CupOfGeo

{
  "_comment": "rtk hook requieres: brew install rtk. media hooks require: brew install nowplaying-cli. Looks for a file `~/.claude/no-media` if present or not to trigger.",
  "hooks": {
    "Stop": [
      {
        "matcher": "",
        "hooks": [
          {
            "type": "command",
            "command": "[ -f ~/.claude/no-media ] || nowplaying-cli pause"

dabit3

See more of my writing here. Also check out Devin

In this post, I'll start from scratch and build up to OpenClaw's architecture step by step, showing how you could have invented it yourself from first principles, using nothing but a messaging API, an LLM, and the desire to make AI actually useful outside the chat window.

End goal: understand how persistent AI assistants work, so you can build your own (or become an OpenClaw power user).

First, let's establish the problem

When you use ChatGPT or Claude in a browser, there are several limitations:

agentsoflearning

name	appsec-guardian
description	Expert Application Security Engineer. Prevents insecure code from reaching remote repositories by enforcing OWASP Top 10 and secure SDLC practices. Runs before git push to block vulnerable code.
tools	view, bash_tool, str_replace, create_file, web_search, web_fetch
model	inherit

You are a senior Application Security Engineer with deep expertise in OWASP Top 10, secure SDLC, and security-by-design principles.

Core Mission

hathibelagal-dev

just-cameron

import outlines
from transformers import AutoTokenizer

model_string = 'deepseek-ai/DeepSeek-R1-Distill-Qwen-7B'
# model_string = 'deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B' # For small machines

model = outlines.models.transformers(
    model_string,
    device='cuda', # also 'cpu', 'mps','auto'
)

vgel

import argparse
import random
import sys

from transformers import AutoModelForCausalLM, AutoTokenizer, DynamicCache
import torch

parser = argparse.ArgumentParser()
parser.add_argument("question", type=str)
parser.add_argument(

stong

Original report

• Affected Vendor: OpenPrinting
• Affected Product: Several components of the CUPS printing system: cups-browsed, libppd, libcupsfilters and cups-filters.
• Affected Version: All versions <= 2.0.1 (latest release) and master.
• Significant ICS/OT impact? no
• Reporter: Simone Margaritelli [evilsocket@gmail.com]
• Vendor contacted? yes The vendor has been notified trough Github Advisories and all bugs have been confirmed:

• https://github.com/OpenPrinting/cups-browsed/security/advisories/GHSA-rj88-6mr5-rcw8

mtisz

base_model: meta-llama/Meta-Llama-3-70B
model_type: LlamaForCausalLM
tokenizer_type: AutoTokenizer

load_in_8bit: false
load_in_4bit: true
strict: false

datasets:
  - path: /home/migel/ai_datasets/tess-v1.5b-chatml.jsonl

seanchatmangpt

import ast
import logging
import inspect

from typing import Type, TypeVar
from dspy import Assert, Module, ChainOfThought, Signature, InputField, OutputField
from pydantic import BaseModel, ValidationError

logger = logging.getLogger(__name__)
logger.setLevel(logging.ERROR)

veekaybee

Anti-hype LLM reading list

Goals: Add links that are reasonable and good explanations of how stuff works. No hype and no vendor content if possible. Practical first-hand accounts of models in prod eagerly sought.

Foundational Concepts

Pre-Transformer Models