A build-time summary of 46 unique engineering articles collected across seven daily digests.
Topic Trends
- AI & Machine Learning — 17 articles
- Developer Tools — 7 articles
- Infrastructure & Scale — 7 articles
- Data Engineering — 4 articles
- Databases & Storage — 4 articles
Source Pulse
- Netflix TechBlog — 10 articles
- Cloudflare Engineering — 9 articles
- Meta Engineering — 7 articles
- GitHub Blog — 6 articles
- Google Developers — 6 articles
Representative Articles
Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI
Meta Engineering · AI & Machine Learning
Last year, we introduced GEM, the industry’s largest recommendation system (RecSys) foundation model GEM established Meta’s first LLM-inspired paradigm for ads recommendation, driving major performance gains The post Meta’s Generative Ads Model (GEM): Scaling the Central Model and Compute Engine for Frontier Ads AI appeared first on Engineering at Meta.
GitHub availability report: September 2026
GitHub Blog · Developer Tools
In September, we experienced two incidents that resulted in degraded performance across GitHub services The post GitHub availability report: September 2026 appeared first on The GitHub Blog.
OCP Summit 2026: Powering AI with Open Innovations in Scale-Up, Scale-Out, and Scale-Across Networks
Meta Engineering · Infrastructure & Scale
At the 2026 Open Compute Project Global Summit, Meta is sharing the next generation of open networking infrastructure, including: Our first liquid-cooled network rack (IcePack), and 102.4T switch platform (IceCube) Flattened Planar Fabric (FPF), a multiplane backend scale-out fabric that fundamentally changes how GPUs connect to the network MetaRoCE, our open-source AI transport protocol The post OCP Summit 2026: Powering AI with Open Innovations in Scale-Up, Scale-Out, and Scale-Across Networks appeared first on Engineering at Meta.
DataJunction as Netflix’s answer to the missing piece of the modern data stack
Netflix TechBlog · Data Engineering
Our initial problem was most evident in our Experimentation Platform team, where the previous system required:extensive business knowledgefamiliarity with experimentation conventionsand complex engineering practicesThis led to metric authoring processes that would take weeks, which was a prohibitive requirement for Data Scientists and Analytics Engineers who wanted to onboard new metrics to the Experimentation Platform.Enter DataJunctionTo address this problem and to set ourselves on the path to a successful semantic layer solution in the future, we looked at several candidates They efficiently deliver both the semantic definitions (of metrics and dimensions) and fast data access It also provides flexible, user-friendly interfaces (including a UI, API, and various clients), making it easy for users to author and manage their metrics and other semantic models.ConclusionsWith experimentation domain well supported by DataJunction, we would like to take on next steps to unifying of the general analytics at Netflix and to make it easier for Netflix users to both:find and use metric definitions across verticals (defined in DJ)easily access, modify and experiment with new metrics across Netflix data stackThat will require building out strong integrations between DJ and other tools, but that has been underway and it is one of the most enjoyable parts of this project, where most of the feature exists and we are simply enabling existing systems to exchange information in a structured and reliable way
TimeSeries Tiered Storage Journey: Kafka/Flink Streams to Native Cassandra Cold Reads
Netflix TechBlog · Databases & Storage
The users still use the same query interface:TimeSeries Tiered Storage Read PathThe Cold Tier Data Plane uses compaction metadata for aggressive planning and pruning, which dramatically reduces the S3 query fan-out The idea for tiering is simple:Read directly from these S3 backups instead of paying a separate data transfer cost to S3.Once TimeSeries marks a hot Time Slice as ready for cold storage, we can customize Cassandra to read directly from its backups, allowing us to delete the on-disk data copy As of Cassandra 4.1, we can leverage pluggable Memtables to redirect reads from “cold” tables directly to the backup storage in S3.— create table for a given Time SliceCREATE TABLE data_20260616_00_04 ( … …, PRIMARY KEY ((time_series_id, time_bucket, event_bucket), event_time, event_id, key)) WITH CLUSTERING ORDER BY (event_time DESC, event_id DESC, key DESC)
Method
Articles are de-duplicated by URL, then classified with a deterministic engineering keyword taxonomy. The report is generated during the site build and does not use a database or an external AI API.