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The company added a warning to prospective investors that a major dilution could be in the cards after it goes public.

Screenwriting and playwriting for movies, television, stage. Discussion | Link

The DORA research programme is the most rigorous empirical study of software delivery performance ever conducted. Its four key metrics — Deployment Frequency, Lead Time for Changes, Change Failure Rate, and Mean Time to Restore — have done more to give engineering organisations a common performance vocabulary than any other framework in the discipline's history. If you work in software and you have not read the State of DevOps Report, stop and read it before finishing this paragraph. Now: the DORA Four were derived primarily from organisations with cloud-native architectures, on-demand deployment infrastructure, and relatively unconstrained ability to release software when it is ready. The research cohort skews toward technology companies that have already made the cultural and architectural investments that make high-frequency, low-risk deployment possible. This is not a criticism of the research. It is an observation about its generalisability — and it has a specific consequence for practitioners who work in regulated enterprises: banks, healthcare systems, utilities, insurance carriers, government agencies. In these environments, the DORA Four are necessary but structurally insufficient. They measure the delivery pipeline accurately. They do not measure the operational sustainability of the team running that pipeline — and in regulated enterprises, operational sustainability is where SRE programmes go to die quietly, years before anyone realises the damage is permanent. This post proposes a fifth metric. Not to replace the DORA Four, but to complete them — to close the measurement gap that leaves regulated enterprise SRE teams flying blind on the dimension that most reliably predicts long-term programme failure. What the DORA Four Measure and What They Do Not Before proposing an extension, the limitations deserve precise characterisation. Imprecise criticism of a well-validated framework is noise. The limitations described here are structural — arising from the d

Scroll through social media today, and you'll likely come across AI-generated images everywhere. From anime-style portraits and fantasy landscapes to hyper-realistic photographs of places that don't even exist, AI image generators have quickly become one of the most fascinating applications of artificial intelligence. What makes this technology so impressive is its accessibility. A few years ago, creating professional-quality artwork required design skills, expensive software, and hours of effort. Today, anyone can generate stunning visuals simply by typing a few words. But what actually happens behind the scenes when you enter a prompt and click "Generate"? Turning Ideas into Images At a basic level, AI image generators convert text into visuals. When a user enters a prompt such as: "A futuristic Mumbai skyline at sunset with flying cars" the AI doesn't search for an existing image online. Instead, it creates a completely new image based on patterns it learned during training. These models are trained using millions of image-text pairs, allowing them to understand concepts such as objects, colors, lighting, artistic styles, and even relationships between different elements within a scene. As a result, the AI can interpret the user's description and transform it into a visual representation. Starting with Random Noise One of the most interesting aspects of modern AI image generation is that the process usually begins with random noise. Imagine the static pattern seen on an old television screen. Initially, the AI starts with something similarly meaningless. It then gradually removes the noise while adding details that match the prompt. This process is known as a diffusion model , and it is the foundation of many modern AI image generators. To understand the idea, consider the following simple Python example: import random prompt = " A futuristic Mumbai skyline at sunset " noise_level = random . randint ( 1 , 100 ) print ( f " Prompt: { prompt } " ) print ( f " Start

We've all been there. You're 45 minutes into a Docker tutorial, feeling great about yourself, and then someone casually drops: "Just pull the image and spin up a container." And you think: "...wait, aren't those the same thing?" First - this has happened to a good number of us if we are to be honest. Even almost every single DevOps engineer, cloud architect, and platform wizard you admire has typed the wrong term in a sentence at least once in their career. It's practically a rite of initiation. There should be a badge for it if you ask me. Why Does This Trip Everyone Up? Here's the sneaky truth: Docker commands blur the line constantly. You type docker run nginx and something called a "container" starts — but wait, didn't you just use an "image" called nginx ? Where did one end and the other begin? The confusion lives in the fact that they are deeply related — one literally gives birth to the other. But they are fundamentally, completely different things. Getting this distinction straight is your official rite of passage into DevOps. Once it clicks, the rest of Docker feels like cheating. Basically, A Docker Image is the blueprint : a frozen, static snapshot of everything your app needs - the OS layer, the dependencies, the config files, your actual code. It just sits there on disk, completely inert. You can't run a blueprint. A Docker Container is the house : the live, running instance that was built from that blueprint. It has processes running, files potentially being written, network ports being listened on. It's alive. And now, just like one blueprint can produce 10 identical houses on different streets - one Image can launch 10 identical Containers simultaneously; and that's where Docker's scaling magic comes from. # The image just sits here, unchanging docker pull nginx # Now we BUILD a house (container) from the blueprint docker run nginx # Build THREE houses from the same single blueprint docker run nginx docker run nginx docker run nginx Here is an exampl

