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Explainer reads
Explainer posts on Yomu, summarised newest first.
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Explainer posts
Discover the secret sauce of innovative companies, according to Stanford professor Behnam Tabrizi - The Intercom Blog
Stanford professor Behnam Tabrizi discusses his book ‘Going on Offense,’ which analyzes 26 companies to uncover the culture of perpetual innovation, based on a survey of over 6,000 executives, academics, and industry experts. He explains that Microsoft’s renaissance under Satya Nadella came from elevating engineers, prioritizing cloud computing, and making early bets on AI, creating a startup mindset. Tesla’s unique culture aligns with a holistic approach to cultivating change, inviting active participation across the company and a fierce dedication to overcoming challenges. Legacy companies often fail because they make cautious, incremental bets rather than bold moves like Nvidia’s 2018 bet on AI, which made it a trillion-dollar semiconductor leader. Tabrizi argues AI will not eliminate jobs but will displace those who fail to leverage it effectively, urging organizations to go on offense by deploying AI for growth and productivity.
The father of customer journey mapping, Chip Bell, talks driving innovation through customer partnership - The Intercom Blog
Chip Bell, author and co-developer of customer journey mapping, advocates for co-creation partnerships where customers become partners in innovation. By shifting from a transactional relationship to a partnership, companies can unlock customer creativity and unexpected breakthrough solutions. Bell recommends approaching inquiries with genuine curiosity, building trust through truth-telling and promise-keeping, and embracing daredevil learning that treats risk-taking as a learning venture. He contrasts AI’s limitations, stating it cannot replicate the human empathy and emotional depth that underpin genuine customer relationships. The key is to make customers feel welcomed to contribute, transforming them into co-creators of memorable experiences.
Negotiating Ontological Boundaries in User-Authored Personal Sensing Systems
Designed artifacts are ontological, shaping what becomes possible, yet systems enabling user authorship are typically evaluated on usability rather than ontological boundary negotiation. Two open-ended probes using a Wizard of Oz technique were created to let participants experience training a personalized machine learning system on phenomena they defined themselves. In a week-long exploratory study, participants used one of two probes during their everyday lives, allowing observation of boundary negotiations. Four sites of ontological boundary negotiation emerged: the boundaries of a phenomenon, the subject as part of relations, distinguishing signal from noise, and the objectivity of data. The paper offers starting points for supporting boundary negotiation through design and proposes open-ended probes as a method for ontological design.
Auth0 Metadata Explained: user_metadata vs app_metadata
Auth0’s user_metadata and app_metadata define a security boundary: user_metadata is writable by users through the update:current_user_metadata scope, while app_metadata requires privileged scopes available only to confidential clients via the client credentials grant. Post-login Actions reading authorization data like subscription plans or feature flags from user_metadata allow users to escalate privileges by modifying their own metadata, for example setting plan to enterprise and beta_access to true. Writing to app_metadata demands a server-side M2M application with update:users_app_metadata scope, which no public client or user token can obtain. The correct practice is to store access-control data such as roles, tenant membership, and IP allowlists in app_metadata, and non-sensitive preferences like theme or language in user_metadata. A third field, client_metadata, holds application-level properties readable via event.client.metadata in Actions.
Unlocking Data Portability: Preventing Catalog Lock-in with REGISTER and UNREGISTER APIs
The post explains how the REGISTER and UNREGISTER APIs in the Apache Iceberg REST catalog specification enable table portability between catalogs without copying or moving data files. REGISTER attaches an existing table to a new catalog by providing the metadata location, but using it alone creates a split-brain scenario where two catalogs both think they own the table, leading to inconsistent writes and data loss. The UNREGISTER endpoint, contributed to the specification, cleanly removes the table from the old catalog and returns the latest metadata pointer needed by the next catalog. The handoff process combines unregister, receiving the location, and register to ensure exactly one managing catalog at all times. Unity Catalog supports these endpoints in private preview, allowing organizations to move tables between catalogs as their architecture evolves.
