Technology
The people, products and decisions shaping technology.
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This reading list brings together items from the sources selected for Technology. Open each item at its original source for the complete article, episode or video. This is a selection of current coverage, not a complete archive.
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Jev is HERE. How to use it
In this episode, I talk with Ryan Vogel about Jev, a new type of AI built for classification. Ryan shows how Jev takes an input plus an output schema and returns a probability for each choice in about 200 milliseconds. He demos Jev sorting 1,700 emails for 18 cents total, then covers lead scoring, support routing, video clipping, and browser control. I push him on the startup angle: find a business with an expensive queue of incoming information and put Jev at the front of it. You leave with a clear mental model, real use cases, and a simple way to try it today. Links Mentioned: Jev/Typeface AI: https://typesafe.ai AI Gateway: https://vercel.com/ai-gateway Timestamps 00:00 – Intro 02:27 – What Jev Is and Why It Matters 04:32 – Email Triage Demo 07:19 – Jev as an AI Decision Maker 15:46 – How to Use Jev in a Business 20:48 – Startup Idea: Local Services Matching and Instant Quotes 22:51 – Use Case 1: Bitcoin Signal Test and Limits 24:03 – Use Case 2: Auto-Clipping Long Videos 25:27 – Use Case 3: Browser Control: Flight Pick in 7.1 Seconds 26:18 – How to Get Access 27:25 – Closing Thoughts Key Points • Jev is a classifier: an input and an output schema go in, and a probability for each choice comes out. • Ryan's demo scores 1,700 emails for 18 cents total. • Each Jev query takes about 200 milliseconds, whatever the input and output structure. • Use Jev at any point where a business makes fast, repeatable decisions on incoming data. • Keep Jev in an advisory role, and save frontier models for high-intelligence tasks like trading. • Instant access runs through the Vercel Gateway, and a waitlist covers direct access. Numbered Section Summaries 1. What Jev Actually Is Ryan explains Jev as a classifier. You define an input and an output schema, and Jev returns a probability for each option, for example 80% orange, 10% red, and 10% blue for the color of an iPhone. 2. The 18-Cent Email Demo Ryan runs Jev on 1,700 of his own emails and scores each one for category,
The Creative Spirit of Who Framed Roger Rabbit
The Creative Spirit of Who Framed Roger Rabbit I love Who Framed Roger Rabbit, the 1988 movie by Robert Zemeckis. I haven't watched it in quite a few years, and Cypress Frankenfeld just pointed out this sequence from early in the movie: It's a pelican riding a bicycle! Look closely and you'll note that the pelican is animated while the bicycle is a real bicycle. Apparently they filled the wheels with water to add stability, then set it running and guided it with a cable. Cypress gathered more details on the scene. What a delight.
Did Google just kickstart the intelligence explosion?
Run GitHub Actions 2x faster and build with Codesmith at https://www.blacksmith.sh/ Google DeepMind just published Dream-RSI, a technique that turns an AI's old discovery logs into a simulator so it can test thousands of exploration strategies. But is this actually RSI or just a search algorithm with caching? Let's dive in. #coding #programming Want more Fireship? 🗞️ Newsletter: https://bytes.dev 🧠 Courses: https://fireship.dev
The State of Startups in 2026
YC works with thousands of founders every year, which gives us an early look at how startups are changing. Right now, the shift is striking: startups are moving from bits to atoms, nearly one in five YC companies has a solo founder, and companies are reaching meaningful revenue faster than ever. In this episode of The Lightcone, Garry, Jared, Diana, and Harj dig into what’s driving these changes and what they mean for founders. They discuss how AI is making it possible for smaller teams to take on more ambitious problems, why experienced founders are having a resurgence, and why knowing what to build is becoming more important than simply knowing how to build it. Chapters: 00:00 — Intro 01:06 — Startups Are Moving From Bits to Atoms 03:29 — Why AI Is Making Hard Tech Easier 05:21 — Defense, Manufacturing, and the Return of Hard Tech 09:48 — AI Compute Is Becoming a Physical Infrastructure Problem 12:34 — Robotics Is Approaching Its ChatGPT Moment 15:40 — Software Isn’t Dead. It’s Becoming the Harness 17:59 — Why AI Startups Are Growing Faster 22:52 — The Hidden Boom in Data and RL Environments 27:08 — Why Robotics Will Need Specialized Models 29:38 — The Rise of the Solo Founder 32:54 — Why Experienced Founders Are Back 34:59 — What Founders Should Do Right Now Apply to Y Combinator: https://www.ycombinator.com/apply Work at a startup: https://www.ycombinator.com/jobs
Databricks’ Ali Ghodsi Never Wanted to Be CEO. Now He’s Among the Best
