I replaced chatgpt with mybrain 1.0 and I'm seeing huge improvements in accuracy and reasoning performance!
Also energy efficiency significantly improved, no?
GitHub cuts AI deals with Google, Anthropic
261–270 of 742 posts
Re: GitHub cuts AI deals with Google, Anthropic
#262Earlier quoted context omitted.
Copilot can choke on my AGPL code on GitHub, that was used for training their proprietary models. I'm still salty about this, sadly looks like the world has largely moved on.
It really feels like a digital form of colonialism; they come in take everything, completely disregard the rules, ignore intellectual copyright laws (while you still have to obey them), but when you speak out against this suddenly you are a luddite that doesn't care about human progress.
If it doesn't work, oh well, you'll get VC money for something else.
If it works, the lawyers will figure it out.
Re: GitHub cuts AI deals with Google, Anthropic
#263Earlier quoted context omitted.
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 slowl…
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 pi…
> 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.
Re: GitHub cuts AI deals with Google, Anthropic
#264This kind of thing is why I think Sam is often misjudged. You can’t fuck around in such a competitive market. If you go in all kumbaya you’ll get crushed by market forces. It’s rare for company/founder ideals to survive the market indefinitely. I think he’s iterated fast and the job is still very hard.
Re: GitHub cuts AI deals with Google, Anthropic
#265This Github purchase was incredible
Re: GitHub cuts AI deals with Google, Anthropic
#266I use cursor and its tab completion; while what it can do is mind blowing, in practice I’m not noticing a productivity boost. I find that ai can help significantly with doing plumbing, but it has no problems with connecting the pipes wrong. I need to double and triple check the updated code - or fix the resulting errors when I don’t do that. So: boilerplate and outer app layers, yes; architecture and core libraries,…
It's the subtle errors that are really difficult to navigate. I got burned for about 40 hours on a conditional being backward in the middle of an otherwise flawless method. The apparent speed up is mostly a deception. It definitely helps with rough outlines and approaches. But, the faster you go, the less you will notice the fine details, and the more assumptions you will accumulate before realizing the fundamental e…
That’s the thing, isn’t it? The craft of programming in the small is one of being intimate with the details, thinking things through conscientiously. LLMs don’t do that.
Re: GitHub cuts AI deals with Google, Anthropic
#267Earlier quoted context omitted.
> in practice I’m not noticing a productivity boost. How can this be possible if you literally admit its tab completion is mindblowing? Isn't really good tab completion good enough for at least a 5% producitvity boost? 10%? 20%? Select line of code, prompt it to refactor, verify they are good, accept the changes
> How can this be possible if you literally admit its tab completion is mindblowing? What about it makes it impossible? I’m impressed by what AI assistants can do - and in practice it doesn’t help me personally. > Select line of code, prompt it to refactor, verify they are good, accept the changes. It’s the “verify” part that I find tricky. Do it too fast and you spend more time debugging than you originally gained.…
I think the essential point around impressive vs helpful sums up so much of the discourse around this stuff. Its all just where you fall on the line between "impressive is necessarily good" and "no it isn't".
Re: GitHub cuts AI deals with Google, Anthropic
#268I use cursor and its tab completion; while what it can do is mind blowing, in practice I’m not noticing a productivity boost. I find that ai can help significantly with doing plumbing, but it has no problems with connecting the pipes wrong. I need to double and triple check the updated code - or fix the resulting errors when I don’t do that. So: boilerplate and outer app layers, yes; architecture and core libraries,…
> in practice I’m not noticing a productivity boost. How can this be possible if you literally admit its tab completion is mindblowing? Isn't really good tab completion good enough for at least a 5% producitvity boost? 10%? 20%? Select line of code, prompt it to refactor, verify they are good, accept the changes
> How can this be possible if you literally admit its tab completion is mindblowing?
If I had a knife of perfect sharpness which never dulled, that would be mind-blowing. It also would very likely not make me a better cook.
Re: GitHub cuts AI deals with Google, Anthropic
#269I use cursor and its tab completion; while what it can do is mind blowing, in practice I’m not noticing a productivity boost. I find that ai can help significantly with doing plumbing, but it has no problems with connecting the pipes wrong. I need to double and triple check the updated code - or fix the resulting errors when I don’t do that. So: boilerplate and outer app layers, yes; architecture and core libraries,…
I'm building a tool in this space and believe it's actually multiple separate problems. From most to least solvable:
1. AI coding tools benefit a lot from explicit instructions/specifications and context for how their output will be used. This is actually a very similar problem to when eg someone asks a programmer "build me a website to do X" and then being unhappy with the result because they actually wanted to do "something like X", and a payments portal, and yellow buttons, and to host it on their existing website. So models need to be given those particular instructions somehow (there are many ways to do it, I think my approach is one of the best so far) and context (eg RAG via find-references, other files in your codebase, etc)
2. AI makes coding errors, bad assumptions, and mistakes just like humans. It's rather difficult to implement auto-correction in a good way, and goes beyond mere code-writing into "agentic" territory. This is also what I'm working on.
3. AI tools don't have architecture/software/system design knowledge appropriate represented in their training data and all the other techniques used to refine the model before releasing it. More accurately, they might have knowledge in the form of eg all the blog posts and docs out there about it, but not skill. Actually, there is some improvement here, because I think o1 and 3.5 sonnet are doing some kind of reinforcement-learning/self-training to get better at this. But it's not easily addressable on your end.
4. There is ultimately a ton of context cached in your brain that you cannot realistically share with the AI model, either because it's not written anywhere or there is just too much of it. For example, you may want to structure your code in a certain way because your next feature will extend it or use it. Or your product is hosted on serving platform Y which has an implementation detail where it tries automatically setting Content-Type response headers by appending them to existing headers, so manually setting Content-Type in the response causes bugs on certain clients. You can't magically stuff all of this into the model context.
My product tries to address all of these to varying extents. The largest gains in coding come from making it easier to specify requirements and self-correct, but architecture/design are much harder and not something we're working on much. You or anybody else can feel free to email me if you're interested in meeting for a product demo/feedback session - so far people really like our approach to setting output specs.
Re: GitHub cuts AI deals with Google, Anthropic
#270Earlier quoted context omitted.
Yes. Thank you for saying it. We're watching Microsoft et al. defeat open source. 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 mo…
> 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, sidesteppi…
> 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!"