Step-by-step guides for connecting apps and automating daily tasks.
TL;DR: Building an AI automation is easy; keeping it from silently breaking three weeks later is where 80% of workflows hit a brick wall. When an automated workflow fails, it rarely throws a clear error message—it quietly drops rows, loops endlessly on expired credentials, or sends unreviewed hallucinations to your executive team. Before deploying any … 더 읽기
TL;DR: After weeks of dissecting automation risks and warning about edge-case failures, it’s time for some good news: Aside on Windows is genuinely fun to use. Unlike early web bots that hijacked your mouse or trapped you in clunky terminal windows, the Windows build feels like an organic extension of your dual-monitor setup. Between background … 더 읽기
TL;DR: Agentic AI browsers like Aside represent a legitimate leap beyond copy-paste chatbots, but the industry hype conceals three major operational traps: the Supervision Paradox, subscription stacking bloat, and severe compliance liabilities. If a web task takes under two minutes manually, watching an AI browser execute it step-by-step makes you slower, not faster. Furthermore, paying … 더 읽기
TL;DR: Following our beginner guide on Aside’s $0 stack, I spent five full workdays testing Aside on a standard Windows enterprise desktop across our company’s messiest authenticated portals. Over 35 multi-step operational tasks, the agentic browser reclaimed 4.2 hours of mind-numbing data retrieval, but cost 1.1 hours in supervision and dynamic DOM troubleshooting — leaving … 더 읽기
TL;DR: Agentic browsers have spent the last year trapped in Mac-exclusive demos. With Aside’s September 2026 Windows release, autonomous web automation has finally arrived on enterprise desktop PCs. Unlike traditional AI chatbots that stop cold at login screens or require fragile API webhooks, Aside navigates your authenticated tabs directly. On the $0 Free Tier (500 … 더 읽기
TL;DR: Automated AI “second brain” tools promised that we could delete our note folders and let semantic algorithms connect our thoughts forever. After running an autonomous PKM setup across 2,400 work notes for six months, I abandoned the automated knowledge graph. AI connects notes by semantic similarity, but human operations run on causal timelines — … 더 읽기
TL;DR: The one habit from my first year in supply chain planning that no AI formula generator has replaced: before I trust any number, I calculate it two structurally different ways and make sure they match. It takes about 30 seconds and it catches exactly the kind of silent errors I found when I tested … 더 읽기
TL;DR: After testing AI formula generation (ChatGPT-4o, Claude 3.5, and Formula Bot) across 50 real enterprise datasets, complex REGEX text extraction and multi-criteria lookups proved 100% reliable. However, dynamic array spills, circular inventory logic, and multi-sheet date offsets consistently created silent calculation errors. Here is where I rely on AI in Excel, where I refuse … 더 읽기
TL;DR: The one document I will never delegate to ChatGPT, Claude, or any optimization algorithm is the Critical Component Shortage & Customer Allocation Report. When physical supply runs short, mathematical optimization fails because it ignores unwritten contract penalties, vendor reliability nuances, and personal accountability when a plant stops. Here is the short answer: I will … 더 읽기
TL;DR: Where a 13-year supply chain planner actually uses AI — and where I deliberately don’t. I use it for spotting demand patterns, but never for final inventory formulas or confidential order-level data. Short answer: yes, but only for spotting patterns in the numbers — never for the actual inventory formulas, and never with real … 더 읽기