Ali Ghodsi never wanted the CEO job. In 2015, Databricks had a wildly successful open source project, $1.5 million in revenue, and a board quietly interviewing other candidates behind his back. He got the seat as a trial run. They didn't even give him a CEO salary. When I had Ben Horowitz on this podcast and asked him who the best CEO in Silicon Valley is outside his own portfolio, he didn't hesitate. He said Ali. We get into how he actually runs the place: find the single biggest bottleneck in the company, then put an absurd, disproportionate amount of attention on it for two or three years. That's how the Lakehouse category got built, back when the strategy consultants, his own sellers, and most of the internet were laughing at the name. Ali breaks down the playbook he ran against Snowflake, why he's said no to phenomenal executives who still hold a grudge about it, and what he learned shipping code himself. He's also blunt that conflict aversion disqualifies you from this job. I over-indexed on being liked at HubSpot. Ali doesn't. 0:00 – Introduction 1:22 – The secret CEO search and why the board bet on a founder 4:11 – Professor or CEO? Always taking the harder option 8:18 – Build your own playbook and pour everything into one bottleneck 12:16 – Killing PLG and learning what great enterprise sellers actually have 19:09 – Hiring ahead of the curve: sales leaders, execs, and back-door references 27:02 – The Snowflake rivalry: study your enemy, never copy them 33:22 – Lakehouse: conviction, ridicule, and the case for second acts 41:02 – The killer instinct and why conflict-averse CEOs fail 44:10 – Dunbar's number and rethinking the org chart around AI 48:00 – AGI is already here — enterprises just use it as a chatbot 52:55 – Does he still code? Two days for a connector vs. three quarters 57:18 – A day in the life, and why the Monday meeting isn't theater 1:04:53 – Why Databricks will go public, just not yet 1:08:09 – Get over conflict aversion, or don't be CEO 1
90% of AI prototypes never reach production (w/ Temporal's Samar Abbas) | AI Basics
The TWiST AI Basics series is made possible by Google for Startups! Build context-aware AI workflows using DeepMind models and orchestration tools with Google for Startups’ new Startup technical guide on generative media. Get the guide here: Today’s show: The demo works, the vibes are immaculate, but then the app falls apart the second real users touch it. On today’s AI Basics, Temporal co-founder and CEO Samar Abbas says the problem isn’t with the model at all.
Instinct AI is For Real. What You Need to Know
Invites while they last to Instinct: https://startup-ideas-pod.link/instinct_ai I sit down with Remy to go through Instinct, the new invite-only personal agent that runs inside iMessage. Remy shares his raw chat history on screen: a haircut booking in Copenhagen, a restaurant reservation, a Bali visa on arrival, and an Emirates Skywards sign-up. We cover the parts that impress us, the points where the agent hits a wall, and the privacy questions that stay open. By the end of this episode you understand what Instinct does today, and you get fresh ideas for personal agents in general. Timestamps 00:00 – Intro 02:08 – Instinct Pros 11:18 – Instinct Cons 13:16 – Simple Onboarding 15:19 – Tools and Connectors 17:43 – Example 1: Booking a Haircut in Copenhagen 20:24 – Example 2: Restaurant Booking and Calendar Entry 23:03 – Example 3: Bali Visa on Arrival and Emirates Skywards 26:00 – Closing Thoughts Key Points • Instinct hides the agent complexity behind a phone number and iMessage, so a first-time user starts in seconds. • Remy gets it to book a haircut, hold a restaurant table, file a Bali visa on arrival, and open an Emirates Skywards account. • A spend-limited virtual card keeps the blast radius small when the agent pays for things. • The agent stalls when a task needs a phone app or an Indonesian checkout page. • Users report that Instinct keeps copies of email after they disconnect Google, so treat privacy as an open risk. • The trusted person network lets one Instinct talk to another, which builds network effects into the agentic era. Numbered Section Summaries 1. The Invite-Only Launch Remy first sees Instinct on X, where large AI accounts talk about it. The site shows a wait list, so he grabs an invite code from a public tweet. Scarcity drives the curiosity, and Remy reads the rollout as a marketing play as much as a compute limit. 2. Onboarding Built for the Mass Market Remy signs in with a phone number and lands straight in iMessage. The agent opens
Box's Aaron Levie: On Reinventing Yourself in the AI Age and Enterprise Diffusion
Starting a company is hard. Reinventing your company for AI as a public company with quarterly earnings results is even harder. Aaron Levie has pulled off the transition with Box and offers hard-won advice for founders. The cofounder and CEO of Box argues the value isn't only in the model; it's in the bridge from a model's raw capability to the actual workflow inside a bank, a law firm, or a pharma company. That's the case for the application layer, and Box is building it: an agent harness tuned so tightly to its own file system, permissions, and search that it beats handing the raw API to Claude or ChatGPT on both accuracy and latency. Aaron explains why token subsidies from the labs can't last, why you want a model-agnostic company routing your tokens rather than the one selling them, and why coding diffused fast while the rest of knowledge work won't. (There's no "give us your GitHub" for a sales rep.) His prediction: within five years, 90% of enterprise tokens go to work no human user ever initiated. Hosted by Sonya Huang, Sequoia Capital 0:00 – Introduction 1:55 – Are application companies the hottest neolabs? 6:56 – Will the labs move up the stack? 12:34 – Box and betting the company on AI 16:50 – Hero use cases: reading a million contracts and long-running agents 18:42 – Work slop: why AI code is embraced but AI content isn't 24:08 – Building Box's agentic harness and the evals that matter 27:23 – The state of the model race 29:25 – Open-weight model adoption in the enterprise 32:34 – Memory, continual learning, and what belongs in the weights 37:29 – Box Labs and systems of record in a world of agents 44:55 – Will chat be the dominant UI for enterprise AI? 48:00 – Why coding diffused fast and the rest of knowledge work hasn't 54:31 – Staying wired in, making a company AI-first, and what it takes to win
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