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As UK police embrace the AI revolution, a WIRED investigation reveals the messy inside story of one region’s experiment with predictive analytics.

It’s been hard to look away from headlines about the European heat wave this week. Temperatures are breaking records across the continent, and the weather is threatening lives, shutting down schools, and in one particularly ironic case, forcing the cancellation of a London Climate Action Week event about extreme heat. As the summer ramps up…

Cloudflare released the Cloudflare One stack, an open-source library of agent skills for planning, deploying, and managing Zero Trust environments. The skills include automated migration logic for Zscaler and Palo Alto Networks, the same logic used in Cloudflare's Descaler program that has moved enterprise customers in hours rather than months. By Steef-Jan Wiggers

Adding an AI feature looks deceptively easy. You sign up for an API key, paste in a prompt, and within an hour you've got a working demo that makes the whole team lean over your shoulder. Then you ship it, traffic arrives, and two things happen at once: your latency graph develops a long, ugly tail, and your monthly bill arrives with a number that makes finance schedule a meeting. The gap between "impressive demo" and "production feature" is almost entirely about cost and latency engineering. The model is the easy part. Here's how to cross that gap. First, understand what you're actually paying for Most LLM APIs bill by tokens — roughly ¾ of a word each — and they bill both directions: the tokens you send (input) and the tokens the model generates (output). Output tokens are usually several times more expensive than input tokens, which has a non-obvious consequence: a verbose prompt is cheaper than a verbose answer. This reframes optimization. People obsess over trimming their prompts while letting the model ramble for 800 tokens when 80 would do. If you want to cut cost, the highest-leverage move is almost always constraining the output : ask for JSON, ask for a single sentence, set a max_tokens ceiling, and tell the model explicitly to be terse. Latency follows the same logic. Generation is sequential — the model produces one token at a time — so output length is the single biggest driver of how long a request takes. A 50-token answer is fast almost regardless of model. A 2,000-token answer is slow even on the fastest infrastructure. Lever 1: Don't call the model when you don't have to The cheapest, fastest LLM call is the one you never make. Two techniques eliminate a startling share of traffic. Caching identical and near-identical requests. Many real-world prompts repeat — the same FAQ-style question, the same document summarized twice, the same classification of similar inputs. A cache keyed on the normalized prompt turns a repeat request into a sub-millisecond

Building a unified market intelligence platform for traders, analysts, researchers, and developers. After months of development, Jungletrade is now publicly available. The idea behind Jungletrade is simple: modern market analysis has become fragmented. Market data, indicators, analytical models, and trading signals are often distributed across multiple platforms, forcing users to maintain several subscriptions, workflows, and dashboards just to build a complete market view. We wanted to explore a different approach. 📊 The Problem Most market platforms focus on a specific layer of the analytical stack: Raw data Technical indicators Quantitative models Trading signals Each layer provides value, but users are frequently required to move between multiple tools to connect the pieces. Our goal was to create a modular ecosystem where these layers can coexist within a single platform. 🧭 The Jungletrade Ecosystem Today, JungleTrade provides four product categories: 📦 Data Structured datasets for market research and discovery. 🧠 Models Analytical frameworks designed to identify patterns and relationships within market data. 📈 Indicators Tools that transform raw information into actionable insights. ⚡ Triggers Event-driven signals designed to highlight potential market opportunities. 🔍 Built for Transparency One design decision was particularly important to us: every product should explain itself. Each product includes: Product description Key features Use cases Interpretation guidelines Methodology overview The objective is not simply to provide charts but to explain the problem being solved and how the underlying analysis works. 🔌 API First All products available through the platform are also accessible through API endpoints. Developers interested in integrating JungleTrade data into their own applications, dashboards, or research pipelines can request a demo API key through the platform. 🏗️ Architecture JungleTrade is built using a modular, service-oriented architecture des

