The latest articles on DEV Community by Quantum Quiver (@quantum_quiver_d2f5a141d8). https://dev.to/quantum_quiver_d2f5a141d8 https://media2.dev.to/dynamic/image/width=90,height=90,fit=cover,gravity=auto,format=auto/https:%2F%2Fdev-to-uploads.s3.amazonaws.com%2Fuploads%2Fuser%2Fprofile_image%2F3702068%2F63e693ee-e78d-4ab6-9af7-6515964ac7ef.png https://dev.to/quantum_quiver_d2f5a141d8 en Quantum Quiver Fri, 09 Jan 2026 10:49:08 +0000 https://dev.to/quantum_quiver_d2f5a141d8/building-a-supply-chain-risk-monitoring-system-with-real-time-alerts-e9a https://dev.to/quantum_quiver_d2f5a141d8/building-a-supply-chain-risk-monitoring-system-with-real-time-alerts-e9a <p>Supply chains are the backbone of global commerce, yet even minor disruptions can cascade into major financial and operational impacts—whether due to port closures, labor strikes, or sudden commodity price shocks.</p> <p>I built the <em>Supply Chain Risk Monitor</em> to help businesses and analysts <strong>detect early indicators of supply chain disruption</strong> using publicly available data sources and automated workflows. Rather than relying on reactive reporting, this system emphasizes <strong>timely, structured risk signals you can act on</strong>.</p> <p>This post walks through the problem space, how the system is architected, and why it matters for data-driven operations.</p> <h2> The Problem </h2> <p>Modern supply chains are highly interconnected, and disruptions can originate from many sources. Some recent examples include:</p> <ul> <li>Port delays and closures affecting shipment schedules </li> <li>Labor actions at key logistics hubs </li> <li>Commodity price spikes triggered by weather or geopolitical events </li> </ul> <p>Most teams don’t become aware of risks until <em>after</em> they have already impacted cost or delivery commitments.</p> <p>The goal here was to build a monitoring pipeline that provides <strong>data-driven alerts without hype—just signal.</strong></p> <h2> How the System Works </h2> <p>At a high level, the Supply Chain Risk Monitor pulls data from multiple public and semi-public sources, normalizes it, and issues structured alerts.</p> <h3> Data Sources </h3> <ul> <li> <strong>Maritime RSS Feeds</strong> – Detect port closures, congestion, and other shipping news </li> <li> <strong>Global Event Databases (e.g., GDELT)</strong> – Identify reported disruptions across regions </li> <li> <strong>Market Data APIs</strong> – Track commodity price shocks and unusual moves </li> </ul> <p>These sources are aggregated and filtered to produce <strong>actionable alerts</strong>, not clickbait headlines.</p> <h2> Alerting and Delivery </h2> <p>Alerts are delivered in near real-time and include:</p> <ul> <li><strong>Event description</strong></li> <li><strong>Data source with a link</strong></li> <li><strong>Timestamp</strong></li> <li><strong>Relevant context (commodity or location)</strong></li> </ul> <p>Alerts can be consumed via:</p> <ul> <li><strong>Private Telegram channel</strong></li> <li><strong>Google Sheets dataset for further analysis</strong></li> <li><strong>Webhook or API ingestion (for connected tools)</strong></li> </ul> <p>The system prioritizes <strong>latency and accuracy</strong> so that teams can incorporate alerts into workflows rather than dashboards.</p> <h2> Why This Matters </h2> <p>For logistics teams, analysts, and risk managers, early warning does not guarantee avoidance—but it <strong>reduces surprise</strong> and supports data-informed decisions.</p> <p>This kind of monitoring is useful for:</p> <ul> <li>Identifying emerging port delays </li> <li>Tracking labor or geopolitical activity that could affect routes </li> <li>Spotting commodity price shocks that might signal upstream issues </li> <li>Integrating risk signals into automated processes </li> </ul> <p>Importantly, this product <strong>does not provide predictions or financial advice</strong>; its focus is on distilled, verifiable signals.</p> <h2> Get Started </h2> <p>I’ve made the Supply Chain Risk Monitor available as a subscription data product so others can benefit from the same pipeline without building it from scratch.</p> <p>If you are interested in:</p> <ul> <li><strong>Real-time supply chain risk alerts</strong></li> <li><strong>Clean, structured data feeds</strong></li> <li><strong>Telegram alerts and Google Sheets outputs</strong></li> </ul> <p>Learn more here:<br><br> 👉 <a href="https://tinyurl.com/ypssuek8" rel="noopener noreferrer">https://tinyurl.com/ypssuek8</a></p> <h2> Final Thoughts </h2> <p>Risk monitoring in complex systems requires both breadth and structure. By automating data ingestion from diverse sources and emphasizing verification, this system aims to get you <strong>closer to the story than the press release</strong>.