标签：#Startup

Instead of writing long build logs or recording traditional vlogs, my co-founder and I wanted to try something different. We're documenting our startup journey by turning it into an AI-generated anime series. Not for fiction. For real startup moments. Episode 2 follows our cold outreach journey: Finding an ICP Testing different niches Sending DMs Getting ignored Learning what works (and what doesn't) We're treating this as an experiment to see whether AI-generated storytelling can make the process of building a startup more engaging than the usual "build in public" content. The goal isn't perfect animation. It's authentic documentation—with AI as the creative medium. We're still figuring it out, improving every episode, and learning as we go. Would love to hear what fellow builders and developers think about this approach. Could AI-powered anime become a new way to document products, startups, and open-source projects? Feedback is always welcome. 🚀

Phia, the shopping startup founded by Bill Gates’ daughter, Phoebe, and her friend, Sophia Kianni, is under fire for a practice known as “cookie stuffing,” which helped the product receive commissions and credit for sales it did not actually generate, per a Bloomberg investigation.

You ship all the time. A fix here, a new setting there, a feature you spent a whole weekend on. And your users mostly don't notice. That gap is expensive. When people can't see a product moving, it feels abandoned, even when you're shipping every week. They churn a little faster, they email asking for things you built a month ago, and all the momentum you're actually creating stays invisible. The fix is boring and old: a changelog. But not a changelog rotting in a Notion doc nobody opens. One that shows up inside your app , where users already are. Here's the approach I settled on. The idea: a widget, not just a page A "what's new" widget is a small button or badge in your UI. Click it, and a panel slides out with your latest updates. Users see it in the flow of using your product, not on some /changelog page they'd never visit. You really want three things: An in-app widget users actually see. A public page and RSS feed you can link from emails and docs. A way to write updates in plain language and publish in a click. The one-tag version I ended up building a tool for this (honest disclosure below), but the integration is the part worth showing, because it's the pattern any changelog widget should follow: <!-- Paste before </body> --> <script src= "https://cdn.patchlog.io/widget.js" data-project= "your-project-id" data-position= "bottom-right" async ></script> One script tag. No SDK, no npm install, no framework coupling. It behaves the same in React, Vue, Rails, or a plain HTML page. Two implementation details matter, whether you build one of these yourself or evaluate an existing one: Render it in a Shadow DOM. A changelog widget should not inherit or leak styles. If it uses the host page's global CSS, it will look broken on half the sites it lands on. Shadow DOM isolates it completely. Fail silently. A marketing widget must never break the host app. If the network call fails, it should quietly do nothing. What to actually write in it The tool is the easy part. T

A year and a half ago, I wasn't trying to build an AI company. I was building a small social platform called spritex-social — nothing fancy, just a side project a handful of friends were testing with me. No grand plan, no investors, no roadmap beyond "let's see if people like this." At some point, users started asking the same basic questions over and over: how do I change my profile, where's this setting, how does that feature work. Instead of writing endless documentation, I thought — why not just let AI answer this? So I wired up Google's Gemini API through Google AI Studio, built a small Retrieval-Augmented Generation (RAG) system, and gave it context about the platform. It was supposed to be a support chatbot. Nothing more. That's not how it went. I found myself spending more time improving the chatbot than improving the actual social app. Every small upgrade made me ask another question: could it remember conversations? Could it use tools? Could it search the web? Could it do things instead of just answering questions? The more I asked, the less interested I became in the social platform I was supposed to be building. Eventually I had to admit it to myself: I wasn't building spritex-social anymore. I was building something else entirely. So I stopped. Not because the project failed — because my attention had already moved somewhere else, and I finally stopped pretending otherwise. That "somewhere else" became RexiO — a Bangla-first AI platform I've been building solo ever since: my own orchestration layer, an intent classifier, 30+ tools, model routing across providers, and eventually our own fine-tuned models trained from scratch on borrowed Colab GPUs. RexiO went public on July 10, 2026. This chatbot pivot is just one chapter of a much longer story — one that actually starts on a Nokia button phone, ২ টাকা data packs, and a ৳20 freelance job that became my first line of code in production. I wrote the whole thing down, unfiltered — the rewrites, the 12-hour

The massive round was co-led by ARCH Venture Partners, Spark Capital, and Khosla Ventures.

Open source AI is booming, according to Hugging Face CEO Clem Delangue. The company has grown into something like a GitHub for AI in recent years, where AI builders can share and download open models and datasets, now used by roughly half the Fortune 500. Delangue has seen the same story play out again and again: companies start […]

Lyzr, a startup that builds AI agents for enterprises, used its own AI agent to raise a $100 million round — proof, evidently, that the product actually works.

One of my favorite words in the startup and product-building world is pivot. For a long time, I thought a failed project meant wasted time. Today, I see it differently. Every project I worked on—even the ones that never gained users or reached the finish line—taught me something I couldn't have learned from books alone. They taught me how to validate ideas, communicate with users, make technical decisions, prioritize features, and, most importantly, when to change direction. I've come to believe that many successful founders didn't succeed because they had the perfect first idea. They succeeded because their previous attempts gave them the experience to recognize a better opportunity. In fact, I think that if many of them had started directly with the project that eventually made them successful, they might have failed. They first needed the lessons, the mistakes, and the discipline that came from building things that didn't work. I'm still on that journey. Some of my own projects didn't succeed the way I had hoped, but I don't consider them failures. They were investments in experience. Every project made me a better builder and helped me better understand what I want to create and how I should create it. One principle that keeps me moving comes from the Quran: «"Indeed, Allah will not change the condition of a people until they change what is within themselves." (Quran 13:11)» And another verse that reminds me to stay patient during difficult times: «"Allah does not burden a soul beyond what it can bear." (Quran 2:286)» If you're building something today and it isn't working, don't be afraid to pivot. Sometimes changing direction isn't giving up—it's applying everything you've learned so far. I'm curious: Have you ever pivoted a project? What did it teach you?