It is clear that Vyshyvanka is more than just code — it is an ecosystem. The true power of an open-source workflow engine lies in its community. Today, we want to talk about how you can get involved, whether you are interested in pushing the boundaries of the core engine or building specialized solutions with custom plugins. The Core Engine vs. The Plugin Ecosystem A common question we get is: 'Should I contribute a PR to the core engine, or should I build a separate plugin?' The answer depends entirely on the scope of your contribution. When to Contribute to Core The core engine ( Vyshyvanka.Core , Vyshyvanka.Engine , Vyshyvanka.Api , Vyshyvanka.Designer ) should be reserved for changes that benefit every user of the platform. Good candidates for core contributions: Performance improvements to the execution pipeline New fundamental port types or expression functions Bug fixes in the engine, validation, or persistence layers Enhancements to the Designer UI (canvas, node editor, property editors) Improvements to the API surface (new endpoints, better error responses) Documentation improvements These changes require careful review and testing because they impact every installation. We encourage PRs here, but we also ask that you open an issue first so we can discuss the architectural impact. When to Build a Plugin Plugins ( ./plugins/ ) are the best way to extend functionality without increasing the maintenance burden of the core. Good candidates for plugins: Integration with a specific third-party SaaS tool (CRM, CI/CD, monitoring) Custom nodes specific to your industry or use case Experimental node behaviors that are not yet ready for core Proprietary integrations you want to keep separate from the open source project Plugins are independent, versionable, and can be maintained outside the core release cycle. They empower you to solve your specific problems immediately without waiting for a core release. Project Structure at a Glance Understanding where things live i

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Slack has outlined how its AI serving infrastructure evolved through four distinct phases, moving from a self-managed Amazon SageMaker deployment to a multi-cloud architecture spanning AWS Bedrock and Google Cloud Vertex AI. By Matt Foster

Why developer tools deserve a design language of their own - and how I built one for my own corner of the web Somewhere along the line, we collectively agreed that "functional" had to mean "boring." Open almost any developer tool, internal dashboard, or technical log and you'll find the same thing: a sterile corporate wiki. Grey on white. The same SaaS design system everyone copied from the same three component libraries. Rounded cards, a sans-serif font, a faint drop shadow. It works. It's also completely forgettable. But here's the thing nobody says out loud: when you're building for engineers - or building your own space on the web - you are under no obligation to follow the standard playbook. The intersection of system design and visual identity is one of the most under-explored areas in frontend architecture. We obsess over latency, bundle size, and runtime dependencies, then slap a default theme on top and call it done. The backend gets all the craft. The interface gets a template. I wanted to do the opposite. Building VOID_PROTOCOL When I put together my own developer log - https://blog.naveenr.in - I deliberately stepped away from the standard minimalist tech blog. Instead, I built out a full design system I call the VOID_PROTOCOL × Manga Editorial Design System: dark-only, type-driven, built on Astro 6, Tailwind 4 (CSS-first @theme tokens), and React 19 islands. The name isn't decoration. VOID_PROTOCOL started on my https://naveenr.in portfolio, which runs in two modes. There's a minimal version, and there's an immersive one - and in immersive mode the background is a real-time 3D simulation of a sentinel entity. It's not a looping video; it actually responds to your movement, clicks, and scroll. When you leave it alone long enough, it sleeps. And when it sleeps, it dreams - it dreams my initials. (Yes, really. It started as a joke and I kept it.) That entity is the soul of the whole identity: black, empty, void-like space and a cool blue palette, a deep-sp

Over the past few months, I've been building a gaming website focused on Elden Ring guides, calculators, and tools. While the project started as a simple hobby, it quickly became an interesting experiment in SEO, content strategy, and web development. Here are some lessons I learned along the way. Building the Site Was Easier Than Getting Traffic Launching a website with Next.js was straightforward. Getting visitors was much harder. Many developers underestimate how competitive search traffic can be, especially in gaming niches where large sites already dominate search results. Publishing a website is only the first step. Why I Chose Next.js The project uses: Next.js TypeScript React Tailwind CSS The biggest advantage was SEO. Server-side rendering and static generation helped ensure that search engines could easily crawl and index pages. Performance was also excellent compared to many traditional CMS solutions. Tools Attract Different Users Than Articles One interesting discovery was that calculators and interactive tools behave differently from standard content pages. For example: Guides answer questions. Tools solve problems. A player may read a guide once, but they might return to a calculator dozens of times while planning different character builds. This makes tools valuable long-term traffic assets. Internal Linking Matters More Than Expected When new content was published, internal links helped search engines discover and understand related pages. For example: Build guides linked to calculators. Calculator pages linked to stat guides. Stat guides linked to weapon builds. This created a stronger topical structure around the Elden Ring ecosystem. Search Traffic Takes Time One of the biggest lessons was patience. Many pages received: Zero impressions Zero clicks No rankings for days or even weeks. Then suddenly search impressions started increasing as Google tested pages across different queries. Traffic growth was rarely linear. Content Clusters Work Well Inst

