标签：#Startup

There are a lot of fast-growing AI startups, but some are growing even faster, they say.

ZML, a hot French AI startup endorsed by Turing Award winner Yann LeCun, has now released ZML/LLMD, software that could make running AI less costly.

AI chip maker SambaNova has raised at an $11B valuation months after Intel was rumored to be trying to buy it for about $1.6 billion.

If you're building something ambitious, this is a fast track to the people who can move your startup forward.

AI law startup Norm has raised a $120 million Series C round led by Khosla Ventures, valuing the startup at $1.2 billion.

Bidbus, which lets dealerships bid on used cars, has raised $15 million in a Series A round that was led by early-stage mobility fund Ibex Investors.

I came into this as a graphic designer, not a software engineer. I didn't have a computer science background, and a lot of what BrandStack needed — authentication, databases, payments, deployment — was new territory for me when I started. What made it possible wasn't some shortcut. It was breaking the problem down into pieces I could actually learn: how user accounts work, how a database should be structured so one person's data never leaks into another's, how to move from test payments to real ones without breaking checkout for actual customers. I made real mistakes along the way. Early on, every user shared the same underlying brand data because I hadn't scoped the database correctly to each account — a serious bug that I only caught by testing with two separate accounts myself. Finding and fixing that taught me more about proper application architecture than any tutorial could have. I don't think being a designer first is a disadvantage for building product. If anything, it means the interface and the experience get real attention, not just the backend logic. But it does mean being honest about what you don't know yet, and being willing to slow down and actually understand a problem instead of copying a fix you don't understand. BrandStack is still a work in progress. But it's a real, working product — built by someone who had to learn most of this from scratch, in public, one bug at a time.

The company just raised $7 million in seed funding, and is launching its app for iPhone and Android on Tuesday.

This is part of my work with 01MVP on OpenNomos — a project that helps founders validate ideas before building. The $0 Launch I once spent three months building a product. It had everything: authentication, payments, a polished UI, dark mode. I was proud of it. Launch day: 27 visitors. Zero signups. I had spent 90 days building and precisely zero days asking anyone if they wanted what I was building. I was solving a problem that existed only in my head. The Hardest Lesson The product wasn't bad. The code was fine. The UI was clean. The problem was that I never validated the core assumption: does anyone actually have this problem, and would they pay to solve it? This is the most common failure mode in indie hacking. You build something you think is cool, polish it to perfection, and launch to silence. The code was never the bottleneck. The validation was. What I Do Differently Now Talk to 10 people before writing code. Not surveys. Not landing page analytics. Actual conversations. "Would you use this? Would you pay for it? Why or why not?" Build a mockup, not a product. A Figma prototype or even a Google Form that simulates the core workflow is enough to test willingness to engage. Charge from day one. Free users will tell you nice things. Paying users will tell you the truth. If nobody will pay, the idea isn't ready. Kill fast. Most ideas fail. The goal isn't to make every idea succeed — it's to fail the bad ones quickly so you can find the good ones. Why This Matters More in 2026 In 2016, building a product was hard. You needed to know how to code, set up servers, handle deployments. The barrier to building kept bad ideas from being built. In 2026, Cursor writes your code, v0 generates your UI, and Replit deploys it. The barrier to building has collapsed to near zero. But here's the problem: AI can help you build anything. It cannot help you figure out what's worth building. The result is a flood of well-built products that nobody wants. The bottleneck shifted from

Hi everyone, I'm Marc, the founder of ClaimMate AI. I've been building an AI software engineering platform that helps developers generate code, explain existing code, debug issues, create tests, review code, and build applications from simple prompts or voice. I'm still in the early stages and would really appreciate honest feedback from other developers. Why I Built It I wanted one workspace where developers could chat with AI, generate code, debug problems, and iterate on ideas without constantly switching between multiple tools. I'd Love Your Feedback If you have a few minutes, I'd appreciate any thoughts on: Is the interface easy to understand? Which feature would you use most? What would stop you from using it regularly? What feature is missing? You can try it here: https://ClaimMateAI.pro I'm not looking for praise—I genuinely want constructive feedback that will help improve the product. Thanks for your time!

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.