</p> <p>If you’re working on similar monitoring pipelines or have ideas for improving signal quality, I’d be interested in hearing your approach.</p> supplychain dataproducts riskanalysis monitoring Quantum Quiver Fri, 09 Jan 2026 09:37:14 +0000 https://dev.to/quantum_quiver_d2f5a141d8/building-a-real-time-bitcoin-whale-tracking-system-and-what-i-learned-403e https://dev.to/quantum_quiver_d2f5a141d8/building-a-real-time-bitcoin-whale-tracking-system-and-what-i-learned-403e <p>Large Bitcoin movements (“whale transactions”) often precede volatility, but most retail traders only see them after the fact—if at all. I wanted to explore whether it was possible to build a reliable, near-real-time whale tracking pipeline using public blockchain data and modern automation tools.</p> <p>This post outlines how the system works, the challenges involved, and what I learned along the way.</p> <h2> Problem Statement </h2> <p>Bitcoin is transparent, but not necessarily accessible.</p> <p>While every transaction is public, extracting meaningful signals in real time is difficult due to:</p> <ul> <li>High transaction volume </li> <li>Mempool noise </li> <li>Exchange internal transfers </li> <li>Latency between detection and confirmation </li> </ul> <p>Most “whale alert” bots either:</p> <ul> <li>Spam unverified mempool data, or </li> <li>Lack context and traceability </li> </ul> <p>My goal was to build something clean, verifiable, and automation-friendly.</p> <h2> Architecture Overview </h2> <p>At a high level, the system consists of:</p> <h3> Blockchain Monitoring </h3> <ul> <li>Watching confirmed BTC transactions only </li> <li>Filtering by configurable thresholds (e.g., 5+ BTC, 10+ BTC)</li> </ul> <h3> Verification Layer </h3> <ul> <li>Every alert includes a blockchain explorer link </li> <li>No unconfirmed or speculative data</li> </ul> <h3> Delivery Layer </h3> <ul> <li>Alerts pushed to Telegram within ~60 seconds of confirmation </li> <li>Structured message format for readability</li> </ul> <h3> Data Persistence </h3> <ul> <li>Each transaction logged to Google Sheets </li> <li>Timestamp, BTC amount, USD value, transaction hash </li> </ul> <p>This design prioritizes accuracy over speed, which is critical when data is used for analysis rather than hype.</p> <h2> Key Challenges </h2> <h3> 1. Filtering Meaningful Transactions </h3> <p>Not all large transfers matter. Exchange reshuffling and internal wallet movements generate noise. While perfect classification is impossible without private labels, thresholding and historical pattern analysis help reduce false positives.</p> <h3> 2. Latency vs. Accuracy Trade-Off </h3> <p>Monitoring mempool data is faster but unreliable. Waiting for confirmations introduces slight delay but dramatically improves data quality.</p> <p>I opted for confirmed transactions only.</p> <h3> 3. Alert Fatigue </h3> <p>Too many alerts reduce usefulness. Tiered thresholds help users choose between:</p> <ul> <li>High-signal, low-frequency alerts </li> <li>More sensitive, higher-frequency monitoring </li> </ul> <h2> Why This Matters </h2> <p>For developers, analysts, and data-driven traders, raw, structured on-chain data is often more valuable than predictions.</p> <p>This kind of system can be used for:</p> <ul> <li>Market structure analysis </li> <li>Volatility studies </li> <li>Correlation research </li> <li>Alert-driven workflows </li> <li>Historical dataset building </li> </ul> <p>Importantly, it does not provide trading advice—only verifiable data.</p> <h2> Making It Accessible </h2> <p>After running this system privately, I packaged it into a small subscription so others could use the data without building the pipeline themselves.</p> <p>If you are interested in:</p> <ul> <li>Real-time BTC whale alerts </li> <li>Clean, verifiable on-chain data </li> <li>Telegram-based delivery with automatic logging </li> </ul> <p>You can find more details here:<br><br> 👉 <a href="https://tinyurl.com/yc6xdpv2" rel="noopener noreferrer">https://tinyurl.com/yc6xdpv2</a></p> <h2> Final Thoughts </h2> <p>Building data products in crypto requires balancing speed, accuracy, and trust. Public blockchains give us the raw materials—but turning that into usable data is an engineering problem, not a marketing one.</p> <p>If you’re working on similar pipelines or have insights into transaction classification, I’d be interested to hear how you approach it.</p> bitcoin onchain dataproducts pipelines