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Trump admin green-lighting $111B deal "reeks of corruption," Sen. Warren says.

AI for API Design & Testing in 2026: Speakeasy vs Swagger AI vs Postman AI — I Built 3 APIs with Each API design is one of the most tedious parts of backend development. Writing OpenAPI specs, generating SDKs, testing endpoints, maintaining documentation — it's all hours of work that could be automated. In 2026, three tools are fighting for your API workflow: Speakeasy , Swagger AI , and Postman AI . I spent 4 weeks building the same REST API with each one, tracking time savings, code quality, and actual developer experience. Here's what actually happened. The Test Setup: Building a Real API I built a simple but realistic ecommerce API (GET/POST products, orders, user management) three separate times, measuring: Time to spec — writing the OpenAPI definition Time to SDK generation — generating client libraries Time to testing — setting up test cases and running them Documentation quality — readability, examples, completeness Iteration speed — how fast you can modify the API and regenerate everything All three tools were given the same requirements. Same laptop, same environment, same skill level (senior backend engineer). Speakeasy: The SDK Generation King Setup time: 8 minutes (CLI install, auth, first config) Learning curve: Low — clear docs, straightforward CLI Speakeasy is laser-focused on one problem: turning your API spec into production-ready SDKs. It generates TypeScript, Python, Go, Java, and more from a single OpenAPI definition. The Good SDK quality is exceptional. The generated TypeScript SDK was production-grade immediately — proper error handling, retry logic, request/response typing, retries built in. No cleanup work needed. Multi-language generation is instant. After writing the OpenAPI spec once, I had Python, Go, and TypeScript SDKs in < 2 minutes total. Each one was framework-idiomatic (using popular libraries in each language). Versioning is smart. Speakeasy automatically versioned SDKs and generated changelogs. When I modified the spec, it detect

Introduction Extracting data from older websites is a technical challenge that goes beyond simple copy-pasting. The example website provided illustrates this perfectly: its outdated design, reliance on mouse-over interactions, and lack of structured export options create a perfect storm of extraction difficulties. This article dissects these challenges and provides a roadmap for extracting both visible content and mouse-over images while preserving data integrity. The Core Problem: Legacy Technology Meets Modern Needs The website's URL parameters ( screen_width=0&screen_height=0 ) immediately signal a legacy system likely built for a bygone era of fixed-width displays. This design choice breaks modern scraping tools that expect responsive layouts. The mouse-over images, critical to the site's content, are dynamically loaded via JavaScript , meaning they don't exist in the initial page source. This requires simulating user interactions to trigger their appearance, a task beyond basic HTML parsing. Why Manual Extraction Fails Attempting to manually save images or copy text from this site is a losing battle. The mouse-over images, for instance, are not directly downloadable – they're embedded in JavaScript events. Even if you could save them individually, maintaining their association with the corresponding visible content would be error-prone and time-consuming. This method also fails to scale for larger websites with hundreds of such elements. The Technical Solution: A Multi-Pronged Approach Effective extraction requires a combination of techniques: Browser Automation: Tools like Selenium or Puppeteer can simulate mouse movements to trigger hover events, capturing both visible and hidden content. This method mirrors human interaction , ensuring all dynamic elements are revealed. Network Request Inspection: Analyzing the website's backend requests using browser developer tools can reveal direct URLs for mouse-over images , bypassing the need for hover simulation. This

Props for That creates live props based things CSS can't normally see in the browser. Things like cursor position, progress values, certain form states, current time, scroll velocity. Prop For That originally handwritten and published with love on CSS-Tricks . You should really get the newsletter as well.

submitted by /u/AmrDeveloper [link] [留言]

Quick Tip: How to Choose the Right Model for Slack AI Workflows in 2026 I've been running Slack-integrated AI workflows in production for about three years now, and the question I get asked most often is deceptively simple: "Which model should I actually use?" Back in 2024, the answer was easy — you picked GPT-4o and moved on. But in 2026, with 184 models accessible through Global API and price points ranging from $0.01 to $3.50 per million tokens, that decision has become a genuine engineering problem. Pick wrong and you're either burning budget or shipping a sluggish experience. Pick right and your CFO actually smiles at you. Let me walk you through how I think about this, what the numbers actually look like, and where I've landed after months of benchmarking across multi-region deployments. Why Slack Workloads Are Weird Most people underestimate what a Slack AI assistant needs to do well. It's not a chatbot. It's a latency-sensitive, always-on, context-heavy workload that has to feel native inside a chat client where users expect responses faster than they can refresh the channel. In my experience, the three constraints that matter most are: p99 latency under 1.5 seconds for the first token — anything slower and users start double-messaging 99.9% uptime across at least two regions — Slack itself is up, so your AI better be too Cost per active user per month under $0.40 — this is the line where finance stops asking questions If a model can't hit those numbers consistently, it's not viable, no matter how clever the benchmark scores look. The Pricing Landscape I Actually Use Here's the table I keep pinned in my team's documentation. These are the models we rotate between depending on the workload. I haven't changed a single number — these are the exact rates as of writing this: Model Input ($/M) Output ($/M) Context DeepSeek V4 Flash 0.27 1.10 128K DeepSeek V4 Pro 0.55 2.20 200K Qwen3-32B 0.30 1.20 32K GLM-4 Plus 0.20 0.80 128K GPT-4o 2.50 10.00 128K The spread is w

