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If you are an active user of AI-powered IDEs like Cursor, VS Code with Copilot, or Windsurf, you already know the sinking feeling of seeing this notification: "You have used 100% of your fast premium requests for this billing cycle." Suddenly, your snappy, context-aware coding assistant slows to a crawl or starts racking up expensive pay-as-you-go API bills. At the same time, you are likely paying $20/month for a ChatGPT Plus or Team subscription that sits underutilized in a browser tab. You use it for general questions, but it lacks direct, real-time access to your local codebase, forcing you to engage in a tedious dance of copying and pasting code blocks. What if you could bridge this gap? What if you could let ChatGPT Web do the heavy reasoning and planning using your local context, while saving your premium IDE tokens for fast auto-completions ? In this article, we’ll explore a highly novel, intermediate-level setup that does exactly this. By leveraging the Model Context Protocol (MCP) , Node.js , and secure Cloudflare Tunnels , you can route heavy code-planning tasks directly to your web-based ChatGPT Plus subscription safely and completely free of extra token charges. The Philosophy: Let ChatGPT Think, Let Your IDE Work When building complex software with AI, your workflow generally splits into two distinct phases: Reasoning & Planning (High Token Usage): This is where you ask the AI to read 10 source files, understand the architecture, design a new feature, or find a subtle bug. This consumes massive amounts of context window tokens. Execution & Autocomplete (Low Latency): This is where the AI writes single lines of code, refactors a function, or autocompletes your imports. This requires fast, inline API queries. Paying premium API rates (per token) for Phase 1 is incredibly expensive. This is where this open-source MCP bridge project shines. It exposes a read-only view of your local project as an MCP server. Your web-based ChatGPT (via custom GPTs or MCP int

Late August I kept seeing the name Dungeon Lootr in places it hadn't been before. I wanted to know whether the game was actually growing or whether I was just noticing it more. Turns out Roblox exposes enough public data to answer that without an API key, so I started pulling it every day. The endpoint is boring in the best way: curl "https://games.roblox.com/v1/games?universeIds=9656201728" You get back visits , playing , favoritedCount , updated and a few other fields. A second call to /v1/games/votes?universeIds=... gives you up and down votes. No auth, no rate-limit drama at once-a-day volume. Here is what a week of that looks like for this one game: Date Total visits Playing right now Favorites Approval Sep 2 4.83M — 25.5k 96% Sep 3 5.39M 10.8k 29.8k 96.1% Sep 4 6.85M 11.4k 38.6k 96.1% That is about 1.4 million more visits than when I checked yesterday, and favorites jumped by almost nine thousand. The updated timestamp moved twice in two days (Sep 2 23:06 UTC and Sep 4 00:56 UTC), so the developers are shipping while the curve is climbing rather than sitting on it. The part that surprised me: the game was created on January 31, 2026. It sat there for seven months doing nothing visible, then flipped in the last week of August. I do not have a clean explanation. No single creator video I can point to, no front-page placement I noticed. It just started compounding. I wrapped the two calls into a tiny CLI so I would stop retyping the universe ID: https://github.com/jackzhouqd/roblox-game-stats — plain Python, no dependencies. Point it at any universe ID and it prints the same table. A few caveats before anyone reads too much into this: visits is cumulative and not deduplicated. It counts sessions, not people. playing is a snapshot at the moment you call it. Hit it at 3am and you will get a different number than at 8pm. One day of movement means nothing on its own. A week of movement in the same direction is when I start paying attention. What I am doing with it: I

I wanted a microphone project with a very small brief: hear a sound, copy it, and see how close you got. That became Mimic Party Online , a browser game where each round gives you a short sound cue and one recording attempt. The cue might be a meme clip, an animal call, a machine noise, or something that is hard to describe without making the sound yourself. It looks like a toy, and it is. It also turned into a useful little audio problem. A score based only on volume would be boring, so the game needs to compare the shape of two sounds while staying fast enough to run in a browser. The round is intentionally simple The player does five things: Choose a sound pack. Listen to the reference. Record one take. Listen to the take. Read the score. The replay is important. People tend to remember the sound they meant to make. The recording tells them what actually came out. A convincing robot alarm can turn into a tired bicycle horn pretty quickly. Quick mode runs for four rounds. Survival mode gives the player three Mic lives and keeps the run going until those lives are gone. The game also has different routes, so a player can protect a streak or accept a shorter recording window for more points. The browser does the audio work The recording stays in the browser. The game uses the microphone stream, converts the take to mono PCM at 16 kHz, and extracts the values needed for scoring. The audio does not travel to a scoring server. For each take, the extractor looks at signals such as: pitch contour timing and active duration attack and energy rhythm and onset positions spectral shape The game does not use every signal for every sound. A pitched cue cares more about contour, while a machine noise depends more on its shape and attack. A rhythmic sound needs the hits to arrive at roughly the right moments. This is also why the score is more useful when it has labels. A result of 68 is not very instructive by itself. "Timing: 74" gives you something to work on in the next atte

