标签：#VR

The National Highway Traffic Safety Administration said emergency scenes are not "edge cases."

Documentation is often the first product a developer experiences. Before they see your architecture, your engineering culture, or your code quality, they interact with your documentation. If that experience is confusing, incomplete, or frustrating, many developers won't make it to their first successful API request. Over the past few weeks, I've been reviewing documentation from AI startups to understand what makes onboarding smooth—and where teams unintentionally create friction. While every company is different, the same patterns kept appearing. 1. Quickstarts assume too much Many Quickstarts jump straight into code without explaining prerequisites. Developers are expected to know: Where to get an API key Which SDK to install Required environment variables Authentication steps A Quickstart should help someone go from zero to a successful request with as little guesswork as possible. 2. Error messages aren't documented Developers don't judge documentation by how it works when everything goes right. They judge it by how quickly it helps them recover when something goes wrong. Instead of only listing error codes, explain: Why the error happens Common causes How to fix it What to try next Good troubleshooting documentation builds confidence. 3. Examples are incomplete Too many examples leave out important details. Developers shouldn't have to infer: Authentication headers Environment variables Request payloads Expected responses Examples should be copy, paste, run, and understand. 4. There's no clear learning path Documentation often feels like a collection of pages instead of a guided journey. A better structure might look like this: Quickstart Core Concepts Tutorials API Reference Advanced Guides Troubleshooting When developers always know what to read next, they make progress faster. 5. Documentation isn't written for AI-assisted development Today, developers increasingly rely on AI coding assistants. That means documentation should also be easy for AI tools to int

The Chevy Silverado EV is a solid first draft of an EV pickup truck. Here's what could make it better.

Walkthrough experience includes visits to stars, exoplanets, and observatories.

Subtitle: Build a Python app with Telnyx AI Inference that turns customer activity signals into churn risk, recommended actions, and retention next steps. Most customer churn is only surprising because the signals were scattered. Usage dropped in one place. Support tickets went up somewhere else. A renewal date got closer. A login did not happen for two weeks. Payment issues started showing up. None of those signals alone proves a customer is leaving, but together they usually tell a story. That is the workflow I wanted to make easier to build: take customer activity data, pass it through an inference model, and return a structured churn assessment that a product or customer success team can actually use. The example is here: https://github.com/team-telnyx/telnyx-code-examples/tree/main/ai-customer-churn-predictor-python It is a small Flask app using Telnyx AI Inference through the chat-completions API. The App Shape The app exposes a few routes: POST /predict for one customer POST /predict/batch for up to 20 customers GET /predictions for recent in-memory predictions GET /health for app health The current default model is set in .env.example : AI_MODEL=moonshotai/Kimi-K2.6 Under the hood, the app calls: POST https://api.telnyx.com/v2/ai/chat/completions The prompt asks the model to behave like a customer success analyst and return JSON only. That is the important part. This is not a chatbot. It is an application endpoint that produces structured output. What Goes In A request can look like this: curl -X POST http://localhost:5000/predict \ -H "Content-Type: application/json" \ -d '{ "customer_id": "CUST-123", "call_volumes": [120, 105, 80, 55], "message_volumes": [450, 420, 300, 190], "support_tickets": 6, "account_age_months": 18, "renewal_days": 21, "last_login_days": 14, "payment_issues": 1 }' Those fields are deliberately simple. The point is to show the pattern, not to pretend this is a full enterprise churn model. The model gets the trend data, support contex

In 2007, humans took roughly 85 billion photos a year. Photography was a specialized craft that...

Microsoft inked a 20-year power purchase agreement with Chevron, locking in decades of carbon emissions from a new natural gas power plant.

Treating docs as a product When documentation lives as an afterthought, it shows. Pages drift out of date, examples break quietly, and release notes scatter across a dozen places no one can find. The fix is not a weekend cleanup. It is a decision to treat docs the way you treat any product people depend on: someone owns it, it has standards, and it gets maintained on purpose. That is the decision I made when the docs came to the Developer Relations team at the end of 2025. Not "let's tidy this up," but "this is ours now, and we are accountable for whether a developer can actually build from it." The work, in the repository The honest record of what a team does to a codebase lives in its git history, so that is where the story starts. Comparing the six months before the handoff to the six months since: Before vs. Under DevRel: Commits: 476 → 1,900+ Merged pull requests: 145 → 447 Unique contributors: 21 → 64 A repository that averaged fewer than 500 commits over half a year is now past 1,900 in the same span. The contributor count tripled, because we treated the docs as something the whole community could improve, not a walled garden. This is what a team that decided to do the work looks like when you measure it. Our proudest metric is what was cut In six months, we added roughly 339,000 lines and removed roughly 281,000. That near balance is the point. A neglected docs site accumulates: dead pages, stale tutorials, examples that no longer compile, three slightly different explanations of the same concept. Adding more on top of that does not help anyone. So we cut nearly as much as we wrote. We rebuilt the Hello World walkthrough from 1,300 lines down to about 300 without losing a thing. We consolidated scattered release notes into a single clean reference. A docs site is judged by what a developer can find and trust, not by how much sits on the shelf. A library you can learn from At the start of the year, the examples library had effectively one usable entry. Today,

Even in a future of robot taxis, someone still has to return the things passengers leave behind.

TL;DR -DevRelCon NYC returns to Industry City in Brooklyn on July 22–23, 2026. It's the flagship...