).
>Or, looking from a different angle: what the complainers are saying is, they're sad they can't extract rent now that their past work became valuable for reasons they had no part in, and if they could turn back time, they'd happily rent-seek the shit out of their code,
Wrong and completely unfair/bitter accusation. The only people rent seeking are the corporations.
What kind of world do you want to live in? The one with "social games" or the one with corporate games? The one with corporate games seems to have less and less room for artists, musicians, language graduates, programmers...
> individually, any single piece of OSS code contributes approximately 0 value to LLM training. But they're somehow entitled to the reward for a vastly greater value someone is providing, just because they retroactively feel they contributed.
You are attributing arguments to people which they never made. The most lenient of open source licenses require a simple citation, which the "A.I." never provides. Your tone comes off as pretty condescending, in my opinion. My summary of what you wrote: "I know they violated your license, but too bad! You're not as important as you think!"
A photocopier provides "vastly greater value" than the people who wrote the books?
> they can't extract rent now that their past work became valuable for reasons they had no part in.
That is not the case at all. If I donate food to Africa, I'm happy if it goes to humans. I'm not happy if a Mafia organization steals the food in transit, repackages it and sells it.
> This is performed with no acknowledgement of authorship or lineage, with no attribution or citation.
GitHub hosts a lot of source code, including presumably the code it trained CoPilot on. So they satisfy any license that requires sharing the code and license, such as GPL 3. Not sure what the problem is.
I deleted my github 2 weeks ago, as much about AI, as about them forcing 2FA. Before AI it was SAAS taking more than they were giving. I miss the 'helping each other' feel of these code share sites. I wonder where are we heading with all this. All competition and no collaboration, no wonder the planet is burning.
I don't understand the case being made here at all. AI is violating FOSS licenses, I totally agree. But you can write more FOSS using AI. It's totally unfair, because these companies are not sharing their source, and extracting all of the value from FOSS as they can. Fine. But when it comes to OSI Open Source, all they usually had to do was include a text file somewhere mentioning that they used it in order to do the same thing, and when it comes to Free Software, they could just lie about stealing it and/or fly under the radar.
Free software needs more user-facing software, and it needs people other than coders to drive development (think UI people, subject matter specialists, etc.), and AI will help that. While I think what the AI companies are doing is tortious, and that they either should be stopped from doing it or the entire idea of software copyright should be re-examined, I also think that AI will be massively beneficial for Free Software.
I also suspect that this could result in a grand bargain in some court (which favors the billionaires of course) where the AI companies have to pay into a fund of some sort that will be used to pay for FOSS to be created and maintained.
Lastly, maybe Free Software developers should start zipping up all of the OSI licenses that only require that a license be included in the distribution and including that zipfile with their software written in collaboration with AI copilots. That and your latest GPL for the rest (and for your own code) puts you in as safe a place as you could possibly be legally. You'll still be hit by all of the "don't do evil"-style FOSS-esque licenses out there, but you'll at least be safer than all of the proprietary software being written with AI.
I don't know what textbook directs you to eliminate all of your competition by lowering your competition's costs, narrowing your moat of expertise, and not even owning a piece of that.
edit: that being said, I'm obviously talking about Free Software here, and not Open Source. Wasn't Open Source only protected by spirits anyway?
It'll be okay. We "destroyed" photography by uploading to places like Instagram and Facebook but photography as a whole is still alive. It turns out even though there is lots of stealing, the world still spins and people still seek out original creators.
The reason here is Microsoft is trying to make copilot a platform.
This is the essential step to moving all the power from OpenAI to Microsoft. It would grant Microsoft leverage over all providers since the customers would depend on Microsoft and not OpenAI or Google or Anthropic. Classic platform business evolution at play here.
Interesting. Humans love memetic patterns so much we’re like chickens with lines drawn in front. We can’t help but say the thing. Like the Nam Shub of Enki it’s entered our minds and now reproduces in all sorts of contexts.
Embrace, Extend, Extinguish we say now without thought. The virus breaks containment. Like fungus on an ant it makes us summit and then spew it forth on others - self—modifying to maximize life span.
Amazing that even when they are giving users maximal choice, even including their arch-nemesis Google, they are evil? Would Microsoft be better if you could only use Microsoft models?
I'd love to see a list of the open standards they've embraced and killed. Your argument is on a weak foundation if it is based on Internet Explorer in the pre IE8 days (15+ years ago), or anything older than that.
