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Razer has finally caved and made its first Hall Effect gaming keyboard. I dug into its switches, features, and gaming performance to see if it was worth the wait.

Razer has insisted that optical keyboard switches are the best choice for competitive esports stars and the sweatiest of try-hards, but it's now relenting slightly by offering cheaper keyboards with magnetic switches and a similar array of gaming features. The $120 Razer Huntsman V3 HE Magnetic Mini and $140 Huntsman V3 HE Magnetic Tenkeyless, both […]

Zoox just got permission to charge for robotaxi rides in its boxy, steering-wheel-less vehicles. On Thursday, the National Highway Traffic Safety Administration announced it has granted the Amazon-owned Zoox a temporary exemption, allowing it to deploy up to 2,500 vehicles annually over the next two years, as reported earlier by Reuters. The NHTSA's decision exempts […]

The Disrupt Stage is where many of the biggest conversations in technology happen, with a legacy that stretches back for more than a decade.

Murat Demirbas discusses the shift toward disaggregated cloud database architectures driven by cloud economics. He explains how decoupling compute from storage enables elastic scaling, cost efficiency, and fault isolation. He shares how classical Paxos roles foreshadowed disaggregation, while analyzing network tradeoffs, shared-memory evolution, and self-assembling database designs. By Murat Demirbas

The U.S. federal consumer watchdog said Hims & Hers, which prescribes for sexual wellness and mental health conditions, used website trackers to share customers' information with advertisers.

Your Roku can probably display what's on your phone screen, if you have Android.

Netflix has ponied up a half billion dollars to extend its streaming rights to The Walking Dead and the show's six spinoffs.

With its latest Smart Play sets, Lego managed put its own spin on one of the world's most popular franchises.

Spotify has introduced a new Running Mode feature that makes it easier to curate playlists around your workout goals, music tastes, and desired beats per minute (BPM). The aim is to help you "spend less time hunting for the right music and more time moving," according to Spotify's announcement, providing customizable running presets and optional […]

If you're looking for a high-performance PC with that clean 'zero-cable' look, Maingear's new pre-built lineup that may suit you.

Over the last few years, I've gradually shifted away from ebooks. It's been part of a general return to physical media, an attempt to create a deeper sense of ownership over the art I love and collect. A Kindle is obviously more convenient than a paperback: It's tiny, holds a huge library, and you can […]

In this episode I talk about developing web application based on well known cro framework and specifically how to create CI pipeline using DSCI tool. Here is example of very simple cro application (taken from cro web site): use Cro::HTTP:: Router ; use Cro::HTTP:: Server ; my $application = route { get -> { content ' text/html ', ' Hello Cro! '; } } my Cro:: Service $service = Cro::HTTP:: Server . new : : host < localhost > , : port < 10000 > , : $application ; $service . start ; react whenever signal ( SIGINT ) { $service . stop ; exit ; } First of all let's create jobs file .dsci/jobs.yaml that would contain list of jobs, in our case this is just a single job: jobs : - id : ci path : . In this case we would have just a single job that: installs apps dependencies runs web application in background runs some end to end tests using http client .dsci/job.raku run_task " install "; run_task " end-to-end "; .dsci/tasks/install/task.bash set -e cd ../ ls -l zef install . --deps-only zef install . echo "done" nohup cro run 1>app.log 2>&1 & </dev/null The first task just installs application dependencies and runs web application in background, now we can create some end to end test. For simplicity I am going to use curl http client here, but feel free choose any languages you like, for example Raku's HTTP::Tiny client, DSCI is super flexible allowing to write tasks on different languages mixing then effectively. .dsci/tasks/end-to-end/task.bash I could have made this simple one line Bash task a part of initial install task, but for claritrty and demonstration of modularity I keep as a separate one. In the future more tests may come (rathe then this trivial one) and it reasonable separate installation and testing logic. set -e # the test should fail if HTTP response is not # successful curl 127.0.0.1 127.0.0.1:10000 -f -L Ok, let's try this out. And the very first run results in ... error: 08:47:39 :: ===> Building: Digest::SHA1::Native:ver<1.0.1>:auth<zef:bduggan> 08:47:39

