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A tool that handles crash recovery, hang detection, and a full status page sounds like it should cost something. Here's why it doesn't, and won't. "What's the Catch?" — Why StayPresent Is Actually Free It's a reasonable question. A tool that handles crash recovery, hang detection, and generates a real status page — the kind of thing you'd expect to see behind a pricing page with a "Pro" tier — is, in fact, entirely free. Not a free tier that nudges you toward upgrading. Not a trial. Just free. Here's the honest answer to "what's the catch," because the skepticism is fair. The MIT license, plainly stated StayPresent is released under the MIT License — one of the most permissive open-source licenses that exists. In practice, that means: use it in a personal project, use it in something you're charging money for, modify it, redistribute it, all without paying anything or asking permission. There's no feature gate waiting behind a paywall, and no functionality quietly disabled unless you upgrade. No account required, no data leaving your deployment There's no account to create, no API key issued by a third party, no external service your bot phones home to. Everything — the web server, the crash recovery, the status page, the hang detection — runs directly inside your own deployment, on infrastructure you already control. There's genuinely nothing to charge for on the vendor side, because there's no ongoing service being provided from outside your own process. Why "free forever" is actually plausible here A lot of "free" developer tools eventually aren't, because they're subsidizing a hosted service somewhere — servers, bandwidth, support staff — and the free tier exists to fund a business built around eventually charging some of its users. StayPresent doesn't have that shape. It's a library, not a hosted service. There's no infrastructure cost scaling with your usage that would ever create pressure to start charging. What you're actually trading The honest trade isn't

📈 Building the Future of Trading: An AI-Powered Platform for Smarter Investments The Introduction: Empowering Every Investor Hello, Builders and tech enthusiasts! I'm thrilled to share my journey as part of the "Meet The Builders" campaign, where innovators are leveraging Google AI to tackle real-world challenges. My project is an ambitious endeavor to democratize effective stock trading through an intuitive, AI-enabled platform. Inspired by industry leaders like Zerodha, I set out to create a comprehensive website that not only facilitates trading but also acts as a smart, AI-powered guide, helping users navigate the often-complex world of stock markets more effectively. This project is my story, a testament to how technology, especially AI, can empower individuals to make more informed investment decisions. The Deep Dive: Why Investors Need a Guiding Hand The stock market can be a daunting place. For many retail investors, it's a whirlwind of data, conflicting advice, and emotional decision-making that can lead to missed opportunities or significant losses. From understanding market trends and analyzing complex financial reports to knowing when to buy or sell, the sheer volume of information can be overwhelming. Many feel like they're trading blind, lacking the expertise and analytical tools available to professional institutions. I believe there's a significant gap here – a need for a personal, intelligent assistant that can cut through the noise, provide actionable insights, and guide users towards more strategic trading choices. This conviction fueled the inception of my project. The Solution: Stock Trading Platform – Intelligent Trading, Engineered for Success My project, Stock Trading Platform, is a robust web-based platform designed to simplify stock trading with the power of artificial intelligence. While currently in its final polishing stages on my local machine and version-controlled with Git and hosted on GitHub, the core functionality revolves around a

Designing Reliable APIs for Production Applications: Lessons From Building Real-World Digital Products APIs are often described as the “bridge” between different parts of an application, but building a production-ready API involves much more than sending data from a frontend to a backend. Through my experience building full-stack applications, I've learned that a good API needs to be designed around reliability, security, maintainability and the actual needs of its users. Here are some of the principles I now consider when designing APIs: Design around resources, not screens An API shouldn't simply mirror the frontend interface. It should expose meaningful resources and operations that can evolve independently from the UI. Validate everything at the API boundary Data coming from a client should never be trusted automatically. Request validation, type checking and clear error responses help prevent invalid data from propagating through the system. Authentication is only the beginning An authenticated user should not automatically have access to every resource. APIs need appropriate authorisation and access-control rules for sensitive operations. Design predictable errors A useful API doesn't just return “something went wrong.” Clients need consistent status codes and structured error responses so that applications can respond appropriately. Think about idempotency This becomes particularly important when an API handles operations such as payments, orders or other actions that shouldn't accidentally happen twice because of a network retry. Don't expose unnecessary data APIs should return what the client needs rather than exposing entire database records. This reduces unnecessary data transfer and can also reduce the risk of accidentally exposing sensitive information. Logging and observability matter An API can appear perfect during development and still fail in production. Good logging and monitoring make it possible to understand what happened when requests fail, la