Frankly, Microsoft hasn't had enough influence to kill any open standard since the height of their dominance in the early 2000s. They aren't that important. I mean, market forces made them adopt Chromium even.
The Copilot team probably thinks of Cursor's efforts as cute. They can be a neat little product in their tiny corner of the market.
It's far more valuable to be a platform. Maybe Cursor can become a platform, but the race is on and they're up against giants that are moving rather surprisingly nimbly.
Github does way more, you can build on top of it, and they already have a metric ton of business relationships and enterprise customers.
A developer will spend far more time in the IDE than the version control system so I wouldn't discount it that easily. That being said, there are no network effects for an IDE and Cursor is basically just a VSCode plugin. Maybe Cursor gets a nice acquihire deal
I'm sure there are multiple reasons, including lowering the odds of antitrust action by regulators. The EU was already sniffing around Microsoft's relationship with OpenAI.
You mean waste a few billion on buying a company that couldn't compete with the market anymore because the iphone made "even an idiot should be able to use this thing, and it should be able to do pretty much everything" a baseline expectation with an OS/software experience to match? Nokia failed Nokia, and then Microsoft gave it a shot. And they also couldn't make it work.
(sure, that glosses over the whole Elop saga, but Microsoft didn't buy a Nokia-in-its-prime and killed it. They bought an already failing business and even throwing MS levels of resources at it couldn't turn it around)
time to revisit the news from back in the day, because they very much did, and the details are an exercise in "wow, how much do you want to split hairs?".
Frankly surprised to see GitHub (Microsoft) signing a deal with their biggest competitor, Google. It does give Microsoft some good terms/pricing leverage over OpenAI, though I'm not sure what degree Microsoft needs that given their investment in OpenAI.
GitHub Spark seems like the most interesting part of the announcement.
Because they previously decreased more under the expectation of another half point cut by the fed. Stronger economic indicators have cut the expectation for steep rate cuts so treasuries are declining.
I replaced ChatGPT Plus with hosted nvidia/Llama-3.1-Nemotron-70B-Instruct for coding tasks. Nemotron produces good code. The cost different is massive. Nemotron is available for $0.35 per Mtoken in and out. ChatGPT is considerably more expensive.
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[ 3.3 ms ] story [ 353 ms ] threadEither way, Claude is great so this is a net win for everyone.
A commenter on another thread mentioned it but it’s very similar to how search felt in the early 2000s. I ask it a question and get my answer.
Sometimes it’s a little (or a lot) wrong or outdated, but at least I get something to tinker with.
Google used to prioritise big comprehensive articles on subjects for desktop users but mobile users just wanted quick answers, so that's what google prioritised as they became the biggest users.
But also, per your point, I think those smaller simpler less comprehensive posts are easier to fake/spam than the larger more compreshensible posts that came before.
Right now, I find each tool better at different things.
If I can only describe what I want but don't know key words, LLM are the only solution.
If I need citations, LLMs suck.
If this is the state of the art of coding LLMs, I really don't see why I should waste my time evaluating their confident sounding, but wrong, answers. It doesn't seem like much has improved in the past year or so, and at this point this seems like an inherent limitation of the architecture.
Codebases are exploding in size. Feature development has slowed down.
What might have been a carefully designed 100kloc codebase in 2018 is now a 500kloc ball of mud in 2024.
Companies need many more developers to complete a decent sized feature than they needed in 2018.
Once you start shifting it to micro services the business logic gets spread out and duplicated.
At the same time each micro-service now has its own code to handle rest, graphql, grpc endpoints.
And each downstream call needs error handling and retry logic.
And of course now you need distributed tracing.
And of course now your auth becomes much more complex.
And of course now each service might be called multiple times for the one request - better make them idempotent.
And each service will drift in terms of underlying libraries.
And so on.
Now we have been adding in LLM solutions so there is no consistency in any of the above services.
Each dev rather than look at the existing approaches instead asks Claude and it provides a slightly different way each time - often pulling in additional libraries we have to support.
These days I see so much bad code like a single microservice with 3 different approaches to making a http request.
Maybe I'll have better luck next time, or maybe I need to improve my prompting skills, or use a different model, etc. I was just expecting more from state of the art LLMs in 2024.
