“I didn't care about like assembly language or memorizing TypeScript syntax”
TypeScript
By Microsoft
0 recommend/use · 4 sourced episodes
Every sourced reference
Short attributed excerpts only. Timestamps are approximate.
“Before Anthropic, at Microsoft, Fiona ran the teams that built TypeScript and Visual Studio.”
“a lot of people write Python and and TypeScript”
“don't use TypeScript, use plain JavaScript, use JS6.”
Related frameworks
Adversarial Dogfooding Loop
Force all your work through your own product even when it's the wrong tool, so it becomes the right tool
Bad vs Sad Quality Tiers
Classify every failure as bad (irrecoverable) or sad (recoverable pain) so teams triage quality across many surfaces.
Build-and-Bake: Ship the Same Feature Until the Model Catches Up
Prototype ambitious features now, let them sit, and re-release the same shape each time models leap
Credits-for-Sharing Loop
Pay users in product credits to publicly share what they built — turn usage into free distribution.
Detune Precision by Time Horizon
The shorter the horizon, the more detail; keep long-range plans deliberately hazy to avoid false precision
Disassemble the Lego Set
Don't digitize the old thing — reassemble the pieces into an experience native to the new platform.
Fear-to-Agency Reframe
When AI change triggers fear, lean in and ask what's within your control — turn it happening to you into happening for you.
Genius-Zone Time Budget
Spend at least 50% of your time on the work you're both great at and love — it's what keeps you showing up.
High Agency, High Accountability
Pair the freedom to solve problems your own way with clear ownership of the hypothesis and the outcome.
Just-in-Time Planning
Shrink long roadmaps to a lightweight monthly priority list and grant explicit permission to kill dead processes.
Label the Process Stage, Not the Polish
A production-looking prototype no longer means it's production-ready — say the stage out loud
Latent Demand Mining
Watch for people jumping through hoops to make your product do something, then make that the smooth path.
Leader Dogfooding for Product Pulse
Live and breathe your product as a real user (or meet customers) and trust the anecdote over the dashboard.
LLM-Optimized Codebase Architecture
Structure your repo so the AI writes the least code possible — infrastructure absorbs the complexity.
Manager-as-IC Onboarding
New managers ship as individual contributors first — and keep doing it — before and while they manage people.
Manager Visibility via an Always-On Agent
Enlist a standing AI session across all repos and channels to stay on top of 8x throughput and drive conversations.
Match Go-To-Market to Buyer-User Alignment
Pick developer-led, PLG, or direct sales by asking one thing: are the buyer and the user the same person?
Match the Medium to the Point
When implementation is cheap, the skill is choosing document vs prototype for the point you're making
Milestone-Gated Launch Sequence
Don't spend a dollar on marketing until users start sharing your product unprompted.
Negotiate From Genuine Indifference
The best position to sell your company is being genuinely fine not selling it.
Outcome-Based Pricing Qualifier
If the work is autonomous and the result is measurable, price the outcome — not the tokens.
Output-Toward-Outcome Measurement
Don't forsake motion for progress — keep asking whether the metric you climb still serves the outcome.
Root-Cause Context Engineering for AI Coding
Don't just fix the AI's bad code — root-cause the missing context so it's right next time.
Single-Channel Build-in-Public Bet
Find the one channel that resonates with your exact audience, then double and triple down.
Spec-as-What-Good-Looks-Like Verification
Check your definition of good into the repo so AI code review can automatically validate work against it.
Speed-to-Aha Over Best Product
Sometimes the correct product decision hurts the aha moment — cut it and get users to magic faster.
The Most Impactful Thing Today (with the Honesty Test)
Wake up asking what maximizes impact — then interrogate your answer for skill-set bias.
The Three-Segment AI Market Map
Frontier models, tooling, or applied agents — pick the layer that fits your capital and your edge.
Turn Advice Into a First-Principles Framework
Don't collect rules — ask why, triangulate three people, and rebuild the reasoning underneath.
Zone Defense for Product Work
Spread taste-makers out to cover the whole field instead of clustering on the same problem
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