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And by viral I mean from $0 to $31. Umami told me Clew Directive got 14 visits last month. AWS told me I owed $31 for it. That works out to $2.21 a visitor, which would make it the most expensive free learning-path tool in California. Spoiler alert: 14 visitors, $31, and not a single one of them was the reason. Something was off. Here is how Amazon Q, Claude, and a few hours of reading my own code untangled it. The app turned out to be innocent. What Clew Directive is, quickly A free, stateless tool that builds you a personalized AI learning-path PDF. You take a 60-second Vibe Check, four questions about your goals and how you learn, and it maps you to free, verified resources and hands you a briefing. No accounts, no database, no paywall, nothing stored about you. It runs on Amazon Nova, which is why it costs close to nothing to operate, which is also why a $31 bill made no sense. The name is the Theseus kind of clew. A ball of thread to find your way out of the maze. Less hype, more direction. Live at clewdirective.com . The number that didn't add up Twelve visitors, 14 visits, 93% bounce, average session about a minute. Referrers from Bing, Google, Yahoo, GitHub. Visitors from the US, India, Netherlands, Egypt, Ethiopia, Singapore. Mostly crawlers stopping by to say hello. A few curious humans and a parade of bots is not a $31 month. So either every visit was doing something enormous, or the bill was never about visits at all. The dashboard lied, politely. An Amazon Q Story My cost tracker said Clew Directive was running on Claude Sonnet. Sonnet is the expensive one. Case closed, right? I opened the repo. Clew Directive does not run Sonnet. The Navigator agent runs Amazon Nova 2 Lite. Scout and Curator run Nova Micro. The IAM policy is scoped to Nova ARNs only, so a Sonnet call from these functions would come back AccessDenied. The app physically cannot bill Sonnet. The math agreed. A full learning-path generation on Nova costs about two-tenths of a cent. Fourtee

Grab's security team built Palana, a Kubernetes-native secure execution platform, to run autonomous AI agents safely. Unlike deterministic software, model-driven agents exhibit unpredictable tool-use, code-writing, and prompt injection risks. Palana contains these threats at the infrastructure level using isolated namespaces, out-of-process control planes, and proxy-mediated, Vault-backed secrets. By Patrick Farry

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Exposing your app to an AI agent over MCP is basically handing someone a master keyring and trusting them to only open the doors they're supposed to. That trust is a bug waiting to happen. This week I wired up a batch of MCP tools over a multi-tenant Laravel app, and the whole exercise was really about one question: how do I let an agent drive the app without letting it drive someone else's data? Here's the thing about MCP tools — each one is an endpoint. An agent calls list_events , publish_event , check_in_participant , and your server runs code on the caller's behalf. The moment you have more than one tenant, every single tool needs to answer two questions before it does anything: are you allowed to do this , and are you allowed to do it *here *. Authorization and scope. Skip either and you've built a confused deputy. The trap: ambient scope doesn't exist under token auth In a normal web request, multi-tenancy is comfortable. You've got a logged-in user, a global scope on the model that quietly appends where organization_id = ? , and you mostly forget it's there. Everything Just Works because there's an ambient "current organization" sitting in the session. MCP tools don't have that. The caller authenticates with a token, there's no session, no middleware stack that set up a current-tenant context. If you lean on a global OrganizationScope that reads "the current org" from somewhere, it reads nothing — and a query you assumed was fenced returns every tenant's rows. That's the kind of bug that doesn't throw an error; it just silently leaks. So the rule I settled on: under token auth, never rely on ambient scope. Filter explicitly, every time, in one place. That "one place" is a small trait every event-scoped tool pulls in: trait ResolvesOrgEvents { protected function resolveOrgEvent ( Authenticatable $user , string $uuid ): ?Event { if ( empty ( $user -> organization_id )) { return null ; } return Event :: query () -> withOrganization ( $user -> organization_id )