标签：#AR

What does an AI company do after one of those not-acqui-hire deals? Groq raised money, is leaning into its neocloud business, and is hiring new execs.

"Winning" bets were made on cloned website and would have lost money, WSJ finds.

An AI agent that can email but can't hold a calendar slot is only half useful. The moment a conversation turns into "let's meet Thursday at 2," the agent needs a real calendar — one that sends invitations people accept in Google Calendar or Outlook, receives invites at its own address, and RSVPs back so the organizer sees a real response next to everyone else's. Bolting a scheduling library onto a shared mailbox doesn't get you there; the agent needs a calendar identity of its own. An Agent Account ships with exactly that. Every account gets a primary calendar that hosts events, accepts invitations over standard iCalendar, and RSVPs with yes, no, or maybe. To a participant, the agent is just another attendee on the invite. This post walks through using that calendar from two angles: the HTTP API for your backend, and the Nylas CLI for the terminal. I work on the CLI, so the terminal commands below are the ones I reach for. The calendar an Agent Account comes with When Nylas provisions an Agent Account, it creates a primary calendar that belongs to the account. You reach it through the same Calendars and Events endpoints at /v3/grants/{grant_id}/... that any other grant uses, so calendar code you've written for a connected Google or Microsoft account works here unchanged. Each account gets: A primary calendar , provisioned automatically. It can't be deleted while other calendars exist on the account. Additional calendars , up to your plan's cap, for separating concerns — a sales-calls calendar and an internal one on the same agent. Free/busy queries , so the agent can check its own availability before proposing a time. Event webhooks — event.created , event.updated , and event.deleted fire on every change, whether it came from the agent or from someone responding to an invitation. List the calendars from the terminal with nylas calendar list , or over the API with GET /v3/grants/{grant_id}/calendars . Both return the primary calendar plus any you've added. List what'

"It runs on my own GPU, so it's basically free." I believed that until I put a meter on it. So I ran a controlled benchmark on one box — an openSUSE machine with a single RTX 3090 — driving three local models through ollama under an identical fixed workload (256-token generations in a loop for ~4 minutes each), while my open-source dashboard priced every run by the real GPU energy it burned : power sampled from nvidia-smi every 10 s, integrated over each run's exact window, multiplied by my actual day/night tariff. One number per model, in euros per million output tokens. Here's the part that made me re-run it. The tiny gemma3:1b came out at €0.118 / 1M tokens — about 5× cheaper than a hosted Flash-class API (~€0.55). But gemma3:27b 's electricity alone was €0.706 / 1M — more expensive per token than just paying the cloud, and that's before a single cent of the GPU's purchase price. "Local" didn't make it cheaper; it made it cost more and I own the depreciation. The mechanism is one line: each token costs watts ÷ throughput , and a big dense model is both slow and thirsty. A newer mid-size architecture ( gemma4:26b ) bought a lot of that back, landing at €0.272 . The full guide is methodology-first and reproducible end to end — minting an ingest key, the stdlib-only client, the exact ollama loop that reads eval_count / eval_duration for real tokens-per-second, reading each run back priced, and the honest caveats (this is marginal GPU energy only — not capex, idle, or cooling — and the absolute numbers round to fractions of a cent; the shape is the finding). Read the full guide on Medium → https://medium.com/@arsen.apostolov/how-much-does-it-actually-cost-to-run-a-local-llm-per-million-tokens-measured-4a90a7f31a48

The Ray-Ban Meta Optics lineup adds a premium fit and a few extra features, but comes with a higher price.

“Coffee” made with functional mushrooms like lion’s mane and chaga is all the rage. We tried the most popular brands to find which were the most palatable.

This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here. For those of you enjoying your summer unaware of Anthropic’s latest feud with the US government, here’s a recap: In April the company said it had built an AI model called Mythos…

Tesla touts Autopilot as lifesaving a day after grandmother died in crash.

Two years after it turned Marg Monday into a daily, the Ninja Slushi is only $200. That’s the cheapest deal ever.

We’ve trawled the depths of Amazon to find the best early Prime Day deals on gear we’ve tested.

Smart glasses are hitting the mainstream, so companies are now getting into the accessories game.

