标签：#Startup

The Uber founder has said that Atoms will allow him to complete "unfinished business."

I kept pricing off gut instinct + one competitor for way too long. You know the move. competitor is $29 so you're $19. or you're $49 because "premium." neither is a strategy. it's just… anchoring with extra steps, so i did something kind of dumb and kind of useful: opened a bunch of public pricing pages and wrote them down. analytics tools, email tools, form builders, newsletter stuff, hosting, that kind of indie/bootstrappy SaaS. no login walls. if i couldn't see a number, i didn't invent one. Sticker prices were less useful than noticing the shape: where free cuts off what the mid tier is actually selling (usually "remove friction," not "more features") whether the unit is seats, usage, projects, subscribers, etc. copying one competitor's number skips all of that. if you want the sheet I packaged it as an excel workbook (pricing tab + a patterns tab + a blank experiments sheet). yellow/uncertain cells mean i refused to make a number up. free 12-row sample: https://payhip.com/b/0Ubzu full ~100 comps: https://payhip.com/b/l72jY ($19) otherwise curious how people here actually did comps when they first shipped. interviews? gut? one rival's page? something else? ended up with ~100 rows in a spreadsheet. product, category, url, model (flat / seat / usage / freemium+paid / whatever), entry/mid/top when it was on the page, free tier y/n, annual discount if obvious, a short note, date checked. what got boring after enough of them some of it stops being interesting once you've seen it twenty times: usable free tiers that do one real job, then you pay to remove branding / raise limits / unlock collab. not the "7-day demo of everything" free. annual is weirdly consistent. like, ~two months free / ~15–20% off shows up a lot. a ton of indie entry prices live in this unglamorous $9–25 band. not always, but enough that "$79 starter" starts looking like a choice, not a default. not everything is per-seat. flat-by-project / flat-by-workspace shows up more than i expected. i wasn't

Building business software in East Africa means dealing with two hard operational facts. First, the Kenya Revenue Authority requires every business invoice to carry a digital fiscal signature and a verifiable QR code via eTIMS. Second, over 80 percent of commercial transactions settle through Safaricom M-Pesa. If your ERP cannot sign invoices in real time or match incoming Paybill payments automatically, your accounting team spends their days doing manual data entry. If your retail POS goes offline when the fiber cuts, you cannot legally issue receipts. To solve these problems, we built and published three production-ready modules on the official Odoo App Store. They support Odoo 17.0, 18.0, and 19.0 across both Community and Enterprise editions. Here is the technical architecture behind how we built them, how we handle network failures, and what we learned along the way. The Three Integrations Module Purpose Edition & Versions JengaStack eTIMS Real-time KRA OSCU invoice signing and fiscal QR codes Community & Enterprise (17.0, 18.0, 19.0) JengaStack M-Pesa Daraja STK Push and C2B Paybill/Till ledger auto-reconciliation Community & Enterprise (17.0, 18.0, 19.0) JengaStack eTIMS VSCU Offline-first virtual control unit and batched compliance sync Community & Enterprise (17.0, 18.0, 19.0) 1. Real-Time Fiscal Signing Without ERP Worker Blocking The standard KRA eTIMS Online Sales Control Unit (OSCU) flow requires sending invoice line items, tax classification codes, and buyer PINs to KRA over HTTPS. KRA returns control unit internal data (CU Information), an invoice sequence number, and a verification URL encoded as a QR code. The immediate trap many developers fall into is making a synchronous HTTP call directly inside Odoo's invoice confirmation method: # The anti-pattern: Blocking the main thread class AccountMove ( models . Model ): _inherit = " account.move " def action_post ( self ): res = super (). action_post () for record in self : response = requests . post (

Vibe Coding Is Easy. Making Money From It Is the Hard Part — Here’s a Practical Developer Guide A developer today can do something that would have sounded ridiculous a few years ago. You can open an AI coding tool on Friday evening, describe an idea, and by Sunday have: a landing page authentication a database an API payments a dashboard deployment maybe even a mobile app That is incredible. But there is an uncomfortable problem. None of those things mean anyone will pay you. AI has dramatically reduced the difficulty of building software. It has not reduced the difficulty of finding a real problem, reaching the right people, earning their trust, pricing your product, and convincing someone to enter their credit card. And this is where I think a lot of developers are getting stuck. Stack Overflow's 2025 Developer Survey found that 84% of respondents use or plan to use AI tools in development , while 51% of professional developers use them daily. At the same time, 46% said they distrust the accuracy of AI output. So yes, AI development is real. But: Being able to generate software faster is not the same skill as being able to create a business. If you are a developer experimenting with vibe coding and wondering how this can realistically turn into income, here is the process I would follow. Step 1: Don't Start With an App Idea This sounds strange. We're developers. Naturally, our brain starts like this: What should I build? Try changing the question to: What problem are people already spending time or money trying to solve? That small change matters. Imagine these two ideas. Idea A An AI-powered productivity dashboard with 17 widgets. Sounds cool. But who desperately needs it? Why would they pay? What are they currently using? No idea. Idea B Small marketing agencies spend hours every Friday manually combining advertising numbers from multiple sources into client reports. Now we have something interesting. There is: a specific user a repeated task wasted time an exis

The round is being raised just months after the robot data startup exited from stealth.

