{
  "academy": "LemOne Academy",
  "location": "State College, PA",
  "mission": "Empowering the next generation of AI Specialists through technical mastery and hands-on building.",
  "verification_portal": "https://lemone.academy/verify",
  "last_updated": "2026-04-05T15:53:17Z",
  "curricula": {
    "historian": {
      "title": "AI Historian",
      "description": "The history of Artificial Intelligence from 1943 to the present.",
      "modules": [
        {
          "id": "h1",
          "title": "The Visionaries (1943–1956)",
          "description": "The mathematical foundations of neural computation and the formal birth of AI at Dartmouth.",
          "learning_objectives": [
            "Understand the McCulloch-Pitts mathematical neuron.",
            "Explain the significance of the Turing Test.",
            "Identify the key figures of the Dartmouth Workshop."
          ],
          "terms": [
            { "term": "Turing Test", "definition": "A test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human." },
            { "term": "Symbolic AI", "definition": "An approach to AI based on high-level, human-readable representations of problems, logic, and search." },
            { "term": "Logic Theorist", "definition": "The first artificial intelligence program, designed to mimic human problem-solving by proving mathematical theorems." },
            { "term": "Dartmouth Workshop", "definition": "The 1956 summer research project where the field of Artificial Intelligence was formally established." }
          ],
          "quiz_sample": [
            { "question": "Who coined the term 'Artificial Intelligence'?", "answer": "John McCarthy" },
            { "question": "What was the primary contribution of McCulloch and Pitts in 1943?", "answer": "A mathematical model of a neuron" }
          ]
        }
      ]
    },
    "basics": {
      "title": "AI Basics",
      "description": "The perfect starting point for AI newcomers. Learn to use AI as a functional partner.",
      "modules": [
        {
          "id": "01-01",
          "title": "The Mental Model",
          "description": "AI isn't a search engine; it’s a reasoning engine.",
          "key_takeaways": [
            "AI is a Reasoning Engine: It predicts patterns rather than retrieving facts.",
            "The Intern Analogy: Use AI for heavy lifting, but verify the output.",
            "Major Tools: ChatGPT, Claude, and Gemini are the primary interfaces."
          ],
          "terms": [
            { "term": "Reasoning Engine", "definition": "A system that uses patterns to generate logical outputs rather than just retrieving data." },
            { "term": "Hallucination", "definition": "When an AI model generates incorrect or nonsensical information with high confidence." },
            { "term": "Context Window", "definition": "The amount of information an AI can 'remember' or consider at one time during a conversation." }
          ]
        }
      ]
    },
    "specialist": {
      "title": "AI Specialist",
      "description": "The core 30-day foundation for building AI applications.",
      "modules": [
        {
          "id": "1",
          "title": "The Evolution of AI",
          "description": "From Turing to Transformers. Plus: Your first 'Master Prompt' execution.",
          "key_takeaways": [
            "Historical Roots: Alan Turing's 'Imitation Game' (1950) set the benchmark for machine intelligence.",
            "Symbolic vs. Connectionist: The early 'AI Winter' was caused by the limitations of rule-based systems.",
            "The Transformer Revolution: The 2017 'Attention Is All You Need' paper introduced the architecture that powers today's LLMs.",
            "Scaling & Alignment: We learned that simply adding more data and compute (Scaling Laws) isn't enough; we must also align AI with human values through RLHF."
          ],
          "terms": [
            { "term": "Turing Test", "definition": "A test of a machine's ability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human." },
            { "term": "Transformer", "definition": "A deep learning architecture that uses the mechanism of self-attention, weighing the significance of each part of the input data differently." },
            { "term": "RLHF", "definition": "Reinforcement Learning from Human Feedback; a method used to align AI models with human preferences and values." },
            { "term": "Emergence", "definition": "The phenomenon where a large language model develops new, complex abilities (like reasoning or coding) as it scales, which were not explicitly programmed." }
          ]
        },
        {
          "id": "2",
          "title": "Inside the Black Box",
          "description": "Neural Networks, Weights, Biases, and LLM mechanics.",
          "key_takeaways": [
            "The Perceptron: The fundamental unit of a neural network.",
            "Weights & Biases: Weights determine the strength of an input, while biases provide an offset.",
            "Backpropagation & Gradient Descent: The core algorithms for training.",
            "Embeddings & Semantic Space: How tokens are represented as numerical vectors."
          ],
          "terms": [
            { "term": "Weights", "definition": "Parameters in a neural network that determine the importance or strength of a specific input signal." },
            { "term": "Backpropagation", "definition": "The fundamental algorithm used to train neural networks by propagating the error back through the layers to adjust weights." },
            { "term": "Embedding", "definition": "A high-dimensional vector representation of a token that captures its semantic meaning." },
            { "term": "Temperature", "definition": "A hyperparameter that controls the randomness and creativity of an LLM's output." }
          ]
        },
        {
          "id": "3",
          "title": "Advanced Prompt Engineering",
          "description": "Mastering Chain of Thought, Few-Shot, and System Roles."
        },
        {
          "id": "4",
          "title": "AI in the Wild",
          "description": "Real-world applications in Business and EdTech."
        },
        {
          "id": "5",
          "title": "Workflow Mechanics",
          "description": "Mapping logic and routing tasks between AI nodes."
        },
        {
          "id": "6",
          "title": "Building AI Chains",
          "description": "Chaining prompts for complex automation."
        },
        {
          "id": "7",
          "title": "Intro to WebLLM",
          "description": "Running local AI with zero API costs and maximum privacy."
        },
        {
          "id": "8",
          "title": "Connecting the Logic",
          "description": "Wiring AI into a light app builder."
        },
        {
          "id": "9",
          "title": "Quality Assurance & Testing",
          "description": "Validating outputs, stress-testing, and handling hallucinations."
        }
      ]
    }
  },
  "verification_system": {
    "method": "Cryptographic Hash Verification",
    "process": "Certificates are issued with a unique ID that corresponds to a hash of the builder's name, completion date, and curriculum version. This hash is verifiable via the /verify portal against our secure database.",
    "api_endpoint": "https://lemone.academy/api/verify-certificate"
  }
}