AI is science but moreover AI works for Science

New Witcher book starts good, hope it delivers like I was 13 again

Success Metrics
Task: Define measurable success criteria, e.g. more valuable molecules are selected for screened library, enhanced user satisfaction, or less manual data processing points.
Reflection: Are these metrics crucial to users and actionable for your team? Is the difference meaningful?

Day 3: Define MVP
Task: Identify the Minimum Viable Product by focusing on essential features that deliver immediate value.

Reflection: Does it align with key values while staying feasible?

Tip: Start with simplified approach to gather feedback or prioritize low-hanging use cases.

Day 2: Key Values You Deliver
Task: List the values the product delivers, such as efficiency, crucial insights, or improved data reliability.
Reflection:
- How does the product enhance workflow or decisionmaking?
- What is improved on customers' side?
- What is reduced?
- Can you put any number?

Day 1: Vision

Task: Write a clear product vision statement. Focus on what the product will enable when completed.

Reflection: Answer these questions:
- What will be available/enabled when the product is done?
- Why is it important to achieve the goal?
- How will it be done, what is the method?

Getting the, but for me it’s also a way to separate interesting stuff here from Polish news and politics (there)

Drug discovery data is not just any data and for sure it's not ecommerce or tinder data ¯\_(ツ)_/¯
So which data platform truly fits your needs?
I've been digging into the top platforms in the field—why keep the insights to myself?

I'm not sure if this platform promotes images of text blues (?) so let's check.

One of the best finds of last days