# distributed-systems

A computing discipline concerning software components that run across multiple networked computers as a single system.

This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles.

## Article: Architecting Secure and Scalable Facial Verification Systems

DevFeed: [Article: Architecting Secure and Scalable Facial Verification Systems](<https://devfeed.tech/articles/article-architecting-secure-and-scalable-facial-verification-systems-42774.md>)

Original publisher: [Read original article](<https://www.infoq.com/articles/secure-scalable-facial-verification/>)

Author: Praveen Kumar Gopalakrishnan

Published: 2026-09-18T09:00:00Z

Content type: article

Language: en

Sources: [InfoQ](<https://devfeed.tech/sources/infoq.md>)

Topics: [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Computer vision](<https://devfeed.tech/topics/computer-vision.md>), [scaling](<https://devfeed.tech/topics/scaling.md>), [Concurrency](<https://devfeed.tech/topics/concurrency.md>), [Zero Trust](<https://devfeed.tech/topics/zero-trust.md>), [API](<https://devfeed.tech/topics/api.md>), [Data Quality](<https://devfeed.tech/topics/data-quality.md>), [client](<https://devfeed.tech/topics/client.md>), [Access Control](<https://devfeed.tech/topics/access-control.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Encryption](<https://devfeed.tech/topics/encryption.md>)

Tags: [access-control](<https://devfeed.tech/tags/access-control.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-ml-data-engineering](<https://devfeed.tech/tags/ai-ml-data-engineering.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [architecture-design](<https://devfeed.tech/tags/architecture-design.md>), [article](<https://devfeed.tech/tags/article.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [computer-vision](<https://devfeed.tech/tags/computer-vision.md>), [concurrency](<https://devfeed.tech/tags/concurrency.md>), [data-quality](<https://devfeed.tech/tags/data-quality.md>), [development](<https://devfeed.tech/tags/development.md>), [devops](<https://devfeed.tech/tags/devops.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [encryption](<https://devfeed.tech/tags/encryption.md>), [latency](<https://devfeed.tech/tags/latency.md>), [ml-data-engineering](<https://devfeed.tech/tags/ml-data-engineering.md>), [risk](<https://devfeed.tech/tags/risk.md>), [scaling](<https://devfeed.tech/tags/scaling.md>), [secure-scalable-facial-verification](<https://devfeed.tech/tags/secure-scalable-facial-verification.md>), [security](<https://devfeed.tech/tags/security.md>), [systems](<https://devfeed.tech/tags/systems.md>), [zero-trust](<https://devfeed.tech/tags/zero-trust.md>)

### AI overview

This article presents a four-layer architecture for high-volume facial verification, covering client-side data validation, asynchronous processing, decoupled detection and verification, risk-based thresholds, and zero-trust handling of personal data.

### Source excerpt

When three thousand employees verify at once, synchronous API calls collapse. This article presents a four-layer architecture for high-volume face verification: client-side filtering that cut cloud costs 30%, decoupled detection and verification enabling 10x scaling, risk-based dynamic thresholds, and zero-trust privacy with consent gates and automated data purging for GDPR and HIPAA. By Praveen Kumar Gopalakrishnan

## Cache invalidation is a distributed systems problem

DevFeed: [Cache invalidation is a distributed systems problem](<https://devfeed.tech/articles/cache-invalidation-is-a-distributed-systems-problem-in-a-convenience-costume-39604.md>)

Original publisher: [Read original article](<https://ankit-rana.com/logs/52-cache-invalidation-distributed-problem/>)

Author: hello@ankit-rana.com

Published: 2026-09-13T00:00:00Z

Content type: tutorial

Language: en

Sources: [Ankit Rana | Mechanical Sympathy](<https://devfeed.tech/sources/ankit-rana-mechanical-sympathy.md>)

Topics: [Cache](<https://devfeed.tech/topics/cache.md>), [Caching](<https://devfeed.tech/topics/caching.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>)

Tags: [cache](<https://devfeed.tech/tags/cache.md>), [cache-invalidation](<https://devfeed.tech/tags/cache-invalidation.md>), [caching](<https://devfeed.tech/tags/caching.md>), [consistency](<https://devfeed.tech/tags/consistency.md>), [distributed](<https://devfeed.tech/tags/distributed.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [ordering](<https://devfeed.tech/tags/ordering.md>), [race-conditions](<https://devfeed.tech/tags/race-conditions.md>), [redis](<https://devfeed.tech/tags/redis.md>), [ttl](<https://devfeed.tech/tags/ttl.md>)

### AI overview

Cache invalidation coordinates updates between a database and a cache without a shared transaction. The article explains how operation ordering can let a concurrent reader repopulate stale data and recommends writing to the database before invalidating the cache, followed by a second invalidation to close the remaining race.

### Source excerpt

A cache and a database are two stores that must agree, which makes every invalidation a distributed transaction without a coordinator. The ordering matters more than the mechanism: invalidating before the database write leaves a window where a concurrent reader repopulates the cache with the old value and it stays wrong until the TTL expires. Deleting the key rather than writing the new value removes a whole class of ordering bug, because two concurrent deletes commute and two concurrent writes do not.

## Building a reliable cloud native foundation for distributed AI training

DevFeed: [Building a reliable cloud native foundation for distributed AI training](<https://devfeed.tech/articles/building-a-reliable-cloud-native-foundation-for-distributed-ai-training-4603.md>)

Original publisher: [Read original article](<https://www.cncf.io/blog/2026/09/11/building-a-reliable-cloud-native-foundation-for-distributed-ai-training/>)

Author: Abhi Kulkarni and Shishir Jindal, Atlassian

Published: 2026-09-11T11:00:00Z

Content type: article

Language: en

Sources: [Cloud Native Computing Foundation](<https://devfeed.tech/sources/cloud-native-computing-foundation.md>)

Topics: [Training AI Models](<https://devfeed.tech/topics/training-ai-models.md>), [Machine learning](<https://devfeed.tech/topics/machine-learning.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [GPU](<https://devfeed.tech/topics/gpu.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [Network design](<https://devfeed.tech/topics/network-design.md>), [Inference](<https://devfeed.tech/topics/inference.md>)

Tags: [ai-training](<https://devfeed.tech/tags/ai-training.md>), [blog](<https://devfeed.tech/tags/blog.md>), [distributed-training](<https://devfeed.tech/tags/distributed-training.md>), [gpu](<https://devfeed.tech/tags/gpu.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [storage](<https://devfeed.tech/tags/storage.md>)

### AI overview

The article explains how to make multi-node AI training reliable by treating inter-node communication, shared storage, hardware placement, network topology, and validation as platform concerns. It identifies RDMA for GPU-node communication and Lustre for concurrent training-data and checkpoint access.

