You’ve built your model, your training code is containerized, and you’re ready to scale up on Google Kubernetes Engine (GKE). You go to provision your nvidia-h100-80gb node pool and... QUOTA_EXCEEDED. It’s one of the most common (and frustrating) roadblocks in modern AI development. High-end accelerators like H100s, A100s, and TPUs are in massive demand, and securing permanent, on-demand quota for them can be difficult. But a lack of on-demand quota doesn't mean you're out of options. GKE provides two powerful, cost-effective strategies for acquiring these scarce resources when you can't get standard, on-demand instances: Spot VMs and the Dynamic Workload Scheduler (DWS) . Let's break down what they are, when to use each, and how to implement them. Strategy 1: Spot VMs Spot VMs are Google Cloud's excess compute capacity sold at a massive discount, up to 90% off the price of standard on-demand VMs. They are perfect for workloads that can be interrupted. The catch is that Spot VMs have no availability guarantee. Google Cloud can "preempt" (i.e., terminate) them at any time if that capacity is needed for on-demand customers. GKE gets a 30-second warning before the node is terminated. Kubernetes uses this window to gracefully shut down your application (giving non-system pods up to 15 seconds to wrap up) before the node vanishes. When to use Spot VMs for accelerators Spot VMs are ideal for workloads that are: Fault-tolerant and stateless: Your application can handle a node vanishing and having its pods rescheduled elsewhere. Batch processing: Jobs that can be easily restarted or have checkpointing built-in. CI/CD pipelines: Running tests or builds that don't need 100% uptime. How to use Spot VMs in GKE You can easily add a Spot VM node pool to your GKE Standard cluster. The key is to use Spot VMs for your workers, not your critical system pods. Create a dedicated Spot VM node pool: When creating a node pool, simply add the --spot flag and apply a taint so standard pods

Hi all, I'm excited to show off Textile, a desktop app I recently built. Textile can combine bits of text using various inputs, such as commands on your computer, the contents of your clipboard, and hard-coded strings that you provide. It lets you carefully build up and modify a dynamic string, step by step, until it's exactly how you need it. The saved steps can then be executed on demand, with the click of a button or using a keyboard shortcut. I built Textile because I was often constructing

If you remember Zeus: Master of Olympus, you'll want to watch this game closely.

Altman has an "utter disregard" for human lives, Florida AG says.

A SaaS team adds “Pay with crypto” to checkout. The first test looks fine: create a wallet address, show a QR code, receive USDT, mark the order as paid. Then production starts. One customer sends the right amount on the wrong network. Another pays after the invoice expires. A third sends 99.80 USDT instead of 100 USDT. Support sees a transaction hash but cannot find the order. Finance sees funds received but cannot match them to an invoice. The backend receives the same webhook twice and unlocks the product twice. That is the moment crypto payment integration stops being a QR-code feature and becomes a payment infrastructure problem. This is the first Dev.to post from Cryptoway . We build crypto payment infrastructure for online businesses, and here we will share practical notes about crypto payment API design, invoices, payment webhooks, stablecoin payments, checkout flows, reconciliation and payment status handling. What is a crypto payment gateway? A crypto payment gateway is the layer between a business event and a blockchain transaction. The business event can be: a SaaS subscription invoice; an e-commerce order; a digital product purchase; a marketplace deposit; a service payment link; an internal billing event. The blockchain transaction is the customer sending BTC, ETH, USDT, USDC or another supported digital asset. The gateway connects the two. It creates a payment request, shows the customer what to pay, monitors the blockchain, updates the payment status and notifies your backend when something changes. In other words: a crypto payment gateway is not the blockchain itself. It is the operational layer that makes blockchain-based payments usable inside real products. Crypto Payment Gateway vs Wallet Address A wallet address is enough for a manual payment. It is not enough for a product that needs order tracking, support visibility and finance reconciliation. Area Wallet address only Crypto payment gateway Order matching Manual matching by amount, address o

