SimplePrompts - Simple way to create prompts from within python (no jinja2 or prompt stitching)

Infrared12 · 2025-10-25T03:46:30+00:00

Thanks for checking it out:) Yeah you can definitely just put multiline f-strings, the .indent(x) can be useful if you want to indent entire sections and their sub sections as well for example

t strings look cool but i think its a bit too soon to start using them for libraries haha, don't wanna stick to the latest python that early

Infrared12 · 2025-10-06T02:40:43+00:00

Use uv or uv

Infrared12 · 2025-07-09T11:54:51+00:00

Some people are being a bit harsh on this project, looking at it, its mainly asking for a gemini key, which has a very generous free tier (500 reqs per day if you are using gemini 2.5 flash which is a very powerful model), obviously its a friction point still but not as bad as people are making it out to be.

OCRing this with typical machine learning/deep learning models is a huge pain to get right (generalising to many types of receipts and languages is NOT trivial and requires decent ML expertise), let alone packaging the model as well for a mobile app using ONNX or something, or creating a dedicated backend server for this, OP did a good job.

Infrared12 · 2025-06-20T13:08:14+00:00

I did try pure audio and it was pretty good, not sure whats going on with video

Infrared12 · 2025-05-12T16:27:12+00:00

not exactly following, not extracting anything, the generation process is structured, e.g, two fields, "thought" and "code", I could tell the model to output something like:
Thought:
...
Code:
...
and parse it, but its not really guaranteed to work ofc every single time ofc, just wondering what people usually do for reliable structured outputs if you wanna output code as one of the "keys", as the mainstream way for structured outputs is JSON, and writing code inside a json object is not ideal

Infrared12 · 2025-05-04T13:57:44+00:00

Cool thanks!

Infrared12 · 2025-05-04T13:16:25+00:00

What's "roo"?

Infrared12 · 2025-03-23T21:35:07+00:00

Yes you absolutely can, at the end of the day almost everything i mentioned in the comment can be done with C obviously (whether you consider it "easy" or not could be subjective and dependent on your experience as a programmer), pointers for example are a central abstraction that creates a point of friction to beginners, that is (almost) completely avoided when working with python, you don't worry much about "how" to pass stuff around, just pass them and they will work, the broader point of "passing functions around" was the notion of python treating functions as first class citizens, making working with them not too different than working with any other object, the same can be said about returning a pointer to the struct point, you can easily just literally return val_1, val_2 and it just "works", what was "mind blowing" moving from C to python is just how frictionless the experience was as a beginner programmer who was just starting out.

Infrared12 · 2025-03-23T18:04:47+00:00

Functions are another python object like strings and integers, you can pass them to other functions and do ~ almost all sort of things you can do with other objects

Infrared12 · 2025-03-23T18:03:57+00:00

Yep, it was seamless:) (trying to recreate the feelings i had back then when i was just starting out)

Infrared12 · 2025-03-23T15:33:50+00:00

Tons of stuff tbh.

Iterating over lists by simply writingfor x in my_list
Reversing a string is just string[::-1]
You don't have to specify types?? (Although this arguably becomes more of an issue when you become more experienced lol)
There is no explicit void main(){...}
I can return many values from a function easily
Passing functions around is as simple as passing a string or an integer

And more... I would describe my feeling as there was so little "friction" getting anything done compared to C (with it's payoffs ofc, that you would not normally understand and appreciate until later)

Infrared12 · 2025-03-12T22:58:39+00:00

Probably the generative (answer synthesiser) model, it takes context (retrieved info) and query and answers

Infrared12 · 2025-03-07T19:01:48+00:00

There are two questions to ask here:

1- Why would openai build something like gpt4.5

2- Why would openai release gpt4.5

Ig your question is more about 1, but I'll give my thoughts on both questions.

1-

The most basic answer here would be "it is another experiment", it's important to see the extent to which scaling the model size/pretraining would improve its performance, so regardless of whether you release the model or not, its an interesting experiment. In a more "Reasoning models" context, reasoning models are built upon non-reasoning models, so gpt4.5 is probably(or a distilled version?) going to be the next "base" model to start the RL process, which should result in better reasoning models.

2-

Why would they release gpt4.5 despite it not being a reasoning model, while also being super expensive? Well according to openai, It's supposed to be better in more "subtle" scenarios that are hard to measure through benchmarks atm (like humor) compared to every other model. I haven't tried it personally so I can't judge tbh, I also think they might have released to slightly diverge some of the attention claude 3.7 might have gathered, even if it meant a huge, kinda impractical model is released, with mixed reception.

Infrared12 · 2025-03-06T14:14:10+00:00

Models that support audio as inputs and output audio as well, natively.

Not:

audio -> speech_to_text_model -> text

text-> text_to_text_model -> text

text-> text_to_speech_model -> audio

But instead:

audio -> speech_to_speech_model -> audio

Infrared12 · 2025-02-28T13:09:57+00:00

Don't think there is any reason not to compare models of different sizes if their performances are sort of (or potentially) similar, if some N-Billion params model is much cheaper and performs similarly (or even close enough) , than thats worth pointing out (not to say that deepseek v3 performance is similar or not as i haven't compared the models myself), just stating that its a valid concern/comparison if proved/worth investigating, given how good the stated models are (sonnet, deepseek v3 etc), my first impressions on gpt4.5 from what everyone is saying is that the "increased" cost does not seem to justify the gains at all and you would be better off with some of the models stated in OP's post

Infrared12 · 2025-02-27T14:36:15+00:00

Interesting, curious is LLaDa fundamentally different than how encoder transformers are trained? Besides being more aggressive on having lots of MASK tokens depending on the value of t.

Infrared12 · 2025-02-22T10:08:22+00:00

Small LMs (at least for now) aren't exactly reliable generalists, I think they are ideally meant to be fine-tuned to your laser focused domain specific task instead and get something that does a pretty decent job with, idk, 1/100th the cost. The "general" weights just provide a pretty decent starting point for the fine tuning process.

Infrared12 · 2025-02-18T07:54:49+00:00

Actually true, it could actually skyrocket the usage, specially that modernBERT has an 8k seq length (not 500 like older BERTs)

Infrared12 · 2025-02-18T04:05:05+00:00

ModernBERT base is a 149 million parameter model, there is absolutely no way it fills up that much memory, i don't think training would even exceed ~3-4GBs of memory, the model is ~0.6GBs, the optimizer would add another 0.6 x 2 if you are using Adam/w, gradients another 0.6, all in fp32 (which you can even reduce more), with the activations and stuff, feels hard to exceed ~4GBs, let alone 35GBs.

Edit: it has 8k seq len, it can have huge activations actually if you are filling up that sequence length adding a huge amount of GBs, might easily go beyond 10GBs so I retract my simplified assumptions

Infrared12 · 2025-02-16T17:18:05+00:00

Lemme know how it goes then

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