### Source excerpt

AI workloads are changing what platform teams need from infrastructure. Provisioning GPUs and standing up a cluster no longer makes a platform "AI-ready." Once training spans more than one node, the bottlenecks show up in places...

## Five Years of Kafka at Razorpay's UPI Switch

DevFeed: [Five Years of Kafka at Razorpay's UPI Switch](<https://devfeed.tech/articles/five-years-of-kafka-at-razorpay-s-upi-switch-24044.md>)

Original publisher: [Read original article](<https://engineering.razorpay.com/tryst-with-kafka-2f5cef766c45?source=rss----6407ad2e59af---4>)

Author: Kshitij Nawandar

Published: 2026-09-07T09:09:58Z

Content type: article

Language: en

Sources: [Razorpay Engineering - Medium](<https://devfeed.tech/sources/razorpay-engineering-medium.md>)

Topics: [Kafka](<https://devfeed.tech/topics/kafka.md>), [Architecture & Design](<https://devfeed.tech/topics/architecture-design.md>), [event driven](<https://devfeed.tech/topics/event-driven.md>), [Amazon Simple Queue Service (SQS)](<https://devfeed.tech/topics/amazon-simple-queue-service-sqs.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Microservices](<https://devfeed.tech/topics/microservices.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>)

Tags: [aws](<https://devfeed.tech/tags/aws.md>), [aws-sns](<https://devfeed.tech/tags/aws-sns.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [event-driven](<https://devfeed.tech/tags/event-driven.md>), [kafka](<https://devfeed.tech/tags/kafka.md>), [microservices](<https://devfeed.tech/tags/microservices.md>), [razorpay](<https://devfeed.tech/tags/razorpay.md>), [redshift](<https://devfeed.tech/tags/redshift.md>), [sns](<https://devfeed.tech/tags/sns.md>), [sqs](<https://devfeed.tech/tags/sqs.md>)

### AI overview

Razorpay describes five years of evolution in its UPI Switch, from a monolith using AWS SQS to Kafka-based infrastructure. The article covers architectural decisions, operational challenges, and optimization work affecting payment-processing performance, reliability, and scale.

### Source excerpt

Preface The UPI Switch at Razorpay has evolved significantly in the five years since we started building it. The Switch is the platform that enables real-time payment processing with NPCI. When the team began, it was little more than an idea. Today it powers more than 70% of Razorpay's total UPI volume. Because UPI is inherently asynchronous, a messaging system sits at the heart of the Switch and has a direct impact on performance, reliability, and scale. What began as a straightforward queue became the core of the system, shaping how every new feature was designed and delivered. This post covers that evolution: the decisions that enabled growth, the ones that slowed us down, the operational issues that forced us to rethink our assumptions, and the optimizations that ultimately stabilized our Kafka-based infrastructure. This is the story of what we got right, what we got wrong, and how we eventually built something stable enough to grow on. The First Version: Monolith and SQS When we began building the UPI Switch, we weren't thinking about massive scale, distributed systems, or elegant event routing. So we built Switch v1 as a monolith. No microservices, no distributed orchestration: just one solid block of code doing everything. That was the right call. We needed to move fast, experiment, and learn, and we followed the Keep It Simple, Stupid (KISS) principle deliberately. For messaging, we picked AWS SQS: reliable, managed, and low on cognitive load. We didn't need ordering guarantees at the time, so a standard queue worked fine. We started with just two queues, and this setup held its ground. It handled a peak of 400 TPS during the IPL. The limitations showed up as the ecosystem grew. A single event, like a successful payment, needed to fan out into multiple workflows: Update NPCI with an API call Send callbacks to merchants about payment status Push structured data into our warehouse (AWS Redshift) To handle this, we started bolting on AWS SNS plus SQS for fan-ou

## How Fixed Intervals Synchronize Distributed Clients and How Jitter Helps

DevFeed: [How Fixed Intervals Synchronize Distributed Clients and How Jitter Helps](<https://devfeed.tech/articles/jitter-is-the-cheapest-reliability-fix-you-are-not-using-39601.md>)

Original publisher: [Read original article](<https://ankit-rana.com/logs/49-jitter-synchronised-clients/>)

Author: hello@ankit-rana.com

Published: 2026-09-07T00:00:00Z

Content type: opinion

Language: en

Sources: [Ankit Rana | Mechanical Sympathy](<https://devfeed.tech/sources/ankit-rana-mechanical-sympathy.md>)

Topics: [reliability](<https://devfeed.tech/topics/reliability.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Network](<https://devfeed.tech/topics/network.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [Redis](<https://devfeed.tech/topics/redis.md>)

Tags: [aws](<https://devfeed.tech/tags/aws.md>), [cache](<https://devfeed.tech/tags/cache.md>), [code](<https://devfeed.tech/tags/code.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [jitter](<https://devfeed.tech/tags/jitter.md>), [network](<https://devfeed.tech/tags/network.md>), [reliability](<https://devfeed.tech/tags/reliability.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [retries](<https://devfeed.tech/tags/retries.md>), [thundering-herd](<https://devfeed.tech/tags/thundering-herd.md>)

### AI overview

Fixed retry intervals, TTLs, restarts, reconnects, and other recurring events can cause independent clients in distributed systems to synchronize, producing repeated load spikes. Adding randomness, or jitter, helps break that alignment; the article cites AWS measurements indicating that full jitter improves total work and completion time under contention.

### Source excerpt

Distributed systems synchronise themselves without being told to: every client that retries on a fixed interval, restarts after a deploy, or reconnects after a network blip ends up firing at the same moment as every other client. The resulting spike is a self-inflicted denial of service that no single component is responsible for. Adding randomness to any repeating interval breaks the alignment, and it is usually one line of code against a failure mode that otherwise requires capacity you never needed.

## EP224: MCP vs RAG vs AI Agents

DevFeed: [EP224: MCP vs RAG vs AI Agents](<https://devfeed.tech/articles/ep224-mcp-vs-rag-vs-ai-agents-17986.md>)

Original publisher: [Read original article](<https://blog.bytebytego.com/p/ep224-mcp-vs-rag-vs-ai-agents>)

Author: ByteByteGo

Published: 2026-09-05T15:30:58Z

Content type: article

Language: en

Sources: [ByteByteGo](<https://devfeed.tech/sources/bytebytego.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [Retrieval Augmented Generation (RAG)](<https://devfeed.tech/topics/retrieval-augmented-generation-rag.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Publish-subscribe pattern](<https://devfeed.tech/topics/pubsub.md>), [Replication](<https://devfeed.tech/topics/replication.md>)

Tags: [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [messaging](<https://devfeed.tech/tags/messaging.md>), [rag](<https://devfeed.tech/tags/rag.md>), [replication](<https://devfeed.tech/tags/replication.md>)

### AI overview

This article compares MCP, RAG, and AI agents. It explains MCP as a standard protocol for connecting AI models to external tools and data, RAG as a way to retrieve current information from external sources, and AI agents as systems that perform tasks and make decisions autonomously. It also summarizes several distributed-systems patterns, including replication, sharding, publish-subscribe, circuit breakers, retries, and leader election.

