标签：#ev

People spend hours watching videos on their smartphones. The wider, shorter, and lighter Galaxy Z Fold8 caters to exactly that.

It's a year of refinement for the Galaxy Watch. With the new Galaxy Watch 9 and Galaxy Watch Ultra 2, which are being announced today, Samsung is more or less taking what people like about its watches and tweaking those elements to be better. The company has been doing this for the past few generations […]

Samsung’s folding phone family has a new middle child with a fresh form factor, but which of its eighth-gen foldables is right for you?

I spent months building an AI agent platform. I launched it on Product Hunt yesterday. Zero signups. The comments were friendly. Three of the four asked for the same thing — not features, not integrations, not a lower price. They wanted to see what the agents did and what they cost . One put it better than my own landing page ever did: they liked that it wasn't "a black box." So I went to make the cost dashboard better. Instead I found out my product had been lying to me for months, and the lies had a pattern. Here's everything, with the code. 1. Every run cost $0.00 The Cost Analytics page reported $0.02 in total across ~100 executions . I'd assumed that meant the platform was cheap to run. It meant the data was being destroyed at write time. cost_cents = int ( ( llm_response . prompt_tokens * 0.5 / 1000 ) + ( llm_response . completion_tokens * 1.5 / 1000 ) ) A typical run on my platform is 56 prompt tokens and 45 completion tokens. That's 0.0843 cents . int() makes it 0 . Not some runs. Essentially every run — because almost every LLM call costs less than one cent. The production numbers: 189 agent runs, 2 with a non-zero cost. 99% of my cost data was zeroes, and the two survivors were just big enough to clear a whole cent. The rates in that formula were correct. I checked them against the providers' pricing pages; the arithmetic is right. The bug is entirely int() on a value that is almost never ≥ 1. A Decimal would have been the textbook fix, but Decimal / float raises TypeError and ~80 call sites do arithmetic on this number, so I widened the column to a float and kept the unit (cents). It's a dashboard estimate, not money — Paddle handles money — so float rounding is irrelevant here. 2. Workflow costs were never recorded at all Truncation at least loses precision. This one lost everything. WorkflowExecution.total_cost_cents and total_tokens_used had no write site anywhere in the codebase . Not a broken write — no write. The columns had been NULL since the feat

Before you rename, move, or delete a Python module: who still imports this? Grepping hits strings and comments. Importing the package can run side effects. whoimports walks the tree with the AST and prints every matching import / from … import line. Install pip install git+https://github.com/SybilGambleyyu/whoimports.git Usage whoimports src/auth/session.py whoimports auth.session -f json whoimports pkg.util -f md Notes Zero dependencies, Python 3.10+ File paths and dotted module names Understands src/ layouts text / markdown / JSON output Pairs with gitchurn and redactx for safe refactor context. Source: github.com/SybilGambleyyu/whoimports · MIT

Cut the noise. Keep the stack traces. CI logs are mostly package installs. The failure is a few dozen lines buried under thousands of Downloading… lines. Scrolling for the real stack trace is a tax paid on every red build. logsnip is a zero-dependency Python CLI that extracts those failure regions and collapses the rest. Install pip install git+https://github.com/SybilGambleyyu/logsnip.git # or the whole toolkit: curl -fsSL https://raw.githubusercontent.com/SybilGambleyyu/devkit/main/install.sh | bash One-liners # Last failure from a GitHub Actions run gh run view --log-failed | logsnip --last # Headlines only logsnip ci-full.log --summary # Safe to paste into an AI assistant gh run view --log-failed | logsnip --last | redactx What it matches Built-in patterns cover pytest E lines and AssertionError , npm ERR! , rustc error[E…] , TypeScript error TS… , GitHub Actions ##[error] , make failures, and common exit-code messages. Stack frames after a hit are pulled in automatically. Design No network, no config files, no dependencies — pipe-friendly --check exits 1 when error-like lines appear (CI gate) --json for machines; --summary for humans in a hurry Pairs with redactx before anything leaves your machine Source: github.com/SybilGambleyyu/logsnip · MIT

