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Station F, the Paris-based startup hub founded by French billionaire Xavier Niel in 2017, is gearing up for a new edition of its selective acceleration F/ai program that will cement its role as a stepping stone for AI startups.

The story behind why we built a WhatsApp CRM that actually understands how WhatsApp works. There's one scene I can't get out of my head. A friend's desk. She runs an online store. On it sat three phones. Not for show. One for customer service, one for the admin, one for the number that was "just for resellers." All three buzzing, nonstop. And there she was, eleven at night, still replying to messages one by one, sighing: "It's the same questions over and over. But if I don't reply, they'll go to the competitor." That's not a rare case. That's the normal state of things for thousands of businesses. We all know one thing CRM software rarely admits: customers here don't live in email. They live on WhatsApp. They ask about prices on WhatsApp, complain on WhatsApp, close deals on WhatsApp, even ask for warranty support on WhatsApp. But the teams handling all of it? They use personal phones. No records, no context, no way to help each other when one person is drowning. Hallo Zetta was born out of that. What Frustrated Us About the Existing Tools Before building our own, of course we looked. Surely someone had solved a problem this simple? Turns out what existed fell into two camps, and both were maddening. Camp one: dumb auto-reply bots. Type "hi," get a template. But the moment a customer asks something slightly off-script, the bot freezes. It actually makes customers angrier, because it feels like talking to a wall. Camp two: bloated CRMs. Loaded with features, dashboards full of charts, but WhatsApp is bolted on as one small tab. As if WhatsApp were an afterthought, not the main battlefield. For most of our customers, WhatsApp is the battlefield. Nothing fit. So we decided to build it ourselves. The Hard Part Isn't "AI Can Reply to Messages" Let me be honest about this. Bolting AI onto WhatsApp is easy. Anyone can wire GPT to a webhook and ship it overnight. If that were the whole goal, this article wouldn't need to exist. The hard part, the thing that made us rethink

InfoQ has opened enrollment for a five-week AI Security & Privacy Engineering cohort for senior engineers and architects in regulated industries, focused on applying security, privacy, threat modeling, observability, and governance practices to production AI systems. By Artenisa Chatziou

The Office of Professional Responsibility has opened more than 100 cases over what ICE officials call “incidents of doxing and threats” against ICE employees.

The mental model most people have is too simple "Chainlink Functions lets smart contracts call APIs." That's true the same way "Ethereum lets people send money" is true. Technically accurate, misses almost everything that makes the product interesting and almost everything that matters for security. Chainlink Functions is better understood as a decentralized serverless compute platform: arbitrary JavaScript runs across every node in a DON, each node executes independently, OCR aggregates the results, and the aggregated output gets delivered back to the consumer contract through a verified callback. The "API call" is just one of the things that JavaScript can do inside that environment. The DON consensus and the threshold-encrypted secrets model are what make it meaningfully different from a centralized API proxy. This is day 9 of the 28-day Chainlink architecture series. Today covers the full request lifecycle, every contract in the chain, how threshold encryption protects secrets without exposing them to any individual node, and the integration mistakes that come from misunderstanding how billing and callbacks actually work. The four contracts you need to understand Before tracing the full lifecycle, it helps to know exactly which contract does what. FunctionsRouter : the stable, immutable entry point for consumers. Manages subscriptions and authorized consumer contracts. Its interface doesn't change when the underlying implementation upgrades, consumer contracts call sendRequest here and only here. Also handles billing: estimates fulfillment cost at request time and finalizes it at response time. FunctionsCoordinator : the interface between the Router and the DON. Emits the OracleRequest event that DON nodes watch for. Handles fee distribution to transmitters via a fee pool. Inherits from OCR2Base , meaning the full OCR consensus machinery runs here. This contract can be upgraded independently of the Router, which is why the Router exists as a stable facade in fro

Even Realities, an ex-Apple team building camera-free smart glasses, raised $150M from Meituan and Tencent at a $1B valuation.

Many people who want Apple's rumored folding iPhone 'Ultra' may not be able to get it at first

Back in February, Uber announced ambitious plans to launch in seven new European markets in 2026 — but now five of those launches are reportedly on hold.

Here's how to find out whether you're charging your Apple mobile devices at top speed.

Baking sourdough bread is inherently old-fashioned, relying on natural fermentation and wild yeast instead of the simple, predictable commercial stuff. So it might sound anathema to bring a gadget into the mix. The trick to the Sourdough Sidekick - backed and branded by King Arthur flour - is that it promises to automate the boring […]

Before Keurig, the coffee in your office was almost certainly terrible. Old, burned, made by someone who would rather poorly eyeball than properly measure. Just altogether gross. After Keurig? You could make your own coffee, a cup at a time, exactly when you needed it. The single-cup brewer was an elegant solution to an extremely […]

It's good to know how long your phone will get updates before you purchase.

With AI igniting an investor frenzy, more startups are achieving unicorn status every month.

