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  1. Short vs. long-term gain, incremental vs. disruptive innovation, and resume-driven development.

  2. I wish I started sooner.

  3. Pointers to think through your methodology and implementation, and the review process.

  4. Three documents I write (one-pager, design doc, after-action review) and how I structure them.

  5. Access, serving, integrity, convenience, autopilot; use what you need.

  6. What the top teams did to win the 36-hour data hackathon.

  7. Design and architecture, tech stack, methodology, results, and lessons learned.

  8. What I learned about hiring and training, and fostering innovation, discipline, and camaraderie.

  9. Stop procrastinating, go off the happy path, learn just-in-time, and get your hands dirty.

  10. Why did I start writing?

  11. How to increase the chances of getting called up by recruiters?

  12. Why real-time?

  13. A public roadmap to track and share my progress; nothing mission or work-related.

  14. Wrapping up 2020 with writing and site statistics, graphs, and a word cloud.

  15. A short story on flying daggers and life's challenges.

  16. Time to clear the cache, evaluate existing processes, and start new threads.

  17. How he switched from engineering to data science, what "senior" means, and how writing helps.

  18. How did you set up your site and what's an easy way to replicate it?

  19. Data cleaning, transfer learning, overfitting, ensembling, and more.

  20. Interview questions you should ask and how to evolve your job scope.

  21. A personal take on their deliverables and skills, and what it means for the industry and your team.

  22. Setbacks she faced, overcoming them, and how writing changed her life.

  23. What questions do they answer?

  24. DNS server snafus led to email & security issues.

  25. Not 'How to build a data science portfolio', but 'Whys' and 'Whats'.

  26. Step-by-step walkthrough on the environment, compilers, and installation for ScaNN.

  27. Building prototypes helped get buy-in when roadmaps & design docs failed.

  28. As careers grow, how does the balance between writing & coding change?

  29. Emphasis on bias, more sequential models & bandits, robust offline evaluation, and recsys in the wild.

  30. What if the alternative was nothingness?

  31. What's an average day like?

  32. For years I've refined my routines and found tools to manage my time.

  33. My tools for organization and creation, autopilot routines, and Maker's schedule

  34. A step-by-step of how to migrate from json comments to Utterances.

  35. Checking for correct implementation, expected learned behaviour, and satisfactory performance.

  36. Should I switch from a regex-based to ML-based solution on my application?

  37. My chat with James Le about my experience, leadership, agile, ML in production, writing, and more.

  38. Why read papers, what papers to read, and how to read them.

  39. Becoming a senior after three years and dealing with imposter syndrome.

  40. How not to become an expert beginner and to progress through beginner, intermediate, and so on.

  41. Examining the broad strokes of NLP progress and comparing between models

  42. Why (and why not) be more end-to-end, how to, and Stitch Fix and Netflix's experience

  43. Updating our FastAPI app to let users select options and download results.

  44. Surprising lessons I picked up from the best books, essays, and videos on writing non-fiction.

  45. Why OMSCS?

  46. I couldn't find any guides on serving HTML with FastAPI, thus I wrote this to plug the hole on the internet.

  47. Ever revisit a project & replicate the results the first time round?

  48. It's not enough to have a good strategy and plan.

  49. I wanted to add my recent writing to my GitHub Profile README but was too lazy to do manual updates.

  50. I thought giving it my all led to maximum outcomes; then I learnt about the 85% rule.