标签：#ki

Developers are owning more of the delivery system around code, not just code itself. Join us during GitHub Universe to meet other devs, learn something new, and explore what's next. The post From coder to orchestrator: How agents shift the role of a developer appeared first on The GitHub Blog .

The problem When you type a single or double quote in Atlassian Jira or Confluence wiki, it's automatically converted to a "smart" quote 🤮 Examples “double” ‘single’ I don’t like it If you delete a smart quote and type it again, it becomes smart again, which can be disturbing. There is no option to disable smart quotes permanently. But... You can get rid of them Type Ctrl-Z immediately after they appear. It also works for the word don't : When you type apostrophe ' it looks normal, then when you type t , the apostrophe changes. Type Ctrl-Z and it reverts back to normal again.

You know the moment. You've handed Claude Code (or Codex, or Cursor, or whatever you're driving today) a real task , not "fix this typo," but "refactor the auth middleware and update the tests." It goes off and works. Even if you've turned off the "ask before edit" prompts and let it run end to end without interruptions, you still want to check in to see what it actually did, watch it reason through a tricky part, catch something before it goes further than you meant. That can take minutes. And in those minutes, you're either sitting there watching a terminal scroll, or you wander off and genuinely forget to come back and check. Nobody was watching. That's the whole problem in one sentence: coding agents got capable enough to work unattended for real stretches of time, but the interface is still "stare at a terminal or forget about it." So, what is SKI SKI is a free desktop app that gives your coding agent a voice - in both directions. You talk to Claude Code, Codex, Cursor, Gemini CLI, and a growing list of other agents by speaking instead of typing. And this is the part that actually changes the workflow - you hear them reply out loud. When the agent finishes a task or hits a decision point, it says so: "Tests pass, want me to ship it?" You don't have to be staring at the terminal to catch that. You can be in another window, another room, or heads-down on a different project entirely. It lives as a small ambient widget on your desktop - a floating pill, or, if you're on a MacBook, a notch bar docked right under the camera housing , you hold a key to talk, and everything - speech-to-text and text-to-speech both runs locally on your machine. Why "voice for coding agents" and not just "voice dictation" This distinction actually matters, and it's the one thing I'd want you to walk away with if you read nothing else. Dictation tools have existed for a while, and good ones too, they turn your speech into text and drop it wherever your cursor is. That's genuinely useful,

When someone says "Bitcoin has over 15,000 nodes worldwide," they mean 15,000+ independent computers are each running Bitcoin software and each maintaining their own full copy of the blockchain. No server owns the truth. Every node checks it for itself. That single fact — every node independently verifies every transaction and block against protocol rules — is the reason blockchains don't need a central authority. If one node tries to cheat, the rest simply ignore it. There's no admin account to compromise because there's no admin. Not All Nodes Do the Same Job Full Node Downloads and stores the entire blockchain, every block since genesis, and independently validates everything against consensus rules. Highest security ~500 GB for Bitcoin ~1 TB for Ethereum This is the backbone of network security. A full node doesn't trust anyone's summary of the chain; it recomputes validity itself. Light Node (SPV) Stores only block headers, not full transaction data. Uses Merkle proofs and relies on full nodes to verify transactions. Low storage, ~50 MB Trusts full nodes for verification What most mobile wallets run Mining/Validator Node A full node that also participates in block creation. Miners (Proof of Work) solve computational puzzles; validators (Proof of Stake) stake cryptocurrency as collateral. Both earn rewards for securing the network. Creates new blocks Earns rewards Requires specialized hardware (PoW) or capital at stake (PoS) Archive Node Everything a full node stores, plus historical state at every block height. Complete history ~15+ TB for Ethereum Used by explorers, analytics platforms, and enterprise tooling Why Peer-to-Peer Instead of Client-Server A traditional web service is client-server: your browser requests data from a company's servers. If those servers go down, the service is unavailable. That's a single point of failure by design. Blockchain networks use peer-to-peer (P2P) architecture instead. Every participant is simultaneously a client and a serv

AI agents don't usually become unreliable all at once. They degrade quietly. One session the agent...

