Startup Beat
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StartUp Beat has relaunched to take a new and improved look at startups from around the world. We will continue to profile the world’s most innovative early-stage startups through company pitches, interviews and guest columns from entrepreneurial experts. StartUp Beat is now based in Medellín, Colombia, one of Latin America’s burgeoning tech hubs.
Of course, none of this would be possible without founder Brian Kovalesky.
Brian founded StartUp Beat in 2007 as both an experiment in digital media and an effort to highlight the innovative work of startups from around the world who didn’t necessarily land on the radar of some of the mainstream startup/technology publications. Brian has a background in journalism, marketing, and communications, and has been working for years on a Ph.D. in U.S. History that is close to completion. His work on StartUp Beat was acknowledged in 2013 by the Columbia School of Journalism’s Tow Center for Digital Journalism when he and the site was part of the school’s Single Subject News conference and research project. Source
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Media Outlet details
| Scope | International |
|---|---|
| Language | English |
| Country | Colombia |
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Recent Articles
Search ArticlesWhy multifamily operators are turning to automation for renters insurance compliance
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Why Python remains the AI industry’s biggest performance bottleneck
This article is about the strangest trade-off in modern engineering: the industry that computes faster than anything in human history conducts those computations in one of the slowest languages in the history of programming. And about what happens once we stop treating that as a law of nature. Let us begin with a scene that plays out every day in thousands of companies. A data centre. Racks of accelerators, each performing hundreds of trillions of operations per second.
How to choose the best autoscaling metrics for AI workloads
On a GPU inference pod, CPU is a decorrelated proxy: it can sit at 30% while the accelerator is pinned and requests pile up behind it. Scale on the signal actually on the critical path—backlog and GPU utilization—with KEDA. On a GPU-served model, the CPU handles tokenization, HTTP, and batch assembly. The work that saturates lives on the accelerator. CPU utilization is a decorrelated proxy.
Toronto startup Provision launches AI scope extraction tool, claims edge over Claude
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Startup profile: Upfirst is using AI to ensure small businesses never miss another call
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The AI era will belong to business integrators, not specialists: report
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Why phishing detection alone won’t save K-12 school districts anymore
A school district in Southern Door County, Wisconsin, cut two checks worth $67,000 to a fake superintendent in November 2025, and learned on July 13 that it was an Ohio woman who had sent staff fraudulent invoices, pretending to be the district’s top administrator.
How AI startup Prezent Vivo is disrupting traditional medical communications agencies
For companies in the life sciences industry, medical communications is a core pillar for sharing clinical trial updates and connecting with peers. Because these materials require strict accuracy and academic rigor, biopharma companies have historically relied on specialized external agencies. This traditional approach guarantees expert messaging but often results in slow turnaround times and high costs that limit the number of projects a company can pursue.
StarApps Acquires AppMaker to Transform Enterprise Mobile Commerce on Shopify
StarApps, the company behind some of the most widely installed product merchandising apps on the Shopify App Store, today announced the acquisition of enterprise mobile app platform AppMaker in an all-cash deal. The deal lands in the middle of a defining shift in ecommerce. With paid customer acquisition getting more expensive every year, brands are moving budget and attention toward retention and channels they own outright.
Why Articulate 2026 is putting scientific communication at the center of the AI conversation
AI has become synonymous with accelerating scientific discovery. Across the pharmaceutical and biotechnology sectors, organizations are investing heavily in AI to analyze clinical data and shorten development timelines. Yet a quieter transformation is underway. As scientific knowledge expands at an unprecedented pace, many life sciences organizations are discovering that their greatest challenge is no longer generating evidence.