6,251 karma · joined August 30, 2012
There’s a lot more code being written now that’s not counted in these statistics. A friend of mine vibe coded a writing tool for himself entirely using Gemini canvas.
I regularly vibe code little analyses or scripts in ChatGPT which would have required writing code earlier.
None of these are counted in these statistics.
And yes AI isn’t quite good enough to super charge app creation end to end. Claude has only been good for a few months. That’s hardly enough time for adoption !
This would be like analysing the impact of languages like Perl or Python on software 3 months after their release.
It’s not gas lighting the latest versions of GPT, Claude, Lama have gotten quite good
I’m tired of electricity - Someone in 1905
I’m tired of consumer apps - Someone in 2020
The revolution will happen regardless. If you participate you can shape it in the direction you believe in.
AI is the most innovative thing to happen in software in a long time.
And personally AI is FUN. It sparks joy to code using AI. I don’t need anyone else’s opinion I’m having a blast. It’s a bit like rails for me in that sense.
This is HACKER news. We do things because it’s fun.
I can tackle problems outside of my comfort zone and make it happen.
If all you want to do is ship more 2020s era B2B SaaS till kingdom come no one is stopping you :P
Next token prediction is more intelligent than it sounds
GPT-4 cannot play a good game of tic tac toe. But it can play passable chess. This is a good point to ponder.
This stuff can already do impressive things and its only getting better.
Douglas Hofstadter and Geoffrey Hinton both think that we are on the path to humans eventually being surpassed.
I would urge everyone to hold back their instinctive reaction to the usual SV hype and go and try GPT-4,Claude+, Mid Journey, RunwayML for a few weeks and come to their own conclusions.
More precisely - It gets the question After irs passed through a matrix that transforms the text description of the image so the LLM can “understand” it.
It maps from the space of one ML model to the other.
It's just a bunch of black boxes AKA "pure functions".
BLIP2's ViT-L+Q-former AKA
//I give you a picture of a plate of lobster it will say "A plate of lobster".
getTextFromImage(image) -> Text
Vicuna-13B AKA //I give you a prompt and you return completion ChatGPT style
getCompletionFromPrompt(text) -> Text
We want to take the output of the first one and then feed in a prompt to the LLM (Vicuna) that will help answer a question about the image. However the datatypes don't match. Lets add in a mapper. getAnswerToQuestion(image, question) -> answer
text = getTextFromImage(image)
prompt = mapTextToPrompt(text)
return getCompletionForPrompt(prompt)
Now where did this mapTextToPrompt come from ?This is the magic of ML. We can just "learn" this function from data. And they plugged in a "simple" layer and learned it from a few examples of (image , question) -> answer. This is what frameworks like Keras, Pytorch allow you to do. You can wire up these black boxes with some intermediate layers and pass in a bunch of data and voila you have a new model. This is called differentiable programming.
The thing is you don't need to convert to text and then map back into numbers to feed into the LLM. You skip that and use the numbers it outputs and multiply directly with an intermediate matrix.
getAnswerToQuestion(image, question) -> answer
text = getEmbeddingFromImage(image)
embedding = mapEmbeddingToInputEmbeddingForLLM(text)
return getCompletionForEmbedding(embedding)
Congratulations you now understood that sentence.The people doing so called alchemy are making far better systems that anyone who’s trying to actually understand things. And we’ve been trying for decades now.