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GPT-4 generates simple app from Whiteboard photo
- deleted 3y ago[deleted]
- Nfthero 3y agoLove the example. Tons of indications how ai will/is able to learn flows and architecture and code etc.
- Maxion 3y agoOnce the context windows for these models grows by one or two orders of magnitude, stuff is going to be very interesting.
- alexpetralia 3y agoThe new register size!
- jesusofnazarath 3y ago[dead]
- nighmi 3y agoWe had seen interesting developpments around vector databases, but then people stopped hyping them as you could just save them in normal databases without real differences. I wonder what will happen when the models can freely access them though.
- ENGNR 3y agoCurious, do it mean saving into Postgres with pgvector, or some other technique?
- EGreg 3y agoI really don't understand how people figure out the vectors to actually store in the databases, regardless of the underlying storage model. Isn't that itself the province of an LLM? Say I have a bunch of text. How do I save the text search "by similarity"? Sphinx and semantic search was hard, I remember. Facebook had Faiss. And here we are supposed to just save vectors on commodity hardware BEFORE using an LLM?
- martythemaniak 3y ago> Isn't that itself the province of an LLM? It is! The steps are: 1. Take a bunch of text, run it through an LLM in embedding mode. The LLM turns the text into a vector. If the text is longer than the LLM context window, chunk it. 2. Store the vector in a vector DB. 3. Use the LLM to generate a vector of your question. 4. Query the vectordb for all similar vectors (that fit in the context window) 5. Get the text from all those vectors. Concatenate the text with the question from step 3. 6. Step 5 is your prompt. The LLM can now answer your question with a collection of similar/relevant text already provided to the LLM in the context window along with your question.
- sterlind 3y agoYou don't even need an LLM. You can use Word2Vec, or even yank the embeddings matrix from the bottom layer of an LLM. And you can use CLIP and BLIP for images and audio, respectively.
- mewpmewp2 3y agoI had scaling issues with pgvector. So would also like to know what regular db can scale?
- RivieraKid 3y ago> stuff is going to be very interesting So no change from today?
- morkalork 3y agoSAP and IBM will be able to boondoggle government projects at scales never seen before!
- l5870uoo9y 3y agoOn a side note, the largest context for GPT4 is 32K. That will cost around $3 (rough estimate). If GPT5 is 20 times the cost of its predecessor as well, then that will cost around $60 per generation. GPT6 will then be $1200.
- kridsdale3 3y agoBut Nvidia making future GPUs on ever smaller TSMC nodes will be a counter-force, converging those future model-use costs to similar zones to today's. Ultimately the market will bear what it will, and if the costs are outside that zone, those products won't launch.
- skilled 3y agoNot sure this is that interesting since it was already revealed during the GPT-4 livestream back[0] in March. It’s my understanding that this feature is still rolling out to everyone, so if there are some use cases beyond “simple” - I am sure we’ll hear about it in a more detailed article / blog post. Not taking anything away, just pointing out the facts. [0]: https://www.youtube.com/live/outcGtbnMuQ https://www.youtube.com/live/outcGtbnMuQ
- famouswaffles 3y agoYou could have written off that demo as using text as a crutch. Just using ocr and pasting the text can get you something similar looking. This as well as these: https://twitter.com/skirano/status/1706823089487491469 https://twitter.com/skirano/status/1706823089487491469 https://twitter.com/GabGarrett/status/1706872805214593173 https://twitter.com/GabGarrett/status/1706872805214593173 are much more impressive demonstrations. It shows the model understanding ui and figma elements to a non trivial degree.
- skilled 3y agoNice! And that’s what I mean also, I’d much prefer a thorough review of what it can do as opposed to individual Twitter links. A lot of people are probably getting excited now but keeping track of them (examples) individually is so hard. There is a submission on the front page, https://news.ycombinator.com/item?id=37673409 https://news.ycombinator.com/item?id=37673409 But it isn’t about code, so I am sure something like that is coming.
- famouswaffles 3y agoThis is what you're looking for. https://arxiv.org/abs/2309.17421 https://arxiv.org/abs/2309.17421
- doctorpangloss 3y agoIt takes only a one token error to have code that doesn't compile, execute, throws exceptions, or to be wrong in some serious way. These use cases are amazing nonetheless. Can the same be said for facts? Hard to generalize. Like lets say I asked GPTV the year a painted was painted. Way back when, I was right, I will never need to know the exact year of a painting - whatever that means - after I finished my modern art history exam. Does GPTV? No. I am happy with ranges in my answers about general knowledge, because at the end of the day, there isn't some error-intolerant process using those outputs anyway. Even order of magnitude errors may be fine, getting the zero wrong in the token sometimes just doesn't matter. It always matters when programming though. Writers increasingly have the same opinion. As more people use GPT4 and learn how stilted and uncreative it is, and that a one token error can make a plot stop making sense, it isn't clear how it's going to deliver on bulk writing. Spam and content farming sure, but even short prose is not in the realm of reality for actual, real breathing human consumption. How do you categorize this? I don't know, I'm not in the field. There's some huge difference between one token error means 0% correct, versus one token error means 99% correct. Maybe transformers can solve this. Maybe they never will.
