标签：#AR

I'm currently building an AI, specifically a large language model (LLM), using PowerShell. This AI will search the internet for code snippets and create databases. It will also have the ability to adjust and improve its own code. With PowerShell, I'm leveraging its scripting capabilities to automate tasks and manage data efficiently. The AI will integrate natural language processing techniques to understand and generate text, making it more user-friendly. Additionally, I plan to develop a simple interface to allow users to interact with the AI easily and provide feedback for continuous improvement. submitted by /u/Electrical-Tap-9224 [link] [留言]

I’ve been experimenting with automating a few small workflows lately (lead scoring, file handling, etc.) One mistake I keep running into is trying to automate things before the process itself is actually clear. At first it feels productive: - add rules - add scoring - connect tools But over time it just turns into: - patching edge cases - fixing broken inputs - adding more conditions to handle weird situations At some point I realized the problem wasn’t the automation, it was that I didn’t really have a clean “manual logic” to begin with. Once I stepped back and tried to define the process in simple human terms, everything got easier: fewer rules, less complexity, way more stable Feels like automation doesn’t fix messy processes, it just exposes them faster. Curious if others ran into the same thing or if I’m overthinking it. submitted by /u/huncho-mohammed [link] [留言]

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The Term Everyone Uses and Nobody Defines Your CTO came back from a conference and said the team needs to "go agentic." A vendor pitched you an "agentic data platform" last week. LinkedIn is full of posts about agentic workflows transforming everything from customer support to supply chain management. And yet, when you ask three people what "agentic" actually means for your data operations, you get four answers. This is not a vocabulary problem. It is a strategy problem. Organizations are making six-figure decisions about agentic AI without a shared definition of what they are buying, building, or hiring for. That gap between the buzzword and the architecture is where most projects fail -- not because the technology does not work, but because nobody agreed on what it was supposed to do. This guide is a practitioner's attempt to close that gap. No vendor pitch, no hand-waving. Just a clear definition, a real example, and a framework for deciding whether agentic workflow consulting is something your team actually needs. What "Agentic" Actually Means (In Plain Language) Traditional data pipelines are deterministic. You define steps, connect them in order, and run them. Step A feeds step B, which feeds step C. If the input changes shape, the pipeline breaks and a human fixes it. The pipeline does not adapt, reason, or make decisions -- it executes. Robotic process automation (RPA) is slightly smarter but still scripted. It records human actions and replays them. Click here, type there, move this file. When the UI changes or an edge case appears, the bot breaks the same way a pipeline breaks: it stops and waits for a human. Agentic workflows are fundamentally different. An agentic system has components that can reason about their task, make decisions based on context, and take actions without a pre-scripted path for every scenario. Instead of "if X then Y," an agentic node can evaluate ambiguous input, choose between approaches, validate its own output, and route work to

This specific undertaking is not fundamentally burdensome in terms of labor; however, this endeavor serves as the crucial support for my unwavering commitment to see it through to its ultimate conclusion. It is precisely the motivation behind my relentless 72-hour shifts and the impetus that prevents me from ceasing my efforts. My affection amidst my grief—my aspiration is to assist others and ensure that the tragedy you experienced is never repeated. Caitlyn Walmsley, RIP. I will love you always.