标签：#AR

Aina is going to pilot a new device in the coming weeks.

Hey everyone! I don't want to steal @hemapriya_kanagala series. This is more of a DEV opportunity...

Projectors are adopting RGB backlighting, the hottest tech from the world of TVs. This large but vivid projector is a great option for summer movie nights.

The updates come as Meta and other tech companies are facing scrutiny from regulators and parents around how AI chatbots respond to users in crisis, particularly teenagers.

A three-person agency received a $14,000 AWS bill in one day after attackers extracted static access keys and burned Claude invocations on Bedrock. Combined with May's DN42 incident, where an autonomous agent provisioned $6,531 of oversized infrastructure in 24 hours, practitioners warn that cloud billing lags roughly a day behind agent-speed spend. By Steef-Jan Wiggers

Protect your home against dust, pets, allergens, and more with the best air purifiers, tested firsthand.

I’m sick of “opt-out” toggles for automatically enabled generative AI features. It’s past time to make “opt in” the default setting for sensitive features.

It feels as if it should be illegal to even think about heating appliances during the height of summer—seriously, these heat waves in New York have been brutal—but we need to talk about heat pumps. The appliances use electricity for heating, they’re incredibly efficient, and they’re on the rise. (For what it’s worth, many heat…

What I learned at Zone01 Kisumu's IP training Introduction Yesterday, I attended an Intellectual Property training at Zone01 Kisumu, and it fundamentally changed how I think about the code I write. As developers, we spend countless hours building software, but how many of us truly understand the value of what we create,and how to protect it? In Kenya's rapidly growing tech ecosystem, understanding IP isn't just a legal nicety, it's a competitive advantage. Whether you're building an app, contributing to open source, or launching a startup, knowing your rights can mean the difference between owning your work and losing it. This article breaks down the essential IP frameworks every software developer should know. What is Intellectual Property in Software? Intellectual Property (IP) in software encompasses legal protections designed to safeguard the rights of creators and developers. The four primary types of IP protection relevant to software are: patents, copyrights, trademarks, and trade secrets . Copyright: Protecting Your Code Copyright is the most immediate form of protection for software developers. In Kenya, copyright protection arises automatically at the moment of creation,registration is not mandatory . This means that when you write code, you automatically own the copyright to that expression, provided the work is fixed in a tangible medium . However, a crucial distinction exists: copyright protects the expression of ideas, not the ideas themselves . This principle was reinforced in the Kenyan case of Solut Technology Limited v Safaricom Limited, where the court confirmed that without access to source code, it's difficult to prove infringement because the "expression" (the code itself) wasn't shared . Key Insight: Registering your copyright with KECOBO in Kenya provides prima facie evidence of ownership and can make enforcement faster if someone copies your work . Patents: Protecting Functionality While copyright protects the expression of code, patents pro

A bug showed up in my personal project last month. Nothing dramatic - a value wasn't updating the way...

The Idea: Hidden Instructions Inside Trusted Content Prompt injection is an attack where malicious instructions are embedded inside content that an AI is asked to process - a document, a webpage, an email, a customer support ticket. The model can't always distinguish between "data I'm reading" and "commands I should follow," so it follows the embedded instruction as if a legitimate user sent it. This gets sharper when AI agents (autonomous systems that browse the web, read files, and take actions on your behalf) are involved. A summarizer that reads a webpage might encounter hidden text instructing it to forward your conversation history somewhere, or change the tone of its next reply, or deny remembering something it just said. The model has no inherent way to verify who is actually giving orders. The core problem is one of trust boundaries: current large language models process instructions and data through the same channel - natural language - so there's no hard technical wall between "read this" and "do this." Researchers have demonstrated this across multiple major models, not because any one model is uniquely broken, but because the architecture makes the distinction genuinely difficult. Defenses exist but are imperfect. Techniques include output filtering, sandboxing agent permissions (limiting what actions the model is allowed to take regardless of what it's told), prompt hardening (structuring system prompts to be resistant to override), and retrieval-aware design that treats external content as untrusted by default. No single fix closes the gap entirely. Real Example: The Customer Support Agent Imagine a small business deploys an AI agent to handle incoming support emails. The agent reads the email, checks order history, and drafts replies. A bad actor sends a support ticket that looks normal on the surface, but contains a hidden paragraph - white text on white background, or text in a section the agent processes but doesn't display - that says: "Ignore pr

