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Sarah Friar, CFO of OpenaAI, introduces a practical AI scorecard to measure ROI through useful work, cost per successful task, dependability, and return on compute.

The City Attorney’s Office sent the tech giants cease-and-desist letters this week telling them to stop profiting from 13 “face-swap” apps that are overwhelmingly used to target women and girls.

Uber explained how it keeps its OpenSearch deployments running during a zone outage. It does this by using OpenSearch's built-in shard allocation and its own isolation-group system, which relies on the Odin container orchestration platform. This way, it maintains both query and ingestion capabilities. By Claudio Masolo

Ask ten AI developers what tools they use, and you'll probably get ten different answers. The AI ecosystem is evolving so quickly that it's easy to believe you need every new framework, model, and application to stay productive. I don't think that's true. Over the past year, I've experimented with dozens of AI tools while building products, writing technical content, managing prompt libraries, and developing AI workflows. Along the way, my stack has become surprisingly simple. It's not built around the "best" tools. It's built around the tools that work well together. Here's the AI stack I rely on in 2026 and, more importantly, why each tool has earned its place. 1. ChatGPT: My Primary Thinking Partner ChatGPT is where most of my work begins. Not because it can do everything, but because it helps me think faster. I use it for: Brainstorming ideas Structuring articles Reviewing technical concepts Exploring architectural trade-offs Refining prompts Research assistance I rarely expect the first response to be perfect. Instead, I treat it like collaborating with a knowledgeable teammate who accelerates my thinking. 2. Cursor: My AI-Powered Development Environment When it's time to write code, I move into Cursor. Its strength isn't just code generation. It's understanding the context of an entire project. Whether I'm building a FastAPI backend, integrating APIs, or refactoring an existing codebase, having AI directly inside the editor removes a huge amount of friction. The less I switch between applications, the more productive I become. In fact, one of the biggest lessons I've learned is that adding more AI tools doesn't automatically improve productivity. Sometimes it has the opposite effect. I explored this idea in The Hidden Cost of Using Too Many AI Tools , where I explain why a smaller, well-integrated stack often outperforms a collection of disconnected applications. 3. GitHub: The Source of Truth Every project eventually ends up in GitHub. Not just source code. I

A few months ago, a founder posted about the SaaS he'd just shipped — built entirely with an AI coding assistant, not a line of it typed by hand. He was proud of it, and he had every right to be. Within days of launch, someone found the API key sitting in plain sight in the client-side code. It got used to bypass the paywall, spam the backend, and write garbage into the database. The founder spent the next stretch rotating every key, moving secrets into environment variables, and locking down the API endpoints that should have been locked down before anyone ever saw the site. Nothing about that story is about the AI being bad at its job. The AI did exactly what it was asked: build a working product, fast. Nobody asked it to think about what happens when a stranger opens dev tools. In the replies, someone made a simple point: AI is a great research aid, but shipping a large application still means understanding the code — copying and pasting isn't programming. The founder didn't push back. He agreed: he'd learned it the hard way. The same story, over and over Swap the platform and the same shape of story repeats. Here's the WordPress version — three separate, ordinary launches, three separate silent failures. A site goes live and Google never finds it. Somewhere in Settings → Reading, "Discourage search engines from indexing this site" got left checked — a setting every staging environment needs and every production site must not have. Nobody notices until weeks later, when someone asks why the brand-new site isn't showing up in search at all. A debug log sits in a predictable place, readable by anyone. wp-content/debug.log collects whatever errors WordPress throws — database credentials, API keys, fragments of user data — in plain text, at a URL automated scanners check within hours of a new site going live. Turning debug mode off doesn't delete the file it already wrote. The admin username is still admin . It's the default nobody bothered to change, and it happens

