Starts August 10
Online
Track
Biotech 101
Learn how teams turn a biological idea into a therapy and a company: choose an intervention, deliver it, test the effect in humans, build reproducible manufacturing, and fund the next proof point.
Private Discord community
Practice, quizzes, and a final project
Mentors and checkpoints
Lifetime access and updates
TrackFor technically curious beginners; no biology degree is required. Build a program's causal chain, compare interventions and delivery routes, design a decisive experiment, interpret early human data, and connect manufacturing to patient workflow, economics, and capital. Evaluate AI and automation claims by how they change the next physical experiment. Finish with an engineering memo on an unfamiliar program. The course includes no lab protocols, medical advice, or investment advice.
- Build a causal chain from a cellular intervention to a measurable patient outcome
- Compare modality, payload, carrier, in vivo/ex vivo delivery, biodistribution, PK/PD, and dose
- Design a decisive experiment with a model, assay, controls, and falsification condition
- Read early clinical data, biomarkers, AI claims, CMC, and manufacturing constraints
- Produce a two-page biotech memo on an unfamiliar program with the next milestone and risk map
Biotech 101
Who this is for
New
Biotech and healthtech founders or operators who need to connect science, product, and capital
Pro
Product and strategy professionals evaluating modality, delivery, clinical data, CMC, and the path to patients
AI engineers and developers who want to see where models help biotech R&D and where they add new risk
Scientists who need a product and business view of a program, pipeline, and company
Beginners ready to work with causal reasoning and public evidence without a wet lab
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Curriculum
One causal line connects the living system, intervention, experiment, human data, AI, manufacturing, economics, and the final engineering memo.
01
Living System as an Engineering Problem
02
Modality, Delivery, and Dose
03
Target, Model, and Experiment
04
Human Data
05
AI and the Development Cycle
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Learning Platform
Short lessons, practice, and final artifacts live on one platform. Wallet login does not require a separate password.
- Wallet LoginConnect MetaMask from any device and resume where you stopped. You do not need a separate password.
- Short LessonsLessons are split into small blocks. Complete one in a short session, then resume from the same point later.
- Practice ThroughoutAssignments apply the material right after a lesson. In larger programs, separate outputs build toward a final project or capstone.
- One WorkspaceLessons, quizzes, assignments, and course materials stay together. Technical tasks can use the editor and syntax highlighting.
CommunityEach block ends with an assignment. Keep the intermediate results and assemble the final piece step by step.
Reviews and Community
01
Practice Reviews
Mentors review code, models, product decisions, and research artifacts in a format that fits each program.
02
Expert AMAs
Developers, researchers, and founders answer questions and show how they make decisions in real projects.
03
Research Briefings
The Guide DAO team follows new papers, tools, protocols, and working practices and connects them to the curriculum.
What You Leave With
Finish the program with a verifiable result you can show to a team or use in your next project.
Final Project
Build the program's main result against clear criteria and take it to a state another person can verify.
Portfolio of Work
Keep intermediate artifacts, sources, and decisions so others can see how you worked and why you reached the result.
Next Step
Record the result's limits, open questions, and next actions for your own project, a team, or a new role.