Smart ring maker RingConn's marketing copy says Lord of the Rings' "enduring narrative highlights a simple but powerful idea: that meaningful transformation often begins with the choices we make each day. RingConn embraces a similar philosophy, believing that lasting change begins with everyday awareness and small, intentional decisions." Of course, the "intentional decisions" Frodo makes […]

I'm 19. In four months I built 128 projects with AI — 61 GitHub repos, 15 MCP servers, a 7-department agent OS, the works. I shipped 5 . Total stars: 6 . Revenue: $0 . That gap bothered me enough that I did the obvious-but-uncomfortable thing: I had an AI audit everything — every repo, every project folder, 4,239 build sessions, 244 memory notes — and pin it all like specimens in a cabinet. No flattery. Here's what the autopsy found. → The full interactive atlas: https://builder-archive.vercel.app/en The number that explains everything 128 built. 5 shipped. It's tempting to read that as a discipline problem. It isn't. The build velocity is real — I once shipped ~20 vertical SaaS in a single weekend on a shared Next.js + Drizzle + Stripe stack. The code works. The UIs are clean. The problem is the last mile . README writing, deployment, the final 10% that turns a repo into a thing a stranger can use — that's where almost everything died. Not ability. Execution. The AI put it in one line: "Can build anything. Finishes nothing." Strength and weakness are the same coin Here's the part I didn't want to see: the thing that makes me fast is the thing that kills me. Because I can build deep, I lose the stopping point. Because building is cheap, I start the next thing before finishing the last. The audit scored two skill axes: Build (design → implementation → automation): advanced Distribution (publish → ship → monetize): beginner Every problem I have lives in that asymmetry. It's not a motivation gap — total commits across repos: ~4,800. The effort is enormous. It just never crosses the finish line into something public. The hardest thing I made is the one I hid The audit flagged a buried asset: a GCC/ZATCA e-invoicing toolkit — Saudi Fatoora Phase 2, EN16931 + Peppol validation, secp256k1 signing, Go compiled to WASM. The single hardest, most verifiable piece of work I've done. It's been sitting in a private repo. That's the disease in one example: the more valuable the th

Everyone says our job got easier. The people doing it are quietly falling apart. Here's the part nobody at the dinner table wants to hear: AI didn't make software engineering easy. It made it relentless. Your uncle thinks you press a button now. Your PM thinks the estimate should be half what it used to be. LinkedIn thinks you're either an "AI-native 10x engineer" or a dinosaur waiting for the meteor. And somewhere in the middle of all that noise is you, doing two jobs at once and wondering when you stopped recognizing the one you signed up for. If that landed, keep reading. This one's for you. 💡 The Lie Everyone Has Agreed To Believe The story the world has settled on is simple: AI writes the code now, so the hard part is over. It's a comforting story. It's also wrong in a way that's hard to explain to anyone who hasn't sat in the chair. Yes, the blank-file problem is mostly solved. Boilerplate, scaffolding, the first rough pass at a function, all of that is faster than it's ever been. The problem is "writing the lines" was never the expensive part of this job. The expensive part was always judgment. Knowing what to build, knowing why it breaks, knowing which of the model's three confident suggestions is the one that quietly corrupts your data at 2 AM is where the engineer earns their salt. AI didn't remove that work. It buried it under a pile of plausible-looking output you now have to review, verify, and own. So the meter didn't slow down. It moved. You spend less time typing and far more time deciding, validating, and cleaning up. To everyone watching from outside, that looks like less work. From inside, it's a heavier cognitive load on a shorter clock. Sound familiar? The Treadmill Nobody Put On the Job Description The cost no one talks about is the half-life of what you know is collapsing. Five years ago you could learn a framework and ride it for a few years. Now a tool you mastered in January has three competitors and a new paradigm by June. New model, new c

The cuts and redundancies are part of a plan to "simplify the company," the CEO says.

WhatsApp gets a new boss, as Will Cathcart moves to a new role at Meta, while Shah steps down as CEO of Indian fintech giant CRED to replace Cathcart.

The Space Force wants to cut the time to field new satellites from years to weeks, days, or hours.

The high-altitude race is a unique test of car and driver.

Save up to $190 on your pass to TechCrunch Founder Summit 2026 by June 26, 11:59 p.m. PT. Designed for founders first on November 4 in Boston. Register here.

After 18 years of focusing on Europe, early-stage investor Seedcamp said that it has raised $320 million for its latest fund which will see it expanding its presence in the United States.