boxabirds

# Project Policy

This policy provides a single, authoritative, and machine-readable source of truth for AI coding agents and humans, ensuring that all work is governed by clear, unambiguous rules and workflows. It aims to eliminate ambiguity, reduce supervision needs, and facilitate automation while maintaining accountability and compliance with best practices.

# 1. Introduction

> Rationale: Sets the context, actors, and compliance requirements for the policy, ensuring all participants understand their roles and responsibilities.

## 1.1 Actors

tysam-code

import os
import sys
with open(sys.argv[0]) as f:
    code = f.read() # read the code of this file ASAP, for logging
import uuid
import time
import glob
import subprocess
import contextlib
from dataclasses import dataclass

al3rez

Security Checklist (What can be done in CursorAI & software-level not infrastructure!)

Configuration Security

• Detect secrets in code
• Identify secrets committed to version control
• Flag hardcoded credentials

Authentication & Authorization

• Identify missing authentication checks
• Detect improper authorization patterns

Artefact2

Which GGUF is right for me? (Opinionated)

Good question! I am collecting human data on how quantization affects outputs. See here for more information: ggml-org/llama.cpp#5962

In the meantime, use the largest that fully fits in your GPU. If you can comfortably fit Q4_K_S, try using a model with more parameters.

llama.cpp feature matrix

See the wiki upstream: https://github.com/ggerganov/llama.cpp/wiki/Feature-matrix

virattt

cjpais

import argparse
import glob
import os
import torch
from safetensors import safe_open
from safetensors.torch import save_file


ap = argparse.ArgumentParser()
ap.add_argument("-m", "--model", help="Path to LLaVA v1.5 model")

Vaibhavs10

!pip install transformers optimum
!pip install auto-gptq --extra-index-url https://huggingface.github.io/autogptq-index/whl/cu118/

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name_or_path = "TheBloke/zephyr-7B-beta-GPTQ"

model = AutoModelForCausalLM.from_pretrained(model_name_or_path,
                                             device_map="auto",
                                             trust_remote_code=False,

nberlette

SvelteKit 🆚 Next.js

Objectives: fast, easy, convention over configuration, & batteries included. Overwhelming choices < providing a clear path forward.

Contenders: [SvelteKit][sveltekit-url] (by [Svelte][svelte-url]) and [Next.js][next-url] (by [Vercel][vercel-url]).

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Comparison of Major Features

stek29

These are just my notes, and described process worked for me on my 1C robot
If anything goes wrong, having USB adapter for FEL flashing might be the only way to restore your robot
this is not an official guide
oh, and I'm not responsible for any damage blah blah
and huge thanks to Dennis and Hypfer, and everyone behind this root :)

0. get uboot shell

to enter uboot shell on 1C you have to: 0. turn robot off normally

1. hook up uart, open console

itzurabhi

Install the dependecies

sudo apt install qemu-system-arm qemu-system-mips qemu-efi-aarch64 qemu-kvm qemu-efi cloud-image-utils

Prepare the EFI partition

dd if=/dev/zero of=flash0.img bs=1M count=64
dd if=/usr/share/qemu-efi/QEMU_EFI.fd of=flash0.img conv=notrunc