标签：#vr

How I automated my content distribution with a DSH plugin I scaffolded myself Posting is easy. Posting everywhere, consistently, is the hard part. I wanted a single command that takes one markdown article and pushes it to Dev.to, GitHub (as a gist), and eventually Bluesky and Mastodon — without my ever touching those web editors again. So I built it as a plugin for DSH (DeepSeek Harness) , using a scaffolding tool that I published myself. Here's the story, the 3 pitfalls that cost me the most time, and how you can get the same thing running in about a minute. Why automate distribution at all? Writing in public is the cheapest compounding asset a developer has. But cross-posting manually has two failure modes: You skip platforms — the "I'll do it later" tab that stays open forever. You lose the content graph — each platform becomes a silo with a slightly different version. A plugin that accepts content + title + [platforms] and returns per-platform status + links removes both. One source, many destinations, audited every time. What I built A DSH content-automation plugin ( dsh-crosspost ) with: Platform adapters : Dev.to (real), GitHub gist (real), Bluesky + Mastodon (stubs, next milestone). BYOK credentials : your tokens live in your DSH profile config — never in code, no platform approval needed from the plugin author. Error classification : every adapter wraps HTTP in try/catch and returns auth / rate-limit / bad-request instead of a raw stack trace, so an agent can decide to retry or skip per platform. Parallel orchestration : one platform failing never blocks the others. The 3 pitfalls that cost me the most time 1. The stale latest dist-tag (the big one) npm install @deepseek-ai/dsh-tools gives you a stale 0.0.1-rc.1 — the real line lives under the next tag. Wasted an evening debugging failures that were purely "wrong version resolved." Lesson: check dist-tags before installing anything in a fast-moving young ecosystem ( npm view pkg dist-tags ). 2. Pure ESM + b

Concertos VR 2026: como o streaming imersivo está redefinindo o show ao vivo Introdução Você já imaginou estar no meio da plateia de um show de rock, sentir o pulsar dos graves e, ao mesmo tempo, poder pausar a cena, mudar de ângulo ou conversar com amigos que estão em outro continente – tudo sem sair da sua sala? Em 2026 isso já é realidade. Graças a headsets 4K mais baratos, plataformas de streaming de baixa latência e a mudança de comportamento do público, os concertos VR deixaram de ser ficção científica e se tornaram a principal forma de consumo musical ao vivo. Neste artigo vamos mostrar, passo a passo, como funciona essa revolução, apresentar casos de sucesso, analisar o impacto econômico e cultural e, principalmente, oferecer um guia prático para artistas, promotores e fãs que querem entrar nesse universo. 1. O que é um concerto VR? Um concerto VR é um evento musical transmitido ao vivo em 360° (ou 180°) e entregue em tempo real para um headset de realidade virtual. O espectador tem liberdade total para olhar ao redor, mudar de ponto de vista e interagir com objetos digitais – como luzes, efeitos e até avatares de outros fãs. Como a transmissão acontece (exemplo de pipeline) # 1. Captura 360° com câmeras Insta360 Pro 2 ffmpeg -i rtsp://camera1 -i rtsp://camera2 -filter_complex \ "[0:v]crop=3840:2160:0:0[left];[1:v]crop=3840:2160:0:0[right];[left][right]hstack=inputs=2[v]" \ -map "[v]" -c :v libx264 -b :v 15M -f rtp rtp://livevrx.com:5004 # 2. Ingestão no servidor de baixa latência (WebRTC) node livevrx-ingest.js --source rtp://livevrx.com:5004 --room concert2026 # 3. Distribuição para o headset (WebXR) <video id = "vrStream" autoplay playsinline webkit-playsinline src = "webrtc://livevrx.com/concert2026" crossorigin = "anonymous" > </video> Esse fluxo garante latência ≤ 30 ms , qualidade 4K por olho e sincronização perfeita entre áudio e vídeo. 2. Equipamento necessário Dispositivo Resolução mínima Preço (USD) 2026 Comentário Meta Quest 3 2 K por olho $399 M

There is a moment in every high-stakes environment when something goes completely, objectively wrong, and the only viable response is to keep working. In my case, it was a pantry clerk who walked into the dry storage room carrying a stack of boxes, clipped a fire sprinkler head, and discharged what I can only describe as an impressive quantity of initially greasy water across an active commercial kitchen. We were told to continue service. It took four hours for the sprinkler system technicians to arrive and resolve the situation. We dried our shoes afterward. I have thought about that shift many times since leaving commercial kitchens for the technology industry. Not because it was the strangest thing I witnessed. It wasn't. Not by a significant margin. However, because the response to it was so instinctively correct. Nobody called an all-hands. Nobody convened a retrospective on the water. We just kept swimming. It turns out that lesson travels extremely well. A few weeks ago I wrote about how a non-linear career isn't actually non-linear, that the industries change but the underlying questions stay remarkably consistent. I want to make that argument concrete. Here's what commercial kitchens specifically taught me about performing under pressure, and why none of it required translation when I showed up in technology. The Kitchen Never Lies I spent years in commercial kitchens before I spent years in technology. Western Culinary Institute. Private golf clubs. A Lebanese restaurant. Bulk production facilities turning out ten thousand pounds of macaroni and cheese a day, five days a week. Country clubs. A casino. Catering. Culinary competitions. The environments were different. The underlying dynamics were identical. High pressure. Constrained timelines. Mismatched team experience levels. Leadership of wildly variable quality and sobriety. Outcomes that mattered regardless of what had happened behind the scenes to produce them. Customers who neither knew nor cared abo