AI Can Write Your Code. Can It Actually Debug It? AI coding assistants have changed how developers write software. You can describe a feature, generate a function, refactor a component, write a test, or explain an unfamiliar codebase in seconds. But there is one part of software development that is still surprisingly difficult: figuring out why something broke. Writing code and investigating a failure are two very different problems. When an application crashes, the answer usually isn't sitting inside the error message. You have to reconstruct what happened. The Problem With "Just Read the Stack Trace" Consider this Node.js error: TypeError: Cannot read properties of undefined (reading 'email') at getUser (/app/services/user.js:42:18) at processRequest (/app/controllers/auth.js:87:12) at async handler (/app/routes/auth.js:31:5) The immediate problem appears obvious. Something is undefined. But what caused it? Maybe: A database query returned no user. An API returned an unexpected response. Authentication middleware failed. A promise returned an unexpected value. A user record exists but its profile doesn't. An earlier function silently produced invalid state. The stack trace tells you where the program finally failed . It doesn't necessarily tell you where the bug began . That's the difference between error reporting and debugging investigation. AI Coding vs AI Debugging Most AI coding workflows look something like this: Developer ↓ Prompt ↓ AI ↓ Code Debugging is different: Failure ↓ Error ↓ Stack trace ↓ Execution path ↓ Application state ↓ Root cause ↓ Fix The AI needs to reason across that chain. Simply asking: "What does this error mean?" usually produces a list of possible explanations. That's useful, but it's not necessarily an investigation. A better question is: "Given this failure and its context, what is the most likely root cause, what evidence supports it, and how can I reproduce it?" That's a much more interesting problem for AI. A Simple JavaScript De

An invite-only event in Austin, Texas, kept online fans in the dark for hours.

Officially, Nvidia's controversial DLSS 5 AI rendering was supposed to launch this evening with only a single game, only on Nvidia's latest RTX 50 GPUs, and with developers in full control of their artistic vision. But unofficially, modders have already ported a leaked version of DLSS 5 to run on just about anything while cranking […]

A headless CMS migration often sounds simple: Contentful → Strapi Move the content, update the API calls, fix a few components, and you're done. Except... you're usually not. The hardest part of a headless CMS migration isn't moving the content. It's managing the contract between the CMS and the frontend . And if your React or Next.js application is tightly coupled to the CMS response structure, changing the CMS can turn into a much bigger project than expected. The problem Imagine your frontend directly consumes Contentful responses: const ProductCard = ({ product }) => { return ( < article > < h2 > { product . fields . title } < /h2 > < p > { product . fields . description } < /p > < img src = { product . fields . image . fields . file . url } / > < /article > ); }; It works. Until you migrate to Strapi. Now the response might look completely different: product . title product . description product . image . url Suddenly, the frontend needs to understand both CMS structures. And this problem isn't limited to simple fields. Things become much more complicated with: Rich text Media and assets References Nested relations Localization Draft/preview content SEO metadata Dynamic components Pagination GraphQL vs REST Different content modeling approaches The architecture I prefer Instead of allowing React components to consume the CMS directly, introduce a layer between the CMS and the application. ┌───────────────┐ │ Strapi │ └───────┬───────┘ │ ▼ ┌───────────────┐ │ CMS Adapter │ └───────┬───────┘ │ ▼ ┌───────────────┐ │ Domain Model │ └───────┬───────┘ │ ▼ ┌───────────────┐ │ React / Next │ └───────────────┘ The frontend doesn't need to know whether the data came from Strapi, Contentful, Shopify, WordPress, or something else. It just receives the data it needs. For example: type Product = { id : string ; title : string ; description : string ; image : { url : string ; alt : string ; }; }; The CMS adapter is responsible for transforming the CMS response into this model

The round came together after the data center developer reportedly secured a $13 billion contract with Jane Street.