I’ve spent thousands of dollars over the years on smart home gear. Like many tech enthusiasts, I started with the usual suspects: smart bulbs, plugs, sensors, voice assistants, and eventually more “advanced” hubs. Every time, the marketing promised intelligence. Every time, I got glorified timers and motion detectors wearing a fancy label. After multiple attempts, I’ve reached the same conclusion many others quietly reach: most “smart home” products are not smart. They are automated, and there’s a massive difference. What “Smart” Actually Means A genuinely smart home system should do three things well: Understand context. Not just that a door opened or motion was detected, but why and what it means right now. Integrate devices meaningfully. Devices shouldn’t just talk to each other; they should share rich, semantic information so the system can reason across them. Be predictive and proactive. It should anticipate needs based on patterns, current state, and human behavior, instead of waiting for a trigger. Current systems almost never do any of these at a level that feels intelligent. The Core Problems (From Someone Who Actually Tried) Take a simple example: the dishwasher. A basic automation might detect the door was opened and then closed, then start the cycle. But it has zero idea whether: Dishes were actually loaded Someone was just checking if the cycle finished More dishes are coming in 30 seconds The person is about to run a quick rinse first The same gap appears everywhere: Lighting at night. The system doesn’t know if you just got up to use the bathroom, you’re wide awake working, or there was an emergency. It just sees “motion after 11 p.m.” and either blasts you with light or leaves you in the dark. Multi-person households. One person’s preference for dim evening lighting conflicts with another person’s need for bright light. Guests have no idea how anything works and accidentally trigger routines. “I’m just doing a quick house tour” vs. actual activity. T

I'm probably not the only one who checks every few months whether a GPU alternative has finally shipped, mostly so I can cancel a few subscriptions. Nobody doubts it's physically possible or that people have tried. The real question is why it hasn't actually happened, and the answer is economic and structural, not technical. GPUs are not uniquely ideal. They're uniquely general LLM workloads are dense matmul, high parallelism, memory-bandwidth-bound compute. GPUs handle this well but weren't built for it specifically. An ASIC purpose-built for transformer inference should beat a GPU on perf-per-watt and perf-per-dollar, and in narrow slices, it already does: Groq's LPU beats GPUs on single-stream inference throughput for models that fit its architecture Cerebras' WSE cuts interconnect overhead by putting the whole model on one wafer Google TPUs have run production workloads for years and are now sold externally via GCP So specialized hardware can win, sometimes even in production. The real question isn't whether something can beat a GPU, it's why none of these have dented Nvidia's share. 1. The capital barrier Custom silicon needs hundreds of millions in NRE cost, access to TSMC's leading-edge nodes with multi-year allocation queues, and several iterations before a design is commercially viable. That caps the field to hyperscaler balance sheets or venture funding measured in billions. The barrier isn't just the chip either. CUDA, the surrounding tooling, and production pipelines took a decade of capital and engineering to mature, and matching that means rebuilding all of it, not swapping a part. That's a second capital sink on top of the silicon itself. There's also a timing risk specific to fixed-function silicon: if the underlying model architecture shifts significantly, an ASIC taped out for today's transformer variant can become dead weight, while a GPU just needs a software update to run whatever comes next reasonably well. That risk hasn't actually played out,

Don’t suffer the buffer. These WIRED-tested home routers will deliver reliable internet across your home, whatever your needs or budget.

The heat of the Hadean may have come from impacts as well as the interior.

The problem with a data breach is not finding evidence. It is connecting it. But let me start where I actually was: 4 AM, last day of the hackathon, staring at this in my terminal. RateLimitError: GroqException - Rate limit reached for model `llama-3.3-70b-versatile` on tokens per day (TPD): Limit 100000, Used 99787, Requested 1616. Please try again in 20m12s. Used 99,787 out of 100,000. My deployment was half done, my demo graph was empty on the server, and the free tier had 213 tokens left. The submission deadline was hours away. I had not slept. I had not eaten. My friends were asleep and I was swapping API keys like a gambler swapping chips. This post is the story of how we got there, and how it ended at 7 in the morning with the best sigh of relief I have ever taken. First, some honesty about how I got here When I joined my first WeMakeDevs hackathon, I did not believe in it. I thought it was one of those ordinary online events. Fake prizes, no follow-through, what would I even get out of it. I joined anyway, mostly out of boredom, got into the Discord, talked to people, made a few connections. I landed in the top 50. A few days later an email showed up: a free Claude Max subscription as a gift. I read it twice. I genuinely could not believe a hackathon had actually delivered something. So when this hackathon opened, I did not hesitate. I messaged my friends and said we are joining as a team this time. Three of us: me (Mehraan), Aqib, and Ubaid. The spark We spent the first evening in our group chat throwing ideas around and shooting most of them down. Then one of my friends dropped a thought that stuck: what happens after a company gets hacked? I started digging into it. The answer is honestly depressing. After a breach, the evidence is everywhere. VPN records. File access logs. The email gateway. Badge readers at the office doors. CCTV. HR notes. Anonymous tips. Each system tells one small piece of the story, and a human analyst has to stitch all of it togeth

The Matic is our favorite robot vacuum by a pretty comfortable margin. If you’ve been thinking about buying one, you may want to plan on doing it sooner than later. The company will raise its price by $250 on September 9th, going from $1,245 to $1,495. Matic told The Verge that the new price reflects […]

Mistral AI, which offers some open source AI models, has raised significant funding since its creation in 2023, with the ambition to “put frontier AI in the hands of everyone.”