Read what ships.

01GitLab · 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 read3 days ago

ArchitectureSecurity

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

03GitLab · 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 read12 days ago

Sign in to save and rate

04Stack 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

05Stack 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

06Figma · tooling

How Lufthansa Systems prototypes realistic maps for the cockpit with Figma Make

Lufthansa Systems struggled to build realistic, dynamic map prototypes for cockpit navigation apps using traditional static design tools. The team experimented with Figma Make during an internal hackathon to integrate external data sources like Mapbox and the OpenSky Network into their designs. As a result, designers successfully tested interactive map features and complex flight traffic data with pilots prior to writing production code.

DEPTH ■■■■■8 min read3 days ago

Sign in to save and rate

07Stack 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

08Yelp · architecture

The making of the Yelp Assistant UI

Yelp developed a chat interface called Yelp Assistant using a hybrid approach combining native UI components and a server-driven UI framework called CHAOS. This architecture allows the team to dynamically update the chat experience without waiting for app store releases. The system separates views from data using reactive datasets and MessageEvents to manage the chat loop, state, and user interactions.

DEPTH ■■■■■12 min read3 weeks ago

Sign in to save and rate

09Stack 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

10Stack 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

11Stack 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

12Figma · tutorial

How to put your design system to work with the Figma agent

This post explains how to provide the Figma agent with proper context through design libraries, explicit guidelines, practical examples, and reusable skills to produce accurate outputs. By connecting libraries and defining rules, teams can ensure the agent uses approved components and follows design patterns. Reviewing agent outputs helps teams identify missing context and refine their libraries and guidelines.

DEPTH ■■■■■6 min read2 days ago

Sign in to save and rate

13Stack 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

14GitLab · tutorial

Three developer tasks worth handing to an agent

The post explains how to use GitLab Duo Agent Platform features such as automated code reviews, CI/CD pipeline failure diagnosis, and cross-project chat. It provides setup instructions for configuration and outlines a consumption-based pricing model using credits. The features aim to automate routine developer workflows using broader project context than standard IDE assistants.

DEPTH ■■■■■4 min read6 days ago

Sign in to save and rate

15Figma · tooling

How Coinbase used Code Connect to guide agents and shrink token costs

The Coinbase Design System team updated their Figma Code Connect mappings to improve agentic design-to-code workflows. By providing agents with better component context, the team reduced token costs by 22.5% and implementation time by about 22% while eliminating common hallucinations.

DEPTH ■■■■■5 min read7 weeks ago

Sign in to save and rate

16Stack 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

17Figma · practice

4 team rituals that drive AI adoption

To drive team-wide AI adoption beyond isolated power users, several companies introduced collaborative team rituals such as peer-led study groups, process-focused sharing forums, cross-disciplinary training, and prototype-first critiques. These forums helped bridge knowledge gaps, reduce intimidation, and build consistent habits. As a result, teams were able to move from individual experimentation to collective AI usage.

DEPTH ■■■■■7 min read2 weeks ago

Sign in to save and rate

18Stack 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

19Stack Overflow · practice

“Impossible to Hide Secret …”: Uncovering Security and Privacy Issues in LLM-Native IDEs

Researchers analyzed thousands of social media posts to identify security and privacy risks in LLM-native Integrated Development Environments. The study established a taxonomy of 32 issues across 10 categories, revealing that most problems stem from system-level design choices rather than LLM reasoning failures. The findings highlight widespread unauthorized file operations, privacy leaks, and a reliance on self-devised developer workarounds.

DEPTH ■■■■■3 min read2 weeks ago

Machine LearningSecurity

Sign in to save and rate

20Figma · tutorial

Workflow lab: Staying in the flow with Figma’s agent

A fictional designer at Offtrail updates a permission settings flow using the Figma agent to handle repetitive maintenance, compliance checks, and component specs. By offloading busywork and context gathering to the agent, the designer focuses on feature logic and design decisions while keeping outputs connected in a shared canvas.

DEPTH ■■■■■8 min read2 weeks ago

Sign in to save and rate