A customer clicks the Pay button, the server sends the request to a payment provider, and the charge succeeds. The trouble begins when the response never reaches the browser because the connection drops. The frontend sees a timeout and retries the same request, while the backend treats it as a brand-new payment. One customer action has now created two valid charges. This kind of failure is easy to miss because every part of the system appears to behave correctly on its own. The browser retries because it never received a response, the API processes a valid POST request, and the payment provider accepts the instruction it was given. The defect sits between those systems, where no component has enough context to know that the second request is a repeat. The same pattern can create duplicate orders, emails, subscriptions, shipments, support tickets, or background jobs. The common advice is to retry failed requests, but that advice is incomplete. A retry is safe only when the server can distinguish a repeated business operation from a new one. For payment APIs, that usually means introducing an idempotency key, storing the original result, and protecting the write path against race conditions. The rest of this article walks through that flow using Node.js, Express, and PostgreSQL. The bug starts with a normal-looking endpoint Consider a small Express endpoint that creates a payment by calling an external provider. The code receives the request body, sends the amount and customer data to the provider, and returns the resulting payment object. Nothing about this route looks obviously unsafe during a basic code review, and the happy-path test is likely to pass without trouble. The risk appears only when the request succeeds remotely but the response is lost before the client receives it. `app.post("/payments", async (req, res, next) => { try { const payment = await paymentProvider.charge({ customerId: req.body.customerId, amount: req.body.amount, currency: req.body.currenc

This week I accidentally created my first vibe coded application. You might ask how one does this accidentally, but it's actually easier than you think. Assumptions Made: The first mistake I made was to assume that Claude remembered my setup and how I like to pair program. I have only had one previous project coded with the help of Claude and GHCP and it's a much more basic application than what I was building this week. I assumed, incorrectly, that Claude would 'remember' how I liked to work and go through the project with me as we make decisions and coding blocks together. The other assumption I made was giving a somewhat blanket approval for Claude to just run with things. I thought at some point Claude would stop, check its understanding during development and then continue but I got excited at the idea of using subagents and once I'd selected that option there was no stopping Claude on its rampage through my code! So What's the Project? I've recently started looking at doing more work with AI rather than just prompting Claude or GHCP to help with the day to day. I wanted to actually call an LLM and sift through information which could then be saved in a DB and recalled at a later point. I set myself a four week plan (with the help of Claude) to help solidify my learning with week one being a basic console application in C# that takes some text, sends to an LLM and then extracts certain information, in this case names of people, which is saved into a Postgres database. Sounds simple right? Where things went right This project started off very interesting. I had a conversation with Claude, same as I would with another developer, about the right LLM to use for this project. We went through the positives, negatives and the potential cost benefits and pitfalls of each option. In the end it was narrowed down to two choices, OpenAI or Gemini. Given that I was experimenting quite a bit and I didn't want to accidentally run up costs, I decided to go with Gemini's free t

Guidelines, not rules. Here's the difference — and why it matters. What is SOLID? SOLID is a set of software design guidelines — not hard rules, but principles that guide how we organize our code. The goal is simple: as your codebase grows and your team scales, things should get easier to change, not harder. SOLID is what makes that possible. Five principles. One goal. Let's walk through each one with real code. S — Single Responsibility Principle A class, function, or method should have one and only one reason to change. The Violation class Bird : def __init__ ( self , name : str , bird_type : str ): self . name = name self . bird_type = bird_type def make_sound ( self ): # two jobs — deciding the type AND making the sound if self . bird_type == " parrot " : print ( " Squawk! " ) elif self . bird_type == " eagle " : print ( " Screech! " ) elif self . bird_type == " owl " : print ( " Hoot! " ) else : print ( " ... " ) make_sound() has two responsibilities — deciding which bird type it is AND making the sound. That's two reasons to change. Add a new bird? Touch make_sound() . Change how sounds work? Touch make_sound() again. Two different reasons, one method. SRP violated. The Fix from abc import ABC , abstractmethod class Bird ( ABC ): def __init__ ( self , name : str ): self . name = name @abstractmethod def make_sound ( self ): pass class Parrot ( Bird ): def make_sound ( self ): print ( " Squawk! " ) class Eagle ( Bird ): def make_sound ( self ): print ( " Screech! " ) class Owl ( Bird ): def make_sound ( self ): print ( " Hoot! " ) # Usage birds = [ Parrot ( " Polly " ), Eagle ( " Sam " ), Owl ( " Oliver " )] for bird in birds : bird . make_sound () Now each class has one responsibility. Parrot.make_sound() only changes if parrots change how they sound. Nothing else touches it. O — Open/Closed Principle A class should be open for extension but closed for modification. SRP and OCP go hand in hand. When you fixed SRP in the Bird example above — you also fixed OCP.

