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#cloud

8218 articles found

· 3 days ago· Dev.to

All GitHub Copilot plans are now on usage-based billing

GitHub officially switched to a usage-based billing model a few days ago. The era of unlimited $10 fixed fees is over 🥲 Recently, GitHub Copilot started introducing more detailed usage-based concepts around premium requests, model access, and AI consumption. Instead of treating all AI interactions

#cloud
· 3 days ago· Dev.to

Integrating a language switcher in my SaaS revealed unexpected bugs

This is the next entry in a build-in-public series where I extract a real, production Laravel CRM into a reusable SaaS core, one module at a time, and ship each piece as a Composer package. So far: the foundation layer, auth on top of Fortify, multi-tenancy, roles and permissions, and the platform a

#cloud
· 3 days ago· Dev.to

Amazon Buy Box Data Scraping in Python: A Complete Dev Guide (2026)

TL;DR: Amazon's Buy Box data requires JavaScript rendering—standard HTTP requests won't work. DIY Playwright scrapers fail at scale (35–55% success rate in 2026 conditions). Pangolinfo Scrape API is the production path: >95% success rate, structured JSON output, 5–15 min freshness. Complete working

#cloud
.com vs .dev: The Strategic Domain Choice That Shapes Your Brand
· 3 days ago· Dev.to

.com vs .dev: The Strategic Domain Choice That Shapes Your Brand

Choosing the right domain extension in 2026 isn't just about availability. Your decision between .dev and .com shapes how developers, investors, and users perceive your brand. It affects everything from SEO performance to investor trust. The domain landscape has evolved dramatically. While .com stil

#cloud
· 3 days ago· Dev.to

llm-cli-gateway 2.0.0: the quiet supply-chain release that matters

llm-cli-gateway 2.0.0 went out on 4 June 2026. npm now reports 2.0.0 as the latest version, and the public GitHub release carries the platform binaries, bundled installers, SHA256 checksums, release manifest, and Sigstore bundles. The headline change is simple: production persistence no longer depen

#cloud
· 3 days ago· Dev.to

India's AI Cloud Breakthrough: Prompt as a Reusable Asset Emerges

A prompt buried in an f-string is technical debt. You can't test it. You can't save it to a file. You can't hand it to a colleague and say "here's the extraction logic." The moment you hard-code a prompt into a string, you've welded your application logic to a single, frozen instruction — one that w

#cloud
· 3 days ago· Dev.to

Railway vs Fly.io: I Wanted More Control After Railway’s May 2026 Outage

TL;DR I no longer recommend Railway for serious production workloads after its recent pattern of incidents. Fly.io is not simpler, but it is one of the first alternatives worth evaluating if you want more control over infrastructure and blast radius. Even if Fly.io is not your final choice, I think

#cloud
· 3 days ago· Dev.to

Preprocessor: IFDEF and Conditional Compilation | Preprocessor: IFDEF e compilação condicional

Bilingual post · Post bilíngue Jump to: English · Português Delphi projects rarely compile as a single flat source file. {$IFDEF}, {$DEFINE}, {$INCLUDE}, and friends select platform code, toggle debug logging, and stitch units together. CrabPascal ships a line-oriented preprocessor (since v1.5.2, re

#cloud
· 3 days ago· Dev.to

LLM Fine-Tuning vs RAG: A Production Decision Framework for Engineering Teams

Key Takeaways Use RAG for knowledge retrieval, changing data, and rapid iteration. Use fine-tuning for style, format, narrow classification, and cost at scale. Start with RAG — 70% of production problems don't need fine-tuning. Fine-tuned Qwen2.5-7B reached 88% accuracy on a proprietary classificati

#cloud
Proof of Compute: The Receipt Is Not the Benchmark
· 3 days ago· Dev.to

Proof of Compute: The Receipt Is Not the Benchmark

Proof of Compute Disclosure: AI tools were used for source collection and editorial review. The article was written by a human author, who checked the facts, code, and conclusions. Crypto risk disclosure: This article is a technical explanation, not investment advice. It is not a recommendation to b

#cloud
AWS Replaces Fat-Tree Data Center Networks with Random Graph Theory, Cutting Routers by 69%
· 3 days ago· InfoQ