A tutorial series, DEV.to blog series — from your first HTML element to production-grade User Interfaces, all in pure Python. Modern Python web frameworks force developers into a split workflow: business logic lives in Python files with full IDE support, while presentation logic is exiled to template files that offer none of it. Template languages like Jinja2 introduce their own syntax for conditionals, loops, and variable access — syntax that your linter cannot check, your type checker cannot verify, and your debugger cannot step through. Every context variable passed across that boundary is a potential KeyError waiting to surface at runtime. Probo eliminates this divide entirely by making HTML a native Python construct — written, validated, and refactored with the same tools you already use for the rest of your codebase. PART 1: Introduction to Probo — Write HTML Entirely in Python What is Probo? Probo is a Python-first, declarative UI rendering framework . Instead of writing HTML in .html files or using template languages like Jinja2, you write everything in pure Python. No template files. No string concatenation. No f-strings full of angle brackets. Just Python functions and classes that are your HTML. The Two Flavors of Every Tag Every HTML tag in Probo comes in two forms: Flavor Example Returns Use Case Function (lowercase) div() , h1() , p() Rendered HTML Quick rendering, lightweight Class (uppercase) DIV() , H1() , P() SSDOM tree node Tree manipulation, streaming from probo import div , DIV # Function: returns a string immediately # return_list=True html_string = div ( " Hello World " ,) # → "<div>Hello World</div>" # Class: returns a tree node, call .render() to get the string node = DIV ( " Hello World " , Id = " main-title " ) # Because it's a Node, you can manipulate it dynamically node . add ( div ( " Subtitle added later! " )) html_string = node . render () # → '<div id="main-title">Hello World<div>Subtitle added later!</div></div>' Note: by adding ret

This button passes every automated accessibility scan we've thrown at it: <button class= "btn-primary" type= "button" > Check availability </button> And it breaks every AI agent that tries to book a room through it. The markup is clean: a real <button> , a proper accessible name from its text content, an explicit type . Nothing to flag. The failure isn't in the button, it's in what happens after the click. And no static scanner ever clicks. What a scanner actually sees Static accessibility scanners evaluate the DOM at a point in time. Usually the initial render: HTML parsed, framework hydrated, nothing interacted with. They check that state against WCAG rules, missing alt text, contrast ratios, label associations, heading order. That's genuinely useful. It's also a photograph of a lobby, when the task happens in the hallways. Here's what never appears in the initial DOM of a typical booking flow: The date picker that mounts when the check-in field receives focus The error message injected after a failed form submit The room-selection modal that opens on "Check availability" The loading state between "Book now" and the confirmation A scanner reports zero issues on all of these, for the simple reason that at scan time, none of them exist. What an agent actually traverses An AI agent completing a booking doesn't evaluate a snapshot. It walks the flow: reads the accessibility tree, decides on an action, performs it, waits for the interface to respond, reads the tree again. Every state transition is a place where the tree can lie to it. Let's look at three patterns we keep finding in real audits. All three pass static scans. All three stop an agent. 1. The modal that exists on screen but not in the tree { isOpen && ( < div className = "modal-overlay" > < div className = "modal" > < h2 > Select your room </ h2 > < RoomList rooms = { available } /> </ div > </ div > )} Visually: a modal. In the accessibility tree: a div soup appended somewhere in the body, with no role="di

True the Vote is working with a Detroit pastor to produce a new documentary called Trap, based on claims that have already been thrown out in court.

