The Buzz

Larger bee colonies don't just have more bees, they're exponentially more efficient pollinators. For growers, this means better crop coverage, extended pollination windows, and higher yields. Learn why hive strength matters more than hive count.

With U.S. beekeepers losing 62% of colonies last year and farms needing to produce 60% more food by 2050, precision pollination monitoring is no longer optional, it's critical infrastructure. Our CEO shares how data-driven hive management protects both bees and yields at commercial scale.

Successful almond pollination depends on thoughtful preparation, clear communication, and disciplined orchard management before, during, and after bloom. This guide outlines best practices to help protect bee health, maximize pollination efficiency, and improve nut set.

BeeHero's Data Hive relies on DBT to take our data analysis and engineering to the next level, implementing a medallion architecture and streamlining access and visibility of the data

As hive shortages and rising costs strain California’s almond industry, a new insight is reshaping pollination strategies: fewer, stronger colonies outperform larger numbers of weak ones. This season showed that hive quality, not quantity, is the key driver of yield, efficiency, and resilience.

Data Science model inference is a crucial component of BeeHero's technology - and scaling inference along with the data posed quite a few challenges. Using queues, batches, concurrency controls and code optimizations helped us to deliver the expected performance

Sample processing is a phase that streamlines and enrich our IoT raw samples, and execute business processes that rely on these samples. Scaling this phase required transitioning from batch processing to single sample processing, separating business logic from the main flow and optimizing our Postgres storage

How BeeHero implemented a Change Data Capture (CDC) solution to ingest data from our production Postgres database into our Data Lake

How We Cracked the Connectivity Challenge in Australia's Major Pollination Zones

One of our early scale optimizations was to improve how we handled that first phase of IoT data processing: from HTTP to SQS queues, from monolith to services and from Postgres to S3 storage

We focused on our IoT devices and server communication, improving our R&D processes and researching common scale issues

AI code editing is fast becoming the practice-of-the-day for developers - here are some thoughts based on my experience with AI coding, and specifically Cursor IDE

Reducing RDS instance size using blue/green deployments is a significant improvement over traditional methods, offering greater speed and reliability. However, it requires careful planning, testing, and execution.

BeeHero's journey to scale our solution and meet customer expectations. Part two: at the beginning there were many sensors but only one server

BeeHero's journey to scale our solution and meet customer expectations. Part one: an introduction to BeeHero IoT and cloud platform and the challenges we faced early on