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From Messy CSV to Clean Dashboard: What I Learned Building My First Power BI + Google Analytics Dashboard (Part 1)

I get distracted easily. If something isn't explained to me properly, step by step, it just doesn't stick, even if I've technically seen the right answer once already. So when I sat down to build a Power BI dashboard for a nonprofit client using their Google Analytics 4 data, I wasn't just trying to get a working file by the end of the day. I wanted to actually understand what I was doing, because at some point my client is going to ask me a question about it, and "I'm not sure, an AI did that p

datapowerplatformai
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ExternalSoftware Engineering
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Somebody asked for help. Nobody came.

This is a submission for Weekend Challenge: Generosity Edition Andrea has been waiting 174 days for someone to reply to his issue. He asked nicely. He labelled it help wanted. Someone who knows Rust could answer him tonight in ten minutes. They just don't know he asked. There are 26,034 issues like his right now. People haven't stopped caring about open source. The ask and the person who could answer it simply never meet. unanswered introduces them. Tell it what you're good at. It finds the main

devchallengeweekendchallenge
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Building Adaptive AI Agents

Building Adaptive AI Agents — Course Glossary Foundations Harness The software scaffolding that surrounds an LLM to turn it into a working agent. It assembles the context the model sees, gives the model the ability to call tools, records results to memory, and re-invokes the model within the agent loop. The LLM supplies the reasoning; the harness supplies the structure around it. Agent Loop The repeating cycle by which an agent completes a task: a user request triggers the model to decide on an

agentsaillm
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‘Comet Has Been Gutted’: A Week of Paid AI Features Quietly Disappearing

For a year the loudest complaints in consumer AI have been about the meter: vanishing limits, tokens burning faster than the pricing page implied, a flat plan quietly behaving like a taxi fare. This week the grievance shifted one notch sideways, from “you’re metering what I paid for” to something blunter: you’re taking away what I paid for. Features removed. Browsers paywalled. Favourite models pulled. And, in the sharpest cases, a plan billed for credits that never arrived. The epicentre was Pe

voicesperplexitypricing
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Where Need Meets Nothing: finding Florida's aid deserts with Snowflake

This is a submission for Weekend Challenge: Generosity Edition Generosity has a targeting problem. Money follows attention, attention follows disasters, and disasters get covered where the cameras already are. The places that quietly need the most are often the places nobody is looking. So I went looking for them. Two Florida counties: County FEMA disasters (10yr) Poverty rate Doctors per 10k Alachua 21 21.2% 351.0 Glades 14 17.2% 20.7 Alachua has more disasters and more poverty than

devchallengeweekendchallengesnowflake
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I checked dozens of UK local business websites by hand. Here's what actually breaks, and the tool I built to do it for me

I'm Alex, 19, Ipswich. For the last month I've been doing something tedious: taking a UK town and a trade - "hairdressers, Wakefield", "accountants, Bristol" - opening every business's own website, and writing down what's wrong with it. Not "the design is dated". Things you can measure: does the bare domain resolve, does www, is the certificate valid, is there a published email address, does the contact form deliver a test message. What breaks, in order of how often I see it: The published web a

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