Clawdemy Lessons

Clawdemy
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Free AI literacy for everyday users. Bite-size narrated lessons that turn fear into fluency, one topic at a time.

  1. 5d ago

    API keys and the OAuth path

    How Clawless connects to AI providers. What an API key actually is, where yours lives once you save it, the BYOK billing model with no Clawless markup, and the OAuth path that lets ChatGPT subscribers skip per-token charges on OpenAI models.

    10 min

  2. 5d ago

    CostGuard and where your data goes

    The two anxieties of the first week with Clawless. CostGuard is the spending safety net that watches your BYOK usage against a monthly cap. The data path is your computer, the AI provider, your computer, with no Clawless server holding your conversations.

    12 min

  3. 5d ago

    How Clawless remembers (and forgets)

    A tour of the memory system. The distinction between conversation history and memory, the four tiers (Pinned, Insights, General, Decayed), the three pathways memories get in, the Memory panel where you control them, and the privacy rule for what should and should not be saved.

    11 min

  4. 5d ago

    Your first conversation and picking a model

    The first hands-on Clawless lesson. Send your first message, find the model picker in the dock row, switch models mid-conversation without losing your place, and use the provider-prefixed pattern to reach off-list models. The capability the rest of the track sits on.

    11 min

  5. 6d ago

    AI governance: the policy layer above any individual deployment

    Lesson 9 of Track 23, the track's closing lesson. Hendrycks Ch 8 brings governance as the layer outside any individual AI system. Four-layer taxonomy: corporate, national, international, compute. Each layer with its mechanisms, its strengths, its limits. Why compute governance has become the field's central lever (compute is physical, excludable, quantifiable). The L9 capability is to situate one real governance proposal inside the taxonomy. Track closure: what the nine lessons produced and what the track does not pretend to be.

    14 min

  6. 6d ago

    AI safety as a field: what it studies and why it is a discipline, not a stance

    Opener of Track 23 (AI Safety and Alignment). Frames AI safety as a field with a subject (catastrophic AI risks), a vocabulary (the four-bucket typology, the specification-vs-proxy-gaming distinctions), a method (descriptive, attributed, cross-disciplinary), and a connection to neighboring fields (safety engineering, complex systems, governance). The capability bar is the paragraph-write: state what AI safety studies and why it is a discipline rather than a stance, in roughly 6-8 sentences.

    13 min

  7. 6d ago

    Beneficial AI and machine ethics: moral uncertainty as the substrate

    Lesson 7 of Track 23, opener of Phase 3 (ethics and governance). Hendrycks Ch 6 turns from 'what fails' to 'what are we trying to do?' Moral uncertainty as the foundational concept: the field does not have a single correct ethical framework to hand to an AI system. Three strategies (My Favorite Theory, expected choiceworthiness, moral parliament). Social welfare functions (utilitarian vs prioritarian) as aggregation tools. Cost-benefit analysis blind spots. Fairness criteria not jointly satisfiable. The L4 callback: outer alignment is harder because there is no single goal to capture.

    13 min

  8. 6d ago

    Brief: Challenges and open problems (closes Phase 3 and Track 18)

    Editorial brief for Lesson 18 of Track 18. The final lesson. Four open frontiers (sample efficiency, safety and alignment, generalization, real-world deployment), how each maps onto T18 algorithms, the tensions across frontiers, T18 syllabus recap, and where the track fits in the curriculum.

    14 min
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Free AI literacy for everyday users. Bite-size narrated lessons that turn fear into fluency, one topic at a time.

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