Video now dominates how we capture information—across social media, education, marketing, and surveillance. Yet, the way we manage video is stuck in the past. Most workflows rely heavily on human labor: people watching screens in real-time, reviewing footage after incidents, or at best using basic object or motion detection.

Despite millions of cameras capturing billions of hours of footage, these outdated methods make it nearly impossible—or prohibitively expensive—to handle video at scale without cutting corners. The sheer volume of data is overwhelming, leaving much of it untapped and underutilized.

Traditional camera systems have a fundamental flaw: they rely on basic motion detection, bombarding users with irrelevant alerts triggered by swaying trees, passing insects, or even shifting shadows. This isn’t just inefficient—it’s a distraction from real threats.
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Enter tl;dw, an advanced API leveraging cutting-edge video language models (VLMs) to transform security video feeds into actionable intelligence. It’s not about adding more cameras or capturing more footage; it’s about understanding what’s happening and why it matters.

Media creators—from YouTube influencers to Hollywood studios—share a common goal: crafting stories that captivate audiences. Yet, much of their energy is consumed by repetitive, time-intensive tasks that have little to do with actual creativity. The solution? Automation that empowers creators to focus on storytelling, not busywork.
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tl;dw is reshaping media production, enabling creators to streamline their workflows and unlock new possibilities. Here’s how.