- leesec 3y agoYou can just ask it about the error, and it will suggest fixes. I don't know why it has to be 100% the first time. Humans almost never are.
- bo0tzz 3y agoThat works for a straightforward compiler error, but not for plot or logic errors in prose, which cannot be detected automatically.
- famouswaffles 3y agoIt works for all kinds of errors to varying degrees. Even "logic" errors can be generated in a go and detected by another instance. Like a "this is what i wanted but you did this" works sometimes too.
- 3y ago
- ActorNightly 3y agoI mean not really surprising. The future programming paradigm is writing high level descriptions of stuff, letting the generative AI write the code for you, and then finetuning it.
- SirMaster 3y agoShould AI really be writing code that was made for writing by humans? Seems inefficient. Shouldn't the AI just be generating machine code for the computer to run?
- ActorNightly 3y agoThe issue though is that while LLMs are good at translation, they aren't good at correctness, because by definition the stuff they generate is somewhat stochastic (they have several answers to a problem with different rating on each one being correct). For generating machine code, there needs to be one output that is the correct answer. Its much easier in the short term to generate larger snippets of Python code based on prompts and let the programmer fine tune it
- dclowd9901 3y agoBleak.
- ActorNightly 3y agoNot really. If you code in Python or Node, you are already relying on a bunch of computer algorithms to optimize your code.
- deleted 3y ago[deleted]
- Someone1234 3y agoThat outlook depends what you think programmers do... Is it the act of writing code or is it creating things? I often wonder this when the threads discussing raw typing speed come up, or the "extra typing" involved in optional curly braces. Even today, without LLMs, only 20% or less of my work is writing actual code. Requirements/research, designing, optimizing, testing, etc make up the lion's share. So you take out that 20%, I'll be more productive, but my work doesn't change.
- satisfice 3y agoChatGPT is basically a buggy app generator. Like the matter replicators on Star Trek, if they occasionally poisoned you or put dirt in your burger. ChatGPT will be next year’s biggest excuse for bugs in products.
- Someone1234 3y agoHumans are also buggy app generators. In both cases testing is key to high quality software, not perfection on the first go.
- huytersd 3y agoHumans are incredible bug generators and they take 1000x longer to do it.
- Capricorn2481 3y agoNow we can lose billions in blockchain even faster than before
- rg111 3y agoYou can fix bugs. And it undoubtedly reduces the number of developers needed. It doesn't replace all human coders yet. But a senior, super skilled engineer can now effectively do the task of a team of 10. This will be true for many teams that I have personally seen. There will be exceptions, too. Not all teams work like that. I can confidently say that LLMs will cause a reduction in number of developers required at least in the short term. Factors that cause an increase in demand might propagate simultaneously in a greater rate and undo this but that is unlikely in my opinion.
- skepticATX 3y ago> But a senior, super skilled engineer can now effectively do the task of a team of 10. Everyone loves to say this and yet I’ve never seen a single concrete example aside from toy demos. Here’s the thing: output is pretty easy to see. If one developer can 10x their output, that means they’re doing in a month what would take them nearly a year without AI. So, show me one example of an individual or team who has done a year’s worth of work in a month.
- teleforce 3y ago[flagged]
- abi 3y agoHoping to add this to my app (https://picoapps.xyz/ https://picoapps.xyz/) once this launches via API. With Pico, you can iterate on hosted apps via LLM text prompts. Being able to circle a button, and ask the AI to add a gradient background or to copy the style of a screenshotted button should really cool! Excited for what's to come.
- deleted 3y ago[deleted]
- fragmede 3y agohttps://nitter.net/mckaywrigley/status/1707101465922453701 https://nitter.net/mckaywrigley/status/1707101465922453701
- mrnotcrazy 3y agoMy big hope for all this AI stuff is that normal people start to use computers to solve problems more often. I work as a sys admin, if I want say a specialized script to organize my music in a special way. I can write that, probably wouldn't be the best code cause it doesn't need to be. The average person could be using computing to solve all kinds of problems but it requires expertise and a time investment that is just not a good value proposition for a lot of people. If GPT or some other thing comes around and makes it so my uncle could say to his computer "hey build me a site so I can show off all my fishing trophies" and it just does it? the world would have a lot more niche software, more weird stuff and overall everyone would be more productive and happy.(or so I hope)
- pylua 3y agoThis looks like what bpmn and other business process rules notation really set out to be originally, but never achieved.