Read what ships.

01ByteByteGo · architecture

A Deep Dive into Salesforce’s Agent Tech Stack

Salesforce transitioned its AI agent platform from a single, complex prompt architecture to a multi-layer design combining deterministic code with reasoning models to overcome instruction loss. The new system delegates rule enforcement, data harmonization, context management, and permissions to dedicated backend components. This approach significantly reduces token consumption and improves operational reliability in production environments.

DEPTH ■■■■■13 min readyesterday

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02GitLab · 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

ArchitectureSecurity

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03GitHub · architecture

The bridge to agent-scale Git infrastructure

GitHub faced infrastructure capacity limits and a 25ms cross-region network round trip when migrating repositories to Azure. The team changed the storage quorum from repository-wide locks to per-reference serialization and introduced ephemeral virtual replicas backed by object storage. This raised write throughput, prevented slow replicas from blocking traffic, and decoupled read scaling from the write quorum.

DEPTH ■■■■■8 min read6 days ago

Infrastructure

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04Grab · architecture

App stability at scale with a layered defense

To maintain app stability amidst rapid weekly releases and AI-generated code, Grab uses a layered defense strategy covering prevention, release gates, and incident response. This pipeline includes static analysis via custom lint rules, multiple tiers of automated and manual testing, a pre-production early access build, staged rollouts, and feature flags with instant kill switches. As a result, critical defects are caught early in the development lifecycle before reaching the wider production user base.

DEPTH ■■■■■16 min read6 days ago

MobileReliability

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05Martin Fowler · architecture

When Software Meets Hardware

A software engineering team modernizing a large-scale retail IoT system of 800,000 battery-powered sensors faced challenges integrating software practices with embedded hardware. By building physical sensor farms, virtual emulators, and self-diagnosing firmware, the team learned to design for intermittent connectivity, power efficiency, and fleet-level observability. The project succeeded by treating the physical world, hardware constraints, and engineering environments as core parts of the system lifecycle.

DEPTH ■■■■■12 min read12 days ago

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06ByteByteGo · migration

Why Shopify Migrated From React Native

Shopify migrated its mobile applications from React Native to native Swift and Kotlin because AI coding agents made the cost of duplicating features across platforms negligible. Using a greenfield approach and a custom tool called Helix to break down the migration into small, reviewable steps, the team successfully rewrote the apps. The resulting native applications showed significant improvements in startup time, crash rates, and app size.

DEPTH ■■■■■11 min read7 days ago

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07GitHub · 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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08Martin Fowler · performance

Are Harnesses Worth the Cost?

An experiment was conducted to evaluate whether behavioral harnesses improve coding agent reliability when building a complex backend-for-frontend service. While frontier models succeeded consistently with or without a harness, the harness significantly increased token usage, time, and cost without improving final output. However, for a cost-optimized model, the harness improved success rates from 30 percent to 100 percent.

DEPTH ■■■■■17 min read2 weeks ago

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09Kubernetes · migration

The Shift to cgroup v2 in Kubernetes: What You Need to Know

Kubernetes has deprecated cgroup v1 in favor of cgroup v2, with complete removal scheduled for future releases. The transition requires migrating Linux nodes, updating container runtimes, and verifying kernel compatibility. Adopting cgroup v2 enables advanced features such as Memory QoS, improved OOM handling, and pressure stall information metrics.

DEPTH ■■■■■12 min read2 weeks ago

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10GitLab · scaling

Make every AI credit count: See, cap, and reduce agent spend

Uncontrolled AI agent spending can be mitigated by combining open-weight models, centralized context graphs to reduce retries, and usage analytics with credit caps. GitLab introduced these capabilities in its Duo Agent Platform to help platform teams trace, limit, and optimize AI credit consumption. The resulting architecture matches specific tasks to lower-cost models while maintaining task completion quality.

DEPTH ■■■■■6 min read2 weeks ago

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11ByteByteGo · architecture

How Uber Built a Genie to Answer Questions Correctly

Uber built an internal AI assistant called Genie to answer engineering support questions directly within Slack. The system uses a retrieval-augmented generation (RAG) architecture powered by Apache Spark, LangChain, and vector databases to fetch and explain documentation. As a result, Genie reduces repetitive on-call support burdens while providing source attribution and fallback paths to human engineers.