### Source excerpt

An AI agent is kind of an AI system where the agent performs the task autonomously and takes the decisions.

## The invisible heartbeat of our networks

DevFeed: [The invisible heartbeat of our networks](<https://devfeed.tech/articles/the-invisible-heartbeat-of-our-networks-10855.md>)

Original publisher: [Read original article](<https://blog.apnic.net/2026/09/01/the-invisible-heartbeat-of-our-networks/>)

Author: Luca Cicchelli

Published: 2026-08-31T23:03:47Z

Content type: article

Language: en

Sources: [APNIC Blog](<https://devfeed.tech/sources/apnic-blog.md>)

Topics: [Networks](<https://devfeed.tech/topics/networks.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Critical Infrastructure](<https://devfeed.tech/topics/critical-infrastructure.md>), [Security](<https://devfeed.tech/topics/security.md>), [5G](<https://devfeed.tech/topics/5g.md>), [Cybersecurity](<https://devfeed.tech/topics/cybersecurity.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [AI Infrastructure](<https://devfeed.tech/topics/ai-infrastructure.md>)

Tags: [5g](<https://devfeed.tech/tags/5g.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-infrastructure](<https://devfeed.tech/tags/ai-infrastructure.md>), [critical-infrastructure](<https://devfeed.tech/tags/critical-infrastructure.md>), [cybersecurity](<https://devfeed.tech/tags/cybersecurity.md>), [data](<https://devfeed.tech/tags/data.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [gnss](<https://devfeed.tech/tags/gnss.md>), [gps](<https://devfeed.tech/tags/gps.md>), [guest-post](<https://devfeed.tech/tags/guest-post.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [network](<https://devfeed.tech/tags/network.md>), [networking](<https://devfeed.tech/tags/networking.md>), [networks](<https://devfeed.tech/tags/networks.md>), [outage](<https://devfeed.tech/tags/outage.md>), [outages](<https://devfeed.tech/tags/outages.md>), [post](<https://devfeed.tech/tags/post.md>), [servers](<https://devfeed.tech/tags/servers.md>), [systems](<https://devfeed.tech/tags/systems.md>), [tech-matters](<https://devfeed.tech/tags/tech-matters.md>), [time](<https://devfeed.tech/tags/time.md>), [transactions](<https://devfeed.tech/tags/transactions.md>)

### AI overview

The article examines how accurate time synchronization underpins modern networks and distributed systems. It uses the Telstra outage to show how misaligned time servers can disrupt communications and rail services, and discusses resilient time sources, terrestrial backups, local atomic clocks, and synchronization requirements for finance, 5G, cloud, and AI infrastructure.

### Source excerpt

Guest Post: The recent Telstra outage highlighted a critical but often overlooked dependency in modern networks: Accurate time synchronization.

## The Essential Guide to Multi-Agent Systems

DevFeed: [The Essential Guide to Multi-Agent Systems](<https://devfeed.tech/articles/the-essential-guide-to-multi-agent-systems-18026.md>)

Original publisher: [Read original article](<https://blog.levelupcoding.com/p/essential-guide-to-multi-agent-systems>)

Author: Nikki Siapno

Published: 2026-08-31T11:38:47Z

Content type: tutorial

Language: en

Sources: [Level Up Coding System Design Newsletter](<https://devfeed.tech/sources/level-up-coding-system-design-newsletter.md>)

Topics: [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [systems](<https://devfeed.tech/topics/systems.md>)

Tags: [ai-agent](<https://devfeed.tech/tags/ai-agent.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [consistency](<https://devfeed.tech/tags/consistency.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [multi-agent-systems](<https://devfeed.tech/tags/multi-agent-systems.md>), [observability](<https://devfeed.tech/tags/observability.md>), [state](<https://devfeed.tech/tags/state.md>)

### AI overview

This guide explains what multi-agent systems are, how they differ from workflow engines and generic distributed systems, and why coordination, communication, shared state, consistency, and observability shape their architecture.

### Source excerpt

Building one AI agent is hard. But what changes when you start adding more?

## Where Kafka Fits in Modern Systems

DevFeed: [Where Kafka Fits in Modern Systems](<https://devfeed.tech/articles/where-kafka-fits-in-modern-systems-18029.md>)

Original publisher: [Read original article](<https://blog.levelupcoding.com/p/kafka-use-cases>)

Author: Nikki Siapno

Published: 2026-08-25T12:02:09Z

Content type: tutorial

Language: en

Sources: [Level Up Coding System Design Newsletter](<https://devfeed.tech/sources/level-up-coding-system-design-newsletter.md>)

Topics: [Kafka](<https://devfeed.tech/topics/kafka.md>), [Event-Streaming](<https://devfeed.tech/topics/event-streaming.md>), [systems](<https://devfeed.tech/topics/systems.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [data](<https://devfeed.tech/topics/data.md>)

Tags: [architecture](<https://devfeed.tech/tags/architecture.md>), [consumer](<https://devfeed.tech/tags/consumer.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [durability](<https://devfeed.tech/tags/durability.md>), [event-streaming](<https://devfeed.tech/tags/event-streaming.md>), [kafka](<https://devfeed.tech/tags/kafka.md>), [parallelism](<https://devfeed.tech/tags/parallelism.md>), [partition](<https://devfeed.tech/tags/partition.md>), [pipelines](<https://devfeed.tech/tags/pipelines.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [systems](<https://devfeed.tech/tags/systems.md>)

### AI overview

This article explains when Apache Kafka is appropriate for distributed systems. It presents Kafka as a durable event log whose replayability, consumer fan-out, and partition-based parallelism support real-time data pipelines and shared event-driven architectures, while warning that Kafka can add unnecessary operational complexity for simpler asynchronous workflows.

### Source excerpt

When should you actually use Kafka? And when is it just unnecessary complexity?