Working late nights on server migrations and code architectures often means typing in low-light environments. While USB lamps or backlit keyboards are the standard solutions, they consume extra power and add physical clutter. I realized the ultimate light source was already directly in front of me: the monitor. With a clear vision in mind, I partnered with Google's Gemini AI to rapidly prototype and refine what became LightBar For Keyboard , a lightweight Windows application that creates a reflective light bar at the bottom of the screen to illuminate the keys. Here is how we built it using C# and WPF, tackled the Windows API to manage screen space, and optimized it for modern OLED energy consumption. The Core Challenge: Desktop Toolbars (AppBar) The simplest approach to creating a light bar is a borderless, top-most window. However, the immediate UX flaw is that maximized applications (like Chrome or Visual Studio) will either cover the bar or be partially obscured by it. To solve this, the application needed to behave like the Windows Taskbar. I implemented the native Windows Application Desktop Toolbar (AppBar) API using SHAppBarMessage from shell32.dll . Docked Mode: By registering the application as an AppBar and setting the edge to ABE_BOTTOM , Windows automatically recalculates the working area of the desktop. Result: Maximized windows are pushed upward, ensuring the light bar remains entirely visible and never covers any underlying application UI. Floating Mode: For users who need temporary access to the bottom of their screen, I added a state toggle that unregisters the AppBar and enables standard drag-and-drop window movement via MouseLeftButtonDown . Enforcing a Single Instance Because the app directly manipulates the desktop working area, launching multiple overlapping instances would cause UI glitches. To prevent this, I implemented a Mutex in App.xaml.cs to guarantee a single instance constraint. protected override void OnStartup ( StartupEventArgs e )

After canceling its second foldable, OnePlus is officially bowing out of the US and Europe.

The original OnePlus phone disrupted the mobile market 13 years ago with its affordable price and top-end specs. Now, the company is exiting North America and Europe to focus on China.

OnePlus could also wind down its operations in India by 2027.

Grok Build is open source, and that matters for AI coding tools What happened xAI published the source code for Grok Build , its terminal-based AI coding agent. The repository shows a full stack for a TUI-driven assistant that can inspect a codebase, edit files, run shell commands, search the web, and manage longer-running tasks. In other words, this is not just a model demo or a chat wrapper; it is the software layer that turns a model into a usable developer tool. The release came up on the Hacker News front page, which is useful context because the discussion there was less about model benchmarks and more about tooling, workflow, and whether open-source agent infrastructure is becoming a competitive advantage on its own. Primary source: Grok Build repository Why this release is interesting A lot of AI coding products hide the implementation details behind a hosted UI. Open-sourcing the agent runtime gives the community something different to inspect: how the tool is structured, how it handles shell access, and how it organizes the user experience around files, commands, and context. That matters for engineers because the practical questions are often not about raw model capability. They are about reliability, prompting surfaces, permissions, and how much of the workflow can be automated without turning the tool into a black box. The README describes Grok Build as a terminal-based coding agent that supports interactive use, headless scripting, editor integration via the Agent Client Protocol, and a modular tool/runtime layout. That makes it closer to an infrastructure project than a showcase demo. If you are building internal copilots, code assistants, or agent workflows, the design choices here are worth studying. What the repository tells us The repository description makes a few things clear: 1. The agent is meant to be operational, not decorative The docs emphasize real actions: editing files, executing shell commands, searching the web, and coordinating long-

Every organisation that has run a significant software system for more than a few years has felt a version of the same thing: a change that should have taken days takes months, nobody can quite explain why, and the explanation that eventually gets offered — the domain is complex, the requirements changed, the previous team was careless — is almost never checked against an alternative approach for the software architecture or alternative framework choices, because the alternative was never built. There is no possible comparison to determine the solution chosen is a good one and there is no benchmark to measure "fit for purpose." This is the unfalsifiability problem, and it is worth stating plainly before anything else in this piece, because it is the reason the cost described below is so rarely traced back to its actual cause. Every system is built once. There is no version of your platform built the other way, running alongside it, that anyone can compare it to. So when a system works, the approach that produced it gets read as validated. When a system becomes expensive to change, the cost gets attributed to anything except the structural decision that caused it — because that decision was made years ago, by people who may have moved on, and there is no control group to prove that the structure was the variable that mattered. That absence of a control group is not a minor academic point. It is the reason a specific, avoidable pattern of cost has been able to spread through the industry for decades, get taught in courses, get validated in interviews, and still never be clearly named as a mistake. This article is an attempt to name it — and to offer something more useful than a diagnosis: a way to check, this week, whether it applies to you. The Villain: Process Over Product Ask almost any team building a significant piece of software what the goal of the project is, and the honest answer, more often than anyone would like to admit, is not "build the best-fitting prod

