AI 资讯
Building Production KRA eTIMS and Safaricom M-Pesa Integrations for Odoo 19
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 (
AI 资讯
Vibe Coding Is Easy. Making Money From It Is the Hard Part — Here’s a Practical Developer Guide
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
AI 资讯
CodePen: CryptoCap Landing Page
Crypto landing page with a sleek dark/light mode toggle, stylish Chart.js market graph, and smooth scroll animations using sal.js. Built with Tailwind CSS for a pixel-perfect, fully responsive design. Design inspired by: https://www.figma.com/community/file/1047142300578798855/cryptocurrency-landing-page-dark-mode
AI 资讯
XDOF, just three months out of stealth, is in talks for a Series B at a $1.2B valuation
The round is being raised just months after the robot data startup exited from stealth.
开发者
Beyond Development: What It Really Takes to Build Enterprise Applications
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
AI 资讯
Architecting memory and storage in the AI era
The era of AI inference has arrived. Imagine a healthcare system analyzing millions of data points in real time to accelerate life-saving medical research, or an intelligent assistant instantly resolving thousands of complex customer needs at once. These real-world breakthroughs rely on advanced infrastructure acting as the engine of continuous intelligence, powering real-time services while…
创业投融资
Krafton doubles down on India with another $250M bet beyond gaming
Krafton's planned investment in India is set to surpass $500 million with its latest commitment.
创业投融资
Less than 24 hours to apply for your TechCrunch Disrupt 2026 Side Event
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.
AI 资讯
We only alert on a 10-spot rank drop. Here's why 1 spot would be worse.
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.
AI 资讯
iRobot unveils the Roomba Duo
The original robot vacuum company showed off a concept robot at the IFA tech show in Berlin today. The Roomba Duo combines a heavy-duty floor-washing machine with a smaller, slimmer Roomba. The two floor cleaners can move around your home together, with the main unit mopping and sweeping larger floor areas and the smaller unit […]
AI 资讯
ICE Wants to Know Everyone Who Bought a Certain Green Beanie From REI in the Last 2 Years
Homeland Security Investigations agents hit the outdoor retailer with a controversial subpoena as part of a dragnet search for the identities of protesters who entered a Minnesota church in March.
AI 资讯
AI Use in the Job Market Is Creating an Infinite Doom Loop
Job seekers are trying to game the application process using AI. It’s not working, and not for the reasons you might think.
AI 资讯
This NAS company wants to run your local smart home
Ugreen, known for its phone power banks, chargers, and NAS storage solutions, is moving into the smart home - in a big way. This week at the IFA tech show, the company launched its HomeAgent smart home platform that combines security camera storage, on-device AI, and smart home control in one system, managed by a […]
AI 资讯
Data from drones in Ukraine is fueling a new Wild West marketplace
Battlefields in Ukraine are littered with the remnants of drones, which are now firmly established as a critical weapon of modern warfare. But behind all that wreckage, there’s a new gold mine for the defense sector. The data drones generate will far outlast the wars in which they are used to fight, increasingly becoming part…
开发者
This rugged smartphone's camera is a removable action cam
RugOne's newest ruggedized phone has a waterproof action cam that you can remove.
开发者
Aqara goes all in on smart lighting
After showing off several smart home firsts at CES, Aqara has returned to IFA with a major lineup of smart lighting compatible with both Zigbee and Thread. One of these new devices is the Floor Lamp T1, which trades the look of a typical lamp for an LED-equipped pole attached to a base. The lamp […]
科技前沿
5 Best Video Doorbell Cameras (2026): Subscription-Free, Video, and More
Never miss a delivery. These WIRED-tested picks will help you keep tabs on your front door from anywhere.
AI 资讯
Nobody Is Saying Why OpenAI and Anthropic Had Outages Today
ChatGPT, Claude, and Grok all suffered outages at nearly the exact same time for reasons that remain murky.
AI 资讯
CanvasKit Layout Traps: The Unbounded Constraint Bug That Only Blanks Release Builds
I shipped eight card and casino games to my portfolio in a single commit — solitaire, roulette, video poker, slots, baccarat, keno, war, higher-lower. All client-side Flutter web, all free, all deployed to Firebase Hosting in one push. flutter analyze was clean. I read the diff twice. The build succeeded. I deployed. Then I opened /games/roulette on the live site and got a page with a header, a subtitle, a bankroll readout, a spin button — and a completely blank rectangle where the betting board should have been. No red error screen. No console exception. No 404. Just an empty region the size of the thing that was supposed to be there, on a page where everything else rendered perfectly. The cause was one enum value: CrossAxisAlignment.stretch on a Row that, four widgets up the tree, was sitting inside a scroll view. In debug that combination throws a loud, well-written framework error. In release the assertion that produces that error doesn't exist, so nothing throws at all — the framework computes with infinity and paints nothing. A layout contract violation is not a type error, and no amount of static analysis is going to find it for you. This post is that bug in full, the family of unbounded-constraint traps it belongs to, why debug builds give you a false sense of safety, and the verification discipline I now refuse to skip. Eight games shipped, one board rendered nothing The symptom is worth describing precisely, because it's what makes this class of bug so slow to diagnose. The route loaded. The page scaffold — nav, page header, back link, related-games strip — was all there and correct. Analytics fired the pageview. The bankroll, the chip selector and the spin control rendered. Only the number grid, the largest single widget on the page, drew nothing at all. The space it occupied wasn't even collapsed to zero; it was just empty. The browser console was clean. Not "clean apart from a warning" — genuinely empty. Chrome DevTools' Elements panel showed what it al
AI 资讯
Startup ARR is less secure than ever, new research shows
The AI era has completely broken enterprise buying patterns, and startups haven't yet figured out how to navigate.