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How I let non-engineers ship AI tools to production — and the boring infrastructure that made it safe. A product manager described a workflow in plain English — "every morning, pull yesterday's failed payments, group them by error code, and post a summary to our channel." Twenty minutes later it was running in production. She never opened an editor. She never saw a line of TypeScript. She talked to an agent, the agent wrote the code, and — once a human had reviewed the pull request — it shipped. That sentence should make you nervous. It made me nervous, and I'm the one who built the thing. The demo is "look, it wrote the code." The operation is "a marketer's tool now has a path to the payments database and nobody reviewed it." The interesting engineering isn't the part where an LLM writes code — that's the easy, demo-able part. It's the guardrails that decide whether the code it writes is allowed to exist. Here's the platform, and the five problems I had to solve to make it safe to hand to people who can't read the code that runs. The shape of the thing The platform is a place where anyone — engineers, PMs, designers, QA — can publish a reusable AI tool, and everyone else can use it. Write once, available to all. A few terms up front, because the whole design leans on them: MCP (Model Context Protocol) is a standard way for an AI client to discover and call your functions. The key detail: there's a step where the client asks the server "what tools do you have?" and the server answers with a list. Hold onto that — half the design hangs off that one list. Cloudflare Workers is code that runs on Cloudflare's servers at the network edge instead of your own. Durable Objects is per-session server-side storage that lives outside the model's context — the finite, token-costing window of everything the model can currently see. None of this is exotic; what matters is where each piece of state lives. Under the hood it's three small Workers speaking MCP: a gateway (auth, routin

Traditional content pages are often designed for a linear reader. The introduction sets context, the middle develops the idea, and the conclusion ties everything together. AI retrieval does not always work that way. A system may identify smaller content units, pull the most relevant section, compare it with other sources, and use that fragment to support an answer. The full page still matters, but the retrievable blocks inside the page matter just as much. A useful Tumblr post explains the idea in simple terms: https://www.tumblr.com/digitalisedsoul/820825642809573376/ai-does-not-read-your-content-like-a-human?source=share For Dev Community readers, the pattern is familiar. Poorly structured inputs lead to weaker outputs. If content is dense, vague, or dependent on surrounding paragraphs, it becomes harder to extract and reuse. If content is modular, clear, and properly scoped, retrieval becomes easier. Marketing teams can learn a lot from this. A strong content page should behave like a set of well labelled components. Each section should answer a specific question. Headings should be descriptive, not decorative. Paragraphs should avoid vague references such as the above point or this approach when the section may be read independently. Definitions should appear close to the terms they explain. Examples should include enough context to stand alone. Proof should be written as text, not only displayed as graphics. Internal links should connect related concepts in a way that helps both readers and systems understand the topic map. A page about AI search visibility, for example, should not only include one broad explanation. It should break the topic into useful blocks: what AI visibility means, why AI systems retrieve passages, how source trust works, what makes content reusable, and how brands should measure answer presence. Each block becomes a possible answer unit. That structure does not weaken the reader experience. It improves it. Developers, marketers, and busi

In 2007, humans took roughly 85 billion photos a year. Photography was a specialized craft that...

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

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

Heat pumps are more than just a way to keep your home comfortable without using fossil fuels.

hey guys i'm a student of class 12 not expert but just interested to learn neural networking as today is my day second so i think to post my progress with you all as i can find the answer of question that i'm facing with. yesterday i have learn the basic of a neuron z=∑(w ⋅x )+b and today i'm learning about Batches, Layers, and Objects. despite =this it's hard to know what weight and bias really are if someone can explain me what they are please explain. that for today (>__<) submitted by /u/Vegetable_Cry_854 [link] [留言]

The gate between your AI agents and the real world Discussion | Link

👋 Hey there, Tech Enthusiasts! I'm Sarvar, a Cloud Architect who loves turning complex tech problems...

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Decode sentences directly from non-invasive brain signals Discussion | Link

A new report finds "high-intensity AI adopters” saw headcount increase 10.2%. Among those companies, entry-level headcount rose by 12%, countering the rhetoric that AI kills junior jobs.

I have been working on Glaze , a small desktop WebView toolkit for Go. The short version: Glaze lets a Go program open a native desktop window backed by the WebView already available on the operating system, without using CGo. It currently targets: macOS, through WKWebView Linux, through WebKitGTK Windows, through WebView2 The project is still young, but the core idea is already useful: keep small Go desktop tools close to the normal Go workflow. No C compiler in the build path. No bundled native helper library. No large application framework around it. Just Go code calling the system WebView. Why I wanted this I write a lot of small tools in Go. Some of them are fine as CLI programs. Others need a basic interface: a form, a preview, a local dashboard, a small editor, or a way to inspect and manipulate data visually. For those cases, HTML is often enough. The browser gives me layout, text rendering, forms, tables, keyboard handling, and a familiar debugging model. But I do not always want to ship a web server as the user interface. I also do not always want to pull in a large desktop framework when all I need is a native window around a local UI. A WebView is a reasonable middle ground. The problem is that many WebView solutions eventually bring CGo, native build tooling, helper libraries, or larger framework assumptions into the project. That is not necessarily wrong. For many applications, those trade-offs are acceptable. For this project, I wanted something narrower. The design constraint The main constraint behind Glaze is simple: Use the WebView already provided by the OS, but call it from Go without CGo. Glaze uses purego to call native platform APIs directly from Go. That means each backend talks to the platform WebView: WKWebView on macOS WebKitGTK on Linux WebView2 on Windows The result is not a full GUI toolkit. That is intentional. Glaze is focused on the window, the WebView, JavaScript-to-Go bindings, and a few desktop helpers that are useful for small t