标签：#startup

The industry is described as a "dual-track" race. On one side are incumbents (Big Tech) with massive infrastructure and deep pockets. On the other is a wave of nimble startups specializing in specific engineering, error-correction, and simulation challenges. The sector is currently transitioning beyond the Noisy Intermediate-Scale Quantum (NISQ) era toward fault-tolerant systems and commercial quantum advantage—the point where quantum machines reliably outperform classical supercomputers for useful tasks. These companies are building the foundational cloud-accessible platforms and hardware: Amazon Braket (AWS) IBM Google Quantum AI Microsoft NVIDIA These players are driving innovation in specific qubit modalities or niches: Superconducting Qubits: Rigetti Computing, IQM, and Atlantic Quantum. Trapped Ion: IonQ, Quantinuum, and Alpine Quantum Technologies. Neutral Atom: QuEra, PASQAL, and Atom Computing. Photonic: Xanadu, PsiQuantum, and Quandela. Silicon/CMOS: Diraq and Silicon Quantum Computing. Error Correction: Riverlane and Q-CTRL are focused on the "noise" problem, helping make unstable qubits behave predictably. Software & Algorithms: Classiq (design automation) and Multiverse Computing (finance/optimization applications). Quantum-Safe Cybersecurity: PQShield and evolutionQ are developing cryptographic solutions to protect data against future quantum threats.

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

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

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.

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

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,

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

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.”

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

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.

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

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

Neo is Bhavin Turakhia’s fifth venture and his latest involving enterprise software. This time he's taking on Microsoft Office, Google Apps with AI.

Pricing is the single most powerful lever you have for growing SaaS revenue — yet most founders treat it as an afterthought. A 1% price increase can yield an 8-12% increase in operating profit, far more than acquiring the same revenue through new customers. This playbook covers the five core decisions every SaaS company must make: monetization model, value metric, tier structure, psychological pricing tactics, and pricing page optimization. Introduction: Why Pricing Is Your Most Important Growth Lever When founders think about growth, they typically reach for familiar levers: more marketing spend, bigger sales teams, viral features. But pricing is the one lever that touches every single customer interaction — and it costs nothing to change. Consider this: if you raise prices by 1% and lose 1% of customers, your net revenue still increases. The math works because the lost customers are often your least price-sensitive ones. In practice, companies that run pricing experiments typically find they can increase prices by 5-15% before seeing any meaningful impact on conversion. Yet pricing is also where most SaaS companies are at their most irrational. We underprice out of fear, copy competitors without understanding why, and avoid changes because we're afraid of customer backlash. Freemium vs Free Trial vs Paid-First Freemium Freemium offers a permanently free tier with limited features. It's a top-of-funnel machine — but it requires low marginal cost per user and a clear upgrade path. Aspect Freemium Best for Products with viral loops, network effects Conversion rate Typically 2-5% free-to-paid Risk High support cost for free users Example Slack, Notion, Canva Free Trial (Time-Limited) Time-limited trials give full access for 7-30 days, then require payment. Aspect Free Trial Best for Products with immediate value delivery Conversion rate Typically 10-25% trial-to-paid Risk Users forget to use the trial Example GitHub, Figma, Intercom The biggest mistake teams make: tre

The company has grown rapidly by acquiring and revamping last-generation tech brands like AOL, Eventbrite, Evernote, Meetup, and Vimeo.

The incident could disrupt a key part of the defense company’s rocket motor business, which designs and tests prototype motors for military customers.

The autonomous vehicle space is starting to feel like a repeat of the 2016 hype cycle. Travis Kalanick is back building a robotics company, and the talent wars and capital are heating up the same way they did the first time around. The money’s flowing back, and it’s the people who lived through that first […]