Lessons from building a small VPN service around standard WireGuard clients instead of a proprietary app When you look at a commercial VPN product, the app seems to be the product: a polished interface, a country list, and a large Connect button. I chose the opposite approach. Instead of building another VPN client, I decided to give users a standard WireGuard configuration that they could import into an existing client. That decision removed a lot of client-side work — but it also exposed where the real complexity of a VPN service actually lives. Why build another app if WireGuard already has one? The usual commercial VPN flow is straightforward: install the vendor's app, sign in, choose a location, and connect. A proprietary client can manage server selection, subscriptions, kill switches, automatic reconnects, diagnostics, updates, and support in one place. But for a small service with one or a few locations, I had to ask a more basic question: do I really need to build and maintain a separate Windows, macOS, Android, and iOS client just to establish a WireGuard tunnel? WireGuard already has mature clients across the major desktop and mobile platforms. A user can import a configuration file or scan a QR code and get a normal VPN toggle. On paper, that looked like a very attractive tradeoff: less client code, fewer update mechanisms, fewer installers, and a smaller attack surface to maintain. What I underestimated was that the app was never going to be the hardest part. A .conf file is not just a settings file The first architectural lesson was simple but important: a WireGuard configuration is effectively a credential. It contains the client's private key. A QR code that represents the same configuration contains the same sensitive material in another form. That immediately creates product problems that have nothing to do with the tunnel itself. How do you show the configuration safely? What happens if the user loses it? Can you issue a replacement without leavin

While the device is pretty and lightweight, it’s not something the everyday person needs due to its hefty price tag and size.

I used to think IPv4 addresses were just random numbers until recently. It blew my mind when I started digging and understanding that they have an anatomy where every number after the dot means something very important. Note: To understand what IP addresses are, please consult this post because I won't be going over them here. IP Addresses: Digital Connectivity What is IPv4 in the First Place? IPv4 is short for Internet Protocol version 4 . As you might have already realized, it's the 4th version of the early test designs during the development of the Internet Protocol in test labs in the 1970s. The first real release, v4, came out in 1981 in a public document called RFC 791. RFC 791: STD 5: Internet Protocol IPv4 is an Internet Protocol that's written in what's called Dotted Decimal Notation (e.g., 172.17.0.3 ), where each portion is separated by a dot, and these portions are called octets. For example, 172 is the first octet and 17 is the second octet (more on this later). Before We Explore What Octets Are, Let's Take a Stroll to the Basics of Binary (Simplified) Have you ever wondered why your computer or phone requires electricity to function? Though electricity can be used as a raw power source for things like fans or speakers, where it's converted into other forms of energy such as movement or sound, it's good to know that electricity can function differently in your computer's RAM or SSD. Inside your computer are billions of extremely tiny transistors. These transistors form circuits that can create and maintain different electrical states, which the computer interprets as 0s and 1s. In simple terms, 0 represents the absence of the electrical state (OFF), while 1 represents its presence (ON). These states, which we represent with 0s and 1s, are called binary digits (or simply bits). 1 bit has the possibility of representing either 0 or 1, which doesn't represent much information, and that's where multi-bits come in. Every additional bit doubles the number of

Microplastics are in everything, especially your coffee. A new generation of plastic-free drip coffee brewers is trying to change this.

A reverse proxy sits between clients and one or more upstream services. Instead of clients communicating directly with your application, every request first passes through the proxy before being forwarded to an upstream. Mature reverse proxies such as Nginx, Envoy, and HAProxy do much more than simply forward requests. They perform tasks such as load balancing, health checks, rate limiting, metrics collection, and much more. I wanted to better understand how some of these concepts work in practice, so I built a reverse proxy in Go. Along the way I implemented request forwarding, multiple load-balancing strategies, health checks, circuit breakers, rate limiting, request logging, metrics, and graceful shutdown. If you'd like to explore Proxify as we go, you can find the project here: https://github.com/Rahmannugar/proxify Table of Contents Request Lifecycle Project Structure Configuration Reverse Proxy Load Balancing Health Checks Circuit Breakers Middleware Graceful Shutdown Running Proxify with Docker 1. Request Lifecycle At a high level, every request follows the same path through the reverse proxy. A client sends an HTTP request to Proxify instead of communicating directly with an upstream service. Proxify receives the request, selects a healthy upstream using the configured load-balancing strategy, forwards the request, waits for the upstream's response, and finally returns that response to the client. Client │ ▼ +---------------+ | Proxify | +---------------+ │ Select Healthy Upstream │ ┌───────┴────────┐ ▼ ▼ Upstream A Upstream B │ ▼ HTTP Response │ ▼ Client Although the overall flow is straightforward, every step introduces additional considerations. Which upstream should receive the next request? What happens when an upstream becomes unhealthy? How can requests be distributed efficiently across multiple upstreams? How do we prevent a failing upstream from continuing to receive traffic? The remainder of this article answers those questions by gradually buildin

These services deliver freshly roasted, delicious coffee picks right to your door—each with its own twist.