A week ago I put a rough, honestly-a-bit-thin version of PulseWatch in front of real people for the first time. Within days, two different strangers — independently, unprompted — found two real gaps in it. Neither was catastrophic. Both were exactly the kind of thing you only find by watching someone else use the thing you built. This is the story of both, and the fixes. Bug one: the run that never ends This first bug came from a friend testing it on a real script. His question was simple: "What happens if start fires twice before end ?" Good question. At the time: nothing good. Here's why. PulseWatch works on two pings — a job calls /start when it begins and /success (or /fail ) when it's done. The server tracks whichever run is currently "open" for a monitor. The bug: if a job's process restarts mid-run — a crash-and-retry, a redeploy that catches it mid-flight, a scheduler firing twice — you get a second /start before the first run ever closes. The old run just sits there, open forever, an orphan with no ending. Worse, because the watchdog was still waiting on that run's expected finish time, it could fire a false "still running" alert for a run that was, for all practical purposes, dead and abandoned. The fix is a small rule with an outsized effect: a new /start supersedes whatever run is currently open. The old run gets marked superseded — a terminal, non-alerting status — and a fresh run begins clean. The watchdog was updated to treat superseded as a dead end: nothing to wait on, nothing to alert about, and it never shows up in a user's run history. It's not a failure and it's not a success. It's just "this run doesn't matter anymore, a newer one replaced it." The logic, roughly: def handle_start ( monitor ): open_run = monitor . get_open_run () if open_run is not None : open_run . status = " superseded " open_run . finished_at = now () new_run = Run ( monitor = monitor , status = " running " , started_at = now ()) db . session . add ( new_run ) db . session .

The tech world has spent the last year buzzing about the complete rewrite of the TypeScript compiler. Now that TypeScript 7.0 is officially out, the headline is clear: a native Go port delivering 8x to 12x build speedups and an instant editor experience. But if we look past the raw performance numbers, TypeScript 7.0 represents something much deeper. It is the most aggressive modernization sweep in the language's history. The TypeScript team used this architectural migration to eliminate a decade of technical debt, kill off legacy web standards, and restructure how the compiler interacts with the JavaScript ecosystem. If you are planning to upgrade your frontend or backend repositories, here is what actually matters, why the team chose Go, and the breaking changes you need to prepare for. 1. The Architectural Plot Twist: Why Go and Not Rust? When Microsoft first announced they were moving away from a bootstrapped JavaScript compiler to a native binary, the collective internet assumed they would choose Rust—the darling of the modern frontend tooling space (used by SWC, Turbo, and Oxc). Instead, the team chose Go , sparking heavy debate across the community. The reasoning reveals exactly how the TS team prioritizes stability over absolute micro-benchmarks: Bug-for-Bug Compatibility: The goal wasn't to write a brand-new compiler from scratch; it was a 1:1 faithful translation of the massive, decade-old TypeScript codebase. Go’s straightforward syntax allowed a clean mapping of existing JavaScript logic. The Memory Model Challenge: Compilers are inherently full of deeply nested, circular object graphs (ASTs, symbol tables, type structures). Managing these in Rust without heavily leaning on unsafe blocks or running into a brick wall with the borrow checker would have taken years. Garbage Collection Alignment: Go’s built-in garbage collection mirrors JavaScript’s memory model elegantly. This allowed the team to achieve multi-threaded parallelism safely and ship a stable p

Hi everyone! I’m a developer at the beginning of my journey, and I’ve just finished working on a small project that brought me a lot of joy: Capybara Game. It’s a cute game where you feed your capybara and improve her happiness level. You can choose between 5 different types of food or pick your own snack. If the capybara likes the snack, her happiness level rises; if she doesn't like it, the happiness level falls. Your progress is saved automatically. Keep in mind that your capybara gets hungry over time, so make sure to check back and feed her regularly! I went for a minimalist, cozy design. The interface is clean and intuitive, focusing on a relaxing user experience that lets the player focus entirely on the capybara. I built this project using HTML, CSS, and JavaScript. Hope you're interested in playing! You can do it here: Play the game here I’d love to hear your thoughts! If you have any ideas for new features or if you find any bugs, feel free to let me know in the comments.