This is a submission for DEV's Summer Bug Smash: Clear the Lineup powered by Sentry . Project Overview I am building a browser-based text removal workspace where a user uploads an image, paints over unwanted text or objects, and sends the resulting mask to an image-editing pipeline. The mask editor uses three stacked <canvas> elements: a base canvas for the uploaded image; an overlay canvas for the painted mask; a cursor canvas for the brush preview and pointer events. All three canvases must have identical dimensions. The pointer coordinates must also map back to the same bitmap coordinate system, or the generated mask will not match the part of the image the user selected. Bug Fix On desktop, the editor had plenty of horizontal space but the uploaded image appeared inside a narrow strip surrounded by a large empty area. The result preview used the available width correctly, so the two sides of the same workspace looked unrelated. The visible symptom was a tiny image editor. The actual failure started before the image was drawn. The initialization code measured the width of the canvas wrapper: const container = canvas . parentElement if ( ! container ) return const containerWidth = container . clientWidth || 1 const containerHeight = 600 It then calculated the largest canvas size that would preserve the uploaded image's aspect ratio: const imgAspectRatio = img . width / img . height const containerAspectRatio = containerWidth / containerHeight let canvasWidth : number let canvasHeight : number if ( imgAspectRatio > containerAspectRatio ) { canvasWidth = containerWidth canvasHeight = containerWidth / imgAspectRatio } else { canvasHeight = containerHeight canvasWidth = containerHeight * imgAspectRatio } The aspect-ratio calculation was correct. The measurement it received was not. Root Cause: The Canvas Measured Itself The wrapper was a relatively positioned element with no declared width: < div className = "relative transition-all duration-500 ease-out" style = { {

I recently read an article by the main maintainer of InversifyJS describing the journey of rebuilding its dependency resolution algorithm . What caught my attention wasn't the performance improvements or the technical details. It was something much more familiar. As I was reading, I realized they were experiencing the exact same phenomenon I had experienced years ago while creating InversifyJS. It reminded me of something that, until now, I had never really put into words. The temptation to solve the hardest problem first Every engineer has experienced it. You're implementing a feature when you encounter a design problem that feels wrong. You know the current approach won't scale, and you know there must be a beautiful abstraction somewhere, so you stop writing code and start designing. Sometimes that's the right thing to do. Many times it isn't. While building InversifyJS, I eventually adopted a different habit. Whenever I found myself thinking, "This feels too complicated, and I can't find a simple, elegant solution right now," I decided to wait. Not because I ignored the problem, but because I didn't think I understood it well enough yet. Instead, I focused on features where I had a reasonable level of confidence. I kept improving the parts of the system that felt obvious, leaving the difficult problems untouched. At first, this almost felt irresponsible. Over time, it became one of the most valuable engineering lessons I have learned. The magic wasn't finding the solution The interesting part is that I rarely came back later with a better idea. Something stranger happened. Implementing those simpler features changed the system itself. New abstractions naturally appeared. Responsibilities became clearer. Concepts that previously seemed unrelated suddenly fit together. Eventually, I would return to the "hard" problem only to discover it wasn't hard anymore. Not because I had become smarter or because inspiration had struck overnight. The problem itself had changed

You ask an AI a question. It answers in fluent, confident prose — complete with a study, a percentage, and a name. Some of it is wrong, and nothing about the wording tells you which part. That's the whole problem with hallucinations: the errors wear the same suit as the facts. The fix is not "trust it less" in some vague way. The fix is a repeatable audit step between AI wrote it and I used it . Below is a short hallucination checker prompt you can copy right now, a test run showing what it catches and what slips past it, and an honest account of where a one-liner stops being enough. What counts as an AI hallucination? Not every mistake is a hallucination. A useful working definition: a hallucination is a claim the model states as fact that has no grounding in reality or in your source material. The common shapes: Fabricated citations — a named study, expert, or paper that doesn't exist. Often dressed with a year and an institution. Plausible-but-wrong specifics — dates, version numbers, statistics that are almost right, which makes them worse. Confident category errors — mixing up two similar things (a library and a framework, one company's product and another's). Invented consensus — "experts widely agree that…" with no experts attached. The dangerous ones are the middle two. Obvious nonsense filters itself; a wrong year in a fluent paragraph does not. The copy-paste hallucination checker prompt Here is the short version, free, no strings. It works on ChatGPT, Claude, or any capable model — paste it into a fresh chat, then paste the answer you want audited: Audit the text below for hallucinations. Do not add new information. 1. Extract every factual claim as a separate numbered line. 2. Label each claim: VERIFIABLE (state how to check it), SUSPECT (state what makes it doubtful), or FABRICATION-PATTERN (named source/study/number with no citation). 3. Flag every name, number, date, and citation for manual checking. 4. Finish with the 3 claims most likely to be wrong