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Skills Are a Mess. Let's Fix That. Here's the problem: you write a skill for zeroclaw. It works locally. You push it. Someone else tries to install it. Nothing works. The error says "missing dependency" but doesn't say which one. Or it installs but the audit fails silently. Or the test harness just... doesn't run. Sound familiar? I've been watching the zeroclaw skills ecosystem grow. More people are authoring skills. More people are hitting the same walls. The v0.7.6 release is about tearing those walls down. What Actually Breaks Let's get specific. Three failure modes I see every week: 1. Install hell zeroclaw skills install my-skill # → Error: Failed to resolve dependency graph # → (no other output) You're left guessing. Is it a peer dependency conflict? A missing Python version? A circular reference in the skill manifest? The loader gives you nothing. 2. Audit blindness zeroclaw skills audit ./my-skill # → Audit complete. 0 issues found. # → (skill crashes immediately on first use) The audit passed. But it didn't check for the actual runtime errors — missing environment variables, incompatible tool signatures, malformed output schemas. It checked the manifest format. That's it. 3. Test harness that doesn't test zeroclaw skills test ./my-skill # → Running 3 test cases... # → All passed. # → (skill still hallucinates in production) The test harness runs your skill against mock data. But the mock data doesn't match real tool outputs. Your skill passes locally, fails in the wild. Why This Happens The current architecture treats skills as static packages. You define metadata in a skill.json , point to some functions, and assume it works. But skills are dynamic. They call tools. They depend on runtime state. They interact with the sandbox. The loader doesn't validate the runtime contract. The audit doesn't simulate execution. The test harness doesn't fuzz inputs. So you get false positives everywhere. "Works on my machine" becomes "works in my specific environment with

If you’ve ever worked with Arabic, Persian, Hebrew, Urdu, or any RTL (Right-to-Left) language on the web, you probably know the pain. Mixed RTL/LTR text rendering breaks unexpectedly. Punctuation looks wrong. Numbers don’t match the locale. Ellipsis appears on the wrong side. URLs inside Arabic text become unreadable. And emails or plain-text environments completely destroy formatting. After dealing with this problem repeatedly, I decided to build: rtl-text-tools A lightweight RTL text processing toolkit for JavaScript and TypeScript. It handles: RTL detection Direction normalization Arabic/Persian digit conversion RTL punctuation conversion Ellipsis fixing Unicode bidi wrapping CSS helpers DOM helpers And it works all the way back to IE11 with zero runtime dependencies. Why This Exists Most internationalization libraries focus on translations and formatting APIs. But very few actually solve the rendering problems of RTL text itself. For example: "مرحبا, رقم 123..." Visually, this often renders awkwardly in mixed-direction environments. You usually want: "...مرحبا، رقم ۱۲۳" That means: move ellipsis to the correct visual side convert punctuation convert digits preserve RTL readability That’s exactly what rtl-text-tools does. Installation npm install rtl-text-tools Quick Example import { fixRTL } from ' rtl-text-tools ' ; fixRTL ( ' مرحبا, رقم 123... ' ); // → "...مرحبا، رقم ۱۲۳" Arabic digits are also supported: fixRTL ( ' مرحبا, رقم 123... ' , { lang : ' arabic ' }); // → "...مرحبا، رقم ١٢٣" If the text isn’t RTL, it returns the original string unchanged: fixRTL ( ' Hello world ' ); // → "Hello world" Features 1. RTL Detection Detect whether text contains RTL scripts. import { hasRTL } from ' rtl-text-tools ' ; hasRTL ( ' مرحبا ' ); // true hasRTL ( ' שלום ' ); // true hasRTL ( ' Hello ' ); // false Supports: Arabic Hebrew Persian/Farsi Urdu Syriac Thaana N’Ko Samaritan Mandaic and more 2. Digit Conversion Convert Latin digits into locale-specific numerals. Persian