A lot of the devs in my office using Claude/gpt are convinced they are so much more productive but they aren't actually producing features or bug fixes any faster.
I think they are just excited about a novel new way to write code.
I ask it questions mostly about libraries I’m using (usually that have poor documentation) and how to integrate it with other libraries.
I found out about Yjs by asking about different operational transform patterns.
Got some context on the prosemirror plugin by pasting the entire provider class into Claude and asking questions.
It wasn’t always exactly correct, but it was correct enough that it made the process of learning prosemirror, yjs, and how they interact pretty nice.
The “complete” examples it kept spitting out were totally wrong, but the information it gave me was not.
I had a suspicion that what I was trying to do was simply not possible with that library, but since LLMs are incapable of saying "that's not possible" or "I don't know", they will rephrase your prompt and hallucinate whatever might plausibly make sense. They have no way to gauge whether what they're outputting is actually correct.
So I can imagine that you sometimes might get something useful from this, but if you want a specific answer about something, you will always have to double-check their work. In the specific case of programming, this could be improved with a simple engineering task: integrate the output with a real programming environment, and evaluate the result of actually running the code. I think there are coding assistant services that do this already, but frankly, I was expecting more from simple chat services.
Specific is the specific thing that statistical models are not good at :(
> how do I do X with library Y?
Recent research and anecdotal experience has shown that LLMs perform quite poorly with short prompts. Attention just has more data to work with when there are more tokens. Try extending that question like “I am using this programming language and am trying to do this task with this library. How do I do this thing with this other library”
I realize prompt engineering like this is fuzzy and “magic,” but short prompts have a consistent lower performance.
> In the specific case of programming, this could be improved with a simple engineering task: integrate the output with a real programming environment, and evaluate the result of actually running the code.
Not as simple as you’d think. You’re letting something run arbitrary code.
Tho you should give aider.chat a try if you want to test out that workflow. I found it very very slow.
I'm aware of that. The actual prompt was more elaborate. I was just mentioning the gist of it here.
Besides, you would think that after 30 minutes of prompting and corrections it would arrive at the correct answer. I'm aware that subsequent output is based on the session history, but I would also expect this to be less of an issue if the human response was negative. It just seems like sloppy engineering otherwise.
> Specific is the specific thing that statistical models are not good at
Some models are good at needle-in-a-haystack problems. If the information exists, they're able to find it. What I don't need is for it to hallucinate wrong answers if the information doesn't exist.
This is a core problem of this tech, but I also expected it to improve over time.
> Tho you should give aider.chat a try
Thanks, I'll do that eventually. If it's slow, it can get faster. I'd rather the tool be slow but give correct answers, than it slowing me down by wasting my time error correcting it.
Thankfully, these approaches can work for programming tasks. There is not much that can be done to verify the output of any other subject.
Anthropic's seems to have addressed the issue using pydantic but I haven't had a chance to test it yet.
I pretty much use Anthropic for everything else.
I agree, this was a tactical move designed to give them leverage over OpenAI.
Still i find very little use from LLMs in this front, but they do come in handy randomly.
Because.. yea, it is. However.. it keeps expanding, it keeps getting more useful. Yea people and especially companies are using it for things which it has no business being involved in.. and despite that it keeps growing, it keeps progressing.
I do find the "stochastic parrot" comments slowly dwindle in number and volume with each significant release, though.
Still, i find it weirdly interesting to see a bunch of people be both right and "wrong" at the same time. They're completely right, and yet it's like they're also being proven wrong in the ways that matter.
Very weird space we're living in.
[1] https://arxiv.org/abs/2110.09485
If these systems showed understanding we would notice.
No one is denying that this form of intelligence is useful.
We just need more monkeys and it will be the same as a human brain.
More than once these tools fail at tasks a fifth grader could understand
You should re-read that very slowly and carefully and really think about it. Calling anyone that's skeptical a 'denier' is a red flag.
We have been through these AI cycles before. In every case, the tools were impressive for their time. Their limitations were always brushed aside and we would get a hype cycle. There was nothing wrong with the technology, but humans always like to try to extrapolate their capabilities and we usually get that wrong. When hype caught up to reality, investments dried up and nobody wanted to touch "AI" for a while.
Rinse, repeat.
LLMs are again impressive, for our time. When the dust settles, we'll get some useful tools but I'm pretty sure we will experience another – severe – AI winter.