The 65% mechanical keyboard has become a popular format for people who want a compact keyboard without giving up the dedicated arrow keys. Compared with a 60% keyboard, a typical 65% layout adds an arrow-key cluster and usually includes a small navigation area. Compared with a TKL keyboard, it removes the dedicated function row and reduces the overall footprint. For keyboard designers, however, reducing the physical size of the keyboard does not simply mean removing a few keys. The PCB has to accommodate the switch matrix, diodes, controller, USB or wireless circuitry, RGB lighting, mounting features, and sometimes hot-swap sockets within a relatively constrained outline. That makes the PCB one of the most important parts of a 65% keyboard design. What Is a 65% Mechanical Keyboard PCB? A 65% mechanical keyboard PCB is the circuit board designed specifically for a 65% keyboard layout. The exact key count and physical arrangement can vary between designs, so the term "65%" describes a form factor rather than one universal PCB specification. A typical board may contain: Mechanical switch footprints A switch matrix One diode per switch position A microcontroller USB connectivity or wireless circuitry Reset and boot controls Indicator LEDs Per-key RGB or underglow lighting Hot-swap sockets, when supported Mounting holes and mechanical cutouts The electrical design and physical design have to work together. A PCB can have a perfectly functional schematic and still fail to fit the intended keyboard case if the mounting holes, switch positions, USB opening, stabilizer locations, or board outline are not correct. Why the PCB Layout Matters So Much Keyboard PCBs are unusual compared with many conventional electronics boards because the PCB also defines part of the physical typing experience. The location of switch footprints determines the key positions. The mounting system affects how the PCB interacts with the case. Flex cuts can change the mechanical response of different

You budgeted six months. It took fourteen. You wanted one thing; you got three things that almost do it. And somewhere between the first sprint and the final invoice, you stopped understanding what you were even paying for. If that sounds familiar, this is the breakdown no one gave you before you started. What custom software development actually means Custom software development is building software from the ground up for your specific business, not configuring Salesforce, not installing a plugin. You're solving a problem your operations have, the way your operations actually work. What trips people up: "custom" doesn't mean "built entirely from scratch." Good dev teams use frameworks, libraries, and third-party services. What's custom is the logic of how your data flows, how business rules are enforced, how users interact. Scope range is huge: Custom dev covers everything from a lightweight internal dashboard to a full-scale multi-tenant SaaS platform. This is why cost estimates vary so wildly. 3 things nobody tells you before you sign 1. Scope creep is almost always the client's fault "Users should be able to manage their accounts" sounds simple. It actually contains dozens of decisions: can they change their email? What verification is required? Can they delete their account? Each one is a feature. Each feature has a cost. The fix: Run a discovery phase (2–4 weeks) before writing a single line of production code. It costs money upfront. It saves far more mid-project. 2. The cheapest bid rarely wins long-term A $40k quote and a $180k quote for the same project both happen. The $40k team isn't lying; they're optimistic, underbidding to win work, or scoping something different. What actually happens: you hit $40k, and you're 40% done. Higher bids from experienced teams often include architecture planning, documentation, testing infrastructure, and post-launch support things the cheap bid omitted. These aren't extras. They're what make the software maintainable in t

