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I started where a lot of us do: a LangChain RAG walkthrough. You chunk some text, embed it, retrieve top‑k chunks, and wire an LLM to answer questions. It clicks quickly, which is exactly why it’s easy to walk away thinking you’ve “done RAG.” What bothered me was that the demo corpus is usually tiny and artificial. I write on DEV.to about things like NLP routing and CNN image classification. If I
I wanted to add live chat to my WordPress sites without loading a 500KB third-party script. So I built my own. GhostChat is an open source embeddable Widget: Vanilla JS, no framework, ~10KB Backend: Cloudflare Workers + Durable Objects for persistent WebSocket connections Payments: Stripe for the hosted tier Self-hostable: Bring your own Cloudflare account Durable Objects give you stateful serve
The more I use AI, the more convincing it feels. Clear answers. Whether it’s: strategy code writing decision support AI rarely hesitates. And over time, I noticed something subtle. I stopped questioning it as much. Breaking the Expectation We assume better tools reduce errors. Smarter systems. And in many cases, that’s true. But there’s a hidden shift happening: As AI improves, our skepticism decr
My first Cloudflare Worker deployed in 47 minutes. Three of those were spent staring at this exact error in a red GitHub Actions log: Authentication error [code: 10000]. I had the API token. I had the account ID. I had copy-pasted the workflow from the official docs. It still failed. The fix was one checkbox I never selected. That checkbox is the entire reason I'm writing this post, because every
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Updated May 2026: Now covers virtual desktop (Spaces) restoration and iCloud sync across multiple Macs, both shipped in ShiftPlus 1.3. TL;DR A complete macOS workspace includes apps, window layouts, browser profiles, virtual desktops, and terminal state. Native macOS saves almost none of it. Most third-party tools cover one slice: Stay and Spencer handle window layouts, Shift handles browser profi
In July 2025, a developer's Claude Code instance hit a recursion loop and burned through 1.67 billion tokens in 5 hours, generating an estimated $16,000 to $50,000 in API charges before anyone noticed. The agent did not crash. It did not throw an error. It just kept calling tools, getting confused, calling more tools, and silently accumulating cost. Old software crashes. LLM agents spend. This is