A self-hosted, compounding-memory AI assistant running on a Raspberry Pi.
NanoClaw is a personal AI assistant built on Anthropic's Claude that runs entirely on a Raspberry Pi. It connects to messaging channels (WhatsApp, Telegram, Slack, Discord), processes voice and images, schedules recurring tasks, and — unlike a standard chatbot — accumulates knowledge over time through a structured memory system.
This is code generated as part of our course on agentic coding. The code below was written by Claude Code using its default model in late Feb 2026.
The purpose of this assigment was to help students realize that, in the absence of explicit prompting to this effect, Claude Code (in its current state) is highly unlikely to generate code with a clean separation of concerns. The specific concern we wanted to focus on was the factoring-out of business rules. This is to set up students to learn about business rules, author them separately, and then figure out how to incorporate them into the program: e.g., using something like the RETE algorithm.
The hope is that once students learn this concept, they will recognize it in future tasks, and think to prompt for it to be done more deliberately. Since Claude doesn't do it automatically, it's something they need to be taught abo
| """ | |
| 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 |
| #!/usr/bin/env python3 | |
| """ | |
| DeepSeek-OCR-2 markdown extraction for olmocr-bench. | |
| Generates markdown files from images. For scoring, see: https://github.com/allenai/olmocr | |
| config: | |
| Prompt: "<image>\n<|grounding|>Convert the document to markdown." | |
| Repeats: 3 |
Use this skill to fetch the transcript of a YouTube video, with or without timestamps.
Use this skill with Claude (by extracting it to .claude/skills/) or with any other agent using Skillz.
Note: This skill is unlikely to run successfully on the Claude web app, since access to YouTube is blocked. Use it with Claude Code or other local agents.
You are Gemini CLI, operating in a specialized Explain Mode. Your function is to serve as a virtual Senior Engineer and System Architect. Your mission is to act as an interactive guide, helping users understand complex codebases through a conversational process of discovery.
Your primary goal is to act as an intelligence and discovery tool. You deconstruct the "how" and "why" of the codebase to help engineers get up to speed quickly. You must operate in a strict, read-only intelligence-gathering capacity. Instead of creating what to do, you illuminate how things work and why they are designed that way.
Your core loop is to scope, investigate, explain, and then offer the next logical step, allowing the user to navigate the codebase's complexity with you as their guide.
You are Gemini CLI, an expert AI assistant operating in a special 'Plan Mode'. Your sole purpose is to research, analyze, and create detailed implementation plans. You must operate in a strict read-only capacity.
Gemini CLI's primary goal is to act like a senior engineer: understand the request, investigate the codebase and relevant resources, formulate a robust strategy, and then present a clear, step-by-step plan for approval. You are forbidden from making any modifications. You are also forbidden from implementing the plan.
So you know how the transformer works, and you know basic ML/DL, and you want to learn more about LLMs. One way to go is looking into the various "algorithmic" stuff (optimization algorithms, RL, DPO, etc). Lot's of materials on that. But the interesting stuff is (in my opinion at least) not there.
This is an attempt to collect a list of academic (or academic-like) materials that explore LLMs from other directions, and focus on the non-ML-algorithmic aspects.
You are an AI assistant tasked with creating a highly engaging, personalized check-in flow for a user. This flow should emulate a beautifully designed iOS app, focusing on simplicity, clear call-to-actions, and an overall delightful user experience. Your role combines that of a personality coach and an expert UX designer.
Here's the theme for today's check-in: {{THEME}}
And here's the context we have about the user: <user_context> {{USER_CONTEXT}}
| # train_grpo.py | |
| # | |
| # See https://github.com/willccbb/verifiers for ongoing developments | |
| # | |
| """ | |
| citation: | |
| @misc{brown2025grpodemo, | |
| title={Granular Format Rewards for Eliciting Mathematical Reasoning Capabilities in Small Language Models}, | |
| author={Brown, William}, |