AWS Replaces Fat-Tree Data Center Networks with Random Graph Theory, Cutting Routers by 69%

AWS disclosed that Resilient Network Graphs, a flat network architecture based on quasi-random graph theory, is now the default for most new data center builds. The design replaces fat-tree hierarchies with direct ToR-to-ToR mesh connections using passive optical ShuffleBoxes, cutting routers by 69%

#cloud
· 3 days ago· Dev.to

How I rotate Sanity draft mode secrets at the edge without redeploying Next.js

Sanity draft mode preview on Next.js App Router works fine when your secret is baked into an environment variable — until a client accidentally pastes the preview URL into a Slack channel and you have to redeploy to rotate it. Vercel Edge Config solves that: you update a single key in the dashboard

#cloud#nextjs#secrets-management#devops
· 3 days ago· Dev.to

Tensor Studio Takes Shape in Cloud Computing Revolution

There's a strange comfort in opening a notebook at 2 AM and pulling color out of a tensor. The model doesn't sleep, doesn't tire, doesn't complain when I revise the prompt for the seventeenth time. It treats every attempt as the first attempt. I don't know yet whether that's generous or empty — prob

#cloud
· 3 days ago· Dev.to

India's Open-Source Revolution: Unveiling XFA Forms on the Web

The problem nobody solved If you've ever opened a government PDF form on Mac or Linux and seen this: "Please wait... If this message is not eventually replaced by the proper You've met XFA. XFA (XML Forms Architecture) is Adobe's format for dynamic PDF forms. Governments and militaries worldwide sti

#cloud
· 3 days ago· Dev.to

Deploying AI Agents on Real Infrastructure Just Got a Whole Lot Safer

Draft flagship post — publish on your blog (canonical), then cross-post to dev.to / Hashnode and as a LinkedIn article. ~1,100 words. This is your single strongest, most differentiated story — it's what makes a hiring manager think "this person actually gets agent reliability." Everyone can get an L

#cloud
AI-Driven Efficiency Hack: When Human Intervention is the Smart Move
· 3 days ago· Dev.to

AI-Driven Efficiency Hack: When Human Intervention is the Smart Move

How a homelab engineer and his AI pair-debugger cornered an RTX 3090 that hard-reset the entire machine the instant it ran inference — and why neither of them could have solved it alone. The first thing Marco noticed was the silence. Not an error. Not a kernel panic scrolling up the screen. Not even

#cloud
Cloud Quality Assurance Bottlenecks Threaten Application Rollouts Across India
· 3 days ago· Dev.to

Cloud Quality Assurance Bottlenecks Threaten Application Rollouts Across India

The dev team is shipping fast. Their sprint velocity has genuinely jumped. But the QA queue is three days long. The pipeline takes 45 minutes to run. Every fast PR is sitting in a holding pattern, waiting for a human tester who has a backlog of 23 tickets. The testers are working harder than ever, a

#cloud
· 3 days ago· Dev.to

AI's Evolving Code: My Six-Month-Old Creations Are Unreadable Now

Last week I opened a project I had built almost a year ago. Not because of a bug. A friend mentioned wanting a self-hosted Slack alternative, and I remembered I had made one. So I opened the folder in VS Code, looked at the file tree, picked the file that seemed like a sensible starting point, and o

#cloud
Next.js 16.2 Boosts Development Speed by 400% with Enhanced AI Tools
· 3 days ago· InfoQ

Next.js 16.2 Boosts Development Speed by 400% with Enhanced AI Tools

Vercel has released Next.js 16.2, featuring performance enhancements that make development startup 400% faster and rendering up to 60% quicker. The update includes AI-assisted development tools, improved Turbopack efficiency, and better error reporting. Migration from Next.js 15 is supported, and co

#cloud
· 3 days ago· Dev.to

NVIDIA Cosmos 3: How a Two-Tower Architecture Unifies Physical AI Reasoning and Generation

Training a robot to pick up an object sounds simple until you realize how many separate systems are involved: a vision model to understand the scene, a reasoning model to plan the action, a dynamics model to predict what happens next, and a policy model to generate motor commands. Each component is

#cloud
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