| """ | |
| 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 |
Note
This does not works in browser for quests which require you to play a game! Use the desktop app to complete those.
How to use this script:
Console tab
A guide how to get and activate Windows 8, 8.1, 10 and 11 Pro for free!
If you see the Windows keyboard button 
Companion prompts for the video: OpenClaw after 50 days: 20 real workflows (honest review)
These are the actual prompts I use for each use case shown in the video. Copy-paste them into your agent and adjust for your setup. Most will work as-is or the agent will ask you clarifying questions.
Each prompt describes the intent clearly enough that the agent can figure out the implementation details. You don't need to hand-hold it through every step.
My setup: OpenClaw running on a VPS, Discord as primary interface (separate channels per workflow), Obsidian for notes (markdown-first), Coolify for self-hosted services.
I wrote an in-depth research prompt to conduct a GPT-Deep-Research on the Manus topic, seeking to replicate it with currently available open source tools. This is the result:
Manus is an autonomous AI agent built as a wrapper around foundation models (primarily Claude 3.5/3.7 and Alibaba's Qwen). It operates in a cloud-based virtual computing environment with full access to tools like web browsers, shell commands, and code execution. The system's key innovation is using executable Python code as its action mechanism ("CodeAct" approach), allowing it to perform complex operations autonomously. The architecture consists of an iterative agent loop (analyze → plan → execute → observe), with specialized modules for planning, knowledge retrieval, and memory management. Manus uses file-based memory to track progress and store information across operations. The system can be replicated using open-source components including CodeActAgent (a fine-tuned Mistral model), Docker for sandbox
Warning
Before we start, this is not an attempt of defamation or the act of public shaming, we are only focusing on safety of spiking "discord refugees" which is why we decided to write our concerns about Kloak.
To begin with, recently we found out that the developers of this site were "scamming" their users by vibe coding almost the entire platform stack and are taking YOUR donations for granted!
For context, here's some info we found so far:
| " ~/.idea-lazy.vim | |
| " LazyVim mappings for Jetbrains IDEs | |
| " Required plugins. https://plugins.jetbrains.com/bundles/7-ideavim-bundle | |
| " IDEAVim | |
| " Which-Key | |
| " IdeaVim-Sneak | |
| " To install, add this to the top of your ~/.ideavimrc: |
| name | description |
|---|---|
pdf-ocr-feedback |
High-accuracy OCR pipeline using Maj@K consensus voting, structured self-evaluation, and adaptive compute budgets to achieve ≥95% transcription accuracy. |
Use when transcribing PDF pages via vision model and you need high accuracy — especially for:
| # Workflow Orchestration | |
| ## 1. Plan Mode Default | |
| - Enter plan mode for ANY non-trivial task (3+ steps or architectural decisions) | |
| - If something goes sideways, STOP and re-plan immediately — don't keep pushing | |
| - Use plan mode for verification steps, not just building | |
| - Write detailed specs upfront to reduce ambiguity | |
| ## 2. Subagent Strategy |
