Your calendar already knows when you worked. A timesheet is those events, for one week or month, with hours added up. Export the week or month to a sheet (start, end, title, hours). Round each event the way you bill: up to 15 minutes, for example. Group by day or week with a pivot table.
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This month, Utexo, a Tether-backed company, plans to launch USDT natively on Bitcoin using the RGB protocol, with Lightning support to follow. Once it does, "USDT on Bitcoin" will refer to at least four different things: Omni (the 2014 original), Liquid, Lightning via Taproot Assets, and RGB. If your system identifies assets by ticker, or even by ticker + chain, that's a data model problem waiting to happen. Most codebases start like this: type Asset = "BTC" | "ETH" | "USDT" | "USDC"; Then multi-chain support arrives: type Asset = { symbol: string; chain: string }; // { symbol: "USDT", chain:
What I Built I built J.O.H.A.N — an AI-powered comeback generator designed specifically for a close friend of mine who often deals with a bully but never quite knows what to say back in the heat of the moment. My friend is a massive cinephile who loves Malayalam cinema, so I built J.O.H.A.N to listen to what the bully says and generate a handful of witty, adaptive comebacks in both English and Malayalam (Manglish). The AI uses a curated Knowledge Base of iconic Malayalam movie punchlines, trending memes, and actor profiles (like Mohanlal and Fahadh Faasil) to deliver culturally relevant, funny
This is a submission for the Hacktoberfest Weekend Challenge: Build for a Friend A friend of mine is preparing for university exams. Like many students, they have to go through long PDFs and notes and often struggle with a simple question: "Where do I even start?" So I built Study Saathi, a simple local AI-powered study assistant that turns study notes into useful revision material. The workflow is simple: Upload Notes → AI Summary → Practice MCQs → Flashcards Study Saathi currently supports PDF and TXT study material and provides three main features: 📝 AI Summary — Creates a concise, exam-fo
The new economics of AI: when hardware, culture, and documentation intersect Practitioners in AI and analytics are navigating a shift where capability is no longer the bottleneck — economics and governance are. The items harvested this week show one pattern: running large models on consumer GPUs, searching visual archives with AI, debating whether agents need memory or documentation, tracking institutional rifts at leading labs, and questioning pure mathematics' future. Each signals a different layer of the same system. The community has noticed that high point scores on technical releases oft
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