Total Articles: 18 from 7 sources
AWS News
1. Amazon SQS turns 20: Two decades of reliable messaging at scale
URL: https://aws.amazon.com/blogs/aws/amazon-sqs-turns-20-two-decades-of-reliable-messaging-at-scale/
Published: 2026-07-13 18:13
Summary:
On July 13, 2006, we launched Amazon Simple Queue Service (Amazon SQS) as one of the first three services available to customers, alongside Amazon EC2 and Amazon S3 We had learned firsthand that distributed systems need a reliable way to pass messages between components without creating tight dependencies If one service called another directly and […]
2. AWS Weekly Roundup: AWS Builder Center at 1 year, Network Scanning in Security Hub, Loom for AWS, and more (July 13, 2026)
Published: 2026-07-13 16:18
Summary:
AWS Builder Center turned one year old last week Launched on July 9, 2025, the platform has grown from a community hub with Wishlist voting, community profiles, and a toolbox into a full ecosystem with sandbox environments, workshops, Spaces, and a Builders’ Library To mark the anniversary, Rick Suttles published a full feature timeline covering […]
3. AWS Weekly Roundup: Claude Sonnet 5 on AWS, Amazon WorkSpaces for AI agents, AWS service availability updates, and more (July 6, 2026)
Published: 2026-07-06 15:46
Summary:
A couple of editions ago I wrote about what I find so energizing about working with startups Last week I got a fresh dose of it: I spent a few days with the AWS Startups team, listening to stories of founders talking about the problems they’re actually solving One story that stayed with me came […]
Dropbox Engineering
1. How we used DSPy to turn AI evaluations into better responses in Dash chat
Published: 2026-06-25 16:30
Summary:
We used DSPy to improve LLM judges and optimize our chat experience, creating an evaluation-driven feedback loop that produced better outputs.
GitHub Blog
1. Better tools made Copilot code review worse. Here’s how we actually improved it.
Published: 2026-07-10 15:57
Summary:
How migrating Copilot code review to shared Unix-style code exploration tools reduced review cost by reshaping agent workflows around pull request evidence The post Better tools made Copilot code review worse Here’s how we actually improved it. appeared first on The GitHub Blog.
2. How GitHub gave every repository a durable owner
URL: https://github.blog/security/application-security/how-github-gave-every-repository-a-durable-owner/
Published: 2026-07-09 16:29
Summary:
GitHub had over 14,000 repositories Here’s how we gave every active repository a validated owner in under 45 days, archived the rest, and made ownership the foundation for everything that followed The post How GitHub gave every repository a durable owner appeared first on The GitHub Blog.
3. Automating cross-repo documentation with GitHub Agentic Workflows
Published: 2026-07-08 21:11
Summary:
Explore how the Aspire team turns merged product changes into SME-reviewed docs pull requests, closing the gap between release and documentation The post Automating cross-repo documentation with GitHub Agentic Workflows appeared first on The GitHub Blog.
Google Developers
1. How the community trained Gemma to “Think” with Tunix and TPUs
URL: https://developers.googleblog.com/how-the-community-trained-gemma-to-think-with-tunix-and-tpus/
Published: 2026-07-14 10:42
Summary:
The Google Tunix Hackathon on Kaggle challenged developers to transform small, non-reasoning base models into general reasoning engines using Kaggle TPUs and a limited compute budget The winning teams achieved this by implementing multi-stage post-training pipelines that combined Supervised Fine-Tuning (SFT) with advanced alignment techniques like GRPO and SimPO Ultimately, the competition democratized AI development by proving that highly capable, structured reasoning models can be successfully trained by the community using accessible, open-source resources.
2. Gemma 4 12B: The Developer Guide
URL: https://developers.googleblog.com/gemma-4-12b-the-developer-guide/
Published: 2026-07-14 10:42
Summary:
The newly released Gemma 4 12B is a dense, multimodal model designed for high-performance local AI execution on consumer devices By introducing a novel, encoder-free architecture, it bypasses traditional visual and audio encoders to feed multimodal data directly into the LLM backbone.
3. Bringing Gemma 4 12B to your Laptop: Unlocking Local, Agentic Workflows with Google AI Edge
Published: 2026-07-14 10:42
Summary:
Google DeepMind’s Gemma 4 12B model brings agentic, multimodal AI capabilities to everyday laptops with 16GB of RAM, enabling local data processing and visual insight generation Users can leverage this model on macOS through the Google AI Edge Gallery for dynamic Python code execution and visualization, as well as via Google AI Edge Eloquent for completely offline voice dictation and text editing Additionally, developer workflows are enhanced by the LiteRT-LM CLI’s new serve command, which creates an industry-compatible local endpoint to power fully-local AI tools and agents.
