All team members present for the retrospective. More small group async in week, based on current work.
Frontend
Jessie
Bansharee
Backend
Kevin
Yizhen
ML
Kevin
Yizhen
Amittai
Recommendation Engine
Kevin
Amittai
Deployment
Colton
Kevin
What worked well
Sticking to small team meetings, continuing distributed workload.
Continuing practice of high documentation (recommendation engine)
Using Tuesday meetings for long term alignment
Using GPT4 to automate some tasks (assigning cross topic complexities and affinities -> what are relation between topics?
What didn't
FrontEnd
Hard to customize search bar, might revert to SwiftUI's searchable instead
Running into some difficulties with Google/Apple auth, team concerned that it will make deploying to App store more difficult
Some models don't align with backend, will need to modify expected request/response types, and update paths
BackEnd
ML
Updated scraping to produce a json of metadata
Updated scraper to take in new topic model
Updated video downloader to take json metadata for download
Recommendation Engine
The model we are trying to use is somewhat massive, so we had to figure out ways of making it less heavy computationally but still retaining its utility.
Deployment
Self-assessment on progress
Where are you in relation to progress towards product and milestones?
FrontEnd
Experimenting with Google authentication and Apple authentication
Found IGListKit, will try learning UIKit and converting Watch for smoother playback
Adding some stretch pages, including FriendsPage
BackEnd
ML
Recommendation Engine
We spent a lot of time working on vectorization of our videos in a way that we can query it for similarities, and building the surrounding infra for the recommendation engine.
Deployment
Give an estimate of how far towards your goals you are, do you think you're on track?
FrontEnd
Main pages drafted/outlined, but need refining
BackEnd
ML
Mostly done on scraping, waiting for topic rework for final bits to finish integration
Recommendation Engine
We are confident that we should have it working end-to-end by the end of the week.
Deployment
Lay out each of the following weeks till end of term with brief goals for each
3 weeks left
Rec System / Webscraping (video and transcript) / Topic Alignment /
Algorithm Clipping / Backend Link to Rec /
Rec/ML/DB Link / Frontend Backend Link
Deployment
Finish Frontend views (this week), link to mock API (by end of next week), link to backend
Sprint 6 Retrospective
All team members present for the retrospective. More small group async in week, based on current work.
Frontend
Backend
ML
Recommendation Engine
Deployment
What worked well
What didn't
FrontEnd
searchableinsteadBackEnd
ML
Recommendation Engine
The model we are trying to use is somewhat massive, so we had to figure out ways of making it less heavy computationally but still retaining its utility.
Deployment
Self-assessment on progress
FrontEnd
IGListKit, will try learning UIKit and converting Watch for smoother playbackBackEnd
ML
Recommendation Engine
We spent a lot of time working on vectorization of our videos in a way that we can query it for similarities, and building the surrounding infra for the recommendation engine.
Deployment
FrontEnd
BackEnd
ML
Recommendation Engine
We are confident that we should have it working end-to-end by the end of the week.
Deployment