## \[Podcast\] DNS cold start

DevFeed: [\[Podcast\] DNS cold start](<https://devfeed.tech/articles/podcast-dns-cold-start-10842.md>)

Original publisher: [Read original article](<https://blog.apnic.net/2026/08/20/podcast-dns-cold-start/>)

Author: George Michaelson

Published: 2026-08-19T20:30:00Z

Content type: article

Language: en

Sources: [APNIC Blog](<https://devfeed.tech/sources/apnic-blog.md>)

Topics: [Caching](<https://devfeed.tech/topics/caching.md>), [Cache](<https://devfeed.tech/topics/cache.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Network](<https://devfeed.tech/topics/network.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Computer science](<https://devfeed.tech/topics/computer-science.md>)

Tags: [cache](<https://devfeed.tech/tags/cache.md>), [caching](<https://devfeed.tech/tags/caching.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [data](<https://devfeed.tech/tags/data.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [dns](<https://devfeed.tech/tags/dns.md>), [network](<https://devfeed.tech/tags/network.md>), [podcast](<https://devfeed.tech/tags/podcast.md>), [tech-matters](<https://devfeed.tech/tags/tech-matters.md>)

### AI overview

A podcast discussion examines DNS behavior during a cold start, when a resolver has no cached data. It explains how caching reduces network delays and explores DNS's hierarchical dependencies, comparing the startup challenge with black-start power generation.

### Source excerpt

Geoff Huston discusses the unexpected behaviours of the DNS when you perform a query after 'cold start' with no cached data.

## ClickStack and Hud bring runtime intelligence to AI-powered development

DevFeed: [ClickStack and Hud bring runtime intelligence to AI-powered development](<https://devfeed.tech/articles/clickstack-and-hud-bring-runtime-intelligence-to-ai-powered-development-5199.md>)

Original publisher: [Read original article](<https://clickhouse.com/blog/clickstack-hud-runtime-intelligence>)

Author: May Walter, Hud.io

Published: 2026-08-13T12:53:04Z

Content type: article

Language: en

Sources: [ClickHouse Blog](<https://devfeed.tech/sources/clickhouse-blog.md>)

Topics: [AI-assisted coding](<https://devfeed.tech/topics/ai-assisted-coding.md>), [observability](<https://devfeed.tech/topics/observability.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [OpenTelemetry](<https://devfeed.tech/topics/opentelemetry.md>), [telemetry](<https://devfeed.tech/topics/telemetry.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Instrumentation](<https://devfeed.tech/topics/instrumentation.md>), [Deployment](<https://devfeed.tech/topics/deployment.md>), [Incident response](<https://devfeed.tech/topics/incident-response.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [clickhouse](<https://devfeed.tech/topics/clickhouse.md>), [log management](<https://devfeed.tech/topics/log-management.md>)

Tags: [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [clickhouse](<https://devfeed.tech/tags/clickhouse.md>), [code](<https://devfeed.tech/tags/code.md>), [coding](<https://devfeed.tech/tags/coding.md>), [deployment](<https://devfeed.tech/tags/deployment.md>), [developers](<https://devfeed.tech/tags/developers.md>), [development](<https://devfeed.tech/tags/development.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [incident](<https://devfeed.tech/tags/incident.md>), [incident-response](<https://devfeed.tech/tags/incident-response.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [integration](<https://devfeed.tech/tags/integration.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [monitoring](<https://devfeed.tech/tags/monitoring.md>), [observability](<https://devfeed.tech/tags/observability.md>), [open](<https://devfeed.tech/tags/open.md>)

### AI overview

ClickHouse and Hud announce an integration that combines ClickStack service-level observability with Hud's function-level runtime context for AI-assisted software development. Shared trace IDs and MCP servers help coding agents assess risky changes before deployment, monitor releases, and investigate incidents using production context.

### Source excerpt

ClickStack and Hud now share trace IDs, pairing service-level observability with function-level runtime forensics so coding agents can assess risky changes before they ship, catch regressions right after deploy, and fix them with real production context.

## Graceful Kubernetes Shutdowns Help Protect In-Flight Orders

DevFeed: [Graceful Kubernetes Shutdowns Help Protect In-Flight Orders](<https://devfeed.tech/articles/when-a-missed-message-becomes-a-missed-meal-23983.md>)

Original publisher: [Read original article](<https://medium.com/mcdonalds-technical-blog/when-a-missed-message-becomes-a-missed-meal-1a44cb0051f3?source=rss----3bac42476d27---4>)

Author: Global Technology

Published: 2026-08-12T14:03:07Z

Content type: tutorial

Language: en

Sources: [McDonald's Technical Blog - Medium](<https://devfeed.tech/sources/mcdonald-s-technical-blog-medium.md>)

Topics: [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-computing](<https://devfeed.tech/tags/cloud-computing.md>), [devops](<https://devfeed.tech/tags/devops.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [messaging](<https://devfeed.tech/tags/messaging.md>), [operations](<https://devfeed.tech/tags/operations.md>), [scale](<https://devfeed.tech/tags/scale.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>)

### AI overview

This article explains how Kubernetes scaling, rolling updates, and cluster maintenance can interrupt in-flight requests and messages. It introduces graceful shutdown patterns in application code and infrastructure to reduce message loss and improve reliability for large-scale order processing.

### Source excerpt

Why graceful shutdowns matter when millions of orders are in flight. by: Samuel Hapgood, Software Engineer III Quick Bytes At McDonald's scale, rare failures in distributed systems can impact real customer and crew experiences Graceful shutdown patterns in Kubernetes -- across application code and infrastructure -- help protect in-flight work during scaling and deployments With the right shutdown strategies, teams can reduce message loss, improve reliability, and ensure orders make it to the kitchen Imagine you're placing a mobile order at McDonald's during a busy lunch rush. You hit "submit," and the kitchen never gets the order. Now you're left waiting -- no food, no updates, and growing frustration. At first glance, it seems like an easy problem to avoid. How hard can it be to just keep track of the order? But in modern, large-scale distributed systems -- especially those that must scale continuously -- that's not always straightforward. Behind the scenes, platforms like Kubernetes (which orchestrate containerized applications in the cloud) are continuously rescheduling pods as part of normal operations such as scaling applications, performing rolling updates, and maintaining cluster health. During these transitions, in-flight requests or background messages can be interrupted in subtle ways. A pod might shut down before finishing a task, a request might be canceled mid-flight, or a message might be acknowledged by a receiving service, but never actually processed. Even with strong messaging platforms and best practices in place, application shutdowns can still interrupt in-flight work if they are not handled carefully. These are edge cases -- but at McDonald's scale, edge cases happen often enough to matter. In the US alone, our platforms support roughly one million orders per hour on a dynamically scaled cluster of hundreds of nodes. At that volume, reliability becomes critical to ensuring a seamless experience for customers, crews, and restaurant operations. Prevent

## ScyllaDB Customer Experience Spotlight: Susie Solis

DevFeed: [ScyllaDB Customer Experience Spotlight: Susie Solis](<https://devfeed.tech/articles/scylladb-customer-experience-spotlight-susie-solis-4878.md>)

Original publisher: [Read original article](<https://www.scylladb.com/2026/08/10/cx-spotlight-susie-solis/>)

