savchenko

Sharing best practices I discovered/found for coding using AI based code generation

Provide code base/project details and up-to-date context.
Separation of concerns in different modules.
Create implementation details for each PR, maintain implementation docs.
Generate small, focused chunks of code (< 1k lines).
Keep functions concise and small.
Comment every function; add context before AI generation.
Keep those comments updated with every change on that code

Sieve Scripting Cheat Sheet

Organize Images into folders using AI

This is a tool that sorts images into folders using "AI". You create the folders you want the images to be put into.

I got 'claude' to write with a couple prompts. Make venv and install deps with pip.

Use microsoft/git-large-coco To turn images into short descriptions of images - https://huggingface.co/microsoft/git-large-coco
Use all-MiniLM-L6-v2 to turn short descriptions into 'embedding vectors' - https://www.sbert.net/docs/sentence_transformer/pretrained_models.html
Compare the embedding vector of the image, with the embedding vectors of the folders - choose the best folder to put the image into

Raspberry Pi 5 - Google Coral Edge M.2 TPU installation guide

To get started with either the Mini PCIe or M.2 Accelerator, all you need to do is connect the card to your system, and then install our PCIe driver, Edge TPU runtime, and the TensorFlow Lite runtime. This page walks you through the setup and shows you how to run an example model.

The setup and operation is the same for both M.2 form-factors, including the M.2 Accelerator with Dual Edge TPU.

Requirements

Raspberry Pi 5 with the following Linux operating system:
Raspberry Pi OS (64-bit) based on Debian 10 or newer

	" How to use: append below code into your ~/.vimrc file

	inoremap ( ()<LEFT> " ( -> ()
	inoremap [ []<LEFT> " [ -> []
	inoremap { {}<LEFT> " { -> {}

	" shift right when input ) and current char is )
	function! RemoveNextDoubleChar(char)
	let next_char = getline(".")[col(".")] " get current char
	if a:char == next_char

	"""
	A micro event loop library implementation from scratch.

	This library provides a minimal but feature-complete asynchronous event loop
	implementation for educational purposes. It demonstrates the core concepts of
	asynchronous programming including:

	- Task scheduling and management
	- I/O multiplexing with non-blocking sockets
	- Timeouts and sleep functionality

	#!/usr/bin/env sh

	URI=$1
	BASE=$(basename $1)

	[ -f ../models/$BASE-f16.gguf ] && exit 0

	(. ../huggingface-cli/bin/activate && HF_HUB_ENABLE_HF_TRANSFER=1 huggingface-cli download $URI --local-dir ../models/$BASE --cache-dir ../models/$BASE/.hf-cache --exclude 'pytorch_model' --exclude 'consolidated' --resume-download) \|\| exit 1

	DTYPE=$(jq -r '.torch_dtype' < ../models/$BASE/config.json)

	#!/bin/bash

	cd /

	sudo apt update

	echo "deb https://packages.cloud.google.com/apt coral-edgetpu-stable main" \| sudo tee /etc/apt/sources.list.d/coral-edgetpu.list

	curl https://packages.cloud.google.com/apt/doc/apt-key.gpg \| sudo apt-key add -

	Requirements:-

	1. Ubuntu 22.04
	2. 7900XT or 7900XTX

	Pre-requests before making any installation.

	Follow step 1-3 if you installed amdgpu.
	1. If you already installed redeon graphic card driver from AMD using amdgpu-install. Completely remove the driver
	and connect your HDMI to motherboard. Then restart your PC