Most write-ups on this subject are about technique. This one is about the three decisions you should make before you write any code, because in my experience every project that went badly went badly for a reason that was decided on day one and not noticed until much later. I have built this kind of collection twice, for competitive analysis and for a partner-vetting workflow. Neither of them needed to touch anything behind a login, and I want to explain why that turned out to be the useful constraint rather than the limiting one. Line one: the login wall is a boundary A login wall is a statement about who the content is for. Treating it as an engineering obstacle to be routed around is the decision that puts a project on the wrong side of everything: terms of service, the platform's own detection, and in several jurisdictions the law. So the first line is simply: if it requires an account to see, it is out of scope. Not "hard," not "for later." Out of scope. I am not going to discuss techniques for getting past one, and I would be sceptical of any article that does. The interesting engineering question here is not how to see more. It is how much you can actually do with what is openly published, and the honest answer is: considerably more than people assume before they check. This constraint also has a practical benefit that is easy to miss. A pipeline built only on openly available data does not break when authentication changes, does not require credential management, and does not put an account at risk. Mine has survived two platform changes that took down colleagues' authenticated collectors. Line two: public does not mean unrestricted The second line is the one developers get wrong most often, and it has nothing to do with access. Data being publicly visible says nothing about whether you may store it, for how long, or what you may do with it. In the EU and UK, information about an identifiable person is personal data whether or not they published it themselves

If you build with Prisma, you already know Prisma Studio. Run one command and you get a clean, visual way to browse and edit rows in your database. It's genuinely useful, and I reach for it every day while developing. But somewhere between "I need to look at my data" and "I need to let a support agent safely edit a customer's record in production," Prisma Studio quietly stops being the right tool. It was never trying to be that tool. It's a database viewer. An admin panel is something else, and the gap between the two is exactly the part that matters once real people and real permissions are involved. I ended up building a small package to fill that gap for my own Express + Prisma apps. Writing it forced me to be precise about what an admin panel actually adds on top of a database browser. Here's the distinction as I now understand it. A database browser shows rows. An admin panel governs them. Prisma Studio connects to your database and shows you everything. That's the point of it, and it's also why you'd never hand it to a non-engineer or expose it in production. It has no concept of who is looking, what they're allowed to do, or which rows they're allowed to touch. An admin panel's whole job is those three questions. The package I built mounts a React UI at /admin and a guarded JSON API under /admin/api/* on your existing Express app. Every single request through that API runs the same pipeline, in the same order: authentication → permission check → tenant scope → validation → Prisma mutation/query → optional audit event That ordering is the entire difference. A database browser skips straight to the mutation. An admin panel refuses to run the mutation until it knows the request is authenticated, permitted, scoped to the right tenant, and valid. Permissions and scope are two different questions This was the design decision I care most about, because collapsing these two into one is how data leaks happen. Permissions decide which actions a role may take. Can an ed

Apple’s leaked camera-equipped AirPods might avoid the privacy pitfalls of other AI wearables by preventing users from recording photos and videos.

Jordan Nanos discusses how semiconductor constraints, data center expansion, and networking bottlenecks impact AI software architecture. Drawing from SemiAnalysis research, he shares insights on benchmark performance, GPU scaling, and tokenomics from chip fab to model inference. By Jordan Nanos

At what point did you start to think you were an engineer? An engineer. What a lofty,...

Moving from AI-Assisted Engineering to AI-Agentic Software Engineering The rise of AI coding assistants has transformed how developers write software. Tools like GitHub Copilot, ChatGPT, Claude, and Gemini have significantly improved developer productivity by helping generate code, explain concepts, and automate repetitive tasks. However, the industry is now entering the next evolution: AI-Agentic Software Engineering . Instead of AI simply assisting developers, AI agents can now take ownership of entire software engineering tasks—from requirement analysis and architecture design to implementation, testing, documentation, and code reviews. The challenge is no longer whether to use AI, but how to integrate AI agents into a structured Software Development Lifecycle (SDLC). This requires moving away from vibe coding toward specification-driven development , where AI agents operate using well-defined requirements, standards, and engineering principles. Today, I'd like to discuss two of the most popular frameworks enabling this transition. 1. Spec Kit Spec Kit is a specification-driven framework designed for Human + AI collaborative software development . The philosophy is simple: define the specification before generating the code . Rather than asking an AI to build an application from a vague prompt, Spec Kit encourages teams to create structured specifications, architectural decisions, and engineering principles that guide AI throughout the development lifecycle. Some key benefits include: Structured and repeatable software development Better requirement traceability Consistent architecture decisions Reduced AI hallucinations Lower development costs through predictable AI interactions Support for selecting the most appropriate LLM based on project requirements Integration of quality engineering practices from the beginning of the SDLC Spec Kit is particularly valuable for engineering teams that want to adopt AI without sacrificing software quality or maintainability.

