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https://www.nytimes.com/2026/07/10/climate/fcc-space-mirror.... https://www.pcmag.com/news/fcc-approves-reflect-orbitals-gia...

It's unclear how the planet avoided its star's bloated red giant stage.

Hi, friends! Welcome to Installer No. 135, your guide to the best and Verge-iest stuff in the world. (If you're new here, welcome, crank the AC, and also you can read all the old editions at the Installer homepage.) This week, I've been reading about cold plunges and Colson Whitehead and the food truck mafia, […]

For the first time since 1995, the Bureau of Land Management is rewriting its grazing regulations.

The Skylight has become the informational and organizational hub of my household. My touchscreen-native kids have also gained more agency over our family activities.

There’s a new fleet of TVs using new mini and micro RBG display tech, and Samsung’s R95H model isn’t as impressive as it should be.

Plus: The Pentagon is training amateurs to become part of its hacker army, a Flock license plate reader error led to cops surrounding a car reviewer, and more.

More dog owners have begun cooking for their canine companions in recent years. When my own dog fell ill, I became part of this growing group.

Hello, I'm Maneshwar. I'm building git-lrc, a Micro AI code reviewer that runs on every commit. It is...

In the previous post I argued that agents are great planners and DAGs are great executors . This one is the practical follow-up: when you actually sit down to wire several LLM calls together, what tool do you reach for? Because the moment one prompt's output feeds the next, you've built a workflow — whether you call it that or not. download transcript → summarize → translate (tool) (LLM) (LLM) That tiny pipeline is already the whole problem in miniature: a non-LLM step (fetch a YouTube transcript), then a model call, then another model call that depends on the first. Run it as one giant prompt and you lose visibility; split it into steps and you gain debuggability — at the cost of more calls and more state to manage. The naming trap Half the confusion is vocabulary. The same idea ships under a dozen labels: Name What it whispers Chain sequential, output → input Pipeline stages, data flowing through Flow branches and conditions Workflow general orchestration Agent workflow the model also decides The word sets expectations. "Chain" promises a straight line; "agent workflow" promises the thing might re-plan on you mid-run. Pick the label that matches how much autonomy you're actually handing over — calling a deterministic two-step pipeline an "agent" only invites disappointment. The real choice: library or orchestrator? There are two families of tools, and they solve different problems. LLM-native chaining libraries — LangChain , LlamaIndex Workflows , Azure Prompt Flow , or visual layers like Flowise . These understand LLM-specific concerns out of the box: prompt templating, passing context between steps, token budgets, streaming, retries on a flaky model. General orchestrators — Airflow , Prefect , AWS Step Functions , Azure Logic Apps . These treat each LLM call as just another task in a DAG, and give you the heavyweight reliability machinery: durable state, scheduling, checkpointing, audit trails, human approval. The rule of thumb that falls out of the last post: F