tgindex
Architecture Weekly

Architecture Weekly

Статистика

Architecture Weekly newsletter originated at https://blog.vvsevolodovich.dev. ~10 articles or videos on solution architecture and system design every week!.

Последний пост
14 авг.
Последнее чтение
14 авг.
Постов за неделю
3
Всего постов
78
Тип
открытый
Язык
английский
Категория
Технологии (по похожим)
В каталоге с
13 авг.
Подписчики
2 987
−1 за 3 дн.
Сутки
+1
+0,03%
Неделя
 
Месяц
 
Просмотров на пост
1 258
40 постов
Вовлечённость
42,1%
к подписчикам
Постов в день
0,4
всего 78
Упоминаний
0
каналов
Охват размещения
оценка
1/24сутки в ленте
504
1/48двое суток
577
1/72трое суток
622

Оценка по просмотрам недавних постов: пост набирает почти всё за первые сутки.

Посты

  • MicroVMs for throw-away jobs MicroVMs is a way to provide you a short-lived isolated execution environment. What are they best for? Right, security related tasks! How about running the virus scanning on docker images? While I am personally skeptical on mere signature-check scans in general and ClamAV in particular, the idea of running high risk payloads in isolated envs is really appealling. Checkout how to leverage MicroVMs for it. #security #aws

  • AWS EC2 Application Status Check 👷‍♂️ After decades of custom monitoring solutions, AWS introduced the every minute status check with HTTP probes. Auto Scaling groups can replace unhealthy instances based on this application status. Try it out and tell me in the comments how it improved your life! #ec2 #observability

  • Should You Split Into Microservices? 👷‍♂️ We recently merged all our microservices(we had a handful) in a monolith. If you consider moving in a back direction, you need to ask yourself at least 5 questions on dependencies, different NFRs for the system parts, teams blocking each other, data boundaries and independent failures. Two candidate services, each owning its database, with red cross-boundary queries between them: one service, cut in half #microservices

  • 5 авг.1 117413

    Running a self-hosted LLM in Kubernetes with vLLM 👨‍💼 With the rise of cost for the LLMs and the privacy concerns more and more enterprises opt to run local models(and I am experimenting with them myself). Grab a guy how to setup an open-source LLM with Kubernetes! #llm #cloud #devops #architecture

  • 4 авг.994511

    SOC2 demystified We recently obtained SOC2 certification for Supplied. Our customers frequently ask how secure their data is with us. Answering this very question in the detailed post #security #soc2

  • 30 июл.1 31613

    We became so faster writing code, but do we ship more? Talking with Baruch Sadogursky about what exactly prevents us from unlocking true productivity, and it's not better agents. 👇 https://youtu.be/a_Kq18ufZzU

  • 22 июл.1 4127

    Clustering Billions of Products for Agentic Commerce with Catalog API 🤓 Shopify Catalog groups billions of listings without a common schema. It first matches products inside each store, then connects them across stores with a Universal Product Identifier (UPI). LLMs assign a structured label to every product. This enables consistent grouping, high precision, and better recall. AI searches based on Catalog data convert twice as often as searches based on scraped data. #ai #architecture #softwareengineering #engineering

  • 21 июл.1 18725

    The State of Streaming to Apache Iceberg in July 2026: Every Path, Its Latency, and What to Do When Seconds Are Not Fast Enough 👨‍💼 In mid-2026, teams no longer ask, “Should we use Iceberg?” They ask, “How current can our Iceberg tables be?”. And this is where the main tradeoff relies dictating your data architecture and tools to go with. From tuned Flink to Kafka connect latency numbers varies from 30 seconds to 15 minutes. How to choose? Well, Alex Merced explains in his piece. #db #distributed #architecture #softwareengineering

  • Architecture Weekly pinned «Most “AI agents” are workflows with an LLM inside. The real difference: who controls the flow? In my new video, I break down the five parts of a real agent—prompt, tools, state, memory and loop—plus the production essentials: tracing, guardrails and evals.…»

  • 17 июл.1 239722

    Most “AI agents” are workflows with an LLM inside. The real difference: who controls the flow? In my new video, I break down the five parts of a real agent—prompt, tools, state, memory and loop—plus the production essentials: tracing, guardrails and evals. Here's the link: https://youtu.be/SmSv_6bI5QM

