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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.”

Some Apple devices are about to get much more intelligent, while others are getting left behind.

The rise of AI has brought an avalanche of new terms and slang. Here is a glossary with definitions of some of the most important words and phrases you might encounter.

We’ve compiled an overview of some of the top alternative browsers available today aiming to challenge Chrome and Safari.

In case you were wondering in which country you should buy a Pixel phone, you world traveler you.

The companies’ Fourth of July plans include celebrating new reactor designs coming online. But there’s still a long way to go before they deliver energy at a meaningful scale.

During negotiations on Wednesday, employees voiced frustrations with what they consider an unwillingness among executives to engage meaningfully with the prospect of unionization.

Every week, over 1,300 US companies file a funding round with the SEC — and most of them never appear in the tech press. If your job involves selling to funded startups, tracking competitors' war chests, or spotting investment trends, you're probably relying on funding newsletters and databases that are days late and hundreds of dollars per seat. There's a better way: go to the primary source. In this tutorial you'll build a real-time startup funding feed from SEC Form D filings — the regulatory document every US company must file when it raises private capital. You'll get exact amounts, industries, locations and investor counts, as clean JSON, for a fraction of a cent per round. Why Form D beats funding news When a startup raises money under Regulation D (the exemption used by virtually all US venture rounds), it must file Form D with the SEC within 15 days of the first sale. That filing includes: The exact amount sold so far — not a journalist's "sources say" estimate The total offering size (or whether it's open-ended) Industry group, city and state Number of investors who participated Date of first sale and year of incorporation Compare that to funding news: TechCrunch covers a tiny, PR-driven slice. Databases like Crunchbase aggregate press and manual research — comprehensive over time, but late and expensive. Form D is the ground truth both of them chase. The catch? EDGAR (the SEC's database) is built for lawyers, not for automation. The filings are XML documents scattered across an archive, discoverable only through a quirky full-text search API with hidden rate limits. That's the part we'll automate. The 5-minute setup We'll use the Startup Funding Feed Actor — it handles EDGAR's discovery API, XML parsing, rate limits and pagination, and returns one JSON record per filing. It's pay-per-event: $0.002 per filing returned (a full weekly sweep of all US rounds costs ~$2.60; a filtered slice costs cents). Failed fetches are never charged. Create a free Apify acc

Avoid phone FOMO with our favorite smartphones that aren’t officially sold stateside but are available in markets like the UK and Europe.

The speakers discuss Agent RFT, OpenAI’s platform for fine-tuning reasoning models via real-time tool interactions and custom reward signals. They explain how reinforcement learning solves complex credit assignment challenges within the context window. They share enterprise success stories, showing how Agent RFT eliminates long-tail token loops and drives extreme efficiency. By Wenjie Zi, Will Hang

If you're going to apply for Startup Battlefield Australia, now is the time. Applications close July 6, and once the deadline passes, the opportunity is gone.

IQM, a full-stack quantum company out of Finland, went public on the Nasdaq today at a valuation of about $1.9 billion.

Not long ago I wrote about how I built a browser-based board game called "Growing City" in three days using AI — and how the hardest part wasn't the code at all. Some time has passed, and I wanted to share what happened next. Layout Bugs While vibe-coding solo, I only tested on my own screen, resolution, and browser. The problem surfaced as soon as real users joined with different setups: some people saw everything misaligned, some things got clipped, some cards overlapped each other. This is how it looked on some screens I had to rewrite the layout to use adaptive sizing so the game looks correct regardless of screen resolution. It should work now — but if something still looks off on your end, let me know and I'll fix it. Bots Started Talking Another change, unrelated to bugs. The service started feeling more alive. Previously, bots just played: rolled dice, bought cards, said nothing. Now they react in the chat to what's happening in the game — if someone's building gets taken, if someone buys an expensive card or runs out of money. It's a small thing, but the game feels noticeably more lively. An empty game with silent bots versus a session where someone's commenting on what's happening in chat — it's a meaningfully different experience, even though the game itself is the same. Thank You to Early Players A special thanks to everyone who tried the game after my first article. And extra thanks to a user with the nickname SHAM, who pointed out that the game rules never said you can't buy multiple purple cards in a row — even though the game itself has that restriction. Fixed! What's Next The project is still going. I'm thinking about ads and other ways to bring in players. Without new users, it's hard to get feedback — and without feedback, it's hard to know what to fix or improve first. The unit economics don't quite work out yet: paid acquisition costs more than I'm willing to invest at this stage. I'll keep figuring it out. If you have ideas on how to find playe