Rye Overly

  Public companies rely on accurate revenue forecasting to guide strategic planning, satisfy investor expectations, and comply with regulatory requirements. Revenue forecasting is the process of estimating future income based on historical performance, market conditions, and internal company strategies. The process begins with a detailed analysis of past sales trends. Finance teams evaluate quarterly and annual revenue, customer retention, pricing models, and product performance. These historical patterns serve as a baseline for projecting future income. Next, companies incorporate forward-looking insights. This includes macroeconomic indicators like inflation, interest rates, and consumer demand trends. Public companies also consider industry-specific dynamics, such as seasonal cycles or regulatory changes, that may impact sales. Internal data plays a key role as well. Sales pipelines, customer contracts, marketing campaigns, and product launches are factored in to create bottom-u...

  Video streaming has revolutionized the way we consume content, from binge-worthy shows on Netflix to real-time gaming streams on Twitch. But behind this seamless viewing experience lies a complex network of technologies working in harmony. At the core of streaming is  video compression . Raw video files are massive, so compression algorithms like  H.264 ,  H.265 (HEVC) , and the newer  AV1  reduce file size without significantly sacrificing quality. This makes it possible to deliver video efficiently over the internet. Once compressed, the video is broken into  small segments , typically just a few seconds long. These segments are then delivered over a  Content Delivery Network (CDN)  — a global system of distributed servers designed to deliver content quickly and reliably. CDNs cache video segments closer to viewers to reduce latency and buffering. Streaming also relies on  adaptive bitrate streaming , a method where the video player ...

  In today’s ever-evolving digital landscape, cyber threats are growing more sophisticated by the day. Traditional firewalls, while essential, often rely on manually updated rules or reactive measures. However, by harnessing historic cyber attack data, organizations can transform their firewalls from static defenses into dynamic, automated protection systems. Historic cyber attack data provides a rich pool of insights into attacker behavior, common vulnerabilities, and evolving tactics. This data includes IP addresses of known threat actors, malware signatures, unusual traffic patterns, and exploit vectors. By analyzing this information, machine learning models can identify patterns and predict the likelihood of future attacks, even before they happen. Automated firewalls can integrate this intelligence to adjust rules in real-time. For example, if an IP range is associated with a past DDoS attack, the firewall can proactively block or rate-limit traffic from that range. If certain...