When it comes to enthusiast-geared Honda hardware, the Civic Si, Civic Type R, and Acura NSX often come to mind first for their revvy VTEC (Honda's form of variable valve timing) engines and playful chassis dynamics. The Prelude, on the other hand, less so. Instead, the Prelude is a technological study - still aimed at […]

Finished up a solid coding sprint tonight working on Safi-Budget a financial management application built around the 50/30/20 budget framework. I'm currently building and training over at Zone01Kisumu , and getting this build updated and deployed live was the main goal for today's session. What Was Updated Today Localized Currency Logic: Updated the core engine defaults from EUR over to** KES** (Kenyan Shillings) to better support local financial tracking workflows. Auth Flow Refinements:** Ironed out session management and routing logic to ensure clean sign-in and logout behavior across the app. Containerization & Deployment:** Confirmed the Go backend containerizes smoothly with Docker and runs cleanly on Render. Tech Stack Language: Go (Golang) Containerization: Docker Deployment Platform: Render Live Demo & Link You can test out the live deployment here: 👉 Safi Budget Engine Live App

Most SaaS products do not need a “crypto payment button.” They need a payment module. That distinction matters. A button can redirect a customer to a payment page. A module has to know which user is paying, which workspace should be upgraded, which plan should become active, when access should expire, how failed or expired payments should be handled, how support can inspect payment status, and how finance can export records later. That is where developers can build a serious product. A Crypto Payment Module for SaaS Apps is a reusable layer that lets SaaS builders add crypto payments without building the whole payment lifecycle from scratch. In this article, I will use OxaPay as the example crypto payment infrastructure because its documentation exposes the primitives needed for this kind of module: invoice generation, white-label payments, static addresses, webhooks, payment information, payment history, SDKs for PHP, Python and Laravel, and automation integrations. This is not a “get rich with crypto APIs” article. It is a practical blueprint for developers who want to build something SaaS founders, indie hackers, agencies, and product teams may actually pay for. The core idea A Crypto Payment Module gives SaaS apps a production-ready way to accept crypto payments and translate payment events into SaaS account states. Instead of selling this: I can integrate crypto payments into your app. You sell this: I can give your SaaS a reusable crypto billing module with invoices, payment status tracking, webhook verification, plan activation, grace periods, admin tools, and payment history sync. That is a much stronger offer. A SaaS founder does not only care that a payment happened. They care that the right account is upgraded, the right plan is applied, the right billing period is extended, the right user sees the right status, and the support team can understand what happened when something goes wrong. That is what your module should solve. Why this is a real developer

