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@karpathy
karpathy / microgpt.py
Last active February 19, 2026 20:58
microgpt
"""
The most atomic way to train and run inference for a GPT in pure, dependency-free Python.
This file is the complete algorithm.
Everything else is just efficiency.
@karpathy
"""
import os # os.path.exists
import math # math.log, math.exp
@ongkiii
ongkiii / IPA-Sources.md
Last active February 19, 2026 20:53
REPOS/TELEGRAM CHANNELS LIST BY u/angkitbharadwaj
@mberman84
mberman84 / PRD.md
Created February 17, 2026 19:59
OpenClaw PRD

PRD.md - Product Requirements & Feature Inventory

Everything built on top of the base OpenClaw platform. Canonical reference for what exists, where it lives, and how it works. Operational use cases and workflow playbooks live in docs/USE-CASES-WORKFLOWS.md.

Table of Contents

  1. Operational Use Cases & Workflows
@matsonj
matsonj / attach_db.sql
Last active February 19, 2026 20:50
NBA Game Quality Explorer
ATTACH 'md:_share/nba_box_scores/c9126ae3-ef30-4b6e-af8e-d2212c1f2797';
@mosure
mosure / ramdisk.md
Last active February 19, 2026 20:49

ramdisk is a scaling primitive for local agent orchestration

local development used to be human-paced: one developer, one editor, occasional builds and tests. consumer ssd endurance assumptions were built around that pattern.

agent-driven development changes the load profile. when you run 4-32 local agents in parallel, each doing build, test, validation, and coding loops, write pressure scales horizontally just like cpu and memory demand.

graph LR;
    a["agent count"] --> e["daily host writes"];
    b["cycles per agent per day"] --> e;
@mberman84
mberman84 / oc.md
Created February 16, 2026 19:42
OpenClaw Prompts

OpenClaw Prompts - Build Your Own AI Assistant

Prompts to recreate each piece of the OpenClaw system. Use these with any AI coding assistant.

1. Personal CRM "Build a personal CRM that automatically scans my Gmail and Google Calendar to discover contacts from the past year. Store them in a SQLite database with vector embeddings so I can query in natural language ('who do I know at NVIDIA?' or 'who haven't I talked to in a while?'). Auto-filter noise senders like marketing emails and newsletters. Build profiles for each contact with their company, role, how I know them, and our interaction history. Add relationship health scores that flag stale relationships, follow-up reminders I can create, snooze, or mark done, and duplicate contact detection with merge suggestions. Link relevant documents from Box to contacts so when I look up a person, I also see related docs."

2. Meeting Action Items (Fathom)