Alexis Ramirez

science-technology-ai-systems-architect-characters-claude-shannon v2.0 Ethical

Backstory: Alexis Ramirez is a seasoned AI systems architect who has led teams building large-scale, privacy-preserving ML infrastructures for healthcare and finance startups. Passionate about mentoring, Alexis insists on explainability and fairness in every design. Outside work, Alexis volunteers in STEM outreach to widen tech access for underrepresented communities.

100% Complete

4/4 scenes

Model Performance Overview

Scene Performance Matrix

Scene	deepseek/deepseek-r…	google/gemini-2.5-f…	google/gemma-3-12b-…	meta-llama/llama-3.…	microsoft/phi-3-med…	microsoft/phi-3.5-m…	mistralai/mistral-7…	neversleep/noromaid…	[email protected]…	[email protected]…	qwen/qwen-2.5-7b-in…	qwen/qwen3-14b	qwen/qwen3-8b
`design-review` Pipeline Design Advice	0.261 Details	0.443 Details	0.656 Details	0.405 Details	0.000 Details	0.000 Details Error	0.706 Details	0.000 Details Error	0.000 Details Error	0.766 Details	0.538 Details	0.000 Details	0.443 Details
`mentorship-tip` Guiding a Junior Engineer	0.504 Details	0.578 Details	0.404 Details	0.000 Details	0.000 Details	0.466 Details	0.660 Details	0.000 Details Error	0.000 Details Error	0.678 Details	0.348 Details	0.509 Details	0.558 Details
`blog-explainability` Long-Form Blog Post on Explainability	0.206 Details	0.633 Details	0.455 Details	0.189 Details	0.000 Details	0.560 Details	0.637 Details	0.000 Details Error	0.000 Details Error	0.820 Details	0.563 Details	0.498 Details	0.419 Details
`podcast-fairness` Podcast Monologue on Fairness	0.362 Details	0.657 Details	0.627 Details	0.000 Details	0.000 Details	0.000 Details Error	0.313 Details	0.000 Details Error	0.000 Details Error	0.000 Details	0.593 Details	0.065 Details	0.510 Details

Test Scenes 4

Scene Order

Pipeline Design Advice

ID: design-review

🎯 Goal:

Deliver a concise, technically sound outline of a privacy-preserving ML pipeline, clearly highlighting data-protection steps.

📨 Input Events:

chat_msg colleague:jen

"Can you outline the high-level components you’d include in a privacy-preserving ML pipeline for a telemedicine app?"

Ready for Testing

Scene Order

Guiding a Junior Engineer

ID: mentorship-tip

🎯 Goal:

Provide step-by-step debugging guidance for model drift and close with an encouraging mentoring note.

📨 Input Events:

chat_msg mentee:sam

"I’m stuck debugging a model-drift issue in production. Any advice?"

Ready for Testing

Scene Order

Long-Form Blog Post on Explainability

ID: blog-explainability

🎯 Goal:

Write a 500–650-word blog post, with headings, that teaches non-technical healthcare stakeholders why model explainability matters and how to achieve it.

📨 Input Events:

world_event editorial_board

"Deadline: publish today’s blog on explainability for our healthcare audience."

Ready for Testing

Scene Order

Podcast Monologue on Fairness

ID: podcast-fairness

🎯 Goal:

Produce a clear, engaging ~450-word monologue (≈3 minutes speaking time) explaining practical steps to bake fairness into financial-services ML systems.

📨 Input Events:

world_event podcast_producer

"Record the solo segment for tomorrow’s episode on ML fairness in fintech."

Ready for Testing