Technology

Technology

Big tech, product launches, and industry shifts

Stories
200
stories
Sources
41
sources
Page
Page 8 of 10
Updated hourly

Why Owl Post covers Technology

Big tech moves on a different timescale than the news cycle wants you to believe. What looks like a product launch is usually the public surface of an organizational bet made eighteen months earlier.

Owl Post reads technology news with a bias toward the structural rather than the theatrical. Leadership changes matter when they signal strategic pivots. Antitrust filings matter when they constrain platform behavior. An SDK update buried in a developer blog can matter more than a keynote. A quiet line in a quarterly earnings call can tell you more about where a company is heading than any press release. That kind of signal is what this digest is built to surface.

Read the full Technology briefing

The beat covers the major platforms (Apple, Google, Microsoft, Meta, Amazon), the infrastructure players that underpin most of the modern internet, the regulatory environment taking shape in Washington and Brussels, and the product shifts that change what people actually use. Owl Post reads across hundreds of publications, developer communities, and regulatory filings, and brings the stories that pass a genuine so-what test.

Your digest adapts to how you like to process this stuff. If you want headlines stripped to what matters with no ceremony, it can read that way. If you want the strategic angle spelled out, including why a platform move matters to adjacent industries, that framing is available too. The curation is the same either way.

A focused technology digest every weekday morning. No press releases dressed up as reporting, no product launches treated as news unless they actually are. The tech beat is consequential enough to deserve better coverage than it usually gets.

Codex CLI now keeps running after you close it. Here's what that means and how to turn it off

Since version 0.157.0, opening OpenAI's Codex CLI starts a shared background server that outlives your terminal and updates itself. It's on by default. Here's what it does and the one command to switch it off. If you use OpenAI's Codex CLI, close every Codex window and check your running processes. There's a fair chance Codex is still there. Since version 0.157.0, released on September 25, opening an interactive Codex session also starts a daemon, a program that runs quietly in the background with no window of its own. It keeps running after your last terminal closes. On most setups, it also updates itself. OpenAI's release notes gave this one line: "Enabled automatic background-server startup for eligible interactive sessions." Developers noticed what that sentence left out, and a GitHub issue asking for it to be opt-in reached the top of Hacker News on October 12. Here's what actually changed, why it bothers people, and how to switch it off if you want to. This part comes from OpenAI's own code and docs, not just user reports. It's on by default. In the Codex source code, the setting daemon_auto_start is marked stable and enabled by default (source). The pull request that made the switch, merged on September 22, is titled simply "Enable automatic daemon startup by default." It updates itself. According to the daemon's README, it checks for new releases five minutes after starting, then every hour. When it finds one, it restarts, and the README warns that "active or queued work may be interrupted." It runs its own copy of Codex. The daemon runs from a separate package inside your ~/.codex folder, not necessarily the version you installed with npm or Homebrew. The person who opened the issue measured that copy at about 374 MB. It remembers the first terminal's environment. The README says it plainly: shared clients use the environment the daemon started with, and "per-client environment isolation is not provided." Start Codex in a project with one GH_TOKEN or proxy

dev.to

GitHub Issues Explained: When Should You Open One in Open Source?

When you first start exploring open-source projects on GitHub, you might notice something: almost every active repository has an Issues tab. But what is it actually for? Should you open an issue whenever you find a bug? What if you have an idea for a new feature? And if you already know how to fix something, should you open an issue first or go straight to a pull request? I used to think that GitHub Issues were solely for project owners to track their own tasks 🫣, but I learned that anyone can open an issue to report a bug, suggest a feature etc.. Let's break it down. A GitHub Issue is a place to report problems, suggest improvements, ask questions about a project, or discuss work that needs to be done. Think of it as a shared record of something that needs attention. Instead of keeping ideas in your head or discussing problems in scattered messages, you create a place where contributors and maintainers can understand the context and discuss possible solutions. I think issues are useful for more than bug reports. They can also help maintainers organize work, collect feedback, prioritize features, and coordinate contributions. There are a few common situations where opening an issue makes sense. Suppose you are using an open-source application and notice that a button stops working after a particular action. Before reporting it, try to reproduce the problem and check whether it has already been reported. A useful bug report should explain: What you were trying to do. The steps needed to reproduce the problem. What you expected to happen. What actually happened. Any relevant error messages or screenshots. For example, instead of writing: "This button is broken." You could write: Title: Analyze button remains disabled after recording stops Description: Steps to reproduce: Start recording audio. Stop the recording. Observe the Analyze button. Expected behavior: The Analyze button becomes available once recording has finished. Actual behavior: The button remains disable

dev.to

VAI Assistant: a non-injection AI customer service tool for WeChat on Windows

VAI助手 (formerly "微信管家", https://vx.cuxn.cn/) is a Windows desktop app that auto-answers routine customer questions in WeChat for online sellers, WeChat merchants and private-domain customer service teams. It does not inject into the WeChat process, does not modify the WeChat client, and does not hook anything. It only reads messages and types replies at the system UI level, the way a person would. Local RAG knowledge base: drop in PDF/DOCX product docs and return policies; the AI checks them before answering and leaves unknown questions to a human. Per-group and per-contact personas and reply styles. Image understanding (product screenshots, invoices, stickers). Human-like typing delay, long-reply splitting, sensitive-word filtering. Chat history and knowledge base stored encrypted on your own PC. Mass messaging, auto-adding friends, spamming. Any WeChat automation carries account risk; we don't promise "no bans". Test on a secondary account, keep reply frequency low, and take over important conversations manually. Demo video: https://youtu.be/ziXTkyEzXhc https://vx.cuxn.cn/

dev.to
SellerBoost AI: Helping Small Sellers Create Better Product Listings

SellerBoost AI: Helping Small Sellers Create Better Product Listings

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 2: Side Hustle. What I Built SellerBoost AI is a project focused on helping small business owners and online sellers create better product listings with less effort. Many small sellers know their products well but may struggle to write compelling descriptions, choose searchable titles, and identify useful keywords. SellerBoost AI aims to simplify that process. The goal is to turn a few basic product details into marketplace-ready content, including: Product titles The potential customers are small online sellers, home-based businesses, and entrepreneurs who want to improve their product listings without hiring a copywriter. The long-term business idea is a simple, affordable tool that saves sellers time when preparing product listings. Demo demo website: SellerBoostAI Screenshots or demo video: Code GitHub repository: SellerBoost-AI The project is being developed as a lightweight web application with a simple interface designed for sellers who want to create listing content quickly. How I Built It I built SellerBoost AI as a web-based project, focusing on a straightforward user experience and a practical problem faced by small sellers. The interface collects product information such as the product name, target audience, and key features. The intended workflow is to transform those details into useful listing content. I also explored connecting the application to a hosted language model without requiring users to run a large model locally. AI implementation status: [Describe the model and provider actually used in the tested version. If live inference is not working yet, state that clearly and do not claim that AI generation is working.] My goal is to keep the application accessible to developers and small sellers who may not have powerful hardware or a large budget. Why Does Open Innovation Matter? Open innovation can make AI experimentation more accessible to independent developers and small b

dev.to

Get Technology delivered to your inbox

Owl Post delivers a personalized technology digest every morning, curated by AI, written in your voice.

Get your free digest
More in Technology
Explore other beats
From the Owl Post blog