Author: Cynthia Dunlop

Published: 2026-08-10T14:28:35Z

Content type: article

Language: en

Sources: [ScyllaDB](<https://devfeed.tech/sources/scylladb.md>)

Topics: [Databases](<https://devfeed.tech/topics/databases.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [cloud-infrastructure](<https://devfeed.tech/topics/cloud-infrastructure.md>), [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [Terraform](<https://devfeed.tech/topics/terraform.md>), [Latency](<https://devfeed.tech/topics/latency.md>), [Linux](<https://devfeed.tech/topics/linux.md>)

Tags: [blog](<https://devfeed.tech/tags/blog.md>), [cloud-infrastructure](<https://devfeed.tech/tags/cloud-infrastructure.md>), [community](<https://devfeed.tech/tags/community.md>), [customer](<https://devfeed.tech/tags/customer.md>), [cx-profiles](<https://devfeed.tech/tags/cx-profiles.md>), [database](<https://devfeed.tech/tags/database.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [latency](<https://devfeed.tech/tags/latency.md>), [linux](<https://devfeed.tech/tags/linux.md>), [on-call](<https://devfeed.tech/tags/on-call.md>), [production](<https://devfeed.tech/tags/production.md>), [streaming](<https://devfeed.tech/tags/streaming.md>), [support](<https://devfeed.tech/tags/support.md>), [terraform](<https://devfeed.tech/tags/terraform.md>)

### AI overview

Susie Solis, a ScyllaDB Technical Support Engineer, discusses her background in technical support, cloud infrastructure, and distributed systems, along with a Kubernetes and Terraform project built to reproduce environments and troubleshoot customer issues. She also describes ScyllaDB's ability to sustain high-throughput production workloads and low latency during major streaming events.

### Source excerpt

Meet Susie Solis, a Technical Support Engineer on the Customer Experience team here at ScyllaDB.

## 34 Amazon Research Awards Build on Trainium recipients announced

DevFeed: [34 Amazon Research Awards Build on Trainium recipients announced](<https://devfeed.tech/articles/34-amazon-research-awards-build-on-trainium-recipients-announced-7614.md>)

Original publisher: [Read original article](<https://www.amazon.science/research-awards/latest-news/34-amazon-research-awards-build-on-trainium-recipients-announced>)

Author: Amazon Research Awards team

Published: 2026-08-05T15:00:00Z

Content type: news

Language: en

Sources: [Amazon Science homepage](<https://devfeed.tech/sources/amazon-science-homepage.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [responsible-ai](<https://devfeed.tech/topics/responsible-ai.md>), [AWS AI chips](<https://devfeed.tech/topics/aws-ai-chips.md>), [AI Research](<https://devfeed.tech/topics/ai-research.md>), [Optimization](<https://devfeed.tech/topics/optimization.md>), [Inference](<https://devfeed.tech/topics/inference.md>), [moe](<https://devfeed.tech/topics/moe.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [synthetic-data](<https://devfeed.tech/topics/synthetic-data.md>)

Tags: [academic-ai-funding](<https://devfeed.tech/tags/academic-ai-funding.md>), [ai](<https://devfeed.tech/tags/ai.md>), [ai-research](<https://devfeed.tech/tags/ai-research.md>), [ai-research-grants](<https://devfeed.tech/tags/ai-research-grants.md>), [ai-safety-and-alignment](<https://devfeed.tech/tags/ai-safety-and-alignment.md>), [amazon-research-awards](<https://devfeed.tech/tags/amazon-research-awards.md>), [ara](<https://devfeed.tech/tags/ara.md>), [aws-ai-chips](<https://devfeed.tech/tags/aws-ai-chips.md>), [aws-trainium](<https://devfeed.tech/tags/aws-trainium.md>), [build-on-trainium](<https://devfeed.tech/tags/build-on-trainium.md>), [data](<https://devfeed.tech/tags/data.md>), [deep-learning](<https://devfeed.tech/tags/deep-learning.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [inference](<https://devfeed.tech/tags/inference.md>), [internal-ara-program-updates](<https://devfeed.tech/tags/internal-ara-program-updates.md>), [llm](<https://devfeed.tech/tags/llm.md>), [machine-learning-research](<https://devfeed.tech/tags/machine-learning-research.md>), [mixture-of-experts](<https://devfeed.tech/tags/mixture-of-experts.md>), [optimization](<https://devfeed.tech/tags/optimization.md>), [research](<https://devfeed.tech/tags/research.md>), [responsible-ai](<https://devfeed.tech/tags/responsible-ai.md>), [synthetic](<https://devfeed.tech/tags/synthetic.md>), [synthetic-data-generation](<https://devfeed.tech/tags/synthetic-data-generation.md>)

### AI overview

Amazon announces 34 recipients of its Build on Trainium program, a $110 million credit initiative supporting AI research and university education. The awards fund work in areas including Responsible AI, language models, synthetic data, distributed systems, model architectures, libraries, and optimization on AWS Trainium.

### Source excerpt

Amazon announces 34 recipients of the Build on Trainium program, a $110 million credit initiative supporting AI research at 30 universities including Stanford, UC Berkeley, UIUC, UCLA, CMU, and MIT, with a focus on Responsible AI.

## On building scalable control planes

DevFeed: [On building scalable control planes](<https://devfeed.tech/articles/on-building-scalable-control-planes-12436.md>)

Original publisher: [Read original article](<https://www.allthingsdistributed.com/2026/08/on-building-scalable-control-planes.html>)

Author: werner@allthingsdistributed.com (Dr. Werner Vogels)

Published: 2026-08-04T14:00:00Z

Content type: article

Language: en

Sources: [All Things Distributed](<https://devfeed.tech/sources/all-things-distributed.md>)

Topics: [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Amazon Web Services](<https://devfeed.tech/topics/aws.md>), [Amazon EC2](<https://devfeed.tech/topics/amazon-ec2.md>), [DSQL](<https://devfeed.tech/topics/dsql.md>), [Cloud](<https://devfeed.tech/topics/cloud.md>)

Tags: [aws](<https://devfeed.tech/tags/aws.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [dsql](<https://devfeed.tech/tags/dsql.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [posts](<https://devfeed.tech/tags/posts.md>), [servers](<https://devfeed.tech/tags/servers.md>), [ssh](<https://devfeed.tech/tags/ssh.md>)

### AI overview

This article explores the engineering of scalable control planes through Zak van der Merwe's experience building them at AWS, first for EC2 and later for DSQL. It explains how control planes record intended state and reconcile it with actual state, bringing together difficult distributed-systems problems and influencing whether services can survive growth.

### Source excerpt

Zak van der Merwe has spent his entire career at AWS building control planes. First for EC2 and now for DSQL. On the surface, the control plane looks quite boring: it records what should exist and reconciles that with what actually does. Nobody leaves school dreaming of building one, but Zak will be the first to tell you that if you like solving hard problems in distributed systems, there are few better places to be. It's where many of those hard problems converge, and where the decisions you make determine whether a service survives its own growth.