Most scraper demos lie by accident. They show the happy path: one URL, one clean page, one neat JSON object. Then the first real user tries a marketplace search page, a login wall, a JavaScript shell, a rate-limited product page, or a site that serves different HTML to every fetch path. The response still comes back as JSON, so everyone relaxes. That is the trap. A JSON response is not the same thing as a useful extraction. The failure mode agents hate AI agents do not just need scraped text. They need to know what happened. Bad extraction output looks like this: { "title" : "Example product" , "price" : "$29.99" , "availability" : "in stock" } That looks fine until you inspect the source and discover the page was a login prompt, a bot challenge, or a thin JavaScript shell. The extractor filled the schema because the schema was requested. Helpful. Like a smoke alarm that hums a little song while the kitchen burns. Better extraction output separates the data from the confidence and the failure class: { "status" : "failed" , "failure_type" : "login_required" , "confidence" : 0.94 , "extracted" : null , "evidence" : { "final_url_type" : "restricted_page" , "visible_content" : "login prompt" , "structured_data_found" : false }, "next_step" : "Use an authorised source, public item URL, feed, API, or sample HTML." } That is less flashy. It is also much more useful. The useful contract is not “scrape anything” “Scrape anything” is usually a warning label wearing lipstick. For agent workflows, the better contract is: Return structured data when the page provides enough evidence. Return a specific honest failure when it does not. Preserve enough metadata for the caller to decide what to do next. Never invent fields just because a prompt asked nicely. This matters for ecommerce, lead enrichment, price monitoring, competitor tracking, procurement, and internal research agents. If the agent cannot tell the difference between “product unavailable”, “page blocked”, “login require

PixToCode is a new Figma plugin that turns the frames you've already designed into production-ready code with AI — React, Angular, Vue, or HTML, all Tailwind-first. Just published on the Figma Community: figma.com/community/plugin/1641790551381890223/pixtocode What it does Select one or more frames in Figma, pick a framework, click Generate. About 10 seconds later you have clean code that uses the exact colors, spacing, typography, and layout from your file — not generic Tailwind utility soup. Highlights: 4 frameworks — React (TypeScript), Angular (standalone + Signals), Vue 3, or semantic HTML5. All Tailwind-first. UI library presets — shadcn/ui, Material UI, Chakra, Ant Design on React, Angular Material on Angular. Output uses the real components , not generic divs. Refine with plain English — type "make the button rounded" or "use green for the active tab" and the AI rewrites the component in place. Multi-frame batch — select up to 5 frames, get them all in one pass. Variants → typed props — a Figma Component Set with Primary / Secondary / Disabled becomes one typed prop-driven component, not three duplicate files. Live browser preview — see the generated component rendered in a sandboxed tab before pasting it into your project. Cloud history — every generation saved to your account, synced across devices. How it works Get a free license key at pixtocode.com (5 free generations, no credit card). Install the plugin from the Figma Community. Paste the key into the plugin's license field. Select a frame, choose a framework, click Generate. Copy the code straight into your project. That's the whole flow. Pricing Free — 5 generations on signup Pro — $20/month for 100 generations Power — $39/month for 250 generations Team — $99/month, 5 seats, 600 shared generations (scales to 10 seats) All paid plans have a 7-day refund guarantee. Tips for best results Frames with auto-layout , named layers , and consistent design tokens produce the cleanest output. For huge dashboard

Whisper is remarkably robust. But "robust" doesn't mean "immune to noise." If you've ever run a meeting recording through Whisper and gotten back garbage — or worse, confidently wrong text — the problem is usually the audio, not the model. Here's the thing: different noise types fail differently. Electrical hum causes Whisper to hallucinate syllables. Echo makes it drop words entirely. Static makes it confuse phonemes. Knowing which noise you have tells you exactly which fix to apply. This post covers: ✅ How each noise type (hum, hiss, echo, wind, static) degrades Whisper output ✅ A Python preprocessing pipeline that detects and removes noise before transcription ✅ How to call the StemSplit Denoise API for cloud GPU noise removal (no local setup) ✅ Measured WER improvements you can reproduce The Noise → Transcription Failure Map Noise Type What It Sounds Like How It Breaks Whisper Hum (50/60 Hz) Constant low-frequency "buzz" Inserts phantom syllables, lowers confidence Hiss High-frequency "shhh" Loses sibilants, confuses "s/sh/f" sounds Echo / Room reverb Words "bounce" and overlap Drops end-of-sentence words, merges phrases Wind Burst plosives, low-frequency rumble Transcribes as "[inaudible]", breaks sentence segmentation Static / crackling Random pops and snaps Breaks word boundaries, causes mid-word cuts These aren't hypothetical. They're reproducible failure modes. Let me show you how to handle each one. Prerequisites pip install openai-whisper requests python-dotenv soundfile numpy librosa You'll need: A StemSplit API key from stemsplit.io/developers (free 5-minute tier, no credit card) ffmpeg installed ( brew install ffmpeg / sudo apt install ffmpeg ) The Preprocessing Pipeline Here's the full pipeline before we break it down: Audio file → [Noise detection] → [Denoise via StemSplit API] → [Post-process: normalize, trim silence] → Whisper → Transcript Step 1: Detect What Kind of Noise You Have Before throwing everything at a denoiser, it helps to know what you

