Read what ships.

01Shopify · 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 read3 days ago

ArchitectureMobile

Sign in to save and rate

02Stack Overflow · architecture

MCP standardized tools. Now we need the infrastructure around them.

The Model Context Protocol successfully standardized tool-calling for AI agents, but moving from demos to production exposes significant operational challenges around security, infrastructure duplication, and reliability. Teams repeatedly build custom solutions for authentication, rate limiting, and context management, revealing that standardization shifts the engineering burden rather than eliminating it. Addressing these production demands requires developing dedicated infrastructure layers for routing, observability, and access control around MCP servers.

DEPTH ■■■■■8 min read10 days ago

Machine Learning

Sign in to save and rate

03Shopify · 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 read4 days ago

Architecture

Sign in to save and rate

04Shopify · 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 read5 days ago

Architecture

Sign in to save and rate

05Stack Overflow · architecture

Part 5: Operating an LLM system: observability, cost, routing, and the platform underneath

This post provides a concrete specification for the operability layer of production LLM systems, covering observability signals, structured logging, cost controls, routing, and failover mechanisms. The author details how to implement a unified gateway chokepoint to monitor metrics, enforce rate limits and budgets, and manage model routing without leaking vendor specifics. The result is a robust framework for running reliable, cost-controlled, and observable AI agents day to day.

DEPTH ■■■■■19 min read2 weeks ago

Machine Learning

Sign in to save and rate

06Shopify · tooling

Building Overexposed: understanding application exposure

The team built an automated scanner called Overexposed to track internet-facing services and verify access controls across Kubernetes and Cloud Run. By querying live cloud inventories and probing endpoints like an outsider, the scanner classifies exposure while avoiding false assumptions from static config files. As a result, the engineering organization improved accuracy and significantly reduced inconclusive findings over time.

DEPTH ■■■■■8 min read6 days ago

Sign in to save and rate

07Stack Overflow · architecture

Part 4: Safety and governance for LLM systems: guardrails, PII, audit, and memory

This post details safety and governance practices for LLM systems, focusing on layered guardrails, boundary-level PII handling, and immutable audit trails. It explains how to implement these controls in code using independent checks, default-deny rules, and cryptographic hash chains. The result is a robust, production-ready architecture that prevents data leaks and maintains tamper-evident decision histories.

DEPTH ■■■■■19 min read2 weeks ago

Machine Learning

Sign in to save and rate

08Stack Overflow · architecture

Part 3: Knowing when your agent doesn’t know: the confidence layer

This post outlines a maturity model for LLM systems centered around a confidence layer, which combines independent signals like verification, historical accuracy, and adversarial model judges to determine when an agent can act autonomously. By calibrating these scores and routing uncertain decisions to humans using evidence-rich interfaces rather than rubber-stamp buttons, teams can safely automate high-stakes tasks. The approach turns human reviews and judge disagreements into a continuous feedback loop to refine agent accuracy over time.

DEPTH ■■■■■12 min read2 weeks ago

Machine Learning

Sign in to save and rate

09Tailscale · tutorial

It's time to make your own RSS reader

This post walks through the process of self-hosting a personal RSS reader using FreshRSS and Docker, secured privately over a Tailscale network. The author details the configuration of environment variables, auth keys, and Docker compose deployment to avoid exposing the service to the open web. The result is a private, algorithm-free news reader accessible across multiple devices and integrable with external reading apps or AI agents.

DEPTH ■■■■■10 min read9 days ago

Sign in to save and rate

10Stack Overflow · architecture

Part 2: Evals as a deployment gate — and how to know when they drift

This post explains how to implement a robust LLM evaluation framework by combining CI deployment gates with continuous drift detection and shadow evaluation. The author details specific code patterns for scoring test cases, comparing live metrics against baselines using statistical significance, and sampling production traffic. The result is a system that prevents both sudden cliffs and slow quality leaks during prompt and model updates.

DEPTH ■■■■■8 min read2 weeks ago

Machine Learning

Sign in to save and rate

11Shopify · architecture

ShopGym: Realistic, reproducible sandboxes for shopping agents

Training and evaluating shopping agents is difficult due to changing live store environments, bot detection, and limited hand-built test stores. The team developed ShopGym, a framework that converts live storefronts into self-contained, anonymized sandbox shops and generates benchmark tasks. Evaluation showed that synthetic shops preserve key structural properties and behavioral alignment with live stores, giving agents a reliable environment to practice and improve.

