~/talk

DevTools for the decade to come

slides:none on file

Predicts machine learning automates roughly a third of everyday coding tasks (McKinsey finds 30% automatable for 62% of dev roles) and surfaces concrete tooling already doing it: IntelliCode, Diffblue, GitHub Actions, CodeSandbox. Shows bots opening and auto-merging PRs, generating unit tests, and running AI code reviews; cloud dev boxes and real‑time collaboration replace local-only workflows; code-as-data and GitHub’s indexed corpus fuel model training; Stripe data flags 42% of dev time in maintenance and a $3 trillion productivity opportunity.

AI-written summary · about

  1. 0:00Introduction
  2. 1:30DevTools for the next decade
  3. 2:30Computing power
  4. 3:37Programming abstractions
  5. 4:41Developer tools
  6. 5:12Specialized roles
  7. 5:45Software eating the world
  8. 6:06Application runtime
  9. 6:39Higher level abstraction
  10. 7:42The notion
  11. 8:33The generational debate
  12. 9:13The new abstraction level
  13. 9:35The crystal ball
  14. 9:50Automation
  15. 11:07Software is eating the world
  16. 11:39We are all software engineers
  17. 12:36The typical engineering fallacy
  18. 12:53Developer tools moving to the web
  19. 13:43Developer tools moving to the cloud
  20. 14:26Intellisense
  21. 15:19Other tools
  22. 15:43diffblue
  23. 16:01bot automation
  24. 16:20GitHub actions
  25. 16:38Automation 30 potential
  26. 16:57No code and low code
  27. 17:30Automating work
  28. 17:41Glide
  29. 17:59Lowcode
  30. 18:15Code as data
  31. 18:55Software
  32. 19:25Training ML
  33. 20:31Software 100 abstraction
  34. 21:13Real time editing
  35. 24:07Hand coding
  36. 24:55Future of code editors
  37. 25:16lobe AI
  38. 25:47the future of GitHub
  39. 26:06developer productivity
  40. 26:27average developer time
  41. 26:39maintenance
  42. 27:04opportunity
  43. 27:26focus
  44. 27:43the next decade
  45. 27:54software engineering as a spectrum
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