Total Articles: 18 from 7 sources
AWS News
1. AWS Weekly Roundup: AWS Heroes Summit, Web Search on Amazon Bedrock, Dogwood, Kiro Crew, and more (August 10, 2026)
Published: 2026-08-10 15:45
Summary:
Last week, we brought together AWS Heroes from around the world to connect, collaborate, and celebrate the builders who go above and beyond for the AWS community The AWS Heroes Summit, an invite-only annual gathering, brings global experts specializing in fields like AI, serverless, and containers together for direct collaboration, technical deep-dives, and feedback sessions […]
2. Runtime instances: persistent compute for production AI agents on Amazon Bedrock AgentCore
Published: 2026-08-06 22:58
Summary:
Announcing runtime instances in Amazon Bedrock AgentCore—persistent, managed EC2 infrastructure for production AI agents with multi-agent collaboration, GPU support, and sessions lasting up to 14 days.
3. Amazon DynamoDB now supports real-time vector search at any scale
URL: https://aws.amazon.com/blogs/aws/amazon-dynamodb-now-supports-real-time-vector-search-at-any-scale/
Published: 2026-08-05 14:45
Summary:
DynamoDB now supports native vector search with single-digit millisecond latency at 99%+ recall It is designed for any scale, even trillions of vectors and requires zero infrastructure management.
Dropbox Engineering
1. How our universal content processing platform Riviera evolved for AI and beyond
Published: 2026-07-20 15:00
Summary:
Riviera is the Dropbox content processing platform that’s been iteratively improving content transformation in our products for roughly a decade.
GitHub Blog
1. Your guide to GitHub Universe 2026 is here: The schedule just launched!
Published: 2026-08-13 19:00
Summary:
The GitHub Universe session catalog is live Explore interactive workshops, community talks, demos, and panels The post Your guide to GitHub Universe 2026 is here: The schedule just launched! appeared first on The GitHub Blog.
2. What 50 open source projects taught us about security in the AI era
Published: 2026-08-13 16:00
Summary:
See how the open source projects in Session 4 of the GitHub Secure Open Source Fund combined AI-assisted workflows, maintainer expertise, GitHub security tools, expert guidance, and funding to improve project security The post What 50 open source projects taught us about security in the AI era appeared first on The GitHub Blog.
3. GitHub availability report: July 2026
URL: https://github.blog/news-insights/company-news/github-availability-report-july-2026/
Published: 2026-08-12 22:17
Summary:
In July, we experienced eight incidents that resulted in degraded performance across GitHub services The post GitHub availability report: July 2026 appeared first on The GitHub Blog.
Google Developers
1. Bridging the Domain Gap: AI Race Coach built with Antigravity and Gemini
Published: 2026-08-14 09:57
Summary:
On May 23, 2026, fresh off the stage at Google I/O, our Google Developer Experts (GDEs) converged on…
2. LiteRT.js, Google’s high performance Web AI Inference
URL: https://developers.googleblog.com/litertjs-googles-high-performance-web-ai-inference/
Published: 2026-08-14 09:57
Summary:
We’re excited to introduce LiteRT.js, the newest member of the LiteRT family Built for JavaScript developers, LiteRT.js delivers state-of-the-art ML model inference performance on WebGPU and upcoming WebNN, with a fallback to WebAssembly for CPU This post provides a quick tour of LiteRT.js and gives web developers everything they need to get started.
3. Systems Engineering Playbook: Optimizing Qwen 3.5-397B MoE on Ironwood (TPU7x)
Published: 2026-08-14 09:57
Summary:
To serve the 397B-parameter Qwen 3.5 Mixture-of-Experts (MoE) model on Ironwood TPUs, engineers developed a modular JAX/Pallas optimization stack that achieved up to a 4.7x inference speedup for prefill-heavy workloads The team bypassed severe hardware sharding constraints by deploying a hybrid Data Parallelism and Expert Parallelism (DP+EP) topology, paired with custom low-level communication fusions like a hierarchical reduce-scatter to optimize cross-device token routing Finally, by executing hardware-aware custom kernels—such as Batched Ragged Page Attention and a fully-fused Gated DeltaNet (GDN) block—they successfully saturated HBM bandwidth and TensorCore MXUs to push system throughput near its theoretical roofline limits.
Meta Engineering
1. How We’re Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees
URL: https://engineering.fb.com/2026/08/12/security/how-were-building-scam-alert-whatsapp/
Published: 2026-08-12 13:00
Summary:
WhatsApp is committed to helping people stay safe while protecting the privacy of their messages As scam tactics evolve — from impersonation to social engineering to AI-generated lures — we’re always evolving as well, so that our protections stay ahead of scammers while protecting people’s personal messages with end-to-end encryption The post How We’re Building Scam Alert on WhatsApp With End-to-End Encryption and Verifiability Guarantees appeared first on Engineering at Meta.
2. From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking
Published: 2026-08-05 19:20
Summary:
Every day, Meta’s recommendation platforms handle billions of user interactions, generating rich temporal signals that capture individual preferences and intent across products, ads, and content In our 2024 post on sequence learning for ads recommendations, we showed how modeling the order and timing of user actions (rather than relying on static, manually engineered sparse features) […] Read More The post From User Sequences to Scaling Laws: A Multi-Stage Architecture for Meta’s Ads Ranking appeared first on Engineering at Meta.
3. GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model
Published: 2026-08-03 18:00
Summary:
Meta’s Generative Ads Recommendation Model (GEM), the foundation model behind ads recommendations across Instagram and Facebook, now trains at LLM scale on several thousand of the latest-generation GPUs This post goes into the details on how we achieved: doubling end-to-end (E2E) training efficiency to 20–25% Model FLOPs Utilization (MFU) while scaling training FLOPs 4x in […] Read More The post GEM Training: How Meta Doubled the Efficiency of Its LLM-Scale Ads Foundation Model appeared first on Engineering at Meta.
Netflix TechBlog
1. How and Why Netflix Built a Real-Time Distributed Graph: Part 3 — Querying the graph with gRPC…
Published: 2026-08-07 16:01
Summary:
It batches requests, runs them in parallel with graph data assembly, and degrades gracefully when an enrichment source is unavailable.When a client sends a query, the request flows through these layers in sequence: the Query Service parses the request into an execution plan, the execution engine walks the graph level by level through the Storage Abstraction Layer, and if enrichments are requested, the Enrichment Layer fetches and merges external data before the response is serialized back to the client.Now, with that mental model in place, let’s follow a query through this system and see how these choices play out in practice.Executing Queries Efficiently: Following a Query’s JourneyTo see how the RDG query layer works in practice, let’s follow a single query end-to-end and focus on one question: how do we make every step fast?We’ll reuse the deep-narrow example from above:For Account X, show me the Stranger Things viewing history across all profiles: which profiles watched it, what they watched, and when.In graph terms, this becomes a 2‑hop traversal:Account X → has_profile → ProfilesProfiles → started_watching → Content (filtered for “Stranger Things”)We’ll walk through how this query moves through the layers we described above:Reading and interpreting the requestReading from storage efficientlyExecuting traversal with breadth‑first levelsRunning many operations in parallel, but safelyFiltering smartly to keep only what mattersMaking repeat queries faster with cachingBy the end, we’ll see how a 2-hop query like our Stranger Things example, with streaming, filtering, and parallel execution, can complete in under 100ms.Step 1: Reading the Request: Deciding What the Query Really WantsEvery query starts as a gRPC request The query was:For Account X, show me the Stranger Things viewing history across all profiles: which profiles watched it, what they watched, and whenSo we still need to fetch each profile’s history and filter it down to Stranger Things sessions That’s why our Stranger Things query completes in under 100ms.Step 4: Parallel Execution: Doing Many Things at Once, SafelyBreadth-first traversal enables parallel work at each level, which is the key to low latency.At Level 2 of our Stranger Things query, we fetch started_watching edges for each profile
2. Modeling Device Capabilities for Analytics
Published: 2026-07-31 16:01
Summary:
To ensure the best possible user experience, we rely on a deep understanding of device capabilities We use a cumulative table to process information about the device’s capabilities By relying on data-driven insights, we can make informed decisions about which features to enable on specific devices, ensuring both performance and reliability.Modeling Device Capabilities for Analytics was originally published in Netflix TechBlog on Medium, where people are continuing the conversation by highlighting and responding to this story.
3. GenRec: Towards LLM-Native Recommendation at Netflix
Published: 2026-07-30 20:10
Summary:
Raw logs of user history, item metadata, and context are transformed via context engineering into natural-language prompts and fed into the GenRec, which runs on vLLM in prefill-only mode and outputs scores for each catalog item, yielding a recommendation ranking.At a high level, GenRec:Verbalizes user histories, item metadata, and context as text.Post‑trains a Netflix‑adapted foundation LLM for ranking.Adds a catalog‑aware scoring head over Netflix titles.Uses reward signals to align with long‑term member value and business goals.Runs in prefill‑only mode on Netflix’s LLM serving stack for cost efficiency.In a large‑scale A/B test against a well‑tuned production ranker, GenRec achieves statistically significant improvements in both short‑term and long‑term online metrics, while using only a small fraction of the Phase‑2 labeled data and input signals As we increased Phase‑2 training data and enriched the input signals, GenRec’s offline metrics continued to improve.Online, we ran a large A/B test on batch‑compute recommendation surfaces, covering ~10% of Netflix traffic over ~4 weeks By verbalizing user histories, context, and item metadata, adding a catalog‑aware ranking head, using reward‑weighted objectives aligned to long‑term satisfaction and business goals, and serving efficiently on our LLM infrastructure, we obtain a model that improves on a strong production ranker while using far fewer Phase‑2 labels and input signals.GenRec is an early but promising step toward a more LLM‑centric recommendation stack at Netflix
Stripe Engineering
1. Mapping the AI economy
URL: https://stripe.com/blog/mapping-the-ai-economy
Published: 2026-08-11 00:00
Summary:
AI companies are undergoing rapid global expansion while achieving unprecedented rates of growth We analyzed Stripe data to understand where global demand is the strongest, and how companies can build to best capture that demand.
2. Analyzing the evidence that helps businesses win “product not received” disputes
Published: 2026-07-21 00:00
Summary:
To understand what can influence win rates, we analyzed evidence packets from one million disputes over a 16-week period Here’s what the data shows and what it means for how you mitigate disputes.
Generated on 2026-08-14 09:57:12