10 paper AI nổi bật nhất trên Hugging Face hôm nay: robot thời gian thực, agentic search, coding agents và học tăng cường thế hệ mới Hôm nay, top paper được upvote cao trên Hugging Face cho thấy một bức tranh rất rõ về hướng đi của AI hiện tại: AI đang rời khỏi các benchmark tĩnh để tiến vào thế giới hành động thực tế — robot phải chạy nhanh hơn, agent phải tìm tài liệu tốt hơn, coding assistant phải hiểu cả repository, còn mô hình huấn luyện phải học được từ phản hồi tinh vi hơn là chỉ đúng/sai. Dưới đây là phần phân tích 10 paper nổi bật, tập trung vào 4 câu hỏi cho mỗi bài: bài toán là gì, ý tưởng chính, điểm mới, và ứng dụng thực tế . 1) HiFi-UMI: Learning Deployable Manipulation Policies from High-Fidelity UMI Data Alone Bài toán: Trong robot manipulation, dữ liệu demo từ con người thường dễ thu thập nhưng chất lượng không đủ ổn định để triển khai thật. Nhiều hệ thống vẫn phải dựa vào dữ liệu bổ sung, tinh chỉnh trên robot, hoặc pipeline phức tạp mới đủ dùng ngoài đời. Ý tưởng: HiFi-UMI hướng tới việc học policy thao tác chỉ từ dữ liệu UMI độ trung thực cao . Tức là thay vì bù đắp bằng nhiều nguồn dữ liệu hỗn hợp, tác giả tập trung nâng chất lượng dữ liệu gốc và thiết kế cách học để policy có thể triển khai trực tiếp. Điểm mới: Điểm đáng chú ý là triết lý “ high-fidelity data alone ”. Đây là một phản đề thú vị với xu hướng “càng nhiều dữ liệu càng tốt”. Bài báo ngụ ý rằng với dữ liệu đủ chuẩn, ta có thể giảm đáng kể phụ thuộc vào fine-tuning tốn kém hoặc domain adaptation phức tạp. Ứng dụng thực tế: Các tác vụ như gắp đặt vật thể, lắp ráp đơn giản, thao tác trong môi trường gia dụng hoặc kho vận. Nếu cách tiếp cận này thực sự bền vững, nó có thể giúp doanh nghiệp triển khai robot nhanh hơn vì giảm chi phí thu thập và hợp nhất dữ liệu đa nguồn. 2) TurboVLA: Real-Time Vision-Language-Action Model at 32 Hz on an RTX 4090 with <1 GB VRAM Bài toán: Vision-Language-Action (VLA) rất hứa hẹn cho robot, nhưng thường quá nặng để chạy real-time. Muốn robot phản ứng mượt,

Originally published on tamiz.pro . The rise of "vibe coding" has democratized software development, allowing developers to build complex applications using natural language prompts. However, this same flexibility introduces a fundamental challenge for enterprise-grade AI agents: non-determinism. When you ask an LLM to "handle this customer support ticket," the model might draft an email, query a database, or call an external API—depending on the temperature, the context window, and the whims of the weights. For simple chatbots, this is fine. For agents that interact with bank accounts, manage server infrastructure, or coordinate multi-step business logic, this unpredictability is a liability. To bridge the gap between the creative, probabilistic nature of Large Language Models (LLMs) and the rigid reliability required by production systems, engineers must introduce structure. The most robust pattern for this is the Finite State Machine (FSM). By decoupling the decision logic from the execution logic , you create agents that are not only smarter but also predictable, auditable, and debuggable. This deep dive explores the architecture of FSM-driven AI agents, why they are essential for moving beyond prototypes, and how to implement them effectively using modern TypeScript libraries like XState and LangGraph. The Problem with Linear Chains In the early days of agentic AI, the dominant pattern was the linear chain: a sequence of LLM calls where the output of one becomes the input of the next. While simple to implement, this architecture suffers from several critical flaws that become apparent at scale: Lack of Error Recovery : If an LLM call fails or returns malformed JSON, the entire chain collapses. There is no defined "state" to revert to, no way to retry a specific step, and no way to pause for human intervention. No Global Context : Each step in a linear chain is often isolated. The second LLM call may not have access to the full history of decisions made in the f

Build a Local LLM Chatbot with Ollama and Python tags: python, ai, llm, tutorial tags: python, ai, llm, tutorial Build a Local LLM Chatbot with Ollama and Python Imagine typing a question into your chatbot and getting a response in milliseconds, completely offline, with zero data leaving your machine. No API keys, no monthly subscription fees, and no privacy concerns about your data being sent to a cloud server. This isn’t a futuristic dream—it’s the reality of running a Local Large Language Model (LLM) on your own computer. With the rise of tools like Ollama , building a private AI chatbot in Python has become as simple as installing a few packages and writing a short script. Let’s dive in and build one together. Why Go Local? Before we write any code, it’s worth understanding why running an LLM locally is a game-changer. Cloud-based AI services like OpenAI or Anthropic are powerful, but they come with trade-offs: you pay per token, your data is processed on their servers, and you’re dependent on their uptime. A local LLM flips this model. You download the model once, run it on your hardware, and you have full control. Ollama is the engine that makes this accessible. It’s a lightweight, open-source tool that simplifies running LLMs like Llama 3, Phi 3, or Mistral on macOS, Linux, and Windows. It handles model downloads, memory management, and inference, exposing a simple API that Python can easily interact with [1][2]. Step 1: Install Ollama and Pull a Model The first step is getting Ollama on your machine. Visit ollama.com , click Download , and install the version for your operating system [2]. Once installed, verify it’s working by opening your terminal or Command Prompt and running: ollama --version If you see a version number, you’re ready to go. Next, you need a model. Ollama supports dozens of open-source models, but for a beginner-friendly chatbot, Llama 3.2 is a great choice. It’s small, fast, and surprisingly capable. To download it, run: ollama pull llam

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