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.
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.
The best computer use MCP servers in 2026, tested: Claude Code's built-in computer-use, Windows-MCP, Cua Driver, Peekaboo, Open Computer Use, macOS-MCP, computer-use-linux and computer-use-mcp, with install commands, tool counts, permissions, and how to reach a remote desktop over a Pinggy tunnel.
Scaffold a Whop app with the Whop CLI, run it on localhost behind Whop's dev proxy, and receive signature-verified payment.succeeded webhooks through a Pinggy tunnel before deploying anything.
TypeSafe's Jev is closed, hosted and waitlisted, so the open reimplementations started landing within a day. Laya, Kev, SemIf, NanoJev and jevlike, ranked by GitHub stars, with latency measured on an M3 Pro, the independent benchmark where Jev still wins on accuracy, and what each one actually needs to run.
Compare 18 B2B SEO agencies helping companies grow through buyer-intent content, technical SEO, link building, AI visibility, and pipeline.
ZuckOff is a free app that listens for the Bluetooth broadcasts camera glasses give off and matches them against a catalogued list of manufacturer IDs. Here is exactly how the detection works, what it can't tell you, and why a solo developer built it.
In-browser LLM inference works in 2026. WebGPU ships in every major engine, WebLLM 0.2.85 carries 163 prebuilt models, and a 4-bit Llama 3.2 1B is a 695MB download needing ~880MB of GPU memory. Working code, real numbers, and the limits.
AI workflow automation: when an off-the-shelf tool is enough and when building your own is worth it. Budget, urgency, scalability and how specific your requirements are decide the answer more than the technology does.
A technical breakdown of what HIPAA-compliant AI systems actually require: BAA scope, encryption, access control, audit logging, and real EHR/FHIR integration challenges.
TypeSafe's Jev is a System One model that returns typed decisions instead of text: 70-500ms latency, $0.042 per million input tokens with output free, and 0% structured-output errors. Here are the three jobs it actually fits, the honest benchmark picture against GPT-6 Astra and Claude, and the tasks where it is the wrong tool.