## Ignition: Flashing 2,300 conference badges with Temporal

DevFeed: [Ignition: Flashing 2,300 conference badges with Temporal](<https://devfeed.tech/articles/ignition-flashing-2-300-conference-badges-with-temporal-35878.md>)

Original publisher: [Read original article](<https://temporal.io/blog/ignition-flashing-2-300-conference-badges-with-temporal>)

Author: Shy Ruparel

Published: 2026-08-04T00:00:00Z

Content type: article

Language: en

Sources: [Temporal Blog](<https://devfeed.tech/sources/temporal-blog.md>)

Topics: [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [ESP32](<https://devfeed.tech/topics/esp32.md>), [Embedded Software Dev](<https://devfeed.tech/topics/embedded-software-dev.md>), [Hardware](<https://devfeed.tech/topics/hardware.md>), [Espressif](<https://devfeed.tech/topics/espressif.md>)

Tags: [conference](<https://devfeed.tech/tags/conference.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [esp32](<https://devfeed.tech/tags/esp32.md>), [espressif](<https://devfeed.tech/tags/espressif.md>), [firmware](<https://devfeed.tech/tags/firmware.md>), [flashing](<https://devfeed.tech/tags/flashing.md>), [hardware](<https://devfeed.tech/tags/hardware.md>), [retries](<https://devfeed.tech/tags/retries.md>), [temporal-voices](<https://devfeed.tech/tags/temporal-voices.md>), [workflow](<https://devfeed.tech/tags/workflow.md>)

### AI overview

The article explains how Temporal powered Ignition, a workflow system used to flash firmware onto 2,300 ESP32 conference badges over two days. It describes the operational challenges of coordinating USB hubs, laptops, workers, retries, device tracking, and validation across the assembly process.

### Source excerpt

How Temporal powered Ignition, the workflow system that flashed 2,300 conference badges in two days using durable execution and retries.

## Temporal CTO: A 20-year shortcut to build reliable agents

DevFeed: [Temporal CTO: A 20-year shortcut to build reliable agents](<https://devfeed.tech/articles/temporal-cto-a-20-year-shortcut-to-build-reliable-agents-1907.md>)

Original publisher: [Read original article](<https://1password.com/blog/build-reliable-agents>)

Author: info@1password.com (Chris Fowler)

Published: 2026-08-04T00:00:00Z

Content type: article

Language: en

Sources: [Blog on 1Password Blog](<https://devfeed.tech/sources/blog-on-1password-blog.md>)

Topics: [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [Processes](<https://devfeed.tech/topics/processes.md>), [Orchestration](<https://devfeed.tech/topics/orchestration.md>)

Tags: [agentic](<https://devfeed.tech/tags/agentic.md>), [agents](<https://devfeed.tech/tags/agents.md>), [ai](<https://devfeed.tech/tags/ai.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [ddos](<https://devfeed.tech/tags/ddos.md>), [developers](<https://devfeed.tech/tags/developers.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [infrastructure](<https://devfeed.tech/tags/infrastructure.md>), [llm](<https://devfeed.tech/tags/llm.md>), [podcasts](<https://devfeed.tech/tags/podcasts.md>)

### AI overview

The article explains why long-running agentic systems encounter familiar distributed-systems failures, especially state loss and retry storms. It presents durable execution, flow control, queues, and rate limiting as mechanisms for making agents recoverable and resilient in production.

### Source excerpt

Zero-Shot Learning is a podcast about how AI gets built, secured, and deployed. Hosted by Nancy Wang, 1Password CTO, and Dev Tagare, Senior Director of Engineering at Google, it's a builder's view of the architecture and the complex decisions it takes to ship with AI. In this episode, 1Password VP, GM of Developer and AI, Jeff Malnick sits in for Dev. When Maxim Fateev, CTO and co-founder of Temporal, joined Zero-Shot Learning, he brought a historical perspective to the challenges developers face when building agentic systems today. From vanishing state to retry storms, Fateev saw that the failures of deploying long-running agents have parallels to the problems he's been working on for decades. Maxim joined Amazon in 2002, where he co-created Simple Workflow Service, the internal orchestration platform that became one of the most widely used services at Amazon. At Uber, he built Cadence, the open-source predecessor to Temporal, the durable execution platform, which he co-founded in 2019. Temporal now runs production workloads for OpenAI, GitLab, Lovable, Docker, and Cloudflare, and has more than 2,500 customers globally. As the industry builds agentic systems, Fateev is watching it rediscover exactly what his infrastructure was built to solve. A brief history of failures Agents become distributed systems the moment they cross a network. Every call to an LLM, every tool invocation, every write to a downstream service crosses a process boundary, and a process boundary is where distributed systems failures begin. Two common ways agents fail in production are state loss and retry storms. While working, an agent builds state, e.g. a record of which tools it called, the results it received, and how far it progressed in a task. When the process crashes, that record is gone. There is no checkpoint to resume from, no record of what was completed, no way to distinguish completed work from incomplete work. The next run starts from scratch, leaving the operator unsure which act

## Kubernetes Control Plane Internals and Reconciliation

DevFeed: [Kubernetes Control Plane Internals and Reconciliation](<https://devfeed.tech/articles/kubernetes-internals-control-plane-explained-18129.md>)

Original publisher: [Read original article](<https://hungrymindsdev.substack.com/p/kubernetes-internals-control-plane>)

Author: Alexandre Zajac

Published: 2026-08-03T15:31:23Z

Content type: tutorial

Language: en

Sources: [Hungry Minds](<https://devfeed.tech/sources/hungry-minds.md>)

Topics: [Kubernetes](<https://devfeed.tech/topics/kubernetes.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Containers](<https://devfeed.tech/topics/containers.md>)

Tags: [container](<https://devfeed.tech/tags/container.md>), [internals](<https://devfeed.tech/tags/internals.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [reconciliation](<https://devfeed.tech/tags/reconciliation.md>), [scheduler](<https://devfeed.tech/tags/scheduler.md>)

### AI overview

This article explains how Kubernetes manages distributed workloads through its control plane, worker nodes, and reconciliation loop. It covers the API server, etcd, scheduling, kubelet, desired-versus-actual state, and automated recovery.