DEPTH ■■■■■13 min read8 days ago

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12Kubernetes · scaling

Scaling Kubernetes Workloads with Node Swap

Kubernetes memory limits often cap node density for bursty agentic AI workloads that sit idle with large memory footprints. To solve this, the engineering team benchmarked Kubernetes v1.34 node swap backed by fast NVMe Local SSDs across CI/CD builds, headless browsers, and Python sandboxes. The approach successfully increased pod density up to three times without significant latency costs.

DEPTH ■■■■■6 min read3 weeks ago

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13Grab · architecture

Powering AI-led research through simulation

The engineering team built a self-contained simulation platform called sim-rs to model marketplace dispatch dynamics and accelerate experimentation. By connecting automated software agents to this simulator with strict safety safeguards, the team successfully optimized strategy policies and simulator performance. This closed-loop lab environment allows agents to test ideas rapidly while ensuring that only valid, production-ready improvements are produced.

DEPTH ■■■■■10 min read3 weeks ago

Machine Learning

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14GitHub · architecture

Building Git infrastructure for agent-scale development

GitHub is redesigning its Git infrastructure to handle the massive surge in concurrent read and write operations driven by agentic software development and high-volume workloads. The team is decoupling storage from compute by leveraging Azure Blob Storage for durability and lightweight compute workers for requests, which separates scaling dimensions. In internal benchmarks, this approach achieves up to 35 times higher write throughput and independent read scalability.

DEPTH ■■■■■9 min read2 weeks ago

Infrastructure

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15ByteByteGo · architecture

LLM Prompt Caching Versus Semantic Caching

This post compares prompt caching and semantic caching for large language models to reduce costs and latency. Prompt caching reuses intermediate KV states for shared prefixes, while semantic caching stores previously generated answers for similar questions using vector embeddings. The article explains the mechanics, pricing models, and trade-offs of both approaches.

DEPTH ■■■■■16 min read6 days ago

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16GitHub · architecture

Staying Evergreen: How we automated secure access and repo hygiene in a shared org

Manual repository housekeeping and access reviews for outside collaborators become difficult to maintain as organizations scale. The team built Evergreen, a GitHub App that automates quarterly access reviews and stale repository archiving using native platform features and IssueOps. This automation successfully enforces secure defaults by revoking unanswered access while maintaining a full audit trail in git history.

DEPTH ■■■■■8 min read11 days ago

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17ByteByteGo · architecture

How Netflix Taught an LLM to Recommend Movies So That You Keep Watching

Netflix developed GenRec, an LLM-based recommendation ranker designed to interpret viewing histories and item metadata in plain text to score movies and TV shows. By adapting an open-source foundation model in a two-phase training process and employing context engineering to reduce token usage, the team improved catalog ranking and satisfaction prediction. The system balances general language knowledge with domain-specific constraints to manage costs and personalization.

DEPTH ■■■■■15 min read2 weeks ago

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18Martin Fowler · practice

Fragments: October 21

This post discusses commentary on AI usage in writing, the cancellation of the Grace Hopper Celebration, and reviews a detailed article by Cindy Sridharan on debugging with AI agents. The reviewed article highlights that AI agents excel at processing raw data rather than reading code first, and outlines a three-step debugging process involving controls, signals, and hypothesis validation. The lesson is that engineers must guide AI agents by setting up verification loops and providing raw data rather than human-centric abstractions.

DEPTH ■■■■■5 min read6 days ago

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19ByteByteGo · architecture

Why LLMs Agree With You Even When You’re Wrong

Large language models often agree with incorrect user claims because their training rewards human approval and helpfulness over strict accuracy. This behavior, known as sycophancy, is driven by reinforcement learning from human feedback and conversational pressure. Teams can mitigate this issue by using synthetic training data, Constitutional AI principles, and internal activation probes to penalize agreeable falsehoods.

DEPTH ■■■■■13 min read3 weeks ago

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20Kubernetes · tooling

Why Is My Kubernetes Job Still Pending? Tracing Kueue Admission with Headlamp

Kubernetes jobs managed by Kueue can remain pending while waiting for admission checks and resource allocation. The team implemented a Kueue plugin for Headlamp to trace pending workloads through local queues, cluster queues, and resource flavors within a unified interface. This approach allows engineers to quickly determine whether a job is blocked by admission policies or standard pod scheduling.

DEPTH ■■■■■9 min read12 days ago

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