If we had some optimistic but also realistic discussions on their limitations, I'd be less skeptical. As it is, we are talking about 'revolution', and developers being out of jobs, and superintelligence and whatnot. That's not the level the technology is at today and it is not clear we are going to do anything else other than get stuck in a local maxima.
There's the question, "is an LLM just autocomplete"? The answer to that question is obviously no, but the question is also a strawman - people who actually use LLM's regularly do recognize that there is more to their capabilities than randomized pattern matching.
Separately, there's the question of "will LLM's become AGI and/or become super intelligent." Most people recognize that LLM's are not currently super intelligent, and that there currently isn't a clear path toward making them so. Still, many people seem to feel that we're on the verge of progress here, and feel very strongly that anyone who disagrees is an AI "doomer".
Then there's the question of "are we in an AI bubble"? This is more a matter of debate. Some would argue that if LLM reasoning capabilities plateau, people will stop investing in the technology. I actually don't agree with that view - I think there is a lot of economic value still yet to be realized in AI advancements - I don't think we're on the verge of some sort of AI winter, even if LLM's never become super intelligent.
I think calling it intelligent is being extremely generous. Take a look at the following example which is a spelling and grammar checker that I wrote:
https://app.gitsense.com/?doc=f7419bfb27c89&temperature=0.50...
When the temperature is 0.5, both Claude 3.5 and GPT-4o can't properly recognize that GitHub is capitalized. You can see the responses by clicking in the sentence. Each model was asked to validate the sentence 5 times.
If the temperature is set to 0.0, most models will get it right (most of the time), but Claude 3.5 still can't see the sentence in front of it.
https://app.gitsense.com/?doc=f7419bfb27c89&temperature=0.00...
Right now, LLM is an insanely useful and powerful next word predictor, but I wouldn't call it intelligent.
Wouldn't this make chimpanzees and ravens and dolphins unintelligent too? You're asking it to do a task that's (mostly) easy for humans. It's not a human though. It's an alien intelligence which "thinks" in our language, but not in the same way we do.
If they could, specialized AI might think we're unintelligent based on how often we fail, even with advanced tools, pattern matching tasks that are trivial for them. Would you say they're right to feel that way?
Animals have the ability to learn and grow by themselves. LLMs are not intelligent and I don't see how they can be since they just follow the most likely path with randomness (temperature) sprinkled in.
Second, it's wrong. LLMs can learn within their context window. The main issue now is the limited size of their context window; animals have a lifetime of compressed context and LLMs only have approximately one conversation.
It honestly made no sense what you were saying so I didn't respond to that directly as I assumed it would be clear from my explanation as to why animals can be intelligent and LLM are not.
> LLMs can learn within their context window.
They don't learn from the context window as much as they use what is in the context window to define a probabilistic path. If you put something in the context window that it was never trained on, it would spit out BS or say it doesn't know.
If these tools boost tue productivity where is the output spike of all the companies, the spike in revenue and profits?
How often do we lose the benefit auto text generation to the loop of That’s wrong Oh yes of course, here is the correct version Nope, still wrong Prompt editing?
When it works, it's pretty good, and sometimes great. But when failure modes look like the above I'm very wary of accepting its output.
But it still does the tasks you asked for, so that's the part that really matters.
I would absolutely choose to use Claude as my model with ChatGPT if that happened (yes, I know it won't). ChatGPT as an app is just so far ahead: code interpreter, web search/fetch, fluid voice interaction, Custom GPTs, image generation, and memory. It isn't close. But Claude absolutely produces better code, only being beaten by ChatGPT because it can fetch data from the web to RAG enhance its knowledge of things like APIs.
Claude's implementation of artifacts is very good though, and I'm sure that is what lead OpenAI to push out their buggy canvas feature.
https://github.com/open-webui/open-webui
--
[0] - https://news.ycombinator.com/item?id=41988306
It also features one-click installation, OpenAI integration, a hub for downloading and running local models, a spec-compatible API server, global "quick answer" shortcut, and more. Really can't recommend it enough!
[0] https://github.com/janhq/jan
[0] https://msty.app
Its most unique feature is its "beam" facility, which allows you to send a query to multiple APIs simultaneously (if you want to cross-check) and even combine the answer.
Sonnet is better in the small, by a lot. It’s sharply up from idk, three months ago or something when it was still an attractive nuisance. It still tops out at “Best SO Answer”, but it hits that like 90%+. If it involves more than copy paste, sorry folks, it’s still just really fucking good copy paste.
