Research task (Business Planning & Research):
Act as a creative technical founder and brainstorm an innovative AI startup idea that meets these criteria:
1. Leverages AI in a meaningful, technically interesting way, not just a thin wrapper around an LLM
2. Uses or integrates APIs (e.g. for data sources, automation, or integration with popular tools)
3. Solves a real, cool problem in people’s lives or work, something practical but original
4. Has potential for viral growth or clear product-market fit

Context: I like startups like Cal.ai (http://cal.ai) (AI calorie counting), Boardy.ai (http://boardy.ai) (AI networking), and AI scheduling assistants, things that automate or enhance daily life or work through smart AI tooling. 

Please:
* Suggest 3 ideas, with a clear description of the product and how it works technically (tech stack, APIs, AI models)
* Explain what problem it solves and why people would want it
* Keep the ideas ambitious but buildable, something that could actually become a startup

For each idea state if any company is doing something similar already (who are the competitors). I want the startup to leverage a technical advantage of using cool ai features only enabled now and that will improve as foundational models and services get better. 

Evaluation rubric:
criterion: The response provides exactly 3 startup ideas as requested.
weight: 2
axis: Instruction Following

criterion: The response includes ideas that can be built using modern available tools (e.g., AWS, Hugging Face, GPUs, LLMs).
weight: 3
axis: Instruction Following

criterion: The response describes how the ideas solve a real, practical problem (e.g., by identifying target users, addressing problems that people can plausibly face, tackling problems that require high compute).
weight: 3
axis: Implicit Criteria

criterion: The response substantiates its core claims with relevant, credible, and up-to-date authoritative sources (e.g., respected magazines like Forbes, credible VC reports / blog posts, economic reports from reputed think thanks, government reports), demonstrating a strong evidence-based foundation for its conclusions.
weight: 2
axis: References & Citation Quality

criterion: The response states the technical advantages enabled by today’s AI advances for each proposed idea (e.g., having pretraining knowledge, in-context learning handling increasingly complex tasks, models having larger context windows, models capable of producing multimodal output in a single call).
weight: 2
axis: Synthesis of Information

criterion: The response outlines how the startup would benefit from future improvements in foundational models and services.
weight: 2
axis: Implicit Criteria

criterion: The response synthesizes information from multiple domains (e.g., technology, market trends, healthcare, user behavior) to generate novel insights or solutions, rather than merely summarizing existing information.
weight: 4
axis: Explicit Criteria

criterion: The response details meaningful API integrations for each idea, using concrete examples (e.g., health APIs by Epic, REST APIs from vendors like Workday, AWS API integrations, or OpenAPI Bench) to illustrate their implementation and impact.
weight: 4
axis: Explicit Criteria

criterion: The response explains the user experience (UX) of each idea in quantifiable or qualitative ways (e.g., learnability, efficiency, errors, memory).
weight: 3
axis: Implicit Criteria

criterion: The response includes a growth or market strategy for each idea (e.g., investing, scaling up hardware, boosting sales through ads, using SEO).
weight: 3
axis: Synthesis of Information

criterion: The response uses unnecessary technical jargon (e.g., RLHF equations, loss functions, attention mechanisms, code) that would confuse the reader.
weight: -1
axis: Communication Quality

criterion: The response lists ideas that are distinct from one another.
weight: 4
axis: Explicit Criteria

criterion: The response discusses risks or limitations of each idea (e.g., training time, costs, hallucinations, potential biases).
weight: 3
axis: Implicit Criteria

criterion: The response speaks in the persona of a "creative technical founder".
weight: 2
axis: Instruction Following

criterion: The response and ideas avoid being a "thin wrapper" around an LLM (e.g., changing only a prompt, not including new data, not defining a use case, not proposing solutions with non-LLM technology).
weight: 2
axis: Instruction Following

criterion: The response’s competitive analysis shows a clear, defensible "moat".
weight: 2
axis: Implicit Criteria

criterion: The response is structured as a practical startup brainstorm (e.g., with revenue, growth plans, sales pitches, product ideas).
weight: 2
axis: Implicit Criteria

criterion: The response presents ideas with a clear, singular "killer feature" to focus initial development.
weight: 2
axis: Implicit Criteria

criterion: The response presents a clear, logical, and coherent structure that effectively guides the reader through its analysis, making complex information easy to follow (e.g., using section headers, specifying the AI models to be used, outlining the required infrastructure, and identifying the exact problem being solved).
weight: 2
axis: Communication Quality

criterion: The response provides specific, actionable details (e.g., naming particular technologies, APIs, or competitors) and avoids vague generalizations, demonstrating a deep understanding of the subject.
weight: 4
axis: Explicit Criteria

criterion: The response uses visuals, diagrams, or mockups to illustrate complex concepts (e.g., mermaid diagrams, architecture diagrams, training flows, or pipeline flows of end-to-end systems).
weight: 2
axis: Communication Quality

criterion: The response formatting is optimized for quick scanning (e.g., use of bolding, short paragraphs, bullet points, section headers).
weight: 1
axis: Communication Quality

criterion: The response critically assesses the practical feasibility of its proposals, acknowledging potential challenges, limitations, and the competitive landscape in a balanced manner.
weight: 4
axis: Explicit Criteria

criterion: The response specifies which specific AI models would be used and explains why they are appropriate for the use case.
weight: 2
axis: Implicit Criteria

criterion: The response includes potential revenue streams and a business model for each idea (e.g., paid downloads, API rates, front-end advertisements, collaborations, subscription models).
weight: 4
axis: Explicit Criteria

criterion: The response defines a specific initial target market segment for each idea (e.g., social media users, stay-at-home parents, high school students, graduate students conducting research reviews).
weight: 2
axis: Implicit Criteria

criterion: The response estimates rough development timeline and key technical milestones.
weight: 2
axis: Synthesis of Information

criterion: The response’s proposed ideas address trivial or ill-fitting problems (e.g., machine translation, already-solved AI tasks like binary classification, non-AI topics like cooking, or unrelated fields like theoretical physics).
weight: -2
axis: Synthesis of Information