Total Articles: 19 from 7 sources
AWS News
1. AWS Weekly Roundup: Welcome DuckLabs to the team, Agentic Resource Discovery (ARD), and more (August 31, 2026)
Published: 2026-08-31 14:45
Summary:
The news that interested me the most last week was the DuckLabs acquisition AWS has signed a definitive agreement to acquire DuckLabs, the Amsterdam-based company behind DuckDB, the popular open source analytical database that runs in-process and executes SQL directly against files like Parquet, CSV, and JSON DuckDB stays open source under its independent foundation […]
2. Happy 20th Birthday, Amazon EC2
URL: https://aws.amazon.com/blogs/aws/happy-20th-birthday-amazon-ec2/
Published: 2026-08-25 16:11
Summary:
On the 20th Anniversary, we recognize how AWS has continued to push the boundaries of what cloud computing can deliver, building custom silicon for general-purpose and AI workloads and expanding EC2 into new form factors and deployment models that our customers in 2006 could not have imagined.
3. AWS Weekly Roundup: Student Rewards on AWS Builder Center, Local Zone in Las Vegas, and more (August 24, 2026)
Published: 2026-08-24 16:20
Summary:
During my time at AWS, I have always looked for opportunities to work with students I have delivered over 50 talks at universities across the region, and watching the potential in the room is always a strong motivator It reminds me of why I do this work, and that the students I meet today may […]
Dropbox Engineering
1. Improving infrastructure efficiency for growing demand in the age of AI
Published: 2026-08-18 17:00
Summary:
As demand for AI continues to grow, so does the infrastructure needed to support it.
GitHub Blog
1. OpenClaw went viral. Meet the maintainers building and securing it.
Published: 2026-08-27 16:00
Summary:
OpenClaw is the fastest-growing project in GitHub history Peter Steinberger and several maintainers share what they learned in the project’s first six months Meet the maintainers building and securing it. appeared first on The GitHub Blog.
2. GitHub Copilot app for Beginners: Automate Dependabot pull request triage
Published: 2026-08-26 20:12
Summary:
Managing library updates can be tedious at times Learn how the GitHub Copilot app can handle this type of repetitive task The post GitHub Copilot app for Beginners: Automate Dependabot pull request triage appeared first on The GitHub Blog.
3. How to evaluate LLMs before production
URL: https://github.blog/ai-and-ml/llms/how-to-evaluate-llms-before-production/
Published: 2026-08-25 21:35
Summary:
These are the lessons we learned evaluating LLMs for real-world secret scanning The post How to evaluate LLMs before production appeared first on The GitHub Blog.
Google Developers
1. Evolving Spec-Driven Development: Conductor Now Supports Antigravity
Published: 2026-08-31 16:41
Summary:
Conductor has evolved from a Gemini CLI extension into a portable plugin, bringing conversational Spec-Driven Development (SDD) to ecosystems like Antigravity CLI and Claude Rather than relying on strict command sequences, developers can now chat naturally with their AI assistant while it dynamically manages persistent markdown artifacts (like spec.md and plan.md) in the background This update eliminates workflow friction while ensuring your repository remains a version-controlled, single source of truth for your project’s architecture and state across different AI tools.
2. Building scalable AI agents with modular prompt transpilation
URL: https://developers.googleblog.com/building-scalable-ai-agents-with-modular-prompt-transpilation/
Published: 2026-08-31 16:41
Summary:
To resolve the scaling bottlenecks and runtime errors caused by monolithic system prompts, engineering teams should treat prompts as build artifacts by modularizing instructions into reusable templates By running these modular “skill files” through a transpiler, developers can enforce static validation, catch missing dependencies at build time, and integrate prompt generation directly into their CI/CD pipelines This deterministic approach prevents code drift and ultimately establishes a safe framework where agents can propose updates to their own logic via standard pull requests.
3. Run Ray on TPU, Part 1: The foundations
URL: https://developers.googleblog.com/run-ray-on-tpu-part-1-the-foundations/
Published: 2026-08-31 16:41
Summary:
Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google’s accelerators using the familiar Ray task-and-actor APIs To handle the strict networking requirement of keeping multi-host TPU “slices” together over their Inter-Chip Interconnect (ICI), the KubeRay Operator on GKE automatically provisions and labels the underlying hardware layout Ray Core utilizes these labels via its slice_placement_group() primitive to atomically reserve complete slices, allowing developers to deploy jobs through KubeRay, Ray Train, or Ray Serve simply by declaring a hardware topology (like “4x4”) without writing custom placement code.
