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learn-claude-code is by shareAI-lab — not by us.

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Indexed Oct 5, 2026 · 78,024 stars at index time. Maintainers: claiming verifies your identity and unlocks a higher assurance tier. Removal requests are honored.

AI agent · for humans & their agents

learn-claude-code

unclaimed listingactively maintainedFreeMIT

Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1

by Open Source Community · New publisher

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shareAI-lab/learn-claude-code is an open-source project by shareAI-lab: Bash is all you need - A nano claude code–like 「agent harness」, built from 0 to 1. Indexed here so it can be found — not resold.

It is free. Get it from the upstream repository: https://github.com/shareAI-lab/learn-claude-code

From the project's own README (excerpt, reproduced for discovery under its MIT license):

English 中文 日本語

Learn Claude Code -- Harness Engineering for Real Agents

Agency Comes from the Model. An Agent Product = Model + Harness.

Before we write any code, one thing needs to be clear.

Agency -- the capacity to perceive, reason, and act -- comes from model training, not from external code orchestration. But a working agent product needs both the model and the harness. The model is the driver. The harness is the vehicle. This repository teaches you how to build the vehicle.

Where Agency Comes From

At the core of every agent is a neural network -- a Transformer, an RNN, a trained function -- shaped by billions of gradient updates on sequences of perception, reasoning, and action. Agency was never bestowed by the surrounding code. It was learned during training.

Humans are the original proof. A biological neural network, refined by millions of years of evolutionary pressure, perceives the world through senses, reasons through a brain, and acts through a body. When DeepMind, OpenAI, or Anthropic say "agent," they all mean the same core thing: a model that learned to act through training, plus the infrastructure that lets it operate in a specific environment.

The historical record is unambiguous: • 2013 -- DeepMind DQN plays Atari. A single neural network, receiving only raw pixels and game scores, learned 7 Atari 2600 games -- surpassing prior algorithms and beating human experts in 3 of them. By 2015, scaled to 49 games at professional tester level, published in Nature. No game-specific rules. One model, learning from experience. • 2019 -- OpenAI Five conquers Dota 2. Five neural networks played 45,000 years of Dota 2 against themselves over 10 months, then defeated OG -- the TI8 world champions -- 2-0 in a live match. In the public arena, the AI won…

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01Capabilities

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Doesn’t

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02Requirements & stack

Depends on

No declared dependencies

Credentials needed

None declared

Stack

python

03Community

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This listing is unclaimed, so publisher identity cannot be verified and it stays below the “verified” tier by design — that is a statement about the listing, not about the project’s quality. Our automated scans still ran; a maintainer who claims it unlocks identity verification.

VersionChannelReleasedNotes
0.0.0stableOct 5, 2026Indexed listing — see the upstream repository for real release history.