At the Black Hat security conference, the AI giant revealed new details about how its agents went rogue, hacked several other companies—and did it all right under the company’s nose.

For nearly two years, researcher Vangelis Stykas has maintained access to North Korean hackers’ servers. His work shows they pulled off intrusions in a shocking number of systems across the globe.

The serial entrepreneur is stepping down a little over a year after taking the "24/7 job" of overseeing X.

If you have ever pointed a coding agent at a multi-day goal, you know the failure mode. It is not that the model writes a bad function. It is that on turn 40, the agent no longer remembers what the objective was, which decision you already made, what is out of scope, or what the last run actually proved. The context window rolled over, and the plot went with it. LoopX is an attempt to fix that specific problem. It calls itself "loop engineering for long-running AI agents," and it is a local control plane that sits above your agent runtime rather than replacing it. The one-sentence version Your agent (Codex, Claude Code, Cursor, whatever) executes bounded loops. Something (a heartbeat, a cron job, you hitting enter) triggers the next loop. LoopX holds the state that has to survive between those loops. The project draws the separation like this: Layer Role Codex / Claude Code / Cursor Execute a bounded agent loop: read, write, run commands, respond Goal mode / automation / CLI / TUI Trigger or schedule the next loop LoopX Preserve goals, gates, todos, run history, quota, evidence, handoff state That third row is the whole product. LoopX is not an executor and not an autonomous production controller. It is a state kernel with a CLI. Why "just use a todo file" isn't enough A TODO.md plus a long system prompt gets you surprisingly far. It falls over once any of these become true: The goal changed halfway through, and nothing recorded why . A decision genuinely needs a human, and that request evaporated into a chat message nobody read. Two agents are touching the same repo and neither knows who owns what. The last run claimed success, and there is no artifact proving it. Some work is safe and read-only, some crosses into writes, production, or private data, and the distinction lives only in your head. LoopX makes those things explicit and machine-readable, which is what lets a loop run longer without becoming less accountable. The concepts, in plain English Lifetime goals

I needed a VPS to run CyberPanel. Simple enough: 1 vCPU, 1 GB RAM, 10 GB SSD, IPv6 only. CyberPanel...

JioHotstar explains the distributed architecture behind its real-time ad request workflow, covering ad decisioning, waterfall tiering, pacing algorithms, latency optimization, and service coordination required to select and deliver personalized advertisements during streaming playback at scale. By Leela Kumili

This is disturbing: …a team of security researchers at UC San Diego, who found that a model of aftermarket car alarm known as the KARR Security System, installed in more than 2 million vehicles across the US by their estimate, can let any hacker within Bluetooth range send radio commands to silently unlock the car at will, turn off its alarm, honk the car’s horn or flash its lights, or even disable its ignition and leave a driver stranded.

If you've experimented with AI-driven testing, you've probably lived this cycle: you ask an AI agent to "test the checkout flow," and it does something — clicks around, declares success, and leaves you unsure what was actually verified. The next day you ask again and it does something different. The browser automation works; the testing discipline is missing. That gap is what Snagly is for. Rather than describe it, I pointed it at softwaretestingtrends.com — my own production site, nothing fixed beforehand — and recorded the whole thing. It found eleven issues, including a critical accessibility bug on my own signup page. One of its findings turned out to be wrong, and I'll come back to that, because it matters more than the ones it got right. 📺 Watch the full walkthrough — installed from an empty folder, run against production, ~20 minutes. What it is Snagly is a free, MIT-licensed set of 30 skills for AI coding agents — GitHub Copilot , Claude Code , Cursor, Codex and 70+ others — that turn "an AI that can drive a browser" into "an AI that tests like a QA professional." A skill, if you haven't met them yet, is a reusable instruction set that teaches the agent a specific working method — when to use it, what rigor it requires, what evidence to capture, and what it must never do. Each skill in Snagly has one job, and they hand off to each other the way a real testing practice does: start-testing is the front door — say "what can you test here?" and it routes you to the right skill, checking prerequisites before handing off. Discovery & strategy : scenario-mapper explores your site and produces a prioritized list of test scenarios; test-case-writer expands any of them into a reviewable spec; test-plan sets strategy, cadence, and release exit criteria; qa-onboarding writes the guide for your next hire. Execution : flow-runner drives real user journeys step by step, asserting outcomes (not just that clicks happened) and capturing evidence the moment anything fails. cru

I’m an omnivore. Purple Carrot’s vegan meal kit offers some of the best cooking I’ve seen from any meal kit, with or without meat.