In this week’s episode of Build Mode, Isabelle Johannessen talks with Precursor Ventures' Charles Hudson about the headwinds facing early-stage founders today and the most common mistakes founders should avoid in order to get funded.

A new $20 billion valuation would be a giant step up from the $10 billion valuation it reached in October.

Benchmark-backed Ollama has amassed 176,000 stars, and nearly 17,000 forks on Github by helping developers easily run AI on their PCs.

Nilekani remains Fundamentum's anchor investor as the firm expands its leadership team and targets AI and fintech startups in India.

AI founders love the glamorous agent stories: coding agents, sales agents, AI doctors, AI lawyers. But if you dig through the YC 2026 batch data, one of the more interesting signals is decidedly unglamorous: insurance . Out of 477 real-ish company records in the current snapshot, 25 match insurance-related keywords — about 5.2% — and 8 companies sit in the Fintech → Insurance subindustry. Not a tidal wave. But it's enough to suggest something worth paying attention to: insurance is quietly becoming one of the better wedges for AI agents that actually ship. The reason is simple. Insurance is wall-to-wall documents, rules, judgment calls, exceptions, approvals, claims, underwriting, and cross-system coordination. In other words: wall-to-wall work that agents can do and humans hate doing. Insurance is not fintech's leftover category Most people file insurance under "slow fintech": aging distribution, legacy systems, long processes, heavy regulation. From an AI builder's perspective, that list of flaws reads more like a list of opportunities. Insurance workflows are highly structured — but not fully structured. Policies, claims files, medical records, photos, repair estimates, payout history, compliance clauses: the inputs are messy and heterogeneous. Yet every step has a crisp objective: is this covered, what documents are missing, how should this risk be priced, can this pass approval. That's not a chatbot problem. It's an agent problem — reading documents, following procedures, calling systems, leaving audit trails, handling exceptions. And precisely because it's complex, insurance is more likely to command real budget than yet another AI writing tool. Agents die without boundaries; insurance comes with them built in The most common failure mode for early agent products: they sound like they can do everything and end up doing nothing well. Insurance workflows hand you boundaries for free: Inventory and asset processes can be automated end to end Medical prior authori

If you could pick only one counterintuitive number from the YC 2026 batches, make it this one: out of 477 real-ish company records, 366 list San Francisco as their location — roughly 77%. For comparison: New York City has 24. London 10. Boston 7. Los Angeles 4. Fully remote? 3 companies. Even if you add the 11 tagged "San Francisco + Remote", the conclusion doesn't budge: AI startups aren't spreading across the map. They're re-concentrating in one city. This isn't Bay Area nostalgia. It's industry structure casting a vote. Remote won work. It didn't win startup density. One of the most popular takes of the past few years: software teams can start anywhere, so companies no longer need the Bay Area. That take wasn't entirely wrong — tooling, cloud services, open models, and online fundraising genuinely lowered the barrier to starting a company. But the YC 2026 location data is a reminder that a lower barrier is not the same as a vanished advantage. Building an AI startup isn't just writing code. It runs on model gossip, talent flow, customer pilots, investor feedback, peer pressure, and extremely fast narrative iteration. Much of that works online. But the densest informal information still travels fastest offline. San Francisco's edge was never the office space — it's collision frequency. AI made same-city learning matter again In the classic SaaS era, most domain knowledge came from customers and product cycles were relatively stable. You could build a vertical software company in any city and grind toward PMF at your own pace. The AI era doesn't work like that. Model capabilities turn over every few months. Agent architectures keep getting rewritten. Inference costs, context windows, voice, tool calling, and eval infrastructure are all on rolling release. A seemingly minor technical shift can redraw your product's boundaries overnight. In that environment, whoever hears real feedback earlier, learns earlier what others tripped over, and understands earlier what inv

We just opened the waitlist for Something, and the part that surprised me most while building it wasn't the multi-agent orchestration — it was how hard it is to make an AI actually disagree. Every model we tested defaults to being helpful, which in practice means agreeable. Even when explicitly prompted to "find flaws," the outputs would soften into "here are some considerations" instead of a real critique. We had to engineer around this specifically: Separate system prompts with opposing reward framing — one agent optimizes for identifying growth potential, the other is explicitly told its only success metric is surfacing a disqualifying flaw Structured output forcing a verdict, not a summary — the skeptic agent (Nothing) has to commit to a specific weakness category (unit economics, timing, technical feasibility) rather than hedging across all of them A reconciliation step where both outputs get merged into one conviction score, so the founder isn't just reading two contradictory paragraphs If anyone's built adversarial agent setups and hit the same "it just wants to agree with me" problem, curious how you solved it. [Everyone who has a brain is a founder here] something-waitlist.vercel.app

The $300 million round is expected to be led by Menlo Ventures, Sifted reported.

Lionel Messi and Cristiano Ronaldo are betting on AI, health tech, and startups. Mohamed Salah is taking a more traditional route beyond football.

Like its U.S. counterpart, the European Chips Act aims to foster the semiconductor industry — in part thanks to state subsidies. One of the beneficiaries is QuantumDiamonds, a German startup that applies a novel approach to inspecting chips.

When it comes to achieving artificial general intelligence (AGI), large language models just don’t have what it takes. Models like ChatGPT and Claude are great at text, but they’re less skilled at understanding how things actually move through space and time — an essential skill for producing intelligence that generalizes. That gap, it turns out, might be filled by gaming data. That’s the bet behind General Intuition, a […]

Founded in 2024, Prime Intellect’s goal is to give organizations capabilities to train their own agentic systems without relying on frontier AI labs.