“GPD 40 doesn't exist anymore or it's going to be deprecated in APIs as well.”
GPT-4o
By OpenAI
2 recommend/use · 4 sourced episodes
Every sourced reference
Short attributed excerpts only. Timestamps are approximate.
“you've probably been on GPD40 for you know a while”
“It was said as GPT4, GPT40 were coming out”
“OpenAI's GPT40 is also good.”
Related frameworks
Agency Over Title: The Force-Multiplier Mindset
Ignore role boundaries — use AI tools to execute your own ideas end to end
Barrels-and-Ammunition Team Design
Staff each team from the gap, not from a fixed PM/EM/designer template
Build Only Where You Can Be Best (Model Sourcing)
Build in-house only where your unique data or position lets you beat the frontier; otherwise buy
Cannibalize Your Own Product Every 6-12 Months
Make your current product look silly on a fixed cadence instead of only shipping what users ask for
Context Loading (Additional Information)
Front-load all relevant task information — and put it at the top for caching
Continuous Calibration, Continuous Development (CCCD)
A CI/CD-style loop for non-deterministic AI: scope, evaluate, deploy, then calibrate against surprises.
Ensembling (Mixture of Reasoning Experts)
Solve the same problem several ways and take the most common answer
Evals as Articulating Success
An eval is just a clear spec of ideal behavior — the shared language of AI product work
Evals-Plus-Production-Monitoring Dual Feedback Loop
Reject the false dichotomy: evals catch what you know, production monitoring catches what you don't.
Few-Shot Prompting
Show the model examples of what good looks like instead of describing it
Hills-and-Valleys: Getting Value From Probabilistic AI Tools
Be patient and explicit, start small, and learn where the model is strong vs weak
Irrationally Optimistic, Uncompromisingly Realistic
Push hard toward a future you believe in while killing your own hypotheses as data arrives
Prompt Decomposition
Make the model list the sub-problems first, then solve each before the whole
Prompt Injection Defense Stack
Stop trying to fix injection with prompts and guardrails — mitigate at the model level
Run Toward the Hard Use Cases
Don't disable high-stakes uses to avoid downside — engineer them to be great
Self-Criticism Loop
Get the model to critique its own answer, then implement its own critique
Ship-to-Learn: The Emergent-Product Loop
When product properties are emergent, launching is how you discover them
The Agency-Control Autonomy Ladder
Ship AI in graduated versions, trading human control for machine agency only as trust is earned.
The AI Success Triangle
Successful AI adoption is a people problem first: great leaders, good culture, and technical progress.
The Dehydrated Company Hiring Model
Only hire when a function is underwater, so headcount forces ruthless prioritization
The Maximally Accelerated Question
A forcing question that separates critical path from what can wait
Two-Mode Prioritization for AI Products
Prioritize backward from model magic AND forward from customer needs
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