Interview, Explained
In-depth breakdowns of tech interviews — section-by-section analysis with diagrams, quotes, and insights.
Peter Steinberger on why fun is velocity and your name is the moat
The creator of OpenClaw retraces eight months from a hungry-night hack to a viral open-source agent that drew 18,000 issue and PR authors, and pulls out the durable lessons: annoyance finds the gaps, your name outlasts any product, a dependency's business model becomes yours, and fun is the real velocity.
Alejandro Maza on running a company on 200,000 agents a day
Kavak's Chief Product & AI Officer, Alejandro Maza, explains how they tore the used-car company down and rebuilt it around long-running agents — one per customer, up to 200,000 a day — betting that the real unit of AI transformation is the whole organization, not the task or the tool.
Anne Hecht on why autonomous agents only pay off when governed
NVIDIA's Anne Hecht explains how enterprises deploy autonomous agents safely: treat each agent as a whole system, engineer the guardrails in from the start, and run open and frontier models side by side.
Dylan Ayrey & Feross Aboukhadijeh on AI Taking the Easiest Way In
Two security founders explain how AI removed the last real barrier to hacking — expertise — turning old weaknesses like leaked credentials and unvetted packages into the fastest, cheapest way into almost any system.
Philip Johnston on Why AI's Next Data Center Is in Orbit
Rejected by 100+ VCs as 'the dumbest idea they'd ever heard,' Philip Johnston bet that collapsing launch costs make solar-powered data centers in orbit inevitable — and turned StarCloud into the fastest-growing unicorn in YC history.
Simon Mo on why open-source inference became AI's control layer
Simon Mo, lead maintainer of vLLM, argues that open-source inference has quietly become AI's critical infrastructure — the control layer that turns commodity GPUs into intelligence, and where capability parity leaves control over speed, guardrails, and economics as the real contest.
Boston Children's on AI that cracked 18 rare-disease cold cases
A Boston Children's and OpenAI workflow ran o3 deep research over 376 hard rare-disease cases, surfaced evidence that led to 18 new diagnoses, and even proposed a novel gene hypothesis, with human geneticists validating every lead.
Patrick Collison on Why It's Never Been a Better Time to Start
Stripe's CEO argues the AI era has lowered the cost of starting a company and widened who can win — making the classic fears, and the classic lean-startup playbook, look overstated.
Three AI Leaders on the New AI Fight: Controlling Cost, Agents, and Robots
Three CNBC interviews across the stack — Cisco, Fireworks AI, and Standard Bots — show the AI fight shifting from raw capability to control over agents, cost, and the physical world.
Dmitri Dolgov on why a working demo is 1% of the product
Waymo's co-CEO turns two decades of self-driving into a seven-lesson playbook for physical AI, where a working demo is 1% of the work, every added nine of reliability costs roughly 10x, and the real moat is the evals, simulation, and hundreds of millions of autonomous miles backed by publicly audited safety proof.
Philip Kiely & Ali Taha on why inference still ships 10x, not basis points
Baseten's Philip Kiely and Ali Taha open up the inference stack — cache-aware routing, error-canceling quantization, speculators, and cross-cluster race conditions — to argue inference is a young enough field to still win in multiples, and one that's collapsing into training to build models that optimize themselves.
Steijn Pelle & Frédéric Renken on doing the job before you automate it
The founders of Lassie explain how they built AI agents that autonomously run dental back-offices — by first doing the paperwork by hand, engineering for full autonomy over years, and reaching the Main Street businesses whose only 'incumbent' was a human who quit.