By default, whenever you request a machine learning model, the underlying architecture saves gigabytes of tensor data into a hidden directory located directly inside your home folder ( ~/.cache/huggingface ). Because standard bare metal and virtual cloud configurations typically isolate the root operating system on a smaller, highly optimized boot drive, pouring 140GB+ of raw weights into the home folder guarantees absolute storage exhaustion. Here is the engineering blueprint to fix it cleanly on Linux. The Cache Location Trajectory When attempting to solve this problem, avoid outdated tutorials recommending deprecated parameters like TRANSFORMERS_CACHE . Environment Route Support Status Architecture Impact HF_HOME Active Master Route Safely redirects all models, datasets, and core assets globally. TRANSFORMERS_CACHE Deprecated Warning Fails to capture datasets and will be removed in version 5.0. HUGGINGFACE_HUB_CACHE Deprecated Warning Legacy routing path that creates unnecessary diagnostic warnings. 🛑 The Symlink Security Risk Creating symbolic links (symlinks) to trick the OS into routing files elsewhere is a common anti-pattern. Mapping these links improperly or running your workflow with elevated rights introduces privilege escalation vulnerabilities, compromising container and host security. Step 1: The Permanent Environment Override To change your Hugging Face cache directory on Linux permanently, target an expansive secondary storage array instead by appending a direct master route into your user profile configuration: # Create a dedicated folder inside your secondary storage array sudo mkdir -p /mnt/massive_drive/ai_model_cache sudo chown -R $USER : $USER /mnt/massive_drive/ai_model_cache # Append the master environment variable to your bash profile echo 'export HF_HOME="/mnt/massive_drive/ai_model_cache"' >> ~/.bashrc source ~/.bashrc Step 2: The Python Import Order Mandate If you declare your custom storage location programmatically inside an application

“Based on simple math, these outbreak responses—which require rapid, timely responses—are going to be greatly diminished,” the former CDC lab director tells WIRED.

TL;DR Welcome back to Dev Opportunity Radar. This is a weekly series where I share opportunities,...

QCon AI Boston 2026 focused on the operational challenges of deploying AI agents, emphasizing the need for robust production infrastructure. Key themes included improving context management, ensuring security through a "harness" around agents, and adopting a comprehensive engineering model for AI. By Tatiana Fesenko

Perimenopause has entered the chat. Perimenopause—and its better-known relative, menopause—used to be considered taboo. Not anymore, thanks at least in part to TV doctors and social media influencers. Perhaps it’s my age, but these days, both my algorithm and my conversations with friends increasingly swing toward perimenopause. Menopause is defined as the life stage that…

Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast. While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. Farmers use…

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A practical, end-to-end walkthrough of taking a read-only slice of an enterprise source system and exposing it to an AI agent as a set of Model Context Protocol (MCP) tools — using Azure Logic Apps, API Management, and Agent Foundry. All identifiers below are placeholders; swap in your own. The goal We wanted a simple outcome: a user asks a business question in plain language and gets a straight answer — no dashboards, no field names, no training on the underlying system. That means an AI agent with safe, read-only access to a backend system of record, exposed as discrete tools the model can call. The constraints shaped every decision: Read-only. The agent can retrieve and analyze, never write. Safe. Records in most business systems contain free text (notes, subjects, descriptions) that could carry prompt-injection payloads. Tool output must be treated as data, never instructions. Composable. The agent should see many small, well-described tools — "list records", "aggregate by category", "record change history" — not one giant "query the system" tool. The architecture Five layers, one direction of flow: Agent (Agent Foundry) │ MCP (JSON-RPC over HTTP) ▼ MCP server (API Management) │ REST operation per tool ▼ API gateway (API Management) │ single POST, routed by body ▼ Tool executor (Logic App) │ OAuth token + REST calls ▼ Source system (REST API) The key design choice: one backend endpoint, many logical tools. The Logic App exposes a single HTTP trigger that accepts { "tool": "records.list", "parameters": { ... } } and routes internally. API Management then fans that single endpoint out into many named operations, and its MCP feature turns those operations into agent tools. This keeps the backend trivial to maintain while the agent still sees a rich, typed tool catalog. Step 1 — Design the tools Start from the questions , not the schema. A useful toolset usually falls into a few families: Records: list / get / search / filter for the core business entities. Activity