9 articles2026-09-29
Bonsai Demo explained: local inference with a fork-specific model
Separate the demo repository, downloaded model weights and required runtime before judging the claims.
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EasyAI / Open source
One project, one complete learning path: discover the idea, run it safely, inspect the architecture and code, then decide what to build next.
Separate the demo repository, downloaded model weights and required runtime before judging the claims.
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Use one learning goal and a runnable artifact to find a sensible entry point in this large course.
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Choose the component that matches your computer-use problem before installing a fleet of tools.
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Use the open book and its experiments without confusing a reading list with a deployed agent.
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Understand how a generated command line can expose a GUI application without pretending to replace it.
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Map goals, agents, tasks and budgets before giving an autonomous team its first assignment.
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Follow a memory from an agent conversation into a bank, then decide when recall or reflection is warranted.
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Separate financial workflow files, paid host services and human approval before choosing an agent.
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Understand the shared core, provider configuration and tool permissions before choosing an interface.
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Understand coverage records, independent verification and the limits of an AI-assisted audit.
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Separate agents, commands, hooks and MCP configuration from the Claude Code host that uses them.
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Learn what proposals, behavior specs, designs and tasks contribute before an assistant edits code.
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