But for sheer “doesn’t stutter every interaction at the worst moment”? You’ve got to hand it to the ops people: 4o can give you second best in industrial quantity on demand. I’m finding that if AI is good enough, then OpenAI is good enough.
Are you sure you're using Claude 3.5 Sonnet? In my experience it's absolutely capable of writing entire small applications based off a detailed spec I give it, which don't exist on GitHub or Stack Overflow. It makes some mistakes, especially for underspecified things, but generally it can fix them with further prompting.
And I’m not sure we disagree.
Vercel demo but Pets is copy paste.
* I'm iOS by experience; my main professional JS experience was something like a year before jQuery came out, so I kinda need an LLM to catch me up for anything HTML
Also, I wanted HTML rather than native for this.
Funny thing, TypingMind was ahead of them for over a year, implementing those features on top of the API, without trying to mix business model with engineering[0]. It's only recently that ChatGPT webapp got more polished and streamlined, but TypingMind's been giving you all those features for every LLM that can handle it. So, if you're looking for ChatGPT-level frontend to Anthropic models, this is it.
ChatGPT shines on mobile[1] and I still keep my subscription for that reason. On desktop, I stick to TypingMind and being able to run the same plugins on GPT-4o and Claude 3.5 Sonnet, and if I need a new tool, I can make myself one in five minutes with passing knowledge of JavaScript[2]; no need to subscribe to some Gee Pee Tee.
Now, I know I sound like a shill, I'm not. I'm just a satisfied user with no affiliation to the app or the guy that made it. It's just that TypingMind did the bloodingly stupid obvious thing to do with the API and tool support (even before the latter was released), and continues to do the obvious things with it, and I'm completely confused as to why others don't, or why people find "GPTs" novel. They're not. They're a simple idea, wrapped in tons of marketing bullshit that makes it less useful and delayed its release by half a year.
--
[0] - "GPTs", seriously. That's not a feature, that's just system prompt and model config, put in an opaque box and distributed on a marketplace for no good reason.
[1] - Voice story has been better for a while, but that's a matter of integration - OpenAI putting together their own LLM and (unreleased) voice model in a mobile app, in a manner hardly possible with the API their offered, vs. TypingMind being a webapp that uses third party TTS and STT models via "bring your own API key" approach.
[2] - I made https://docs.typingmind.com/plugins/plugins-examples#db32cc6... long before you could do that stuff with ChatGPT app. It's literally as easy as it can possibly be: https://git.sr.ht/~temporal/typingmind-plugins/tree. In particular, this one is more representative - https://git.sr.ht/~temporal/typingmind-plugins/tree/master/i... - PlantUML one is also less than 10 lines of code, but on top of 1.5k lines of DEFLATE implementation in JS I plain copy-pasted from the interwebz because I cannot into JS modules.
Which app are you talking about here?
Phind is useful as you can switch between them -- but only get a handful of o1 and Opus a day which I burn through quick at moment on deeper things -- Phind-405b and 3.5 Sonnet are decent for general use
Wonder if we'll ever see a standard LLM API.
At this point its just the OpenAI API
https://github.com/cline/cline for the agent thing
I expected little from Copilot, but now i find it indispensible. It is such a productivity multiplier.
To each their own.
Also ambiguous title. I thought GitHub canceled deals they had in the work. The article is clearly about making a deal, but it's unclear from the article's title.
I assume they just aren't at the point where they have the ability or want to host the compute to offer up Llama as an option as opposed to OpenAI, Anthropic and Google who are all offering the model as a service.
or a fat chance?
Big part of competitors' (eg. Aider, Cursor, I imagine also jetbrains) advantage was not being tied to one model as the landscape changed.
After large MS OpenAI investment they could just as easily have put blinders on and doubled down.
GitHub’s article: https://github.blog/news-insights/product-news/bringing-deve...
Google Cloud’s article: https://cloud.google.com/blog/products/ai-machine-learning/g...
Weird that it wasn’t published on the official Gemini news site here: https://blog.google/products/gemini/
Edit: GitHub Copilot is now also available in Xcode: https://github.blog/changelog/2024-10-29-github-copilot-code...