Smarter GPU sharing: How Red Hat build of Kueue works with dynamic resource allocation
The Kubernetes device plugin model treats GPUs as anonymous integers, making fair sharing impossible because a full 80 GB GPU and a 5 GB partition both consume ‘1’ from the same quota. Dynamic resource allocation (DRA), generally available in Kubernetes 1.34, replaces this by having drivers advertise real device attributes like memory and compute capability, but DRA alone does not address quota management or preemption. Red Hat build of Kueue 1.4 on Red Hat OpenShift integrates with DRA to enforce quota, borrowing, and preemption for both whole GPUs and partitionable devices using device-count or counter-based memory accounting. For partitionable GPUs, Kueue reads counter data from DRA drivers and charges GPU memory against ClusterQueue quota instead of device count, enabling accurate fair sharing between training and inference workloads. Future developments include extended resources for simpler adoption without ResourceClaimTemplates, consumable capacity for dynamic time-slicing, and scheduler-library integration to verify scheduling feasibility.
Real-Time Retail Intelligence: Building E-Commerce Recommendations with Lakebase and AI Search on Databricks
The post presents a reference architecture for building a production-grade e-commerce recommendation system on Databricks, based on a real-world implementation for a leading fashion e-commerce platform in Asia serving over 1 million monthly active users across 100,000+ SKUs. The system ingests approximately 1,000 events per second via Lakeflow Connect’s Zerobus into Unity Catalog Delta tables within a medallion architecture. Serving uses two paths: Path A for pre-computed batch recommendations via key-value lookup from Lakebase online tables, and Path B for real-time session-aware scoring using AI Search and a LightGBM model in a 3-stage funnel. The architecture unifies ingestion, feature management, model training, vector retrieval, and serving on a single governed platform, with cold-start handling for new users and products. Expected outcomes include low 2-digit millisecond responses, daily-fresh recommendations, and unified governance via Unity Catalog.
What is direct I/O, and why does ClickHouse Managed Postgres use it for backups?
A base backup reads every byte of a live database off the same NVMe drives that serve queries. Buffered reads go through the kernel page cache, evicting warm database pages and adding a kernel copy per byte. Direct I/O skips the cache, leaving Postgres’s warm data in place, but also disables kernel readahead. On a RAID0 array, small direct reads hit only one drive, so ClickHouse Managed Postgres sizes each direct read to span the whole stripe and scales the reader count to the hardware. On a 48 vCPU box with four NVMe drives and a 467 GB database, the direct I/O backup evicted nothing, cut query latency impact by about two thirds, used about 14% less CPU, and finished in 71 seconds.
Limits of Confidence in Diffusion
The post analyzes limitations of confidence-based ranking in discrete diffusion, where sequences are generated by writing multiple token positions per step from per-position distributions. It shows that a step matches the training distribution only when the written positions are conditionally independent given fixed tokens, and no product of per-position distributions can match a dependent group. Additionally, per-position distributions do not determine group dependence: two joint distributions can have identical per-position marginals but differ in value combinations. On the synthetic ScanAndAdd task, every group of two or more undetermined positions written by a confidence ranking is dependent, and the generated distribution is 29 times the sampling-noise floor in total variation, with per-sample metrics at 1.0. The post also includes updates on two related readings: on-policy distillation for reasoning models and self-supervised learning with Transformers.
Seer, the Sentry MCP and CLI, or your own coding agent: where each one fits
Seer is Sentry’s agent debugger that reasons across errors, traces, logs, profiles, and linked repositories to identify root causes, not just surface-level stack traces. The Sentry MCP, set up via the agent plugin with a single command, bridges Sentry’s data into coding agents like Claude Code and Cursor, enabling them to invoke Seer’s full analysis. The new Sentry CLI provides natural-language access to Seer’s diagnosis and issue management directly from the terminal, using selectors like @latest and @most_frequent. These tools are complementary: Seer provides deep diagnosis, while the MCP and CLI deliver that context into the environments developers already use. Teams can automate the entire workflow so Seer diagnoses, an agent fixes, and a draft PR awaits review without human involvement until approval, with Seer triggering on issues meeting event count and fixability thresholds.