Lessons from building 24hours.lk and its companion mobile suite, the 24 Eco System Building an application is easy when everything is predictable. The database is clean. The API works. The user follows the expected flow. The server has enough resources. Nothing changes. Real enterprise applications are nothing like that. While working on 24hours.lk and the 24 Eco System mobile applications, I started to understand the difference between building something that works and building something that can survive in a real production environment. The most valuable part of the experience wasn't creating screens — it was dealing with everything that happens behind them. It Started Looking Like a Normal Application At first, an application can look deceptively simple: a user opens the app, authenticates, views some data, submits something. The backend processes the request. The database stores it. Done. But that simple flow hides a much larger engineering problem: What happens if two requests arrive at exactly the same time? What happens if the database becomes slow? What happens if the mobile app is running an older API version? What happens if a user closes the app halfway through an operation? What happens when thousands of records need to be retrieved? What happens when one service goes down but the rest of the ecosystem keeps running? That's where enterprise development really begins. 1. The Architecture Becomes More Important Than the Feature One of the biggest mindset changes I experienced was realizing that a feature is never really isolated. A new feature can touch mobile UI → API → authentication → business logic → database → storage → notifications → infrastructure, all at once — and changing one part can unexpectedly affect another. Because of that, I had to think about things like: Separation of concerns API contracts Service boundaries Database relationships Reusable business logic Error propagation Authentication flows Backward compatibility Deployment strategy

Krafton's planned investment in India is set to surpass $500 million with its latest commitment.

Less than 24 hours left to apply to host a Side Event during TechCrunch Disrupt 2026 and make your mark in the Silicon Valley scene. Apply before the application closes tonight at midnight PT.

Rank tracking tools love to notify you the instant a number changes. We deliberately don't — our drop alert only fires once an app falls 10 spots or more between two measurements. The tempting, wrong version A 1-spot threshold sounds like the more attentive product. In practice it turns every notification channel into noise: App Store search rank has real day-to-day jitter that has nothing to do with anything you did — a competitor's own rank shifting, a re-index, sampling timing. Alert on every 1-spot move and within a week the alert is something people mute, which defeats the entire point of having one. Why 10, specifically 10 spots is large enough to almost never be pure noise and small enough to still catch a real problem while it's still cheap to fix — a keyword field edit, a screenshot swap, a review-response push. Wait for a 30-spot collapse before alerting and you've waited past the point where the fix is simple. The threshold is symmetric: the same 10-spot rule fires on a jump upward, so a keyword field change you made on purpose gets confirmed by the same mechanism that would have warned you if it went the other way. The trade-off we're making explicit This means small real movements — 3 spots, 5 spots — genuinely don't page anyone. That's intentional, not a limitation we're hiding: an alert system tuned to catch everything catches nothing anyone still trusts by week three. A threshold set high enough that every alert is worth opening is worth more than a lower one that trains you to ignore your own notifications. If you're building anything similar — uptime, price, rank, any noisy time series — the question worth asking isn't "how sensitive can I make this," it's "what's the smallest move that's still cheaper to catch early than to catch late." That number is rarely 1. We build Storelift , where this threshold governs both the in-app alert and the rank-drop email.

The AI era has completely broken enterprise buying patterns, and startups haven't yet figured out how to navigate.

The high-profile startup's annual revenue run rate stands at over $100 million.

The grid has been straining under the weight of new AI data centers, and that has utilities courting fusion startups.

Ultrahuman is targeting a $200 million annual revenue run rate by January 2027 as it builds a new Qualcomm-powered smart ring.

The family-focused AI assistant wants access to the details of your everyday life, but says it won’t use that data to train AI models or share it with others.

Our rebranded Boston event, TechCrunch Founder Summit (formerly All Stage), is back on November 4th! And we are looking for some incredible volunteers to help us make this event happen. If you are interested in finding out what goes into building tech events, apply to volunteer. If you are selected, not only will you get […]

Using GPT-6 Astra, Playco built three themed game prototypes from one grey box foundation and reported 50% fewer manual fixes than with the previous model.

Legora used GPT-6 Astra to review 41 documents in minutes, find all four planted errors, and improve performance by nearly 40% in this financial-review workflow.

Let me start with a question. Why did I fear development done by artificial intelligence? The answer is plain. AI can build software, and on top of that, it never rests. AI has no labor law People rest. There are labor laws. We sleep at night. We need weekends. Work too many days in a row and the body breaks. So there is a ceiling on how much work a person can move forward in a day. For a long time, we treated that ceiling as a given. But AI has no labor law. It works at night. It works on weekends. Give it an instruction once, and it does not stop until morning. It never says it is tired. It takes no breaks. It keeps working for hours at the same quality. This difference did not fit inside the word "convenient." What I felt was fear. This was not a story about one more handy tool. It was a story about the ground under the speed of work changing at the root. Claude Code came out about a year and a half ago. That is when I understood. The company that runs it 24 hours takes the first-mover advantage. And the company that can punch with money wins. This is not a cynical take. It is the obvious consequence. The first mover wins — that story is not new. Whoever enters a market early takes the ground. They set the standard. Everyone after them chases the gap. AI widens that gap by the day. A company that moved ten hours forward overnight and a company that stood still overnight are ten hours apart by morning. The gap compounds daily. Can you catch up by hiring more people? You cannot. Hiring takes time. Post the opening, interview, teach, wait for people to settle in. That takes months. Meanwhile, the other side's AI keeps moving through the night. The speed of adding people cannot match the speed of adding AI. So the moment a small company steps into a contest of headcount, it loses. It was a ring we should never have entered. Companies that can punch with money win — obviously Why can I say it becomes a contest of money? Because there is no ceiling on how fast you can

The acquisition also leaves Sequoia-backed Serval as the de facto startup leader in AI IT service automation, industry watchers believe.

The internet has a trust problem, and it’s not just because social media feeds are filling up with AI slop. AI-generated text and images are now making their way into job applications, product reviews, and even insurance claims, leaving platforms and users alike scrambling to figure out what’s real. A handful of startups have cropped up in the past couple of […]