Have you ever wondered how your smartwatch actually knows you're stressed? Most of us treat the "Stress Score" on our wrists as a source of truth, but the logic remains hidden behind proprietary algorithms. Today, we are pulling back the curtain. We are going beyond basic heart rate tracking to perform PPG signal processing and HRV frequency domain analysis using Python. By the end of this guide, you’ll know how to ingest raw data via Bluetooth Low Energy (BLE) , apply digital filters with SciPy , and calculate the elusive LF/HF ratio to determine autonomic nervous system balance. If you are interested in advanced biometric algorithms or Python signal analysis , you’re in the right place. 🚀 The Architecture: From Photons to Stress Metrics Unlike standard heart rate (BPM), which just counts peaks, Stress Scores rely on Heart Rate Variability (HRV) —the millisecond-level variations between heartbeats. We'll be moving from raw light intensity data to a frequency-based stress index. graph TD A[Wearable Sensor / PPG] -->|Raw BLE Stream| B[Data Acquisition - Bleak] B --> C[Preprocessing - Bandpass Filter] C --> D[Peak Detection - Find R-R Intervals] D --> E[Cubic Spline Interpolation] E --> F[Fast Fourier Transform - FFT] F --> G[LF/HF Ratio Calculation] G --> H[Final Stress Score] 🛠 Prerequisites To follow this advanced tutorial, you’ll need: Hardware : A pulse oximeter or wearable that exposes raw PPG via BLE (e.g., Polar OH1, MAX30102 with an ESP32). Stack : NumPy & SciPy : For heavy-duty math and signal processing. Bleak : For cross-platform Bluetooth Low Energy communication. Matplotlib : To visualize the pulse waves. Step 1: Capturing the Raw PPG Stream (BLE) Photoplethysmography (PPG) works by shining green or red light into the skin and measuring the light absorption. First, let's grab that raw stream. import asyncio from bleak import BleakClient # UUID for the Raw PPG Characteristic (Device specific) PPG_CHAR_UUID = " 00002a37-0000-1000-8000-00805f9b34fb " def no

More than 200 people in roles such as engineering, finance, and communications will now be represented by the Communication Workers of America.

An email file is not just text plus a few attachments. It can contain HTML, nested MIME parts, misleading filenames, inline resources, remote tracking pixels, malformed encodings, and enough data to exhaust a browser tab. Moving parsing into the browser removes an upload from the architecture, but it does not automatically make the viewer safe. It changes the security job: untrusted content is now being interpreted next to the user’s active web session. This is the checklist I use for a local EML and winmail.dat/TNEF reader. Treat every parsed field as untrusted The sender, subject, recipient, filename, MIME type, and message body all came from a file. Render headers and filenames as text, never by concatenating HTML. The same applies to errors. A parser exception can include a filename or fragment of malformed input. Showing that message verbatim may leak data into logs or turn it into markup. Map parser failures to stable error categories, then display a controlled explanation. Normalize into one internal model EML and TNEF have different container structures, but the UI should not contain two independent security implementations. Both parsers can produce a common message model: subject, sender, to, cc, date, plain body, sanitized HTML candidate, attachments[] { safe filename, MIME type, bytes, inline flag, content ID, content location } The normalization layer is the right place to enforce per-source limits and reject unsupported structures. The viewer and download code then work against the same constrained data regardless of input format. Sanitize HTML as hostile input Email HTML was designed for mail clients, not for direct insertion into an application DOM. A conservative policy removes: scripts and event handlers; forms and interactive controls; iframe , object , and embed elements; styles and CSS URLs; unsafe protocols; executable or unexpected embedded content. Use a maintained sanitizer with a pinned version, but do not stop at its default configuration.

The agent is already in your workflow. The failed build is where it goes blind. AI coding agents have moved from novelty to daily tool. In Stack Overflow's 2025 Developer Survey, 84% of developers said they are using or planning to use AI tools in their development process, up from 76% the year before, and about one in seven professional developers now use AI agents at work every day . Among developers who have used agents at work, roughly 70% agree the agents have reduced the time they spend on specific tasks . There is one place, though, where that agent still tends to go dark: the failed CI run. The pipeline turns red, and your agent (like you) is handed a wall of log output from jobs it did not write, covering steps it did not touch. It has to reconstruct what actually broke before it can fix anything. That reconstruction is the expensive part, and it is exactly the part Latchkey is built to remove. This piece is about a clean division of labor. Latchkey's self-healing CI repairs the failures that are about your environment, not your code. For the failures that are genuinely about your code, Latchkey does not guess and patch on your behalf. Instead it hands your own coding agent a complete, structured account of the failure over the Model Context Protocol, so your agent can fix the bug with full context instead of starting from a log file. Two kinds of red build, and only one of them is yours to fix Almost every failed build is one of two things. Either the environment let you down (a flaky network, a full disk, a process killed for memory, a missing tool, configuration that drifted), or your code is actually wrong (a compile error, a failing test, a broken assertion). These two cases want opposite treatment, and conflating them is how teams end up rerunning pipelines and hoping for green. Latchkey's self-healing CI handles the first case. When a step fails on a Latchkey managed runner, Latchkey detects the failure, diagnoses the cause, and applies a fix while t

