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