Meta Engineering
1. MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet
URL: https://engineering.fb.com/2026/08/24/networking-traffic/metaroce-rdma-transport-ai-ethernet/
Published: 2026-08-24 18:02
Summary:
To meet this challenge at scale, Meta designed MetaRoCE – a clean-sheet RDMA transport protocol purpose-built for AI workloads on commodity Ethernet We’re releasing the MetaRoCE specification, a reference software implementation and a compliance test […] Read More The post MetaRoCE: A New RDMA Transport Built for AI-Scale Ethernet appeared first on Engineering at Meta.
2. MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines
URL: https://engineering.fb.com/2026/08/24/networking-traffic/mtia-300-meta-training-chip-built-in-nics/
Published: 2026-08-24 17:45
Summary:
MTIA 300 is the first of Meta’s family of in-house training and inference accelerators optimized for training ranking and recommendation models We’re sharing how MTIA 300’s built-in NIC chiplets allow it to meet the communication needs associated with training recommendation models with superior performance over general-purpose GPUs The post MTIA 300: Meta’s First Training Chip with Built-in NICs and Communication-Offloading Engines appeared first on Engineering at Meta.
3. 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.
Netflix TechBlog
1. MAPS: Netflix’s Multimodal Asset Personalization at Scale
Published: 2026-08-28 16:01
Summary:
A single unified model can therefore pool interaction signal across every canvas, so a member’s affinity learned on a high-traffic canvas immediately informs the artwork we pick on a sparse one One unified model over all five canvases, with image embeddings in its asset representation.As the chart below shows, each idea helped exactly where we expected: on the data-starved short-panel canvas and landscape-panel canvas We have since shipped MediaFM as the default video preview embedding across all platforms.Relative offline IPS lift for the two content-aware video preview embeddings, each measured against the ID-only baseline at the zero rule
2. A Tale of Two Flink Autoscalers
Published: 2026-08-21 16:01
Summary:
Each autoscaler node handled the metrics for a subset of Flink jobs, and we never had to write custom sharding or coordination logic to keep up with a growing Flink fleet Starting from the sources, the autoscaler walks the job graph and uses each operator’s TPR, its input/output ratios, and a target utilization to compute the parallelism every vertex needs so that no operator becomes the bottleneck, rather than resizing the whole cluster as a unit.Figure 2: Flink job DAG: current → desired parallelism per vertex, based on busynessThe two approaches make a different contract, summarized below.Table 1: Comparison of the two Flink autoscalersThe decisive difference for us is the last two rows: the OSS autoscaler can scale exactly the stateful, multi-operator jobs our homegrown system could not, and it lets each job carry its own configuration — stabilization periods, thresholds, and other scaling behavior tuned to the workload Having started supporting Flink 2.2 at Netflix, we plan on experimenting with this new state backend to see if it can help eliminate state recovery bottlenecks when scaling large stateful jobs.Looking ahead, we aim to migrate all internal scaler use cases onto the new one based on OSS autoscaler to simplify our operational surface area.Key TakeawaysAlong the way, three lessons that generalize beyond Flink:Metric choice matters more than algorithm sophistication
3. 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
Stripe Engineering
1. Five monetization trends from global pricing leaders
URL: https://stripe.com/blog/five-monetization-trends-from-global-pricing-leaders
Published: 2026-08-20 00:00
Summary:
As AI transforms software economics, the standard revenue playbook is breaking down Learn how leaders around the world are preparing for agent buyers, updating processes for faster pricing iteration, and building more flexible infrastructure.
2. Why global workers are driving demand for stablecoin payouts
URL: https://stripe.com/blog/why-global-workers-are-driving-demand-for-stablecoin-payouts
Published: 2026-08-19 00:00
Summary:
Platforms like DoorDash, Meta, and Deel already enable stablecoin payouts for global workers We surveyed 2,300 workers in 20 countries to see what’s driving stablecoin demand, where the opportunity is highest, and how other platforms can adapt.
3. New currency capabilities for global businesses to cut FX costs
URL: https://stripe.com/blog/reduce-fx-costs-with-stripe
Published: 2026-08-17 00:00
Summary:
Two product upgrades make it easy for global businesses to manage FX entirely on Stripe We’re expanding multicurrency settlement to more markets and currencies, and we’re introducing the ability to convert currencies instantly—all on Stripe.
Generated on 2026-08-31 16:41:16