tl;dr: because of LLM’s.
Certainly his use of LLM was stupidly egregious, but he found that even by those standards the math results underpinning the LLM were way off.
The core functionality is simple:
Automatically, upon each payment, add the expense to my app
Update an Apple Watch complication with the % of my monthly budget spent
Categorize the purchase for later analysisCan someone enlighten me? I don’t understand why you need AI for this in the first place.
Reading the payment and turning it into structured data
You guys know the joke about how pets and their owners start to resemble each other?
I went with quantized Gemma
Well, was it quantized in a way that iphone 16 supports?
Often it’s the quantization where things break down, and the hardware needs to support the quantization, can’t run FP16 on int8 hardware… And sometimes the act of quantization can cause problems too.
And yeah, LLMs are likely going to be very hit or miss anyway.
He combines LLMs with numbers and wonders why this does not work? Under which rock does he live?
I think you missed the point of his post. His issue is that the numeric operations the phone executes to run the LLM is producing garbage. Arguably this could break all kinds of neural networks, such as voice transcription. He’s not complaining that the LLMs are themselves unable to properly perform math.
Under which rock does he live?
Under the rock where reading comprehension exists apparently.
Where he was prompting for “What is 2+2?” to the LLMs, the accuracy of the answer was immaterial. At that step he was comparing two systems and simply needed a static question to give both system to compare the internal processes to determine why they arrived at different outputs (or a what appeared to be race condition/infinite loop for one) when the result should be identical to both irrespective of how right or wrong the answer is to the prompt. The LLM answer from the LLM could have been “ham sandwich” and it still would have served his purposes.
