aashari

The Autonomous Agent Prompting Framework

This repository contains a disciplined, evidence-first prompting framework designed to elevate an Agentic AI from a simple command executor to an Autonomous Principal Engineer.

The philosophy is simple: Autonomy through discipline. Trust through verification.

This framework is not just a collection of prompts; it is a complete operational system for managing AI agents. It enforces a rigorous workflow of reconnaissance, planning, safe execution, and self-improvement, ensuring every action the agent takes is deliberate, verifiable, and aligned with senior engineering best practices.

I also have Claude Code prompting for your reference: https://gist.github.com/aashari/1c38e8c7766b5ba81c3a0d4d124a2f58

aashari

You are a powerful agentic AI coding assistant, powered by GPT-4o. You operate exclusively in Cursor, the world's best IDE.

You are pair programming with a USER to solve their coding task. The task may require creating a new codebase, modifying or debugging an existing codebase, or simply answering a question. Each time the USER sends a message, we may automatically attach some information about their current state, such as what files they have open, where their cursor is, recently viewed files, edit history in their session so far, linter errors, and more. This information may or may not be relevant to the coding task, it is up for you to decide. Your main goal is to follow the USER's instructions at each message.

1. Be concise and do not repeat yourself.

karpathy

LLM Wiki

A pattern for building personal knowledge bases using LLMs.

This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.

The core idea

Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.

jakekarnes42

pedramamini

#!/usr/bin/env python3
"""
rocky_say — Text-to-speech using Rocky's cloned voice (Project Hail Mary)

Transforms input text into Rocky's speech patterns ("text GAN"), then
synthesizes audio using XTTS v2 voice cloning. Rocky is the Eridian alien
from Andy Weir's Project Hail Mary, voiced by James Ortiz in the 2026 film.

His speech patterns are distinctive: dropped articles, simplified grammar,
word tripling for emphasis ("good good good", "bad bad bad"), and the

lbrame

Arch Linux tweaks

This is a collection of the tweaks and modification I've made to my Arch Linux installation over the months. These may be applicable to other distros, but please check first before doing anything. I also included Arch Wiki references for all the procedures I mentioned. My recommendation is not to blindly follow this gist but to always check with the Arch Linux wiki first. Things move fast and by the time you're reading this my gist may be out of date. Lastly, the golden rule: never execute a command you don't understand.

Installing the KDE Plasma desktop

My current DE of choice is KDE's Plasma. I find it just about perfect.

There are various ways to install it on Arch. The most popular one is to install plasma and plasma-applications, but I don't like doing that because it comes with too many programs I'll never use. I, instead, install the base plasma group, remove the few extra packages that come with it, then I finish off by installing a few KDE apps that don't come with th

devilankur18

TokenZip — PRD, HLD, LLD

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📋 PRD — Product Requirements Document

1. Executive Summary

TokenZip v2 transforms Karpathy's llm wiki concept into a gzip like token compression engine on top of entire codebase, which can reduce the LLM input token cost upto by 95% when using with Coding Copilots like Claude Code, Codex etc. Instead of generating a flat text summary, it builds a multi-level, queryable, chainable knowledge graph — from repo → modules → files → symbols — stored locally in .tokenzip/db, exposed as an MCP server for any AI copilot, and kept fresh via git hooks