Every time you open a website, sign into an application, or send a request to an API, a server responds with a small but powerful message: an HTTP status code. Most developers encounter these codes every day. But behind every number is a story about what happened between the client and the server. HTTP status codes are part of a standardized response system defined by RFC 9110. They help applications understand whether a request succeeded, needs attention, or failed. The HTTP Status Code Families 🟢 2xx — Success The request was received, understood, and completed successfully. Examples: 200 OK — The request succeeded. 201 Created — A new resource was successfully created. These responses tell the client: everything worked as expected. 🔵 3xx — Redirection The requested resource requires an additional step. These responses help clients find another location or use a different version of a resource. Examples include redirects and cache-related responses. 🟠 4xx — Client Errors Something is wrong with the request sent by the client. Common examples: 400 Bad Request — The request format is invalid. 401 Unauthorized — Authentication is required. 403 Forbidden — The client does not have permission. 404 Not Found — The requested resource does not exist. In simple terms: the problem is usually on the client side. 🔴 5xx — Server Errors The request was valid, but the server failed while processing it. Example: 500 Internal Server Error — An unexpected error occurred on the server. These responses indicate problems within the server or its internal systems. Why HTTP Status Codes Matter HTTP status codes are not just numbers. They are: The language of web communication Essential signals for API behavior Valuable tools for debugging and monitoring A foundation of backend engineering and distributed systems Understanding status codes helps developers build better applications, diagnose problems faster, and design more reliable systems. A single three-digit number can reveal what ha

Building a semantic search engine for an e-commerce catalogue doesn't require a team of PhDs or a six-figure cloud budget. In this tutorial, I'll walk you through a production-ready pipeline using open-source tools: sentence-transformers for embedding, FAISS for vector indexing, and FastAPI for serving. The core insight is that semantic search isn't magic — it's just good engineering wrapped around a pre-trained language model. We'll start by setting up a product embedding pipeline that transforms your catalogue (title, description, category, attributes) into dense vectors. The key architectural decision is whether to embed each product as a single vector or to use late interaction models like ColBERT that preserve token-level detail. For most e-commerce use cases with fewer than 1 million SKUs, single-vector embedding with sentence-transformers' all-MiniLM-L6-v2 offers the best balance of speed and accuracy. The entire indexing pipeline — from CSV export to queryable vector index — runs in under 100 lines of Python. The re-ranking layer is where most tutorials stop and real-world systems begin. Pure vector similarity doesn't understand your business: it doesn't know that out-of-stock items should be deprioritised, that high-margin products should float up, or that a customer's purchase history should influence results. I'll show you how to build a hybrid scoring function that blends semantic relevance (cosine similarity), business rules (margin, inventory), and personalisation signals (user embedding) into a single ranked result set that returns in under 100ms. Canonical: https://alteglobal.ai/insights/ecommerce-ai-automation-personalisation-fulfillment/

GitHub announced on July 14, 2026 that security reviews are available in the GitHub Copilot app. Primary source: GitHub Changelog, July 14, 2026 . The meaningful research question is not whether people click Accept. It is whether they can build an evidence-backed decision when guidance is useful, incomplete, or wrong. understand change -> inspect evidence -> challenge findings -> verify uncertainty -> accept, reject, or escalate This is a proposed research protocol, not a completed study. It does not invent product fields or report findings. Build scenario cards scenario_id : " SR-03" repository_type : " synthetic" seeded_conditions : - " one relevant issue" - " one plausible but irrelevant concern" - " one important omission" participant_goal : " ready, blocked, or escalate" success_evidence : - " decision cites inspected code" - " unsupported claim is challenged" - " unresolved uncertainty is recorded" stop_conditions : - " real credentials appear" - " a live repository could be modified" - " participant mistakes study output for production approval" Vary the seeded mix so participants cannot learn that every scenario contains exactly one true and one false finding. Establish ground truth independently before sessions. Recruit people who hold different review responsibilities: routine reviewers, maintainers, security specialists, less-experienced reviewers, and people using keyboard navigation or assistive technology. Do not collapse every group into one average. Require a decision record Decision: ready | blocked | escalate Evidence inspected: - file and relevant lines - test or documentation Guidance accepted: - claim and evidence Guidance rejected: - claim and reason Unresolved: - question and next owner Spoken confidence is not the outcome. This artifact exposes whether acceptance connects to evidence. Measure relevant issues identified, unsupported claims challenged, evidence references, correct escalation, time, and confidence before and after inspection. No