This is a follow-up to SynaptoRoute: A Study in Local Semantic Routing . If you haven't read it, the short version is: SynaptoRoute is a zero-token semantic routing engine that classifies user queries into intents using local embeddings instead of LLM API calls. SynaptoRoute v0.3.0: Matching Semantic Router While Scaling to 50,000 Routes What Changed Since v0.2.0 When I published the first post, SynaptoRoute had just shipped dynamic batching and O(1) hot-reload. The throughput numbers were promising, but the accuracy story was incomplete. I had internal benchmarks but no comparison against a widely adopted baseline under identical, reproducible conditions. That gap is now closed. v0.3.0 is live on PyPI: pip install synaptoroute == 0.3.0 The Benchmarking Journey Getting to these numbers took multiple benchmark revisions. Early synthetic datasets produced catastrophic accuracy collapse and initially suggested that both SynaptoRoute and Semantic Router were performing poorly. After deeper investigation, the root cause turned out to be flaws in the dataset generation pipeline rather than limitations of the routing engines themselves. Several rounds of validation, failure analysis, threshold tuning, adversarial testing, and external benchmarking followed. All final results presented in this article come from independent public datasets with strict train/test separation, eliminating dataset leakage and benchmark inflation. That process was valuable because it forced the project to validate assumptions against real-world data instead of relying on synthetic benchmarks. The Benchmark That Actually Matters I evaluated SynaptoRoute against Semantic Router on two standard NLU datasets. Same embedding model ( BAAI/bge-small-en-v1.5 ). Same hardware. Same evaluation script. Same train/test splits loaded from HuggingFace. CLINC150 150 intents spanning 10 domains, plus an out-of-domain class. This is the standard stress test for intent routers. Metric SynaptoRoute Semantic Router

Large language models (LLMs) understand and generate text from prompts. OpenAI exposes models through the Responses API . The official openai npm package is the practical way to call it from Node.js. This post covers common patterns beyond a single prompt string. Prerequisites OpenAI account Generated API key Enabled billing Node.js version 26 openai package installed ( npm i openai ) For Markdown output: marked , dompurify , and jsdom ( npm i marked dompurify jsdom ) Client setup Create a client with your API key (read from the environment in production). import OpenAI from ' openai ' ; const client = new OpenAI ({ apiKey : process . env . OPENAI_API_KEY }); The same SDK can target other hosts that implement a compatible API by setting baseURL and apiKey : const client = new OpenAI ({ apiKey : process . env . LLM_API_KEY , baseURL : ' https://your-gateway.example/v1 ' , }); Azure OpenAI uses AzureOpenAI instead. Many third-party gateways support Chat Completions only; the examples below use client.responses.* , so confirm your provider supports the Responses API (especially for tools like web search). Basic integration Pass a string as input and read output_text from the response. const response = await client . responses . create ({ model : ' gpt-5.5 ' , input : ' Write a one-sentence bedtime story about a unicorn. ' , }); console . log ( response . output_text ); System prompt Use top-level instructions for stable behavior (tone, format, role). They take precedence over casual wording in the user message. const response = await client . responses . create ({ model : ' gpt-5.5 ' , instructions : ' Reply in one short sentence. Use plain language. ' , input : ' Explain what an LLM is. ' , }); console . log ( response . output_text ); Few-shot prompting Pass prior turns as an input array with user and assistant roles, then the new user message. Keep task rules in instructions . const response = await client . responses . create ({ model : ' gpt-5.5 ' , instructions :