Microsoft Execution Containers provide a cross-platform framework for isolating AI agents within secure sandboxes to protect private data and system integrity. This technology allows developers to manage the lifecycle of autonomous code while ensuring that unpredictable agentic workflows do not access sensitive local files or unauthorized network resources. The Evolution of Agent Security and Isolation Trust remains a significant hurdle for developers building modern AI agents, particularly those operating on edge systems. When agents combine local processing with cloud-based intelligence, they often require access to sensitive information to be effective. However, granting this access creates a risk that the agent might call unintended APIs or compromise private user data. Historical attempts to launch autonomous agents in the 1990s largely failed because of these security concerns. Delivering arbitrary code to local machines proved too risky for mainstream adoption. Today, hardware-assisted virtualization has changed the landscape. This technology serves as the foundation for modern security models, including isolated operating system components and cross-platform tools like the Windows Subsystem for Linux. Microsoft now utilizes these virtualization advancements to build a more reliable framework for agent operations. By running agents in secure containers or microVMs, the system separates their activities from the primary operating system. This isolation ensures that even if an agent receives a poorly constructed prompt, it cannot delete critical system files or leak sensitive information. Managing Developer Environments Developers need a way to build code in flexible environments while still planning for restricted production deployments. Microsoft Execution Containers (MXC) address this by offering a policy-based restriction model. This framework allows for the creation of managed, isolated containers that follow specific security protocols. Applying Policy-Ba

The Pipeline Became the Attack Surface For most of the last decade, we treated the CI/CD pipeline as plumbing: invisible, trusted, and mostly ignored until it broke. That assumption is no longer safe. The clearest signal came in 2025, when attackers stopped going after the software a pipeline builds and went after the pipeline itself. This week's research pass pulled together three shifts that are all landing at once: a supply-chain attack that redefined the threat model, GitHub's response in its 2026 security roadmap, a pricing change that quietly rewrites the cost math, and a persistent gap between how much teams trust AI in general and how little they trust it inside CI. Here is what the sources actually say. The tj-actions attack changed the threat model On March 14, 2025, researchers discovered that the popular tj-actions/changed-files GitHub Action had been compromised. According to Palo Alto Networks Unit 42, the action was used by over 23,000 GitHub repositories at the time ( Unit 42 ). The mechanics are worth understanding, because they explain why this matters beyond one action. Attackers injected code that dumped the CI/CD runner's memory and wrote sensitive environment variables and secrets straight into the workflow logs. They retroactively modified multiple version tags to point at a single malicious commit, so pipelines that pinned to a tag rather than a commit SHA pulled the payload ( Unit 42 ). The incident is tracked as CVE-2025-30066, described as allowing remote attackers to discover secrets by reading action logs ( GitHub Advisory Database ). The compromise did not start with tj-actions. Unit 42 traced it back through a leaked personal access token that reached reviewdog/action-setup , a dependency in the chain, with earlier steps going back to late 2024 ( Unit 42 ). In other words, the pipeline's own dependency graph was the delivery vehicle. The takeaway is not "avoid one bad action." It is that the automation running your builds is now a firs

Most "best CI/CD tools" lists are twenty logos in a table, ranked by nothing in particular, with the author's product at the top. This is not that. It is a map of the categories, what each one exists to solve, and how to tell whether you need it yet. I work at Latchkey, so I will say plainly where we sit: we are one option inside one of the six categories below, and I will tell you when we are the wrong answer. Read the rest as a map, not a pitch. A note on what is missing here: I have not invented benchmark numbers or quoted prices for tools I do not operate. Vendor pricing changes often enough that any figure I write today is wrong by the time you read it. Where a number matters, go to the vendor's own pricing page. The mistake most teams make Teams usually shop for CI/CD tools by asking "which one is best." That question has no answer, because the tools are not competing with each other. They are stacked on top of each other. A team that adopts a build accelerator to fix a slow pipeline, when the actual problem is that half their failures are flaky, has bought a faster way to fail. A team that adds pipeline observability before they have enough pipeline to observe has bought a dashboard nobody opens. The useful question is narrower: which layer is currently your constraint? Answer that, and the tool choice inside the layer is usually obvious. Here is the whole landscape in one view. Layer What it solves When it becomes your bottleneck CI platform Running the pipeline at all Never; this is where everyone starts Runners and compute Where jobs run, and how fast they start Queue time or runner cost is visible Build acceleration Doing less work per run Full rebuilds dominate your wall clock Supply chain security What the pipeline is allowed to reach You ship to production or touch customer data Observability and cost Where time and money actually go You cannot answer why last week was slow Artifacts and registries Storing what the pipeline produces You publish images

