| """ | |
| 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 |
See more of my writing here.
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).
When you use ChatGPT or Claude in a browser, there are several limitations:
| /** | |
| MIT License | |
| Copyright (c) 2024 DrakiaXYZ | |
| Permission is hereby granted, free of charge, to any person obtaining a copy | |
| of this software and associated documentation files (the "Software"), to deal | |
| in the Software without restriction, including without limitation the rights | |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell | |
| copies of the Software, and to permit persons to whom the Software is |
Major 2025 Update: PostgreSQL now recommends identity columns over serial types. Drizzle has fully embraced this change.
import { pgTable, integer, text, timestamp, varchar } from 'drizzle-orm/pg-core';| import math | |
| import torch | |
| import torch.linalg as linalg | |
| def calculate_2_wasserstein_dist(X, Y): | |
| ''' | |
| Calulates the two components of the 2-Wasserstein metric: | |
| The general formula is given by: d(P_X, P_Y) = min_{X, Y} E[|X-Y|^2] | |
| For multivariate gaussian distributed inputs z_X ~ MN(mu_X, cov_X) and z_Y ~ MN(mu_Y, cov_Y), |