  • 14 июл.1 34071

    I bought a setup for running local LLMs. Grab the unpacking video! https://www.youtube.com/shorts/xcbmp1p06jM

  • 2 июл.1 626214

    People go to the technical conferences and the only value they get are free snacks and some talks missing the true purpose of such events. I published a guide how to actually prepare the conferences and what to do there depending on your career aspirations. https://open.substack.com/pub/softwarearchitectureweekly/p/capturing-value-out-of-technical?r=1m9i62&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

  • 29 июн.1 519112

    Stragglers, Not Failures: How Adaptive Hedged Requests Reduce p99 Latency by 74 Percent 🤓 In fan-out microservice architectures, the dominant cause of high p99 latency is stragglers — slow-completing requests rather than failures — because one straggler in a fan-out blocks the entire composite response. While retries are a solution for failed requests, the stragglers require a parallel request if slow response is detected. Indeed new issues come with the solution, like write amplification, but if the goal is p99 optimized, raced requests are a way to go. The result is a 74% reduction in p99 latency with zero call-site configuration changes, with a reference implementation available as an open-source Go library. #distributed #architecture #engineering #softwareengineering

  • 24 июн.1 49227

    The Inference Paradox: How Split-Brain LLMs Are Killing Your GPU ROI 🤓 LLM inference has a structural hardware mismatch: the prefill phase is compute-bound (processing all input tokens in a single forward pass) while decode is memory-bandwidth-bound (reading the full KV cache to emit one token per step), so coupling both phases on the same GPU means each permanently starves the other. Kubex's enterprise audits surface average GPU utilization near 5% — monolithic vLLM serializes all prefill before decode can continue, and under high concurrency the delay compounds across every request in the batch. Disaggregating prefill and decode onto separate hardware-optimized node pools — as llm-d (now a CNCF sandbox project) implements on Kubernetes with prefix-cache-aware routing — yields 2–3x throughput at high concurrency by keeping the decode pool continuously active while the prefill pool handles bursts in parallel. #ai #llm #engineering #cloud

  • 2 июн.1 6873

    https://open.substack.com/pub/softwarearchitectureweekly/p/building-a-stripe-app-for-data-sync?r=1m9i62&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true

  • 2 июн.1 570612

    ScyllaDB Clusters at Discord, Zero Trust for AI Agents, CloudFlare Data Platform and many more in Architecture Weekly #198 https://www.youtube.com/watch?v=71AXRNKlg5c

  • 1 июн.1 406426

    Zero Trust for AI Agents 🍼 Each new technology bears security risks and the agentic systems is no exception. Both common vulnerabilities like supply chain attacks and excessive permissions stay; new ones like direct and indirect prompt injection, RAG poisoning and others comes. Follow the Antropic guide on applying Zero Trust to your agentic systems. #security #ai

  • 31 мая1 29325

    Migrating Data Ingestion Systems at Meta Scale 👨‍💼 Big migrations are scary and effortful. Imagine one with the biggest MySQL deployment in the world like at Meta’s data ingestion system. The team ran both legacy and new systems in a shadow testing mode, diffing outputs for correctness so individual pipelines could be validated and migrated independently; partition-level metadata flags automatically halted new delta landings and forced merges with known-good partitions whenever a partition was flagged as bad, bounding data quality risk during the transition. At @boltapp we followed the same shadow testing approach during our migration from MySQL to TitaniumDB. #db #architecture #distributed #engineering

  • 28 мая1 2976

    How Discord Automates ScyllaDB Clusters at Scale 🤓 A small infrastructure team operating 20+ ScyllaDB clusters with nearly 500 nodes cannot afford purely manual runbooks for cluster-wide operations. Discord’s Scylla Control Plane (SCP) encodes operations — rolling OS upgrades, cluster expansion, shadow cluster provisioning, node recovery — as YAML-defined workflows with explicit retry counts, parallelism controls, and abort-on-failure semantics; idempotency is a hard requirement for every task so that any retry is safe. Shadow clusters — temporary production replicas receiving real traffic — let the team validate new ScyllaDB versions before they touch live data; SCP automates the full shadow cluster lifecycle, cutting what once required more than a day of continuous engineer attention to largely unattended runs. #db #distributed #architecture #engineering

  • 22 мая1 517117

    Decided to give another shot to video versions of Architecture Weekly. Check it out and leave me a comment if you like this format! https://youtu.be/oSkLhBRqWIY

Architecture Weekly — tgindex