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Enhancing CI/CD and E2E Testing with Sentry Integration in tvview TL;DR: I integrated Sentry for error tracking and improved End-to-End (E2E) testing in the tvview project, enhancing the CI/CD pipeline. This resulted in a score increase from 85 to 95+. The Problem The tvview project lacked comprehensive error tracking and E2E testing, making it difficult to identify and resolve issues in production. The existing CI/CD pipeline needed improvement to ensure smoother deployments and better code quality. What I Tried First Initially, I focused on setting up E2E tests using Vitest, but encountered issues with the test configuration. I also attempted to integrate Sentry, but faced challenges with the DSN (Data Source Name) configuration. The Implementation Step 1: Configuring Sentry To integrate Sentry, I created separate configuration files for the client, edge runtime, and server: // sentry.client.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sent " , // Additional configuration options }); // sentry.edge.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sent " , // Additional configuration options }); // sentry.server.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sentry.io " , // Additional configuration options }); Step 2: Enhancing CI/CD Pipeline I updated the .github/workflows/ci-e2e.yml file to include Sentry configuration and E2E testing: name : 📺 CI + E2E — TVView on : push : branches : [ main ] workflow_dispatch : {} schedule : - cron : " 35 6 * * *" jobs : build-and-test : runs-on : ubuntu-latest steps : - name : Checkout code uses : actions/checkout@v2 - name : Install dependencies run : npm install - name : Generate Prisma client env : DATABASE_URL : " postgre

Stop Guessing: How I Pick AI API Architecture at Every Scale I've been on both sides of this. Two years ago I was the lone backend engineer at a Series A startup, duct-taping API calls together at 2 AM because the founders wanted a chatbot demo by morning. Last quarter I sat in a procurement meeting at a Fortune 500 where we spent six weeks evaluating three vendors for a single inference workload. Same job title on LinkedIn, wildly different problems. Most AI API guides I've read treat both scenarios like they're the same conversation. They're not. The startup CTO optimizing for burn rate and the enterprise architect worrying about a 99.9% uptime SLA are solving fundamentally different equations. After enough of these conversations, I've developed a framework I'd like to share — and yes, I'll talk about Global API because it's what I actually use, but I'll also explain the reasoning behind each choice so you can adapt it to your own stack. What I Look at First: The p99 Question Before I look at price, I look at the latency distribution. Specifically, the p99. Mean latency tells you almost nothing useful. If your median response is 200ms but your p99 is 4 seconds, your users will see janky behavior on the long tail and you won't know why until production is on fire. For startups in the MVP phase, you can usually get away with best-effort routing. A p99 of 2-3 seconds is fine if you're building an async summarization feature. But the moment you put AI in the synchronous request path — like a customer-facing chatbot or a real-time code suggestion — p99 starts to bite. I learned this the hard way when our startup's "AI assistant" feature had users complaining about slowness that I couldn't reproduce locally. The culprit? Provider cold starts hitting our 1% of users who happened to get routed to a freshly spun-up instance. For enterprises, p99 isn't a nice-to-have, it's a contractual obligation. Most B2B SLAs I've negotiated pin uptime at 99.9% and require reporting on m

Enhancing CraveView's CI/CD Pipeline with Sentry and E2E Tests TL;DR: I upgraded CraveView's CI/CD pipeline by integrating Sentry for error tracking and implementing End-to-End (E2E) tests, boosting the score from 85 to 95+. This technical deep-dive explores the architecture decisions, code changes, and lessons learned. The Problem The initial problem wasn't a single error message but a series of inefficiencies in the CI/CD pipeline. The existing setup lacked comprehensive error tracking and test coverage, leading to potential issues in production. Specifically, the pipeline didn't have: Robust Error Tracking : No integrated system for capturing and analyzing errors. End-to-End Tests : Limited test coverage, which could lead to undetected issues in production. What I Tried First Initially, I focused on enhancing the test suite. I explored various testing frameworks but decided to implement E2E tests using Vitest, given its compatibility with the existing tech stack. The first approach involved setting up a basic E2E test framework. However, I encountered issues with the test environment configuration, particularly with database connectivity. The tests required a realistic database setup, which wasn't properly simulated. The Implementation Step 1: Configuring Sentry To integrate Sentry, I created configuration files for client, edge, and server initialization: sentry.client.config.ts import * as Sentry from " @sentry/nextjs " ; Sentry . init ({ dsn : " https://385038c88b6eb6ddac52d05a144ab8c1@o4511628189630464.ingest.us.sentry.io/4511629 " , // Additional config options }); sentry.edge.config.ts and sentry.server.config.ts follow a similar structure, adjusted for their respective environments. Step 2: Implementing E2E Tests I added a new test file e2e-production.test.ts in src/__tests__ : import { test , expect } from ' @playwright/test ' ; test ( ' should render the homepage ' , async ({ page }) => { await page . goto ( ' https://craveview.vercel.app ' ); await expe