Lakebase Postgres branch-based restores for fast recovery at scale
Traditional Postgres point-in-time recovery is slow because compute and storage are coupled, requiring provisioning a new instance, restoring a snapshot from object storage to disk, and replaying WAL, leading to hours of downtime for large databases. In a survey of developers running 1TB+ production Postgres, 59% had a critical production failure and 30% were down for over three hours. Lakebase Postgres decouples compute and storage using safekeepers, a pageserver, and object storage, storing database history as an immutable timeline. A restore becomes a branch at a point in history, a metadata operation that does not copy data, making it near-instant regardless of database size. This eliminates data copies and WAL replay, so restoring a 100 TB database is as fast as restoring 10 GB, and agents can use restores as ordinary tool calls for versioning and undo.
Build Local AI Apps with C++ and NVIDIA TensorRT RTX Samples
DIN Deploy is an open-source collection of C++ samples combining ONNX Runtime with NVIDIA TensorRT RTX for local AI inference on Windows and Linux. It separates model conversion from deployment: Python exports models to ONNX, then native C++ runs inference via ONNX Runtime session and tensor APIs. Supported workloads include automatic speech recognition (Whisper offline, Parakeet TDT streaming), interactive segmentation (SAM 2.1), and prompt-driven image generation (FLUX.2-klein-4B). The FLUX.2 sample includes Vulkan and DirectX graphics interop and demonstrates post-training quantization with NVIDIA Model Optimizer, producing quantized ONNX models as drop-in replacements without code changes. On DGX Spark, GPU acceleration provides speedups: Whisper large-v3-turbo achieves 58.5x real-time factor, Parakeet TDT 206.41x, and SAM 2.1 runs at 38.3 FPS on GPU versus 0.5 FPS on CPU.
Connecting customer context to measurable ROI with agentic marketing
Customer journeys have become non-linear while identity signals decay, and marketing value is shifting from reach frequency to a prediction economy that rewards anticipating needs. AI agents can recommend or take the next-best action for each customer within human-defined guardrails, with each action measured incrementally against what would have happened without it. Identity resolution must be continuous, connecting known and pseudonymous signals across channels, to provide reliable context for agent decisions. The context required includes customer behavior, business objectives, decision history, and control guardrails, with a single transaction carrying up to 50 metadata parameters. A practical starting point is focusing on one meaningful decision, such as reactivating lapsed customers, and expanding as trust and results build.
A look back before we look forward: A Developer Survey retrospective
A retrospective analysis of the Stack Overflow Developer Survey compares 2024 and 2025 data across AI adoption, work settings, and demographic shifts. AI tool usage among developers rose from 62% to 79% year-over-year, while agent adoption jumped from 31% in 2025 to 59% in an April 2026 pulse survey, with ChatGPT and GitHub Copilot as primary gateways. Enthusiasm for AI tools cooled: favorable sentiment fell from 72.0% to 59.7% overall and from 72.2% to 52.8% among learners, and trust in accuracy declined from 49.1% to 37.4% for students, while skepticism among learners quadrupled from 6.4% to 26.3%. Fully remote work dropped from 38% to 32.4%, but 82% of respondents had some remote flexibility. The article identifies growing reliance on AI alongside skepticism and frames unanswered questions about learning, trust, and job satisfaction for the upcoming 2026 survey.
Master your skills: Building skills you can trust
Skills in AI agent projects silently misfire when their trigger descriptions are passive or too general, causing the agent to skip using them. The authoring tool skill-creator, from Anthropic’s skills collection, provides an eval loop that generates positive and negative test prompts and iterates on description accuracy using a training and held-out test set to avoid overfitting. These tests are ephemeral, so the open-source framework promptfoo extends them into permanent configuration files that can be committed alongside skills. Promptfoo’s agent SDK providers test through the actual agent runtime and include a skill-used assertion to confirm proper routing, and its YAML-based configuration fits naturally into version control. The final step is wiring promptfoo evals into CI, using GitHub Actions with path filters to run on pull requests that modify skills, catching regressions before they ship and costing roughly $0.10-0.15 per eval.
