Hands-on guides, protocol deep dives, and field notes on tunnels, SSH, reverse proxies, self-hosting, self-hosted LLMs, and AI - from the people building Pinggy.
Netlify moved Edge Functions from V8 isolates to Firecracker microVMs and cut median warm latency from 25-40 ms to 5-6 ms. Here is how microVM snapshots, EROFS and rendezvous hashing make that work.
AI agents and webhooks let attackers automate phishing end to end: personalized messages, AI-built fake login and crypto portals, real-time follow-ups, and campaigns at scale. Here is how it works and what defends against it.
ChatGPT plugins are back, built on MCP servers. Build a working plugin in Node.js, test it in developer mode through a Pinggy tunnel, package plugin.json, and submit it to the directory.
AI Overviews cut top-result clicks by 58% and AI Mode passed 1 billion users. What SEO, AEO and GEO actually mean, how AI answers pick their sources, and tested steps to get your pages cited.
How an open-source cluster of seven ESP32-S3 microcontrollers splits a Qwen2-0.5B model into 1.58-bit ternary layers, why it fits in 16 MB of flash per board, and why it is slow.
A technical comparison of cloud computers for AI agents in 2026: machine0, OpenComputer, E2B, Daytona, Fly.io Sprites, Vercel Sandbox and Modal. Isolation, persistence, lifetime limits, GPUs, desktops and real per-hour prices, checked September 2026.
How AI agents control a computer in 2026: the screenshot-and-click loop, Claude and ChatGPT desktop computer use, a tested Python agent on Claude's computer_toolset_20260801, OpenAI's computer tool, open-source agents, and safe sandboxing.
F-Droid shipped a from-scratch Kotlin and Compose rewrite on September 24, 2026, six days before Android developer verification starts blocking unverified apps in Brazil, Indonesia, Singapore and Thailand.
Workers toggle between apps nearly 1,200 times a day and lose about 9% of their working year to it. Why teams are consolidating tool stacks into one AI workspace, and how to choose and pilot one in 2026.
Five practical steps for developers to build and launch an AI startup: pick a narrow problem, define target customers, prepare clean data, customize a pretrained LLM, and launch with a website and brand.
