I had a lot of fun couple months ago building a talent insight newsletter:
Then I lost interest doing the same analysis on different companies. So I want to see how I can leverage the crowd intelligence by
- Exposing a UI for public access to the Database queries. (Think Dune Analytics for LinkDB)
- Public users will be able to make a few queries to the dataset everyday (rate limited) so I can see how much interest people have.
So far, I managed to build a UI dashboard using Redash connected to my dataset backend:
Next step is to expose to public and implement rate limiting. Will update as I make progress.
Engineering:
- Will Larson: https://lethain.com/
- Uber Tech Blog: https://eng.uber.com/crisp-critical-path-analysis-for-microservice-architectures/; https://eng.uber.com/service-oriented-architecture/
- https://www.softwareatscale.dev/
- https://www.educative.io/learn
Management:
Communities:
- https://www.learninpublic.org/ — I recently joined the community, been finding so many Gems here. Def recommend
Motivation:
- It’s a Reinforcement Learning framework to solve explore/exploit problems.
- Eg. What strategy to utilize to optimize returns when playing N slot machines each with unknown distributions of expected returns?
Example Solutions:
- epsilon = 0.1 : use 10% of coins for explore, rest to exploit the best known slot machine
- UCB method
Further discussions:
- How is RL different from regular ML?
- I wonder if Facebook utilizes this in any of their ML systems.
- Netflix uses this for their recommendation systems[2]