Modern development teams use CI/CD pipelines, automated testing, feature flags, and AI-assisted coding to release new functionality multiple times per week. Yet despite all of these improvements, one part of the delivery process often remains surprisingly inefficient: website feedback. Clients still send screenshots through email. Designers leave comments in Slack. QA engineers create tickets manually. Developers spend time figuring out where an issue actually occurred before they can even begin fixing it. As AI becomes increasingly integrated into software development, another technology is beginning to reshape this workflow: the Model Context Protocol (MCP) . Rather than treating website feedback as disconnected conversations, MCP makes it possible for AI systems to understand the context surrounding a reported issue, helping development teams reduce unnecessary back-and-forth and resolve problems faster. Why Website QA Still Creates Bottlenecks Most website review processes haven't changed much over the past decade. Someone spots an issue, takes a screenshot, writes a short description, and sends it to a developer. The developer then has to answer a familiar set of questions. Which page? Which browser? Which screen size? Can you reproduce it? What exactly were you clicking? The actual bug may only take a few minutes to fix, but understanding the issue can consume considerably more time. As websites become increasingly dynamic, reproducing reported problems becomes even more difficult. Personalization, authentication, browser differences, JavaScript frameworks, and responsive layouts all introduce variables that aren't captured in a simple screenshot. Why Context Matters More Than Ever AI coding assistants have dramatically improved developer productivity. However, AI is only as useful as the context it receives. If an assistant only receives a vague message such as: "The button doesn't work." there is very little it can do. Now compare that with a report containi

I'm excited to share that I'll be speaking at React Day Berlin 2026 this December! My talk How I Brought React Into a Preact Form Engine: A Production Bridge Pattern I'll walk through how we integrated React into an existing Preact-based form engine in production — the bridge pattern we used, the trade-offs, and what I'd do differently next time. Duration: up to 20 minutes Dates: December 4 & 7, 2026 Format: remote or in-person (TBD) Join me (free stream access) You can register through my speaker badge and get partial free access to watch the stream: 👉 My React Day Berlin 2026 badge Speaker page: reactday.berlin/#person-sam-abaasi ReactDayBerlin #react #preact #javascript #webdev See you there!

A practical workflow for turning release artifacts into an accurate, reviewable SaaS product demo without inventing product details. Shipping a feature and explaining it are different kinds of work. The code may have tests and a clean deployment path. The material needed to explain the feature is scattered across tickets, release notes, draft docs, screenshots, and a rushed screen recording. Consider a fictional SaaS team releasing workspace permissions. The feature adds Owner, Editor, and Viewer roles, plus a log of permission changes. By release day, most inputs already exist. The challenge is turning them into one accurate product demo without asking the workflow to guess. Treat release material as a versioned input bundle, then review the script and storyboard before generating video. Why shipping the feature is easier than explaining it Engineers and product managers see the feature as a diff. Viewers do not. A ticket might say "enforce role checks at the workspace boundary," while a customer needs to know where to choose Viewer and what that person can access. That translation is where demos drift. A script can inherit an internal name, skip a prerequisite, show an old label, or turn a planned benefit into a shipped claim. Set one rule before starting: the video cannot introduce a product fact that is absent from the release bundle. Every UI action must also map to the released build. Build a release-to-video source bundle Store the material in a release-specific folder with the release tag or build date. Give each input an owner who can resolve conflicts. Input What it contributes Owner Release note Shipped scope and exclusions Product manager Audience and job One viewer and the task they need to complete Product or PMM Approved claims Language the team can defend Product and legal, if needed Help-center draft Prerequisites, steps, and edge cases Docs or customer education Three current screenshots Exact labels and important UI states Designer or feature owne

For a while now, an idea has been gaining traction: with artificial intelligence, anyone can build an app without knowing how to code . The promise is incredibly seductive: with just a few prompts, we can generate code and instantly turn an idea into a product. It’s no coincidence that this vision took hold so quickly and gave rise to services like Lovable.dev , Blot.new , v0 , and others. Every new technological evolution that narrows the gap between an idea and software tends to make developers' work look like an arcane ritual waiting to be dismantled by a simpler formula. There is something deeply familiar about all of this. Something that reminds me of a line from a song many of us grew up with, with its slightly childish enthusiasm: everybody wants to be a cat ! Today, it seems like everybody wants to be a dev. The real question is whether everybody can be a dev. Joking aside, the attempt to make programming accessible to everyone is an old story, one that certainly didn't start with the advent of AI. A World Without Developers The idea that technological evolution can democratize programming is a recurring theme in the history of computer science. Every time a new abstraction emerges, someone proclaims that the job of writing software is about to become obsolete . Sometimes the promise is alluring; other times, it's just a clever way to sell a new tool. Yet, the core premise remains the same: if computers get closer and closer to understanding human language, then perhaps those seemingly indispensable technical skills are no longer needed . I’ve seen this pattern repeat itself multiple times. A demo takes half an hour to build, a prototype seems to work, and suddenly, the idea of building an app feels within anyone's reach. It’s fascinating, but the problem is that what you see at the beginning is often just the surface-level work: the interface, the screens, the user flow. What remains hidden is the hardest part, the work that determines whether the applicati