During maintenance tasks like backup or inspection, you often need to switch a gbase database cluster to READONLY mode and back to NORMAL afterwards. This post shows how to perform the switch online with gcadmin — no downtime required. Tools and Prerequisites Tool : gcadmin , located on any Coordinator node. User : Must be the cluster installation user (default gbase ). Pre‑check : Ensure gcware services are running ( gcware_services status ). Scope : The mode change applies cluster‑wide; no need to repeat per node. Step‑by‑Step (READONLY → NORMAL) Step 1: Check the Current Mode # Cluster‑wide gcadmin showcluster # Specific VC in a multi‑VC environment gcadmin showcluster vc vc1 Look at the VIRTUAL CLUSTER MODE field. Proceed only if it shows READONLY . Step 2: Switch to Normal Read/Write Mode # Single VC or whole cluster gcadmin switchmode normal # Specific VC gcadmin switchmode normal vc vc1 The switch takes effect in seconds. The cluster synchronises the new mode to all nodes automatically — no process restart is needed. Step 3: Verify the Change Run gcadmin showcluster again. When VIRTUAL CLUSTER MODE shows NORMAL , the cluster is fully writable again. Production Notes Business impact : An online switch does not interrupt running read queries. Pending write operations queued during READONLY mode are executed automatically after the switch. Node to run on : Any single Coordinator node is enough. Privileges : Only the gbase user can switch modes. Reverse switch : To return to read‑only mode, use gcadmin switchmode readonly [vc vc_name] . Troubleshooting : If the command fails, check gcware service health and node status first, then retry. Companion Commands # Check all cluster processes gcluster_services all info gcware_services all info # View detailed VC information gcadmin showvc The mode‑switch mechanism in GBase 8a is built for high availability. Following the "verify → switch → re‑verify" workflow lets you change cluster modes safely and transparently in a g

A while back I set three targets for my engineering team. Not velocity. Not story points. Not "things shipped." Just three numbers. Together they tell me whether the work is moving the way it should, or whether next week is shaping up to be a fire-fighting week. I check two of them most days. The third I used to watch closely...until we lost the tool that measured it. Here they are, and why they earned their spot. Why these and not just velocity The first metric most engineering managers reach for is velocity. Story points completed, tickets closed, work merged. Velocity is worth watching. It is a lagging indicator...it tells you what already happened...but it still shapes what comes next. When a sprint's work doesn't get finished, it rolls into the following one, and that rollover eats into whatever you had planned. What velocity doesn't tell you is how the work moved...whether it moved in a way that's going to come back and bite you. For that you need numbers that describe the shape and quality of the work, not just the amount of it...ideally ones that flag a problem while there's still time to act. These three do that. 1. Average PR size Target: under 300 lines changed per PR. What it tells me: how well the team is decomposing work. A team consistently shipping oversized PRs isn't producing more... they're producing PRs that no reviewer can read carefully. Big PRs get rubber-stamped. Rubber-stamped PRs are where production bugs hide. The 300-line target isn't magic. It's roughly the size below which most reviewers will actually read every line. I tell my team to aim for under 300 changes and to treat 500 as a hard ceiling, give or take a handful of genuine exceptions. Past 500 changes, I consistently see quality, review time, and thoroughness all drop sharply...the PR stops getting read and starts getting skimmed. When the team's average creeps up over a few weeks, I have an early signal that one of three things is happening: Stories are too coarse. The work does

If you've spent any time building React Native apps, you've already bumped into the question. You're setting up a new project, you need to move users between screens, and suddenly you're 40 minutes deep in a documentation rabbit hole wondering whether to go with the familiar React Navigation setup or take the leap to Expo Router. This article is going to break it all down, no hype, no fanboy energy, just an honest look at both options so you can make the call that actually fits your project. First, What Does "Routing" Even Mean in a Mobile App? On the web, routing is pretty intuitive. You type a URL, the browser goes to a page. Simple. In mobile apps, there's no URL bar, no browser history button, nothing like that. But users still need to move between screens, go back to where they came from, open modals, tab between sections, and all of that has to feel smooth and natural. So in mobile development, routing is basically the system you use to manage how screens stack on top of each other, how state is preserved when you go back, how deep links work, and how the app knows which screen to show based on where the user is in the flow. Think of it like this: routing is "moving between screens while keeping your app's memory intact." When someone logs in, fills out a form, gets distracted and opens a modal, then comes back, the app should remember all of that. Routing is the machinery underneath that makes it happen. In React Native, this doesn't come out of the box. The framework gives you the building blocks, but you have to wire up the navigation yourself. That's where libraries come in. Why Navigation Is Such a Big Deal in React Native React Native apps are essentially single-page applications. Everything runs in one JavaScript thread, rendered to native views. There's no browser doing the heavy lifting of managing history or transitions. You're responsible for it all. Bad navigation kills good apps. A janky transition, a broken back button, a modal that doesn't dismi

