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Most social-media automation tutorials stop at “call the API on a cron job.” That works, but it leaves the hard questions unanswered. Which account is the automation using? How does it avoid posting the same story twice? What happens when an API request times out after X has already accepted the post? And where should an AI agent’s editorial freedom end? I recently built a scheduled X publishing workflow with Codex and xurl , the official command-line client for the X API. The result is not just a timer attached to an AI prompt. It is a small publishing system with four distinct layers: An X developer application with read-and-write user authentication. xurl , which stores the credentials and communicates with the X API. A fixed-account Codex skill that verifies the identity before every write. A Codex scheduled task that researches, checks history, drafts, and publishes. That separation is the important part. Codex can make editorial decisions, but it cannot casually choose an account or improvise the publishing command. The skill owns the deterministic write boundary, while the scheduled task owns timing and editorial policy. In this article, I’ll show you how to build the same architecture. X developer settings, API packages, Codex features, and command-line options can change. The workflow below was verified in August 2026, but you should check the current upstream documentation before using it in production. What You Will Need Before starting, you will need: Codex on a Mac with access to Scheduled tasks. An X developer account and an application with read-and-write permissions. Homebrew. A dedicated or clearly identified X account for the automation. A local project containing the source material or editorial context the agent should use. You should also decide what the automation is allowed to publish before you give it access to an account. A good editorial policy is specific enough to reject a story, not merely broad enough to describe a topic. For example,

On Friday, Amazon customers received an email alerting them to an update to the site's terms and conditions. Most notably, it stated that disputes would now be resolved through arbitration and said users agree to a class action waiver. Amazon framed this as a "fast and efficient" way to resolve issues, but it notably would […]

The new Apple Upgrade program makes the monthly cost of an iPhone even cheaper than before.

"Flamingo missiles were used. A good achievement."

Meta released Glimmer this week, an open-weight AI model anyone can download and run on their own hardware — a contrast to Muse Spark, the company’s more powerful model that stays locked behind its own APIs. The release landed alongside a letter from Mark Zuckerberg arguing AI should be “for everyone” rather than controlled by a handful of labs, but as Equity’s […]

Natural gas prices could triple in some parts of the U.S., which could saddle hyperscalers with massive bills to power their AI data centers.

Scheduled LLM curation is the least glamorous agent workload you run. A cron job wakes up at 3am, reads a pile of memory, asks a model to dedupe it, summarize it, re-rank it, and writes the result back. Nobody is watching. There's no chat window, no streaming tokens, no human to click a button. It just has to work, quietly, every night. That "nobody is watching" part is exactly what makes the cloud-versus-local decision harder than it looks. When you have a human in the loop, a failed API call throws an error you can see and retry. In a headless cron context, the same failure turns into a job that hangs on an approval prompt no one will ever answer, or a pod that curated three months of context into an emptyDir that vanished on restart. I've run curation both ways: nightly jobs hitting a hosted API, and the same logic pointed at a local model on my Kubernetes cluster. Both work. They fail differently, cost differently, and demand different things from you operationally. Here's the actual tradeoff, not the marketing version. The decision point You reach this fork once your agent memory stops being a toy. Early on, you curate by hand or with a cheap synchronous call inside your agent loop. Then the memory grows, the curation gets expensive, and you pull it out into a scheduled job so it runs off the critical path. Now you're paying an API on a timer, and two things start to bug you. First, the data. Curation reads your entire memory store to make decisions. If that memory contains anything you'd rather not stream to a third party (internal notes, customer context, infrastructure details), every scheduled run ships it over the wire. I wrote about the general version of this problem in privacy-routed LLM inference , and scheduled curation is the workload where it bites hardest, because it touches everything, repeatedly, forever. Second, the cost shape. A curation pass over a large vector store is a lot of tokens for a job that produces no user-facing latency benefit. Yo

Study finds wait times for cancer surgeries hit 10-year high.

Streaming platform says user-generated content "may be used for future Gen AI model improvements."

"If this was opt-in, nobody would opt in," Twitch CPO Mike Minton said on a livestream responding to user feedback. "That's honestly the answer."

Amazon is handing off MMOs Lost Ark and Throne and Liberty to other publishers.

There's now an opt-out toggle for something that should have been opt-in to begin with.

Amazon is fully stepping back from MMOs. After saying last year that it would be halting "a significant amount" of its work on first-party AAA games, "specifically around MMOs," Amazon will be handing over live operations of Throne and Liberty and Lost Ark in the West to other companies, according to announcements on Wednesday. Throne […]

Earlier this summer, Amazon customers began noticing that emails related to their online orders looked sparse: Order confirmation emails didn't name specific items anymore, and instead listed only item categories. "Your Beauty item is confirmed!" an email about my retainer cleaning tablets read. Shoppers have posted other iterations of the redacted emails as well: "Ordered: […]

Amazon is unsurprisingly fighting measure to stop use of contractors for last-mile delivery in NYC.

As part of a planned Texas data center, Amazon is investing in an on-site power plant that could reportedly become the largest source of climate pollution in the United States.

To power its new West Texas data center, Amazon is investing in the construction of a new power plant that could be one of the largest single producers of greenhouse gases in the US, according to the New York Times. The new gas-burning plant in Pecos County, Texas has received significant investment from Amazon and, […]

A malicious Ollama model is not a virus you double click, but it is untrusted input handed to a C parser, a template engine and your filesystem in one request. The realistic damage from a hostile /api/pull is disk exhaustion, VRAM starvation, blob writes under ~/.ollama/models , a poisoned chat template that silently rewrites every prompt, and memory corruption in the GGUF loader if the file is crafted for it. None of that requires a vulnerability in your app code, only an Ollama daemon that trusts whoever can reach port 11434 and whichever registry a tag points at. Bind the daemon to localhost, pin models by SHA256 digest, run the container as a non root user with a read only root filesystem and a capped model volume, and the entire class collapses to a bad model that answers badly. TL;DR by reader profile: Solo developer running Ollama on a laptop, for example a contractor testing llama3.1:8b locally: leave OLLAMA_HOST at 127.0.0.1:11434 and pin digests, because your only real exposure is pulling a model whose tag moved under you. Two person startup running Ollama on one rented GPU box, for example a founder pair serving an internal assistant: run it in Docker as UID 1000 with --read-only , --cap-drop ALL and a sized model volume, because a single unbounded pull can fill the disk that also holds your Postgres data. Team fronting Ollama with Open WebUI or Continue, for example five engineers sharing one workstation: put model management behind the proxy and block /api/pull , /api/create , /api/push and /api/delete for normal users, because chat access and registry access are not the same privilege. Anyone building agents or RAG on Ollama, for example a support bot with tool calling: treat the Modelfile TEMPLATE and SYSTEM blocks as attacker controlled text, because a poisoned template reaches the model before your prompt does. Consultancies holding client data, for example a two person shop under an NDA: keep model pulls on a staging host, mirror approved blobs int

Ring has debuted a new version of its smart doorbell camera that's designed to be easily installed as a replacement for a door's peephole without drilling or running wires. The Peephole Cam 2K is a replacement for the brand's Door View Cam that first debuted in 2019 and, alongside a sleeker design, it features a […]

NJ antitrust suit accuses Amazon of unlawfully wielding power over delivery contractors .