Two things are true about software delivery in 2026, and they are pulling in opposite directions. The first: AI is now writing a large share of the code that reaches your pipeline. CloudBees' 2026 State of Code Abundance Report found that AI generates or assists in writing 61% of the average enterprise codebase, and that 81% of enterprise leaders report an increase in production issues tied to AI-generated code. The same report names a confidence gap worth sitting with: 92% of leaders say they are confident in the production readiness of that code, even as the failures climb ( CloudBees, 2026 ). The second: the place best positioned to catch those failures, the CI/CD pipeline, is where teams trust AI the least. JetBrains' TeamCity team reported that 73% of organizations do not use AI in their CI/CD pipelines at all, and 78.2% do not delegate tasks to AI in CI/CD workflows, even though general AI usage in development work exceeds 90%. When asked why, 60% cited unclear use cases or value, 36% cited a lack of trust in AI-generated results, and 33% cited data privacy concerns ( JetBrains TeamCity, 2026 ). That is the trust gap. More machine-written code is arriving, more of it is breaking in production, and the pipeline that should be the safety net is the one room teams will not let automation into. This piece is about why that hesitation is rational, and what automation has to look like to deserve a different answer. Why the pipeline is different The JetBrains analysis put its finger on the reason cleanly: development workflows tolerate experimentation because feedback is immediate and cheap. CI/CD is the opposite. It demands consistent, reproducible signals, and the cost of an error is high. A coding assistant that guesses wrong wastes a few seconds of your time. A pipeline that guesses wrong can hide a real defect, ship it, or erode the one thing a pipeline exists to provide: a trustworthy answer to the question "is this build good?" So the bar for automation in CI/

At a private, closed-door event in Austin, Texas today, Tesla officially launched its gilded car of the future. It's a huge milestone for Elon Musk, who has been hyping the imminent arrival of driverless cars for years and has bet the future of his company on AI, autonomous vehicles, and humanoid robots. It was an […]

From 0 to 1,000 Filipino Couples: SEO and Growth Lessons From a Niche SaaS Quick Answer: We grew our bootstrapped wedding planning SaaS from zero to 1,000 registered Filipino couples in 18 months by focusing on hyper-local SEO, long-tail keyword targeting, and AEO-optimized content that answered specific questions couples were already searching for. Introduction: The Niche SaaS Problem Building a SaaS for the Philippines wedding market sounds like a terrible idea. The market is fragmented, couples are price-sensitive, and you're competing against Facebook groups, Excel templates, and WhatsApp broadcasts from vendors who've been in the game for decades. But here's the thing: fragmentation is an opportunity if you own the search intent. When we started WedPlanner , we had no ad budget, no influencer partnerships, and no existing audience. What we had was a thesis: Filipino couples planning their weddings were asking Google very specific questions, and nobody was answering them well. Not the big wedding directories. Not the international apps. Not the Facebook groups where the same questions get asked 47 times a week. So we decided to answer them. Systematically. And it worked. Why Local SEO Matters for Niche SaaS Most SEO advice assumes you're building for the US market. Keywords like "best wedding planner app" are dominated by The Knot, Zola, and apps with $50M+ in funding. You can't outbid them. You can't out-backlink them. You can't out-brand them. But "wedding planner app Philippines"? "best wedding organizer Manila"? "Filipino wedding budget spreadsheet"? Those are different games entirely. The Long-Tail Keyword Goldmine Filipino wedding planning searches are hyper-specific and underserved: "wedding checklist Philippines 2026" — 720 monthly searches, virtually no competition "how much does a wedding cost in Manila" — 1,900 monthly searches, forum posts from 2019 as top results "wedding venue Cavite budget 100k" — 480 monthly searches, Facebook group threads as SE

Last September, Steve Ballmer insisted in an ESPN interview that the Clippers weren't involved in a shady-looking $28 million endorsement deal for his star player, Kawhi Leonard, that involved no actual endorsing. Now, the former Microsoft CEO has been suspended by the NBA for a year, while the league says his team will forfeit five […]

Never miss a delivery. These WIRED-tested picks will help you keep tabs on your front door from anywhere.