On Tuesday, Warp introduced Warp Factories, a new infrastructure system designed to make building AI software factories as easy as possible.

Developers write far more legally consequential prose than they think, and almost none of it is code. It's the SECURITY.md in the repo root. It's the "Security" page someone in marketing asked you to fill in three years ago. It's the status page update typed at 2 a.m., and the sentence in a customer notice specifying exactly which data was affected. The research summarized in this overview of what the evidence shows about cyber incident disclosure treats post-breach communication as a measurable discipline with predictable failure modes — and the enforcement record of the last three years has quietly turned it into an engineering discipline too. In the most closely watched cybersecurity case of the decade, the only allegation that survived a motion to dismiss concerned a technical description of access controls posted on a website. The claim that survived was written by engineers On July 18, 2024, Judge Paul Engelmayer of the Southern District of New York issued a 107-page opinion in the SEC's case against SolarWinds and its CISO. Most of it was a defeat for the agency. Claims built on blog posts, press releases, and podcast appearances were dismissed as non-actionable corporate puffery — statements too general for any reasonable investor to lean on. The theory that cybersecurity controls fall under "internal accounting controls" was rejected outright. Post-incident 8-K disclosures were held to be reasonable given what was knowable at the time. One thing lived: the company's "Security Statement," a technical page describing its practices to customers. The court let claims proceed specifically on its representations about access controls and password policy , because those were concrete enough to rely on and, as pled, contradicted by internal presentations, security assessments, and Slack messages. The SEC ultimately dismissed the whole action with prejudice in November 2025, so no liability was ever established — but the legal line drawn in 2024 didn't go anywhere.

The Fairphone 6+ is priced at $649 and will be available on Amazon.

Well, first real job and I still haven't gotten used to waking up for a 6:30 AM shift... I've been...

I’ve been building a side project called OopsCalorie , an AI-powered calorie and meal tracking app. The idea sounded simple enough: User logs or takes a photo of their food. AI identifies the meal. Estimate calories and macros. Save the entry. Done. Simple, right? Well... Then we started testing it with Filipino food. 😂 AI Meets Filipino Food 🇵🇭 One of the funniest parts of building OopsCalorie has been testing the food recognition. At one point, our AI confidently looked at dinuguan and decided: That's champorado. Okay. I can kind of see where you were coming from. Both are dark, both can be served in a bowl... But still. 😂 Then came bagnet . AI: Lumpiang Shanghai. Bro. Not even close. 😂 These bugs are funny, but they also exposed one of the more interesting engineering problems behind OopsCalorie: Image recognition is only the first step. Correctly identifying a meal — especially regional dishes — requires much more context than I initially expected. The Real Problem Isn't Just Calories When I started the project, I thought the difficult part would be estimating calories. Turns out, before you can estimate: You need to know what the food actually is. And food can be surprisingly ambiguous from an image. A photo might contain: multiple dishes sauces hiding ingredients visually similar foods regional dishes that aren't well represented in training data different cooking methods unknown portion sizes ingredients completely hidden underneath other ingredients Even humans sometimes need context. "Is that pork adobo or humba?" "Is that fried pork belly or bagnet?" Now imagine asking an AI to determine that from pixels alone. Building Around AI Instead of Blindly Trusting It This changed how I'm approaching the system. Instead of treating the AI response as absolute truth, OopsCalorie is evolving toward a workflow where AI provides an intelligent estimate while the user still has the ability to provide context and correct it. We're experimenting with things like: Image +