### Source excerpt

PLUS: Ray 2x throughput boost ⚡, take-home interview breakdown 🔬, great work principles 📚

## AI speeds up code writing, but coordination across distributed services remains a system-design challenge

DevFeed: [AI speeds up code writing, but coordination across distributed services remains a system-design challenge](<https://devfeed.tech/articles/glue-work-is-the-new-system-design-why-alignment-is-the-premium-skill-in-the-age-of-ai-39582.md>)

Original publisher: [Read original article](<https://ankit-rana.com/logs/30-glue-work-new-system-design/>)

Author: hello@ankit-rana.com

Published: 2026-08-02T00:00:00Z

Content type: opinion

Language: en

Sources: [Ankit Rana | Mechanical Sympathy](<https://devfeed.tech/sources/ankit-rana-mechanical-sympathy.md>)

Topics: [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [backends](<https://devfeed.tech/topics/backends.md>), [Microservice](<https://devfeed.tech/topics/microservice.md>), [migration](<https://devfeed.tech/topics/migration.md>), [OpenAPI Specification](<https://devfeed.tech/topics/openapi.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [alignment](<https://devfeed.tech/tags/alignment.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [backend](<https://devfeed.tech/tags/backend.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [migration](<https://devfeed.tech/tags/migration.md>), [openapi](<https://devfeed.tech/tags/openapi.md>), [services](<https://devfeed.tech/tags/services.md>), [system-design](<https://devfeed.tech/tags/system-design.md>)

### AI overview

The article argues that AI assistants have accelerated implementation work while leaving coordination between teams and services largely unchanged. It presents ownership boundaries, contract stability, migration coordination, and failure semantics as central system-design concerns because failures often occur between otherwise correct components.

### Source excerpt

AI made the write path fast and left the coordination path untouched, so failures now live in the gaps between services: two event schema versions across two regions, three teams, and one assumption that someone else handled the migration window. Alignment work, deciding ownership boundaries, contract stability, and failure semantics before anyone opens an IDE, is system design, and it produces concrete network cost when skipped.

## Durable Digest: July highlights

DevFeed: [Durable Digest: July highlights](<https://devfeed.tech/articles/durable-digest-july-highlights-35798.md>)

Original publisher: [Read original article](<https://temporal.io/blog/durable-digest-july-2026>)

Author: Temporal Technologies

Published: 2026-07-30T00:00:00Z

Content type: release

Language: en

Sources: [Temporal Blog](<https://devfeed.tech/sources/temporal-blog.md>)

Topics: [releases](<https://devfeed.tech/topics/releases.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Cloud Run](<https://devfeed.tech/topics/cloud-run.md>), [Langgraph](<https://devfeed.tech/topics/langgraph.md>), [datadog](<https://devfeed.tech/topics/datadog.md>), [API](<https://devfeed.tech/topics/api.md>)

Tags: [announcements](<https://devfeed.tech/tags/announcements.md>), [api](<https://devfeed.tech/tags/api.md>), [cloud](<https://devfeed.tech/tags/cloud.md>), [cloud-run](<https://devfeed.tech/tags/cloud-run.md>), [datadog](<https://devfeed.tech/tags/datadog.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [explore](<https://devfeed.tech/tags/explore.md>), [langgraph](<https://devfeed.tech/tags/langgraph.md>), [releases](<https://devfeed.tech/tags/releases.md>)

### AI overview

Temporal's July Durable Digest summarizes product releases and updates, including pre-release Serverless Workers for GCP Cloud Run, public-preview LangGraph and LangSmith integrations, generally available billing and metrics APIs, Datadog and Vantage integrations, and improvements to the Worker Status UI.

### Source excerpt

Highlights from July include major product releases that make it easier to build and operate durable applications, and enhanced visibility into your Workers.

## Formal methods with Hillel Wayne

DevFeed: [Formal methods with Hillel Wayne](<https://devfeed.tech/articles/formal-methods-with-hillel-wayne-18172.md>)

Original publisher: [Read original article](<https://newsletter.pragmaticengineer.com/p/formal-methods-with-hillel-wayne>)

Author: Gergely Orosz

Published: 2026-07-29T16:22:31Z

Content type: article

Language: en

Sources: [The Pragmatic Engineer](<https://devfeed.tech/sources/the-pragmatic-engineer.md>)

Topics: [Formal methods](<https://devfeed.tech/topics/formal-methods.md>), [Formal verification](<https://devfeed.tech/topics/formal-verification.md>), [Software Engineering](<https://devfeed.tech/topics/software-engineering.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>)

Tags: [ai](<https://devfeed.tech/tags/ai.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [formal-methods](<https://devfeed.tech/tags/formal-methods.md>), [formal-verification](<https://devfeed.tech/tags/formal-verification.md>), [software-engineering](<https://devfeed.tech/tags/software-engineering.md>), [version-control](<https://devfeed.tech/tags/version-control.md>)

### AI overview

Hillel Wayne discusses why formal methods such as TLA+ matter for reliable software, how formal verification tools fit into software development, why distributed systems are difficult to reason about, and whether AI could make these methods more accessible.

### Source excerpt

Hillel Wayne explains why formal methods like TLA+ matter, how they help build reliable software, and whether AI will finally bring formal verification into the mainstream.

## The Gremlin app for Dynatrace: resilience testing and reliability scoring, built on the observability you already trust

DevFeed: [The Gremlin app for Dynatrace: resilience testing and reliability scoring, built on the observability you already trust](<https://devfeed.tech/articles/the-gremlin-app-for-dynatrace-resilience-testing-and-reliability-scoring-built-on-the-observability-you-already-trust-11572.md>)

Original publisher: [Read original article](<https://www.gremlin.com/blog/gremlin-app-for-dynatrace>)

Author: Ryan Detwiller

Published: 2026-07-28T00:00:00Z

Content type: article

Language: en

Sources: [Gremlin Blog](<https://devfeed.tech/sources/gremlin-blog.md>)

Topics: [Resilience](<https://devfeed.tech/topics/resilience.md>), [dynatrace](<https://devfeed.tech/topics/dynatrace.md>), [observability](<https://devfeed.tech/topics/observability.md>), [Instrumentation](<https://devfeed.tech/topics/instrumentation.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>)

Tags: [announcements](<https://devfeed.tech/tags/announcements.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [dynatrace](<https://devfeed.tech/tags/dynatrace.md>), [engineering](<https://devfeed.tech/tags/engineering.md>), [gremlin](<https://devfeed.tech/tags/gremlin.md>), [incident](<https://devfeed.tech/tags/incident.md>), [instrumentation](<https://devfeed.tech/tags/instrumentation.md>), [metrics](<https://devfeed.tech/tags/metrics.md>), [observability](<https://devfeed.tech/tags/observability.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [resilience](<https://devfeed.tech/tags/resilience.md>), [safety](<https://devfeed.tech/tags/safety.md>), [systems](<https://devfeed.tech/tags/systems.md>), [testing](<https://devfeed.tech/tags/testing.md>), [uptime](<https://devfeed.tech/tags/uptime.md>)

### AI overview

The Gremlin app for Dynatrace adds resilience testing and reliability scoring to Dynatrace workflows. Teams can run reliability tests, observe their impact in real time, and track service-level reliability scores using existing Dynatrace metrics, alerts, instrumentation, and health checks.

