标签：#Startup

South Korean chip startup Xcena is betting that AI's real bottleneck is not compute, but memory.

If you've ever built or set up a subscription experience on Shopify for a coffee brand, you've probably run into the same problem most merchants face: The signup flow works great. The first order goes out. And then subscribers start quietly disappearing before the third delivery. This isn't a coffee problem. It's a subscription infrastructure problem — and it's almost always caused by the same handful of missing pieces in the system underneath the storefront. In this guide I'll walk through the practical setup decisions that actually move the needle on retention for coffee subscription businesses on Shopify — from choosing the right model and pricing structure to the fulfillment calendar, dunning logic, and cancellation flow that most setups skip entirely. Why Coffee Works So Well as a Subscription Product Before getting into setup, it's worth understanding why coffee is genuinely one of the better products to build a subscription around — when the infrastructure supports it. Predictable consumption cycle. A 12-oz bag of whole beans lasts roughly two to three weeks for a single drinker. That natural rhythm makes it easy to design a billing and delivery schedule that matches actual usage patterns. Daily habit. Roughly two-thirds of American adults drink coffee every day. A subscription removes the friction of reordering, and that convenience compounds into real retention over time. Freshness as a retention argument. Coffee quality degrades noticeably after roasting. Subscribers who care about quality genuinely prefer a recurring shipment over buying retail — which means freshness becomes a built-in reason to stay subscribed that most product categories simply don't have. The global coffee subscription market reached $808.8 million in 2024 and is projected to surpass $2.2 billion by 2033. The infrastructure opportunity for developers and merchants building on Shopify is real and still early. Step 1 — Choose the Right Subscription Model Before You Build The model choic

The enterprise AI search startup tripled its annual revenue even as tech giants entered the category.

Startup Battlefield applications are due tomorrow, so now's the time to put the finishing touches on your submission!

In its latest update, Bluesky is getting into long-form content.

LinkerBot makes dexterous robotic hands for as little as $600. It wants to become the standard for humanoids and automated factories—and eventually replace human labor altogether.

I was looking at my bank statement one day and realised I was paying for 4 things I completely forgot about. Combined it was around 40 euros a month just silently leaving my account. I'm a 17 year old developer from Cyprus and I spent the last few weeks building Capsule, a simple subscription tracker that shows you everything you pay for, alerts you before renewals, and tracks how much you save by cancelling things. No bank connection required. You just add your subscriptions manually. Privacy first. It's not on the Play Store yet but the waitlist is live at capsule.crickdevs.com if anyone wants early access. Would genuinely love feedback from real people before I launch.

CEO Ariel Katz argues that while AI can replicate workflow SaaS, it can't copy H1’s unique doctor data.

Europe’s startup ecosystem has matured significantly; its founders are increasingly willing to scale companies domestically instead of immediately looking to relocate to the U.S.

StrictlyVC Los Angeles is on June 18. Join for meaningful networking and fireside chats with leaders from Mach Industries, Shinkei Systems, and more. Register today.

While startups raising back-to-back rounds at steep step-ups have become almost routine, a company whose valuation doubles in three weeks is unusual enough to raise questions, particularly given the investor set in both rounds is the same.

Every week another "AI agent for X" launches. Email triage. Calendar coordination. Sales follow-up. PR reviewer. Slack monitor. Meeting summarizer. I've installed enough of them to see the pattern. Here's the dirty secret nobody mentions in the launch posts: These tools don't reduce your work. They multiply your notifications. Each AI tool is configured to be helpful by default. "Helpful" means: "I noticed this thing — here's a notification." Stack a dozen of those, and instead of one inbox to ignore you have twelve. The signal-to-noise ratio gets worse every time you add an AI to your workflow. The mainstream answer is "just configure each one." Sure. Spend four hours tuning notification settings every time you add a tool, and another four hours when one of them ships a "smarter notifications" update. That's not productivity. That's notification janitorial work disguised as setup. This is a structural problem. Not a configuration problem. The wrong question Every AI tool asks the same thing: "Is this important?" Wrong question. There is no objective "important." Importance depends on you, right now. A Stripe webhook is important when you're debugging a checkout flow. The same webhook is pure noise during a deep work block. A Slack message from your cofounder is critical at 11am Tuesday and irrelevant at 11pm Friday. The right question is: Is this urgent enough to interrupt me, right now, given what I'm doing? That's not a question any individual AI agent can answer. It's a layer above all your AI agents. None of them have the context. None of them know what the others are doing. None of them know how you're spending the next hour. So they all default to "I'll just send you a notification, you decide." Which is exactly the experience you have right now: drowning. What an AI firewall actually looks like I'm building that layer. It's called Klorn . Here's how it works in practice. Every signal — email, calendar invite, agent action, webhook, push from another tool — g

Enterprise AI is entering a different phase now, one where enterprises are no longer evaluating whether AI is exciting. They are evaluating whether it is safe to deploy broadly.

After overwhelming demand from founders around the world, TechCrunch has extended the Startup Battlefield 200 application deadline to June 8. Nominate a standout startup or apply yours today.

Savings of up to $410 on TechCrunch Disrupt 2026 tickets end tomorrow, May 29, 11:59 p.m. PT. Register now to save and join 10,000+ tech leaders on October 13-15 in San Francisco.

Trisha Ballakur discusses her journey from a backend software engineer to CTO and CEO, using her startup Pointz as a case study. She explains how to implement bottom-up customer discovery to find product-market fit, effectively delegate to global contractors to reduce build times, customize open-source repos like Valhalla, and apply engineering test-case models to business development. By Trisha Ballakur

We let AI agents loose on a payment platform. They crushed the boring stuff. Then they silently broke the stuff that matters. A survey came out last week. 54% of all code is now AI-generated. Up from 28% last year. I read that number and thought: yeah, that tracks. We're probably in that range too. But here's the thing nobody's asking — which 54%? Not all code carries equal weight. A CRUD endpoint for fetching merchant details? Low risk. The webhook handler that transitions a payment from pending to complete ? That's someone's rent. Someone's payroll. Get that wrong and money moves where it shouldn't, or worse, money doesn't move at all. I'm the CTO of a payment platform. FCA-authorised, processing real money, real merchants, real consequences. We run NestJS microservices, Docker, Traefik — the usual stack. And we've been using AI agents aggressively for over a year now. I'm not here to tell you AI is dangerous. It's not. I'm here to tell you it's dangerous when you forget what it's actually good at. The 80% Where AI Agents Are Genuinely Brilliant Let me give credit where it's due. AI agents have made our team faster in ways that would have seemed absurd two years ago. API scaffolding. Generating service boilerplate. Writing Zod validation schemas. Spinning up new endpoints. Creating test stubs. Refactoring imports. Migrating patterns across repos. We run multiple microservices. When we need a new service, an agent can scaffold the entire thing — module structure, base configuration, Docker setup, Traefik labels — in minutes. What used to be a half-day of copy-paste-and-tweak is now a conversation. When we overhauled our env management across all repos, AI agents did the grunt work. They mapped every .env file, found naming conflicts, identified common variables, and generated a unified Zod schema. What would have taken a team days of grep-and-spreadsheet work took hours. For this 80% of the codebase — the predictable, pattern-following, structurally repetitive code

The Series B round was led by Battery Ventures.

As Cognition reaches $492 million in annualized revenue run rate, it more than doubled its valuation in eight months, it says.

Trajectory is betting the rapid iteration cycle that supercharged vibe-coding can help all kinds of companies build AI products that learn continuously.