What Is Software Testing? 🧪 Software testing is the process of checking software to make sure it works correctly and does what it is supposed to do. For example, when we use a login page, we can test: Correct username and password Wrong password Empty username Empty password Forgot password option The goal is to find bugs and problems before the software is used by customers. Why Is Testing Important? Testing helps developers and companies: Find bugs Improve software quality Provide a better user experience Prevent problems after release Even a small bug can sometimes cause a big problem, so testing is an important part of software development. Manual Testing In manual testing, a tester checks the application manually without using automation scripts. For example, a tester can open a website, enter different inputs, click buttons, and check whether the expected result appears. Automation Testing In automation testing, we use tools and programming to test software automatically. Some popular tools are: Selenium Playwright Cypress Automation is useful when the same tests need to be performed many times. Conclusion Software testing is an important part of creating reliable software. If you are a beginner, you can start with manual testing , then learn SQL, API testing, and automation testing .

The most interesting thing about AI coding agents right now is not that they can write code. It is that they are starting to operate computers. That sounds like a small distinction until you feel it in the workflow. A code generator lives inside a text box. It waits for a prompt, returns a patch, and leaves the rest of the job to you. A software operator can inspect the app, click through the broken flow, read the console, run the server, reproduce the issue, change the code, and check whether the thing actually works. That is a different kind of tool. OpenAI's May 29 Codex update points in that direction. Codex now supports computer use on Windows in the Codex app for eligible users, so it can see, click, and type in Windows applications while testing and refining software. The same release also expands remote control, letting a user steer work from ChatGPT on mobile or Codex on Mac while the Windows machine remains the host for the project files, shell, app server, and local context. I do not think the important part is Windows support by itself. The important part is the new shape of work. Coding Was Never Just Typing For a while, the AI coding story was mostly about generation. Could the model write a component? Could it scaffold an API route? Could it refactor a file without losing the plot? Useful, but narrow. Real software work has always been messier than text generation. You open the app. You notice the layout is wrong. You click a button. Nothing happens. You check the terminal. The dev server crashed. You restart it. The page loads, but the empty state is off. You resize the browser. The mobile nav breaks. You skim the network tab. The request is fine, but the UI state is stale. None of that is "write code" in the pure sense. It is operating the system around the code. That is why computer use matters. It gives the agent access to the loop that human engineers actually live in: observe, diagnose, change, verify. The text editor is only one stop in that lo

Introduction Your program keeps opening transactions. A signup writes a new user row. A checkout debits one account and credits another. A form submission updates three related tables at once. You wrap it all in BEGIN and COMMIT and move on, trusting that the database will handle whatever happens in between. Most of the time it does. But what is it actually promising you when it handles that? And what does it have to do behind the scenes to keep that promise? Say a user transfers ₹1,000 from Account A to Account B. The application runs two updates: subtract 1,000 from A, add 1,000 to B. Now say the server crashes right after the first update runs but before the second one does. Account A: -₹1,000 Account B: +₹0 That money didn't move. It vanished. No error message fixes that, and no user accepts "the server restarted" as an explanation for their missing balance. This is the exact problem a set of guarantees called ACID was built to solve. Most developers can recite the acronym, Atomicity, Consistency, Isolation, Durability, without being able to explain what any of the four words actually promise, or what the database has to do internally to keep those promises. This article tries to fix that. -- 1. What Is a Transaction? Before ACID makes sense, you need to understand what a transaction actually is. A transaction is a group of one or more database operations treated as a single logical unit of work. Either the whole group succeeds, or none of it does. The bank transfer above is a textbook transaction: two updates that only make sense together. In SQL, a transaction usually looks like this: BEGIN ; UPDATE accounts SET balance = balance - 1000 WHERE id = 1 ; UPDATE accounts SET balance = balance + 1000 WHERE id = 2 ; COMMIT ; BEGIN tells the database "everything from here on is one unit." COMMIT tells it "we're done, make it permanent." If something goes wrong in between, a constraint violation, a crash, the application deciding to cancel, the database can issue a RO

SpaceXAI has introduced Grok Bot, a system of persistent AI agents that operate on dedicated cloud computers and can interact with websites, applications, inboxes, and other tools. By Daniel Dominguez