Discussion here: https://news.ycombinator.com/item?id=41987404
https://i.imgur.com/z01xgfl.png
https://cloud.google.com/blog/products/ai-machine-learning/g...
https://i.postimg.cc/RVWSfpvs/grafik.png
As long as that happens, their competitors light money on fire to build the model while GitHub continues to build / defend its monopoly position.
Also, given that there are already multiple companies building decent models, it’s a pretty safe bet Microsoft could build their own in a year or two if the market starts settling on one that’s a strategic threat.
See also: “embrace, extend, extinguish” from the 1990’s Microsoft antitrust days.
It's so interesting that even after that early mover advantage they have to go back to the foundation model providers.
Does this mean that future tech companies have no choice but to do this?
I think their version of gpt-3.5 was a fine tune as well. I doubt they had a whole model from scratch made just for them.
Custom models still have use cases, e.g. situations requiring cheaper or faster inference. But ultimately The Bitter Lesson holds -- your specialized thing will always be overtaken by throwing more compute at a general thing. We'll be following around foundation models for the foreseeable future, with distilled offshoots bubbling up/dying along the way.
Do you have a source for that, I'd love to learn more!
Cross-lingual transfer learning for multilingual voice agents[2]
Large Language Models Are Cross-Lingual Knowledge-Free Reasoners[3]
An Empirical Study of Cross-Lingual Transfer Learning in Programming Languages[4]
That should get you started on transfer learning re. languages, but you'll have more fun personally picking interesting papers over reading a random yahoo's choices. The fire hose of papers is nuts, so you'll never be left wanting.
[1] https://www.amazon.science/publications/evaluating-cross-lin...
[2] https://www.amazon.science/blog/cross-lingual-transfer-learn...
[3] https://arxiv.org/pdf/2406.16655v1
[4] https://arxiv.org/pdf/2310.16937v2
Also, after Anthropic and Google sold massive amounts of pre-paid usage credits to companies, those companies want to draw down that usage and get their money's worth. GitHub might allow them to do that through Copilot, and therefore get their business.
GitHub doesn’t even support using those azure managed APIs for copilot today, it is just a license you can buy currently and add to a user license. The best you can do is pay for copilot with existing azure commits .
This seems about not being left behind as other models outpace what copilot can do with their custom OpenAI model that doesn’t seem to getting updated .
And people still support it by uploading to GitHub.
It’s slowly, but noticeably moving from GitHub to other sites.
The network effect is hard to work against.
Truth be told, Git is a major pain in the ass anyway and I’m very open to something else.
The irony is of course that open source is what they used to train their models with.
That line of thought would get thrown out of court faster than an AI would generate it.
Me teaching my brain someone’s way of syntactically expressing procedures is analogous to AI developers teaching their model that same mode of expression.
If I can't feed a news article into a classifier to teach it to learn whether or not that I would like that article that's not a world I want to live in. And yes it's exactly the same thing as what you are accusing LLMs of.
They should be subject to laws the same way humans are. If they substantially reproduce code they had access to then it's a copyright violation. Just like it would be for a human doing the same. But highly derived code is not "stolen" code, neither for AI nor for humans.
To me, the argument is a LLM learning from GPL stuff == creating a derivative of the GPL code, just "compressed" within the LLM. The LLM then goes on to create more derivatives, or it's being distributed (with the embedded GPL code).
The AI companies don't care much about this. When the time comes, they will open their models or stop using sources that don't meet the appropriate licensing. Their current concern is learning how to build the best models, and win the race to become the dominant AI provider - who cares if they need to use polluted sources to reach their goal. They will fix it later.
Large language models are used to aggregate and interpolate intellectual property.
This is performed with no acknowledgement of authorship or lineage, with no attribution or citation.
In effect, the intellectual property used to train such models becomes anonymous common property.
The social rewards (e.g., credit, respect) that often motivate open source work are undermined.
Embrace, extend, extinguish.
You mean people making contributions to solve problems and scratch each others' itches got displaced by people seeking social status and/or a do-at-your-own-pace accreditation outside of formal structures, to show to prospective employees? And now that LLMs start letting people solve their own coding problems, sidestepping their whole social game, the credit seekers complain because large corps did something they couldn't possibly have done?
I mean sure, their contributions were a critical piece - in aggregate - individually, any single piece of OSS code contributes approximately 0 value to LLM training. But they're somehow entitled to the reward for a vastly greater value someone is providing, just because they retroactively feel they contributed.