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Stop letting the prompt be your state machine You shipped an LLM feature six months ago. Now the same user input produces wildly different outputs depending on... nothing you can point to. Something in the sampling? The time the context filled up and a chunk got dropped? Nobody knows. This is what happens when the prompt becomes your runtime. The trap: the prompt as an accidental runtime Here is what the trap looks like in TypeScript: async function handleUserRequest ( input : string ): Promise < string > { const prompt = ` You are a helpful assistant. The user said: ${ input } Previous context: ${ someGlobalContext } Decide what to do, gather any information you need, format the response, and return it. ` ; return llm . complete ( prompt ); } The model is doing everything here: deciding the intent, gathering data, formatting output, choosing what to persist. That is a footgun. You handed the runtime to a stochastic function. Gartner attributes many failed agentic AI projects to unclear value and inadequate risk controls. Deterministic, testable workflows address both. The fix is not a better prompt. The fix is to stop using the prompt as an architecture. What "deterministic" can and cannot mean here Be honest about what you can and cannot control. You cannot control: the model's exact output. It is probabilistic by design. You can control: The shape of the output (structured output plus schema validation) The steps that run before and after the model call What data enters the model What happens when the output fails validation Whether a human reviews the result before it commits to anything irreversible Determinism here means: the same inputs, the same workflow steps, the same guardrails every time. Not the same tokens every time. That is a realistic and achievable target. It is also the thing teams skip when they are moving fast. Typed workflow steps around the model call Break the work into discrete typed steps. Each step has a clear input type and a clear output

Google’s Android 17 update includes highlights like new floating “Bubble” app windows for easier multitasking, a Screen Reaction recording mode, and a 50/50 split gaming mode for foldable phones. Meanwhile, Wear OS 7 brings Live Updates, better battery life for smart watches, and prepares connections for new Android XR smart glasses that will launch this […]

A security researcher said a flaw in FIFA’s online platforms allowed her to access several internal systems, including one that could have allowed her to take control of the TV stream of every World Cup match.

Pentagon also claims 1.5 million personnel are using generative AI tools.

Following its official debut last month, Google is now rolling out Android 17 to compatible Pixel phones, alongside additional exclusive features as part of the June Pixel Drop. Not every feature announced alongside the OS at the pre-I/O Android Show is available today though. Android 17 itself is arriving on Pixel phones today, and Google […]

Google's Wear OS 7 update is starting to roll out today for the Pixel Watch 2, 3, and 4 , adding a new Live Updates feature that tracks live events from your Android smart watch. Live Updates will now sync with Wear OS devices, so updates like sports scores or an ongoing meal delivery will […]

Google has released Android 17 and Wear OS 7, introducing new multitasking features, parental controls, security tools, and smartwatch upgrades. The launch is also accompanied by a Pixel Drop that brings Google’s latest AI models to its devices.

Mobileye apparently wants to own some of the robotaxi market, even if that puts it in direct competition with companies it supplies its self-driving system to.

The company said the cuts were part of a restructuring meant to help scale to profitability. Rivian recently pushed back its profitability goal to invest in autonomy.

Nimble’s thoughtfully designed SharePower is a modular power bank you can share with a friend.

Now that we're clear of WWDC and all of the new AI-powered features coming to Apple's platforms, Bloomberg reporter Mark Gurman has more details about rumored new hardware, like the camera-equipped AirPods he'd previously written about. He says they are currently on schedule for a late 2027 launch, and that while we're checking out beta […]

I wrote this technical deep-dive to explore the paradigm mismatch between declarative, set-based processing and sequential, depth-first search algorithms. The write-up walks through the mechanics of forcing a relational database engine (DuckDB) to handle chess logic, specifically: Data Representation: Mapping 64-bit bitboards into a relational model using UBIGINT types. The Pruning Blocker: Why the stateless nature of relational sets prevents sibling nodes from communicating, making true Alpha-Beta pruning impossible inside a single query. The Workaround: Offloading the stateful control flow to an external orchestrator to implement Batched Principal Variation Search (PVS) across query boundaries without violating the declarative nature of the core chess math. The resulting chess engine is obviously not competitive, but the goal was to document the architectural trade-offs, the performance walls encountered with recursive CTEs, and how relational algebra behaves when pushed entirely out of its comfort zone. submitted by /u/swing_bit [link] [留言]

For over a decade now, Snap has been working on this device. Now the glasses are finally here. So what stands out on first impression?