⚠️ This article contains affiliate advertising (promotions). A portion of revenue generated through linked sites is paid to the author, but this does not affect the purchase price for readers in any way. Hey — I'm a working engineer running a side hustle in tech writing and e-commerce. Here's the bottom line upfront: by the time you finish this article, you'll have a Python script that extracts "side-hustle promo noise" from your X (Twitter) timeline and auto-adds it to a mute list , plus a Claude Haiku classifier that labels each tweet as signal or noise for roughly ¥0.02 per tweet — copy-paste ready, just swap in your API keys. My own information-gathering time dropped from 90 minutes to 12 minutes a day (7-day average; details below). Why Manual Muting Breaks Down on X: The 30-Item Wall Muting on X via the GUI is one entry at a time. In my case, roughly 70% of the 480 accounts I follow are genuinely useful — but 30% are promotional, making them "almost good" accounts. Muting at the account level kills the useful tweets too. So I turned to keyword muting, which becomes unmanageable past 30–40 keywords manually. Add "free," "limited time," "LINE sign-up," and "#RT please" to the list and you start catching legitimate tech tweets as collateral damage. One failure story: early on I added "side hustle" as a mute keyword, missed an entire high-quality thread squarely in my interest zone, missed the viral wave, and conservatively lost about ¥3,000 in affiliate opportunity. Word filters don't have the precision. That's the starting point for this article. Extracting "Promo Templates" Mechanically with Tweepy and Filter Rules First, using the X API v2 (read access is available even on the free tier) and Tweepy, I pull tweets equivalent to my home timeline and numerically score structural features common in promotional content. The trick is to score on three axes — emoji density, URL count, and call-to-action verbs — rather than keyword matching. import re import tweepy cl

We did what most engineering teams do. Bought an OpenAI API key. Shared it on Slack. Told everyone to start using AI in their workflow. It felt like the right move. Productivity went up. Developers were happy. Managers were impressed. Then the invoice arrived. Nobody could explain it. We could not tell which team spent what, which model was being used, or whether anyone had accidentally sent customer data to an external provider. We had full AI adoption and zero visibility. That is when we realized we had confused access with governance. The problem is not the AI. It is the missing layer between your team and the API. Most teams operate with raw provider keys floating around in .env files, Slack messages, and IDE configs. When someone leaves, you hope they did not take the key with them. When a pipeline misbehaves overnight, you find out from the billing alert, not from your own monitoring. We started asking ourselves some uncomfortable questions: Who on the team is using GPT-4o versus a cheaper model? Is anyone sending PII to an external provider without knowing it? What happens if our OpenAI key gets exposed in a public repo? Can we switch to Anthropic without rewriting half our tooling? None of these are exotic concerns. They are the natural consequences of scaling AI access without an infrastructure layer to govern it. What actually helped We needed something that sat between our developers and every AI provider, handling authentication, enforcing limits, logging every request, and letting us swap providers without touching application code. Think of it the way an API gateway manages microservices. Same idea, but for LLM traffic. Developers point their tools like Cursor, Continue.dev,...at a single endpoint. Two environment variables. Nothing else changes. Behind the scenes, every request is logged, every token counted, every provider key protected. Governance without friction. That is the only kind developers will actually tolerate. If your team is using AI wit

You bought the hardware. Now you’ll need to subscribe for “expanded access” to the most advanced features.

According to the Financial Times, Sam Altman is floating the idea of having leading AI companies give US sovereign funds a five percent equity.

The speakers discuss Netflix’s architecture for surviving extreme traffic spikes. They explain the mechanics of prioritized load shedding embedded in their Envoy sidecar proxy, allowing user-initiated requests to steal capacity from non-critical traffic. They share automated platform strategies for continuous chaos load testing, config generation, and retry storm mitigation. By Anirudh Mendiratta, Benjamin Fedorka

Europe's highest court of appeal has upheld a record-setting $4.7 billion fine imposed on Google back in 2018.

Amazon says it now has enough satellites operating in low-Earth orbit to light up its Starlink internet competitor. With last night's launch, Amazon Leo has 396 satellites deployed, which is "enough to support continuous service across initial latitudes," according to Chris Weber, VP heading up business and product for Amazon Leo. That puts the company […]

Something stinks in California’s climate policies. Years ago, the state set up a system that pays cattle farmers across the country to turn the methane emitted from cattle manure into natural gas, encouraging the dairy sector to produce a gas we burn instead of one that just pollutes the air. It’s become wildly popular because…

Long hours outdoors, day drinking, and World Cup matches are among the factors raising the risks of heat-related illness, as hot weather spreads across the eastern US.

I recently started doing some research about data oriented design and I find material mostly from gamedevs. I understand that it became popular by Mike Acton, but I think the principles could be applied to more than one domains. For example for statistics libraries and quant data analysis. Do you use this approach in non gamedev related areas. Could you please mention real world examples? TIA submitted by /u/codingbliss12 [link] [留言]

submitted by /u/fagnerbrack [link] [留言]