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Cooper Kunz
Cooper Kunz

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Upcoming Livestream - Using Blockchain to Track (Real) Bugs πŸ›

Developers often mistakenly only consider blockchain as an inefficient, append-only data structure or log. In this worldview, they are left to wonder when and why they would leverage blockchain technology - especially when they're already using familiar technologies like Apache Kafka and standard REST APIs.

One of my favorite use cases for blockchains is related to the public access to datasets, particularly when many clients or users want to "speak up" about the dataset's contents.

By logging data in realtime in a low-trust environment like a blockchain, all kinds of users (in this case, researchers, farmers, and consumers) can access a raw data stream with a pseudonymous record of who added that information at what time. This enables compounding network effects - for example, reputation systems (how trustworthy is the data being added) or analytics systems (what is this data telling us), in a permissionless environment where they don't have to ask or authenticate for access to the data.

These are just a few of the reasons why I'm so excited about the Public Pest Network (PPN) - a team of University students and researchers leveraging the Hedera Consensus Service to provide easy ways for entomologists to track and make decisions about pest data. If farmers, researchers, and consumers, can all have better insights into where bugs are migrating to, it can drastically improve outcomes for our food supply and other essential processes like disease outbreaks.

I'll be hosting a livestream with the PPN team next Tuesday, October 27th, where they will dive into what they're building. Join us if you're curious about how blockchain is going to be utilized in the real world!

Sign up for the virtual meetup!

I've also recorded a podcast episode with the PPN team, if you'd like a preview of what we'll cover on the live stream. Give it a listen!

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Get a practical walkthrough of how to identify bottlenecks, split tasks into multiple parallel tasks, identify slow AI model calls, and more.

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