AI is moving fast, and it feels like there's a new concept to learn every week. In an effort to actually understand this whole new world instead of just skimming past it, I've been writing ELI5 articles breaking down concepts that show up constantly in AI conversations but rarely get explained simply. This time, a term that gets thrown around a lot without much explanation: the context window. AI 101 Recap The context window is the total number of input and output tokens an LLM can consider while generating a response. Your prompt, the conversation history, and even the model's response all share that same "budget" of tokens. As a conversation with an LLM grows longer, more tokens live in this window. So far so good. Every AI model has a limit on how many tokens it can hold in its "working memory" at once. So the interesting part with the context window is what happens when you reach these limits. Why are there limits? There are several reasons models have context window limits. Processing more tokens requires more memory and computation, making every request slower and more expensive. On top of that, today's models struggle to use very long contexts effectively. They naturally pay more attention to the beginning and end of a conversation than the middle ("lost in the middle" problem). The Amnesia Problem An AI model has no persistent memory between conversations. Within a long conversation, it only sees whatever still fits inside its context window. Think of it as a sliding window: as new messages come in, older ones eventually slide out and are no longer visible to the model. This is why a long conversation can feel like the model has "forgotten" something you told it early on. It doesn't actually forget, it just no longer has access to that part of the conversation, unless the product you're using has built something extra on top to handle it. How RAG helps RAG (retrieval-augmented generation) sounds technical, but the idea is simple: look it up before you answer

It didn’t keep my coworkers at bay, but Flipper’s expensive productivity tool did help me focus on the task at hand.

Google Cloud has published a new blueprint setting out how organisations should secure artificial intelligence workloads running on Google Kubernetes Engine, arguing that the shift from prototype to production has outpaced traditional security models. The document sets out a three layer approach covering infrastructure, model integrity and application security. By Matt Saunders

Over the last 14+ years, I've been working with the Microsoft technology stack, designing and delivering enterprise applications for procurement, inventory management, warehouse operations, and EPOS systems. As a Tech Lead, I've worked on projects involving: Procurement & Purchase Order Management Inventory & Warehouse Management EPOS integrations Accounting integrations REST APIs & Microservices Azure cloud solutions Performance optimization and secure application design While enterprise software has always been my primary focus, I've recently been expanding my work with Next.js, React, and TypeScript by building browser-based productivity tools. One of my goals is to build applications that are fast, privacy-friendly, and solve real business problems directly in the browser whenever possible. Some of the tools I've been building include: YAML Studio for Kubernetes, Docker Compose, GitHub Actions, Azure DevOps, Helm, Prometheus, and Grafana configuration generation. JSON ↔ Excel Converter with support for nested JSON, multi-sheet exports, and parent-child relationships. Multilingual OCR for business documents. PDF to Excel with structured table extraction. JSON Formatter & Validator. CSV, Excel, and other data conversion tools. One thing I've learned while building document-processing tools is that file conversion is the easy part. The real challenge is preserving document structure—detecting tables, handling multi-line descriptions, reconstructing wrapped product codes, and generating output that users can actually work with instead of spending time cleaning it up. My experience in procurement has made this especially interesting because Purchase Orders, Delivery Notes, Goods Receipts, and Invoices all have different layouts and business rules. Building reliable tools requires understanding both the technology and the business process behind the documents. Alongside application development, I'm also continuing to strengthen my DevOps knowledge with Docker, Azure D