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Deep, recent reads ranked by depth and freshness.

  1. OpenTelemetry Collector Configuration for LLM Observability

    LLM applications emit bulky telemetry containing sensitive user content and disparate attribute names. The team uses an OpenTelemetry Collector pipeline to normalize, enrich, redact, and export this data to Honeycomb. This centralizes control, protects data before it leaves the network, and maintains consistent observability.

    DEPTH ■■■■■12 min read2 weeks ago

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  2. 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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  3. 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 read2 weeks ago

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  4. 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

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  5. How Honeycomb Private Cloud Drinks From the Fire Hose

    The Honeycomb Private Cloud team struggled to manually track and integrate rapid infrastructure and configuration changes produced by upstream teams. To solve this, they built custom scripts to generate automated drift reports and eventually integrated AI to help triage and track the necessary follow-up work. As a result, the team successfully scaled their integration process into a reliable daily workflow.

    DEPTH ■■■■■10 min read2 weeks ago

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  6. 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 read11 days ago

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  7. 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

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  8. Our Limits Are Not Our Imagination

    Software development practices are shifting as artificial intelligence makes advanced rigor, such as formal verification and property-based testing, an affordable default for greenfield projects. Developers can leverage AI agents to thoroughly review code and enforce strict architectural boundaries rather than just writing code faster. However, successful outcomes require comprehensive development systems and thinking rather than relying on AI alone.

    DEPTH ■■■■■3 min read5 days ago

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  9. 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

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  10. Dr. Cat Hicks on the Psychology of Software Teams

    Engineering leaders must establish clear structures and cultural norms to reduce the psychological identity threat and anxiety developers experience when adopting AI tools. Research indicates that organizational trust regarding AI is currently fractured but repairable through realigned expectations and accountability. Furthermore, engineers are advised to build durable sources of professional identity rather than relying solely on mastery of specific languages or legacy codebases.

    DEPTH ■■■■■3 min read3 weeks ago

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  11. 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

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  12. How to lighten design system upkeep with the Figma agent

    Design systems often suffer from maintenance overhead like documentation debt, naming inconsistencies, and drift between product screens and libraries. The Figma agent automates these upkeep tasks by drafting specs, auditing token and component usage, and bulk-replacing disconnected elements. This reduces manual toil and helps teams keep their design systems aligned with product work.

    DEPTH ■■■■■5 min read2 weeks ago

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  13. Behind the build: Generative plugins and shaders at Figma

    Figma developed generative plugins and shaders by replacing a prototype WebGL system with an enterprise-scale WebGPU pipeline, sandbox isolation, and custom UI kits. Through rapid prototyping and cross-functional collaboration, the team shipped the feature suite in two months for Config.

    DEPTH ■■■■■8 min read7 weeks ago

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  14. How do you actually measure the impact of AI on design?

    Figma conducted a multi-year research project to track the impact of AI on product design by building an AI sentiment index and running hundreds of AI-moderated interviews. To process the massive volume of qualitative data, the team engineered a custom thematic analysis tool and an internal dashboard to serve as a cross-functional source of truth. The results showed that AI's impact on workflows and collaboration nearly doubled over three years.

    DEPTH ■■■■■6 min read8 weeks ago

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  15. Workflow lab: Moving between design and code with agents

    A fictional engineering team struggles with AI-generated code that frequently strays from design specifications. By integrating Figma dev tools, an MCP server, and Code Connect into the workflow, the developer provides the coding agent with accurate design context and existing components. As a result, the agent produces production-ready code that matches design intent and reduces rework.

    DEPTH ■■■■■6 min read8 weeks ago

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