# Engineering stories behind the Medium Daily Digest Algorithm: Part 2

DevFeed: [Engineering stories behind the Medium Daily Digest Algorithm: Part 2](<https://devfeed.tech/articles/engineering-stories-behind-the-medium-daily-digest-algorithm-part-2-20318.md>)

Original publisher: [Read original article](<https://medium.engineering/engineering-stories-behind-the-medium-daily-digest-algorithm-part-2-c977ad0b134f?source=rss----2817475205d3---4>)

Author: Raphael Montaud

Published: 2025-08-28T11:31:36Z

Content type: article

Language: en

Sources: [Medium](<https://devfeed.tech/sources/medium.md>)

Topics: [Algorithm](<https://devfeed.tech/topics/algorithm.md>), [migration](<https://devfeed.tech/topics/migration.md>), [Databases](<https://devfeed.tech/topics/databases.md>)

Tags: [algorithm](<https://devfeed.tech/tags/algorithm.md>), [bloom-filter](<https://devfeed.tech/tags/bloom-filter.md>), [database](<https://devfeed.tech/tags/database.md>), [dynamodb](<https://devfeed.tech/tags/dynamodb.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [migration](<https://devfeed.tech/tags/migration.md>), [recommendation-system](<https://devfeed.tech/tags/recommendation-system.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>)

## AI overview

Medium explains how it reduced the cost and maintenance burden of filtering for its Daily Digest by replacing Bloom Filters with user-based direct database queries. The article is part of a four-part series about incremental improvements to the recommendation algorithm.

## Source excerpt

How we made our filtering 10x cheaper by removing our Bloom Filters Bloom Filters are great tools to make fast and cheap filtering. They also come with plenty of problems and can easily get expensive and cumbersome. We switched to user-based direct database queries, which made our filtering cheaper and easy to maintain. Here's the full breakdown of that migration. Intro: This is a 4-part series breaking down improvements to the algorithm behind the Medium's Daily Digest over the past year. When we started this work, the Digest was suboptimal -- and since it's a huge distribution surface, reaching millions of readers every day, we started working on incremental improvements.By the end of these projects, the digest was 10% more likely to convert users to paying members, less expensive to run, more flexible and easier to maintain and it's now providing higher quality recommendations for all our users, including our "power readers".This is told through the lens of our engineering team tackling a series of challenges one by one. Medium has a small team but we operate on a big scale. We're working our way through some technical debt and at the same time, striving to provide the best experience for our readers. This is the source of many interesting challenges.I hope this series helps you understand how the recommendations algorithm work and can help others who are facing similar technical challenges. This is probably the most technical story in the series, but I will keep it as simple as possible and hopefully this is interesting for non-technical readers too. Some Concepts Here's a little cheat sheet with some concepts you may need to follow along with this story You may need this to understand the rest of this postBloom Filters at Medium A lot of the filters I mention in this series are backed by Bloom Filters (I've described some of those filtering rules in Part 1 if you haven't read it already). We use Bloom filters to remove stories we think won't interest readers fro