You know the moment. You push the branch, open the PR, and immediately see it — the undefined return on the refund path, the token logged to the console, the TODO that was supposed to be temporary six weeks ago. The reviewer catches it four hours later and you reply "good catch, fixing now" as if someone else wrote that line. The first reviewer on most pull requests should have been the author. Half the comments you will receive — the missing null check, the untested error branch, the duplicate logic that could be extracted, the import that now goes nowhere — are things you would have caught with one more careful read-through. You skip that read because you have been in the code for two days and your brain completes the sentences for you. You see what you meant to write, not what is on the page. This post is about closing that gap with a structured AI-assisted self-review before the PR opens. Not to skip the human reviewer — to walk into the review with the obvious problems already gone, the test gaps already filled, and the PR description already written. So the reviewer's attention can land on what actually needs a second pair of eyes. The tool is branchdiff : a local browser app that runs your diff on localhost , stores everything in ~/.branchdiff/ , and keeps the AI surface controlled through an explicit branchdiff agent command API. Nothing leaves your machine until you decide to push it. Why "before the PR" is the right moment If you review after opening the PR, every AI fix becomes noise: a force-push, a re-read for your reviewer, another commit in the audit trail. If a teammate is already mid-review when you discover the bug, you look careless. The patch that should have been in the original push becomes a distraction for everyone downstream. If you review before opening the PR, the AI's output is a private workspace. You act on what matters, commit the fixes into your own history (often as fixup! commits you squash before pushing), and the PR that goes up i

I want to tell you something that took me years to learn, so you can learn it on a Tuesday afternoon instead of during a production incident: most developers who build REST APIs do not actually know all the security protections their API needs. I did not know them when I started. I learned them slowly, usually right after something broke. I am a Filipino fullstack developer, about ten years in, now based in Norway. I built DaloyJS ( @daloyjs/core ) partly so that newer developers do not have to learn security the painful way I did. This post is a gentle walk through the problem and how DaloyJS helps. No gatekeeping, I promise. First, what even is a "security protection"? When your API is on the internet, anyone can send it anything. Most people are nice. Some are not, and a few are running automated tools that poke at every API they can find. So your server needs some basic defenses. Here are a few, in plain words: Body-size limit: stop someone from sending a giant 2GB request that fills up your server's memory and crashes it. Timeouts: if a request takes forever, give up on it so it does not clog everything. Prototype-pollution protection: block a sneaky trick where a special key in the JSON ( __proto__ ) can mess with your whole app. Header safety: reject weird characters in headers so attackers cannot inject their own. Path-traversal protection: stop a path like ../../etc/passwd from reading files it should not. Hiding error details in production: do not show strangers your stack traces and internal info. Rate limiting: stop one person from hammering your API thousands of times a second. Secure headers and CORS: tell browsers how to safely talk to your API. You do not need to memorize all of these today. The point I want you to take away is simpler: this list exists, it is longer than most people think, and nobody hands it to you when you write your first endpoint. Why this is a trap, especially with AI tools Here is the part that matters most for you right now,

Get ready for some gaming news. It’s officially June, which means splashy new events from PlayStation, Xbox, and gaming hype man Geoff Keighley. But this season doesn’t just feature the big tentpole shows; there will be a bunch of smaller events, too, and they might feature some promising games as well. But this year’s events […]

There are many solid Bluetooth trackers for iPhones that tap into Apple’s expansive Find My network. Some are thin, some are a bit chunkier. And, evidently, some look like tiny soccer balls. Ugreen’s FineTrack 2 glows in the dark, and it has a loud 110-decibel alarm when you need to find it. It’s just $14.99 […]

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Alternative search engine DuckDuckGo launches 'no AI' web extensions for Chrome and Firefox users.

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Nvidia's new chips will power laptop workstations and mini desktop PCs at first.

The British fintech has built a waitlist of about 450,000 users in India as it prepares for a broader launch.

Microsoft is heading to San Francisco this week in a bid to win back developers at its Build conference. I've been attending Build since the days when Microsoft called it the Professional Developers Conference, and I can't remember a more pivotal moment. As Microsoft continues to reshuffle its entire business around AI, it's moving Build […]