Or, looking from a different angle: what the complainers are saying is, they're sad they can't extract rent now that their past work became valuable for reasons they had no part in, and if they could turn back time, they'd happily rent-seek the shit out of their code, to the point of destroying LLMs as a possibility, and denying the world the value LLMs provided?
I have little sympathy for that argument. We've been calling out "copyright laundering" way before GPT-3 was a thing - those who don't like to contribute without capturing all the value for themselves should've moved off GitHub years ago. It's not like GitHub has any hold over OSS other than plain inertia (and the egos in the community - social signalling games create a network effect).
Wrong and completely unfair/bitter accusation. The only people rent seeking are the corporations.
What kind of world do you want to live in? The one with "social games" or the one with corporate games? The one with corporate games seems to have less and less room for artists, musicians, language graduates, programmers...
You are attributing arguments to people which they never made. The most lenient of open source licenses require a simple citation, which the "A.I." never provides. Your tone comes off as pretty condescending, in my opinion. My summary of what you wrote: "I know they violated your license, but too bad! You're not as important as you think!"
> they can't extract rent now that their past work became valuable for reasons they had no part in.
That is not the case at all. If I donate food to Africa, I'm happy if it goes to humans. I'm not happy if a Mafia organization steals the food in transit, repackages it and sells it.
GitHub hosts a lot of source code, including presumably the code it trained CoPilot on. So they satisfy any license that requires sharing the code and license, such as GPL 3. Not sure what the problem is.
Free software needs more user-facing software, and it needs people other than coders to drive development (think UI people, subject matter specialists, etc.), and AI will help that. While I think what the AI companies are doing is tortious, and that they either should be stopped from doing it or the entire idea of software copyright should be re-examined, I also think that AI will be massively beneficial for Free Software.
I also suspect that this could result in a grand bargain in some court (which favors the billionaires of course) where the AI companies have to pay into a fund of some sort that will be used to pay for FOSS to be created and maintained.
Lastly, maybe Free Software developers should start zipping up all of the OSI licenses that only require that a license be included in the distribution and including that zipfile with their software written in collaboration with AI copilots. That and your latest GPL for the rest (and for your own code) puts you in as safe a place as you could possibly be legally. You'll still be hit by all of the "don't do evil"-style FOSS-esque licenses out there, but you'll at least be safer than all of the proprietary software being written with AI.
I don't know what textbook directs you to eliminate all of your competition by lowering your competition's costs, narrowing your moat of expertise, and not even owning a piece of that.
edit: that being said, I'm obviously talking about Free Software here, and not Open Source. Wasn't Open Source only protected by spirits anyway?
Embrace, Extend, Extinguish we say now without thought. The virus breaks containment. Like fungus on an ant it makes us summit and then spew it forth on others - self—modifying to maximize life span.
What a creature, the meme.
Frankly, Microsoft hasn't had enough influence to kill any open standard since the height of their dominance in the early 2000s. They aren't that important. I mean, market forces made them adopt Chromium even.
It's far more valuable to be a platform. Maybe Cursor can become a platform, but the race is on and they're up against giants that are moving rather surprisingly nimbly.
Github does way more, you can build on top of it, and they already have a metric ton of business relationships and enterprise customers.
(sure, that glosses over the whole Elop saga, but Microsoft didn't buy a Nokia-in-its-prime and killed it. They bought an already failing business and even throwing MS levels of resources at it couldn't turn it around)
Satya never wanted the acquisition and nuked WP as soon as he could.
GitHub Spark seems like the most interesting part of the announcement.
> Claude 3.5 Sonnet runs on GitHub Copilot via Amazon Bedrock, leveraging Bedrock’s cross-region inference to further enhance reliability.
https://www.anthropic.com/news/github-copilot
Also diversifying is always a good option. Even if one cash cow gets nuked from orbit, you have 2 other companies to latch onto
This is kind of a cynical tech startup take:
- ragging on VC's - calling something a bubble
Interest rates are on their way back down btw.
https://www.federalreserve.gov/newsevents/pressreleases/mone...
https://www.reuters.com/world/uk/bank-england-cut-bank-rate-...
Funding has looked to be running out a few times for OpenAI specifically, but most frontier model development is reasonably well funded still.
completely wrong, where have you been the past month? 10Y t-notes are actually UP after the fed's hysterical 50 basis point cut lol