I used to spend hours rewriting AI-drafted video scripts that sounded nothing like me. At best, I might finish one or two that were just okay, but most of the work was still on my shoulders. Now, with my new system, I can get 15 scripts done in one session. They match my writing style, my voice, and my company's knowledge. Instead of full rewrites, each script just needs a quick review. I built the system in about a week, and the difference showed up before the week was out. The first project was an internal video series to teach people about the software factory and LaunchDarkly, and I barely had to edit those scripts. For the first time, the AI handles most of the work. My recent projects have mostly involved agentic software delivery. The software factory is where all of it was heading, and my company spent months preparing to help customers build their own. We had internal material, public documentation, and ongoing conversations I wanted to add clarity to. So my challenge wasn't just learning, it was learning while producing content at the same time. Essentially, devrel. By the end, I had built what I like to call my own personal content factory. At first, it was separate from the software factory it describes, but over time, the line between them blurred. My approach is to let AI handle the bulk of the work, while I step in for the important decisions. This is the only way I've found to make AI content sound like me. The AI creates the drafts, and I step in at three key points: checking the voice, the facts, and the overall feel. The quality ceiling is set before the first draft. I didn't just ask for a video script about a topic. Instead, I gave Claude access to every source I had—public docs, internal notes, and product requirements. It used research agents to read everything at once and came back with clear, organized notes I could use. I set two important rules for this step. First, every part of the research was labeled as either public-safe or internal-o

Telnyx ships over 700 Ultra voices across 36 languages with sub-100ms time-to-first-byte. The voices are not the problem. Hearing them is. The docs list three. The Voices API returns 4,000+ across every provider. Voice pickers play a fixed sample sentence per voice. None of that tells you how a voice handles emotion, pacing, or character inside a real scene. So I built a small app that lets you do exactly that. You write a short scene with a few characters, assign each character a different Telnyx Ultra voice and an SSML emotion, and render the whole thing into one MP3. Every voice speaks in character, in context, in one continuous audio file. The Telnyx code example is: https://github.com/team-telnyx/telnyx-code-examples/tree/main/multi-character-narrator-python The Use Case Voice pickers exist. They play a fixed sample sentence per voice. What they do not do is let you hear a voice inside a real scene — a tense argument, a calm narrator, a panicked character, a reassuring guide — because a single sample sentence does not tell you how a voice handles emotion, pacing, or character. This example solves that. You write a short scene with a few characters. Each character gets a different Telnyx Ultra voice. Each character gets an SSML emotion. You hit render. The app fans out parallel TTS calls, stitches the per-line audio in script order, and plays you one continuous MP3 with every voice speaking in character. The default scene is the Ides of March from Julius Caesar. Five characters, ten lines, five distinct voices, five different emotions: Cassius — determined, plotting the assassination Caesar — surprised, realizing the betrayal Brutus — apologetic, justifying the act Mark Antony — angry, mourning the fallen leader Narrator — calm, setting the scene One render, one MP3, every voice in context. That is the demo. The Eight Curated Ultra Voices The app ships with eight pre-built Telnyx Ultra voices curated for the most common use cases. Each one is a real Telnyx voice

This is a submission for DEV's Summer Bug Smash: Smash Stories powered by Sentry . 🦄 I debated writing this for a long time, but I finally talked myself into really writing again after a hiatus, and there's no better way than story time. So here's one of the most challenging bugs—or really, the series of them—I've run into in the enterprise world. Grab some popcorn and Skittles, because this one takes a while. Better yet, cue up Jerry's actual theme song— Jerry Was a Race Car Driver by Primus , because of course it is —and let the best bass player on the planet score the whole mess while you read. And yes, it's the Summer Bug Smash and my entire cast is dressed for Christmas. Stay with me. Meet Jerry 🪦 If you work with software any length of time, you already know the particular nightmares that come with legacy applications. This one is no different. It started life as a rewrite of some antiquated, bash-flavored system back when Java 8 was the coolest kid at the table. Let's call him Jerry. Jerry is a well-rounded app—or he was, before he let himself go. He came up on a then-modern Java stack and served exactly one purpose: get data from upstream into the database, correctly and on time. He was good at his one job. Then his one job got split into parts, and the sum of those parts did not add up to a whole—Jerry just expanded along the midline with no particular purpose or direction in life. You can imagine how it goes: a few retirements, a couple of half-finished rewrites, several well-meaning somebodies who swore they'd whip him into shape and left him half-done every time. Take your eyes off him at Christmas and he's the weird uncle who shouldn't have been left alone with the punch. That's about when Jerry and I met, more than three years ago. The Infestation Begins 🪰 Jerry did his best to keep up with everything we kept piling on him, but communication was never his strong suit—a patch here, an upgrade there, enough to keep the lights on and the punch bowl full.

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...