The RL Flywheel That Actually Works Here's what's breaking: You've got a reinforcement learning setup that trains, validates, deploys, and then... nothing. No feedback loop. No automatic retraining. No safety gates. Just a model that gets stale the moment it hits production. Sound familiar? I've been building RL systems for a decade. The pattern is always the same: great training pipeline, terrible deployment loop. You spend weeks getting 95% validation accuracy, push to prod, and three days later the distribution shifts. Your agent starts making garbage decisions. You scramble to retrain. Rinse. Repeat. This sucks. I know. The Real Problem The issue isn't training. It's the feedback loop . Most RL systems have: Training pipeline — works fine Validation — mostly works Deployment — fire and forget Observation — maybe some metrics Strategy update — manual, if ever Step 4 and 5 are broken. You're flying blind after deployment. The Flywheel Architecture Here's what a real RL flywheel looks like: Train → Simulate → Validate → Gate → Deploy → Observe → Analyze → Train Every arrow is automated. Every gate is a hard check. Every observation feeds back into training strategy. Let me show you the actual implementation. The Training Loop class RLFlywheel : def __init__ ( self ): self . model = Model () self . buffer = ReplayBuffer ( 1_000_000 ) self . safety_gate = SafetyGate () self . observer = OnlineObserver () def train_epoch ( self , episodes = 1000 ): for ep in range ( episodes ): states , actions , rewards = self . simulate_episode () self . buffer . store ( states , actions , rewards ) batch = self . buffer . sample ( 256 ) loss = self . model . update ( batch ) # Validate against known failure modes validation_score = self . safety_gate . validate ( self . model ) return loss , validation_score Notice the validation happens during training, not after. That's the first gate. The Safety Gate class SafetyGate : def __init__ ( self , thresholds ): self . thresholds = thre

The test passed. The runbook completed. Infrastructure came back online inside the RTO window. None of that means the organization can recover from an actual disaster. Disaster recovery testing is designed to succeed. Clean environments, pre-staged dependencies, known failure modes, available staff — each design decision is operationally reasonable. Collectively they remove the conditions that make real recovery hard. What the test validates is test completion, not recovery capability. The Test Is Designed to Pass Every design decision in a standard DR test tilts toward a successful outcome. The test window is pre-announced, so the right engineers are available. The scope is pre-defined, so unexpected systems don't surface mid-exercise. The environment is either isolated or pre-staged, so competing failures don't complicate the recovery sequence. The data state is known and clean, so integrity issues don't slow the restore. The declaration point is assumed, so nobody has to make an ambiguous call under pressure. A test designed to remove the variables that make recovery hard cannot produce evidence about what happens when those variables are present. What Disaster Recovery Testing Actually Excludes Declaration threshold. In a DR test, recovery starts at a pre-agreed time. In a real incident, recovery starts when someone decides the situation has crossed the threshold for declaration — a decision that is rarely clean and routinely delayed 45 minutes to several hours. That delay is inside the real outage window and outside the test clock. Dependency assumptions. DR tests run against known, pre-cleared dependencies. Real incidents surface undocumented dependencies that were never in scope — a configuration service that hasn't been touched in two years, an authentication endpoint that wasn't in the architecture diagram. Data state. Test environments use clean or pre-staged data. Real recovery requires handling whatever state the data was in at the moment of failure — pa

Several users on social media reported having their Instagram accounts hacked over the weekend. Meta's own support chatbot was blamed for allowing hackers to hijack accounts.