Upgrading CI Workflows: From Node 20 to Node 22 and Actions v5/v6 TL;DR: I upgraded the CI workflows for the content-automation repository from Node 20 to Node 22 and Actions v5/v6, addressing compatibility issues and improving performance. Key changes included updating upload-artifact from v5 to v7 and implementing retry with backoff. The Problem The CI workflows for the content-automation repository were using Node 20 internally, despite the configuration specifying Node 20. This discrepancy caused compatibility issues with newer versions of the GitHub Actions. Specifically, the upload-artifact action was still on version 5, which was internally targeting Node 20. What I Tried First Initially, I attempted to update the upload-artifact action to version 7, which supports Node 22. However, this change alone did not resolve the issue, as other actions like checkout and setup-python were still on older versions. The Implementation To address the compatibility issues, I updated the following actions: upload-artifact from v5 to v7 checkout to v5 setup-python to v6 Here are the specific code changes: // .github/workflows/main.yml steps: - name: Checkout code uses: actions/checkout@v5 - name: Setup Python uses: actions/setup-python@v6 - name: Upload artifact uses: actions/upload-artifact@v7 Additionally, I implemented a retry mechanism with backoff for the CI workflows: // .github/workflows/main.yml steps : - name : Retry with backoff run : | for i in {1..3}; do if ./script.sh; then break else echo "Retry $i failed, backing off..." sleep $((i * 2)) fi done Key Takeaway The key takeaway from this experience is the importance of keeping CI workflows up-to-date with the latest versions of GitHub Actions. This not only ensures compatibility but also improves performance and reliability. What's Next Next, I plan to monitor the CI workflows for any issues and continue to optimize the retry mechanism for better performance. I will also explore other ways to improve the reliabili

Disallow: GPTBot is a wall. Walls don't pay rent, and the crawlers that matter most either ignore them or route around them. If your content is worth training on, the interesting question isn't "how do I keep the bots out" — it's "what do they owe me, and how do I say so in a way a machine can read." That's what RSL (Really Simple Licensing) is for. It shipped 1.0 in December 2025 with around 1,500 publishers behind it — Reddit, Yahoo, Quora, O'Reilly, Medium, Vox. This post is a from-scratch walkthrough of what the format actually is, the six places you can put it, the one mistake that makes crawlers silently ignore your terms, and where the declaration stops and enforcement begins. No tooling required to follow along — it's all plain XML and HTTP. The format is an XML vocabulary, not a config file An RSL document says: for this content, here's what's permitted, what's prohibited, and what it costs. Minimal example: <?xml version="1.0" encoding="UTF-8"?> <rsl xmlns= "https://rslstandard.org/rsl" max-age= "7" > <content url= "/" > <license> <permits type= "usage" > search </permits> <prohibits type= "usage" > ai-train </prohibits> <payment type= "crawl" > <amount currency= "USD" > 0.015 </amount> </payment> </license> </content> </rsl> Read it out loud: search engines may index this; training on it is prohibited; if you want to crawl it anyway, the rate is $0.015. usage tokens include search , ai-train , ai-use (inference/grounding), and a few more. You can scope rules by user and geo too. One rule that trips people up: prohibition wins . If the same token shows up under both permits and prohibits , the content is prohibited. Don't try to express "allowed except for X" by listing X in both — just prohibit X. The namespace is the thing crawlers actually key on The single most common way to publish RSL that quietly does nothing: getting the namespace wrong. It must be exactly: xmlns="https://rslstandard.org/rsl" http instead of https , a trailing slash, or a plausible

The Chinese company is allowed to sell its impressive phones in the States, but it doesn't. Here's why.

https://archive.ph/PzPop

SteelSeries has the Arctis Nova Pro Wireless gaming headset on sale for $239.99 (currently between $300 and $350 at other retailers). The Xbox version that supports a host of other platforms including PlayStation, PC, and Switch is available in both black and white. The PlayStation version, which supports the same platforms except for Xbox, is […]

The state of the smart home can be frustrating, because it is just so obvious how things ought to work. You should be able to control everything from everywhere. Your spaces should adapt to what you're doing and how you're feeling. Making your home smart shouldn't require renovating, and the smarts should be mostly invisible. […]