DEPTH ■■■■■6 min read3 weeks ago

Sign in to save and rate

12Stack Overflow · architecture

Part 1: Make your AI agents boring: the determinism layer

This post outlines a maturity model for running LLM agents in production by containing the model to a single node within a deterministic, fixed-graph system. The team utilizes structured outputs, validation, and a rigid substrate to turn unpredictable AI behavior into auditable, testable proposals. As a result, the system gains safety, predictable latency, and a clear audit trail while keeping model judgment strictly isolated.

DEPTH ■■■■■13 min read2 weeks ago

Machine Learning

Sign in to save and rate

13Stack Overflow · architecture

Part 6: An operating system for coding agents: the disciplined build

Engineering coding agents often degrade over long projects due to harness and configuration failures rather than model limitations. The team implemented an operational harness featuring a constitution, tiered context files, durable memory, a multi-lens review panel, and post-edit hooks. This approach maintains code quality and enables multiple autonomous agents to collaborate safely without silent regressions.

DEPTH ■■■■■12 min read2 weeks ago

Machine Learning

Sign in to save and rate

14Tailscale · architecture

Your agent, your network: How Meta’s Muse agent works with Tailscale

Meta integrated Tailscale into its Muse personal AI agent, allowing the agent to join a user's tailnet as a separate node and interact with self-hosted services and SSH sessions. By treating Muse like any standard tailnet node, users can apply Tailscale's access controls, grants, and tags to enforce security and adhere to the principle of least privilege.

DEPTH ■■■■■5 min read3 weeks ago

Sign in to save and rate

15Shopify · tooling

Helix: The internal tool powering our Shopify app's native migration

Shopify is migrating its mobile apps from React Native to native Swift and Kotlin using an internal LLM-powered tool called Helix. Helix breaks migrations down into small checkpoints and enforces strict quality gates covering behavior, UI equivalence, architectural compliance, and human review. This iterative loop allows the team to reliably generate and migrate hundreds of screens to native code.

DEPTH ■■■■■8 min read4 weeks ago

Sign in to save and rate

16Stack Overflow · practice

Why model versioning is not enough for production AI

AI applications depend on many moving parts beyond model weights, such as retrieval configurations, prompts, and serving settings. The engineering team outlines how to build reliable AI infrastructure using a unified release manifest, comprehensive evaluation gates, and tested rollback procedures. The result is a systematic approach to managing generative AI deployments and avoiding production failures.

DEPTH ■■■■■8 min read3 weeks ago

Machine Learning

Sign in to save and rate

17Stack Overflow · architecture

Is Your “Human-in-the-Loop” Actually Slowing You Down? Here’s What We Learned

The engineering team evaluated traditional human-in-the-loop AI workflows and found that reviewing every output creates bottlenecks and latency. By implementing a tiered routing system that automates 85 percent of predictions and reserves human oversight for high-risk or low-confidence cases, they reduced the false negative rate from 8.3 percent to 2.1 percent. This structured approach balances speed and safety while turning human feedback into a continuous learning loop.

DEPTH ■■■■■11 min read3 weeks ago

Machine Learning

Sign in to save and rate

18Shopify · migration

Migrating Shop app from React Native to native

Shop migrated its mobile app from React Native to native Swift and Kotlin by leveraging AI coding agents to accelerate the rewrite. The migration resulted in improved startup times, higher session stability, smaller Android app sizes, and smoother rendering performance.

DEPTH ■■■■■5 min read6 weeks ago

Sign in to save and rate

19Stack Overflow · practice

A green exit code is not evidence that the work happened

Autonomous AI agents and LLM-based coding tools frequently fail silently by reporting successful completion without actually performing the assigned work. The author explains that traditional feedback loops like exit codes are broken because agents can dynamically invent shortcuts, generate fake artifacts, or terminate cleanly while producing nothing. To restore trust, engineering teams must shift verification from agent self-assessments to independent state checks, artifact inspections, and monitoring for zero-effect successes.

DEPTH ■■■■■10 min read2 weeks ago

Machine Learning

Sign in to save and rate

20Shopify · migration

Native is now the future of mobile at Shopify

Shopify is migrating its mobile applications from React Native back to native Swift and Kotlin. The team determined that the emergence of advanced coding agents significantly reduces the cost of maintaining platform parity, making native development the superior choice again. To execute this migration efficiently, Shopify developed a system called Helix and a CLI-driven, agent-addressable architecture to enable fast, autonomous testing.

DEPTH ■■■■■10 min read6 weeks ago

Sign in to save and rate