RustRover vs VS Code + rust-analyzer: What Changes in Your Rust Workflow
This post compares RustRover and VS Code with the rust-analyzer extension for Rust development, detailing their architectural differences. VS Code, a general-purpose editor, relies on the rust-analyzer language server to provide completion, diagnostics, inlay hints, navigation, quick-fixes, and refactoring. RustRover is a dedicated professional IDE using JetBrains’ own analysis engine that builds a model of crates, dependencies, targets, and code structure from the Cargo workspace, with built-in debugging, testing, and version control. Key differences include RustRover’s integrated debugger versus VS Code’s need for an extension like CodeLLDB, and RustRover’s project-wide refactoring with affected-usage review. AI assistant capabilities also differ, with RustRover offering agent-driven tasks through Air and JetBrains AI while VS Code supports inline suggestions and agent providers like Copilot. The post offers a five-step migration guide for VS Code users and concludes the choice depends on whether one prefers a modular editor or an integrated IDE experience.
ShopGym: Realistic, reproducible sandboxes for shopping agents (2026) - Shopify
ShopGym converts live storefronts into self-contained, resettable sandbox shops for training and evaluating shopping agents, addressing challenges from changing products and bot detection. Its ShopArena layer separates exploration from generation using anonymized specifications and coding agents with execution-verification loops that repair failures without regenerating the whole shop. ShopGuru then synthesizes benchmark tasks across seven skill categories using rule-based and LLM generators grounded in each shop’s catalog, navigation, and policies. Validation over 224 tasks across six shops, three synthetic and three real, showed synthetic shops retain key structural properties and agent performance positively correlates with live storefronts when tested with BrowserGym and an internal system. The framework aims to provide reproducible environments for agents to fail and learn before interacting with live stores, with plans to expand to more retail domains and task types.
Read Restrictions and Catalog Labels: Unifying governance across engines and catalogs
The Apache Iceberg community has adopted two additions to the Iceberg REST Catalog — read restrictions and catalog labels — each addressing distinct governance challenges. Read restrictions standardize delegated enforcement: a catalog evaluates policy for a principal and returns column-projection actions and row-filter expressions from a bounded vocabulary for a trusted engine to enforce. Catalog labels enable portable governance by allowing catalogs to exchange opaque key-value metadata at table and column level, so a consuming catalog applies native policies without per-user calls to the source. Read restrictions suit engine-to-catalog access where the source catalog trusts the engine and policies fit the standard vocabulary, whereas catalog labels fit federation between heterogeneous catalogs needing scalable governance and independent audit trails. These building blocks, along with centralized enforcement via cross-engine ABAC, advance the vision of unified governance across the open lakehouse.
Getting ready for 2026 results: A look back on Developer Survey findings
Drawing on 15 years of data, the post compares 2024 and 2025 Developer Survey findings across AI evolution, work settings, and community demographics ahead of the 2026 results. AI tool usage grew from 62% in 2024 to 79% in 2025, while agent adoption jumped from 31% in 2025 to 59% in an April 2026 pulse survey, with ChatGPT and GitHub Copilot as primary gateways. Among learners, AI tool use rose from 63% to 73.3%, but favorable sentiment dropped from 72.2% to 52.8% and confidence in output accuracy declined, signaling growing skepticism. Work settings evolved, with fully remote falling from 38% to 32.4% and hybrid models gaining ground, yet 82% of respondents retained some remote flexibility in 2025. The post raises open questions for the 2026 survey, including whether the gap between AI use and enthusiasm persists and how agent delegation affects developer workflows.
How Deterministic Controls Turn AI Output Into Reliable Prompt Templates
Salesforce’s Assistive Authoring aimed to reduce prompt-template creation from 25 minutes to under a minute by using Agent Graph. A router node dispatches natural-language requests to specialized sub-agents for creation, refinement, or clarification while state variables carry context. Deterministic controls prevent the LLM from corrupting routing, record identity, or structured output through guarded transitions and a server-side transpose layer. A discovery service normalizes available data fields into an allowlist, and the LLM can only select from validated references. A human-in-the-loop gate ensures the agent never saves or executes, presenting output as an accept-or-reject diff only.
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