Meta Engineering
1. Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler
Published: 2026-07-13 16:00
Summary:
TL; DR At Meta’s scale, a few milliseconds of latency degradation can have a significant negative impact on ads performance When a Linux kernel upgrade risked regressing latency across Meta’s ad serving fleet, we turned to sched_ext — the upstream, BPF-based extensible scheduling framework — to build a scheduling policy customized to the Ads delivery […] Read More The post Modernizing the Meta Ads Service With an Open-Source Kernel Scheduler appeared first on Engineering at Meta.
2. Meta’s AI Storage Blueprint at Scale
URL: https://engineering.fb.com/2026/07/01/data-infrastructure/metas-ai-storage-blueprint-at-scale/
Published: 2026-07-01 16:00
Summary:
During the past year or so, the time between new-frontier-model releases has gone down from months to weeks Reliable and fast access to storage is important to both the speed and computational cost of this AI innovation The post Meta’s AI Storage Blueprint at Scale appeared first on Engineering at Meta.
3. 10 Years of Meta’s Commitment to Python
URL: https://engineering.fb.com/2026/06/30/open-source/10-years-of-metas-commitment-to-python/
Published: 2026-06-30 16:00
Summary:
This year marks Meta’s 10th consecutive year as a sponsor of the Python Software Foundation (PSF), the charitable organization dedicated to advancing, supporting, and protecting the open-source Python programming language and the community that sustains it Python is one of the world’s most influential programming languages, and we use it across our engineering stack, from […] Read More The post 10 Years of Meta’s Commitment to Python appeared first on Engineering at Meta.
Netflix TechBlog
1. Building Service Topology at Scale: Architecture, Challenges, and Lessons Learned
Published: 2026-07-13 22:44
Summary:
Instead of batch jobs that process historical data, we continuously ingest flow records from multi-region Kafka streams and IPC metrics as Server-Sent Events, process them through reactive pipelines with backpressure handling, and provide near real-time topology updates, typically within tens of minutes, compared to the hours-old or day-old data that batch processing approaches provide.This wasn’t just about freshness, it was essential for our use cases When data arrives with the right partitioning, you can aggregate directly; when it doesn’t (like network flows requiring intermediary resolution), you need shuffle/redistribution stages.Dynamic Load Distribution: How Hashing Works with Auto-ScalingHow do we decide which instance receives which aggregator when our Auto Scaling Groups dynamically add or remove instances GC logs showed pauses consuming significant CPU time, in some cases, more than our business logic.Root Cause: Multiple factors contributed: objects accumulating in heap while waiting for 5-minute aggregation windows to complete, unnecessary conversions between different object types as data flowed through stages, and immutability overhead, following Scala best practices, we used immutable data structures for aggregators, but every update created new objects, overwhelming the garbage collector at millions of records per second.Investigation: Heap dumps and GC logs revealed flow log objects retained beyond their useful lifetime, unnecessary intermediate conversion objects, and constant creation/disposal of immutable aggregator versions
2. GenPage: Towards End-to-End Generative Homepage Construction at Netflix
Published: 2026-06-29 13:01
Summary:
This sequence includes the full structured homepage layout, with multiple rows and the entities inside them, so the model can generate the page holistically rather than scoring each row or entity in isolation.Figure 2 At a high level, WBC turns generation into token-level value prediction: given the user context and the tokens generated so far, the model learns to estimate the value of generating each possible next row or entity token.This objective is easier to optimize than page-level RL WBC post-training loss as we progressively enrich the user context tokens
3. Toward More Controllable AI Video Editing: An Early Research Exploration at Netflix
Published: 2026-06-23 00:31
Summary:
We believe this work can help advance the field in a way that’s both meaningful and responsible:Vera: a layered video diffusion model Vera generates only what needs to change as separate edit layers while leaving the rest of the video untouched, preserving the identities, performances, and other details from the source footage exactly as filmed.VOID: a video inpainting model for video object and interaction deletion Specifically, the counterfactual videos are generated by re-simulating the exact scene from the original video, but with the target object(s) or human removed
Stripe Engineering
1. Four travel and hospitality trends from HITEC 2026
URL: https://stripe.com/blog/trends-from-hitec
Published: 2026-06-23 00:00
Summary:
More than 6,000 hospitality executives and operators gathered in San Antonio last week for the HITEC conference The big topic: whether the industry’s AI investment is actually working Across four days and over 50 meetings, four trends stood out.
2. What Link data tells us about AI spending
URL: https://stripe.com/blog/what-link-data-tells-us-about-ai-spending
Published: 2026-06-18 00:00
Summary:
We analyzed spending patterns across the 250 million customers paying with Link We found that Link customers are spending more on AI than they were three months prior, investing heavily in platforms that let them build with AI.
Generated on 2026-07-14 10:42:34