### Source excerpt

With the Gremlin app for Dyantrace, you get resilience testing and reliability scoring built on the observability you already trust.

## Multi Agent Systems for Platform Engineering, Patterns and Architecture

DevFeed: [Multi Agent Systems for Platform Engineering, Patterns and Architecture](<https://devfeed.tech/articles/multi-agent-systems-for-platform-engineering-patterns-and-architecture-17487.md>)

Original publisher: [Read original article](<https://kodekloud.com/blog/multi-agent-systems-platform-engineering-patterns/>)

Author: Pramodh Kumar M

Published: 2026-07-26T16:16:42Z

Content type: tutorial

Language: en

Sources: [Kubernetes - KodeKloud Blog | DevOps, Cloud, Kubernetes, AI Tutorials & More](<https://devfeed.tech/sources/kubernetes-kodekloud-blog-devops-cloud-kubernetes-ai-tutorials-more.md>)

Topics: [Platform Engineering](<https://devfeed.tech/topics/platform-engineering.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>), [systems](<https://devfeed.tech/topics/systems.md>), [Model Context Protocol](<https://devfeed.tech/topics/model-context-protocol.md>), [Provisioning](<https://devfeed.tech/topics/provisioning.md>)

Tags: [a2a-protocol](<https://devfeed.tech/tags/a2a-protocol.md>), [agent](<https://devfeed.tech/tags/agent.md>), [agent-orchestration](<https://devfeed.tech/tags/agent-orchestration.md>), [agent-to-agent-communication](<https://devfeed.tech/tags/agent-to-agent-communication.md>), [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [architecture](<https://devfeed.tech/tags/architecture.md>), [audit-trail](<https://devfeed.tech/tags/audit-trail.md>), [automation](<https://devfeed.tech/tags/automation.md>), [devops](<https://devfeed.tech/tags/devops.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [hierarchical-agent-systems](<https://devfeed.tech/tags/hierarchical-agent-systems.md>), [internal-developer-platform-automation](<https://devfeed.tech/tags/internal-developer-platform-automation.md>), [kubernetes](<https://devfeed.tech/tags/kubernetes.md>), [langgraph](<https://devfeed.tech/tags/langgraph.md>), [langgraph-multi-agent](<https://devfeed.tech/tags/langgraph-multi-agent.md>), [mcp](<https://devfeed.tech/tags/mcp.md>), [mcp-for-platform-engineering](<https://devfeed.tech/tags/mcp-for-platform-engineering.md>), [multi-agent](<https://devfeed.tech/tags/multi-agent.md>), [multi-agent-architecture-patterns](<https://devfeed.tech/tags/multi-agent-architecture-patterns.md>), [multi-agent-systems](<https://devfeed.tech/tags/multi-agent-systems.md>), [multi-agent-systems-for-platform-engineering](<https://devfeed.tech/tags/multi-agent-systems-for-platform-engineering.md>), [platform-engineering](<https://devfeed.tech/tags/platform-engineering.md>), [platform-engineering-automation](<https://devfeed.tech/tags/platform-engineering-automation.md>), [provisioning](<https://devfeed.tech/tags/provisioning.md>), [supervisor-agent-pattern](<https://devfeed.tech/tags/supervisor-agent-pattern.md>)

### AI overview

A practical guide to designing multi-agent systems for platform engineering. It compares supervisor, pipeline, fan-out, hierarchical, and swarm patterns; explains how agent contracts, identity boundaries, audit trails, and verification address common failure modes; and distinguishes MCP from A2A.

### Source excerpt

Studies of real agent systems put failure rates between 41 and 86 percent, and almost none of it is the model's fault. Here are the patterns that hold up for platform work, and the discipline that decides whether yours does.

## Partnerships can keep open source sustainable

DevFeed: [Partnerships can keep open source sustainable](<https://devfeed.tech/articles/partnerships-can-keep-open-source-sustainable-2195.md>)

Original publisher: [Read original article](<https://stackoverflow.blog/2026/07/24/partnerships-can-keep-open-source-sustainable/>)

Author: Phoebe Sajor

Published: 2026-07-24T07:40:00Z

Content type: article

Language: en

Sources: [Stack Overflow Blog](<https://devfeed.tech/sources/stack-overflow-blog.md>)

Topics: [Open Source](<https://devfeed.tech/topics/open-source.md>), [Cloudflare](<https://devfeed.tech/topics/cloudflare.md>), [JavaScript](<https://devfeed.tech/topics/javascript.md>), [Developer experience](<https://devfeed.tech/topics/developer-experience.md>), [distributed-systems](<https://devfeed.tech/topics/distributed-systems.md>)

Tags: [acquisition](<https://devfeed.tech/tags/acquisition.md>), [cloudflare](<https://devfeed.tech/tags/cloudflare.md>), [dev-tools](<https://devfeed.tech/tags/dev-tools.md>), [developer-experience](<https://devfeed.tech/tags/developer-experience.md>), [developer-tools](<https://devfeed.tech/tags/developer-tools.md>), [distributed-systems](<https://devfeed.tech/tags/distributed-systems.md>), [javascript](<https://devfeed.tech/tags/javascript.md>), [leaders-of-code](<https://devfeed.tech/tags/leaders-of-code.md>), [open-source](<https://devfeed.tech/tags/open-source.md>), [partnerships](<https://devfeed.tech/tags/partnerships.md>), [podcast](<https://devfeed.tech/tags/podcast.md>), [se-stackoverflow](<https://devfeed.tech/tags/se-stackoverflow.md>), [se-tech](<https://devfeed.tech/tags/se-tech.md>), [typescript](<https://devfeed.tech/tags/typescript.md>)

### AI overview

Ryan welcomes VoidZero's Evan You and Cloudflare's Dane Knecht to discuss Cloudflare's acquisition of VoidZero, the implications for JavaScript development, and how partnerships can help open-source projects remain maintained and sustainably monetized.

### Source excerpt

Ryan welcomes VoidZero's Evan You and Cloudflare's Dane Knecht back to the show to discuss Cloudflare's recent acquisition of VoidZero and what it means for JavaScript development, how partnerships like theirs can help open-source projects stay maintained and sustainably monetized, and how Cloudflare's distributed systems are helping to improve developer experience in Vite and beyond.

[Next page](<https://devfeed.tech/topics/distributed-systems.md?cursor=WyIyMDI2LTA3LTI0VDA3OjQwOjAwKzAwOjAwIiwgImM4NWU5ZTNjLWQ0YWMtNDkxZC05NDBhLTU5YmM1Mjg3YzMwNCJd>)