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Deep, recent reads ranked by depth and freshness.

Architecture Observability Testing Microservices Data Engineering Clear all

01The Pragmatic Engineer · architecture
The Pulse: Anthropic builds its own source control system
Anthropic experienced a massive increase in code merges due to the heavy use of AI coding agents, which quickly overwhelmed standard Git scaling limits and CI checkouts. To solve this, a small engineering team built a custom centralized version control system called Scion backed by Google Spanner and Cloud Storage, along with a code review tool called Antique. This move successfully migrated the entire monorepo off Git and eliminated bottlenecks, though the architecture drew controversy over similarities to Meta's Mononoke.
DEPTH ■■■■■12 min read5 days ago
Architecture Distributed Systems
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02Shopify · architecture
We gave AI agents a mobile dev environment
Engineering teams outgrew their local machines while running parallel AI mobile development agents, constrained by heavy builds and lack of iOS tools on Linux. To solve this, they built Remote Dev using warm pools of remote Macs connected via SSH alongside separate Linux VMs for Android virtualization, paired with agent skills and live browser streaming. This setup shifted workflow bottlenecks, increasing AI-merged pull requests from 6 percent to 61 percent.
DEPTH ■■■■■9 min read5 days ago
Architecture Mobile
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03GitLab · tutorial
PHP2Shell: GitLab helps find and remediate critical PHP flaws
Critical remote code execution vulnerabilities known as PHP2Shell affect the PHP engine across multiple versions, requiring immediate runtime updates to fixed versions or patched distribution packages. The team utilized platform features like container scanning, code search, and automated agent workflows to locate PHP projects and remediate vulnerabilities. Operators must also check logs and environments for signs of compromise as framework updates and input validations cannot prevent these engine-level flaws.
DEPTH ■■■■■10 min read5 days ago
Architecture Security
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04GitHub · architecture
From 2,500 requests to 24: Rebuilding star history without the stargazers
GitHub faced abuse and performance issues because tools were scraping user identities to reconstruct repository star history. The engineering team collaborated with maintainers to build a purpose-built aggregate endpoint that returns daily star counts without identities and filters by time before aggregation. This reduced request volume from thousands to a few dozen and eliminated heavy database scans.
DEPTH ■■■■■8 min read12 days ago
Microservices
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05Shopify · architecture
How Shopify turns open models into task experts
Shopify uses a continual learning flywheel to post-train open models, such as NVIDIA Nemotron, to serve as dedicated subagents for specific tasks like GraphQL generation. By optimizing inference and utilizing automated self-healing to generate training data from production failures, the team successfully created task experts that outperform frontier models while reducing serving costs by 90-98% and improving response times.
DEPTH ■■■■■5 min read6 days ago
Architecture
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06Anthropic · architecture
Redesigning source control at Anthropic
Anthropic experienced a massive surge in code generation due to AI coding tools, causing git to become a critical bottleneck for their monorepo. To scale, the team built Scion, a custom version control system optimized for online clients and server-side computation. This new architecture reduced infrastructure resource requirements by 200x and drastically improved checkout speeds for CI jobs.
DEPTH ■■■■■17 min read2 weeks ago
Architecture
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07Zalando · postmortem
Thundering herd of circuit breakers
An engineering team investigated why a client-side circuit breaker repeatedly failed to close on its own after an outage. By exporting OpenTelemetry traces into a custom visualization tool, they discovered that low probe traffic caused the autoscaler to scale down the service to minimum capacity, triggering a thundering herd of requests and immediate timeouts the moment the circuit breaker closed. Adjusting the system to account for capacity and probe representativeness prevents this oscillation.
DEPTH ■■■■■10 min read2 weeks ago
Microservices Observability Reliability
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08Shopify · architecture
Cross-store product clustering across millions of stores
Shopify needed to group identical product listings from millions of different merchants under a single global identifier. To solve this at scale without comparing every possible pair, the team implemented a multi-stage pipeline using vector retrieval, a tiered scoring system of ML models, and a bounded graph clustering approach. This architecture successfully standardizes product data across the entire platform while controlling computational costs.
DEPTH ■■■■■13 min read7 days ago
Architecture
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09Stack Overflow · practice
The Regression Tests Nobody Writes: Capturing How Software Actually Behaves
Standard regression tests usually only verify developer intent rather than the actual, unwritten behaviors that systems accumulate in production over time. By capturing real production traffic and network-level interactions, teams can build characterization test suites to automatically detect unintended behavioral shifts. This approach prevents accidental regressions during legacy refactoring, rewrites, and major framework upgrades.
DEPTH ■■■■■6 min read4 days ago
Testing
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10Stack Overflow · practice
Your next job doesn’t have a title yet: Emerging role skills Q&A with Cory Hymel of Andela
Researchers analyzed thousands of job postings to understand how artificial intelligence is changing technical skill requirements. The findings indicate that instead of creating a workforce of pure generalists, the market is developing distinct new specialized skill bundles and hybrid roles. Consequently, organizations are facing talent debt as the half-life of technical skills rapidly shrinks.
DEPTH ■■■■■15 min read12 days ago
Testing
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11Grab · architecture
Smarter personalization: How property data helps us understand user price sensitivity
Existing user personalization systems fail to systematically account for residential property values, leading to one-dimensional profiles and regional blind spots. A conceptual four-step method is proposed that combines public property data, large language models, building-level price estimation, and hierarchical clustering to classify user affluence. The approach remains unimplemented, with future viability depending on technical validation, data quality, and privacy approvals.
DEPTH ■■■■■4 min read3 weeks ago
Data Engineering Machine Learning
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12Grab · architecture
GrabMart data for faster disease outbreak detection
The organization proposes a conceptual data pipeline to analyze aggregated GrabMart transaction data for symptom-related product demand, aiming to supplement clinical surveillance for disease outbreaks. The design relies on data aggregation, statistical baseline comparisons, and privacy safeguards like differential privacy without involving individual health inference. The system remains in an early planning phase requiring rigorous validation, privacy review, and public health oversight.
DEPTH ■■■■■5 min read2 weeks ago
Data Engineering Machine Learning
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13Stack Overflow · practice
Taking a look under your agent’s hood
This post discusses the challenges and methods of applying observability to AI agents, which combine traditional distributed systems telemetry with new requirements for monitoring LLMs and multi-step tool calls. The discussion highlights how non-deterministic agent behavior and increased speed of execution make continuous production feedback loops essential for upstream development. Ultimately, integrating observability data into automated testing and development loops helps teams iterate on agent performance.
DEPTH ■■■■■25 min read2 weeks ago
Machine Learning Observability
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