---
title: "Latest reads"
description: "The engineering internet, summarised so you can actually read it."
---

# Latest reads
> The engineering internet, summarised so you can actually read it.

## Articles

### [Your diff has a demo now with Junie /demo](https://yomu.fyi/post/your-diff-has-a-demo-now-with-junie-demo.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Aliaksei Kartynnik
- Published: Sep 18, 2026
- Type: Tutorial

Junie /demo adds a mode to Junie CLI that builds and launches an application, drives its UI, and records a requested check. It produces an HTML report, screenshots, and video, while leaving reviewers able to inspect steps, outcomes, and evidence. In the example, a prompt selects two issues, marks them Done, checks counter changes, reloads the page, and verifies persistence. Initial setup analyzes the repository and generates configuration after a confirmed build and launch plan; Docker-based environments can use custom templates for complex repositories. The mode also supports GitHub Actions workflows for pull-request demonstrations and release smoke tests, but an explicit PASS verdict is required for a scenario to pass.


### [Ktor 3.6.0 Is Now Available!](https://yomu.fyi/post/ktor-3-6-0-is-now-available.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Simon Vergauwen
- Published: Sep 18, 2026
- Type: Announcement

Ktor 3.6.0 introduces experimental typed authentication with full type safety, including role-based access and anonymous users, plus a dedicated OpenID Connect plugin that supports sessions and auto-refreshing tokens, and experimental HTTP/3 support for the Netty engine via QUIC. Request-parameter conversion now supports Kotlin’s Uuid, Byte, and unsigned numeric types, while receive() accepts nullable types, deprecating receiveNullable(), and more flexible routing syntax. The ContentNegotiation plugin gains a ContentTypeMergeStrategy.SkipIfPresent to preserve explicit Accept headers. Kotlin Multiplatform projects benefit from the ktor-client-engine-defaults artifact and file-based HttpCache storage using kotlinx-io Path, simplifying cross-platform configuration. Additional improvements include respondHtmlPartial for partial HTML output, asynchronous DNS resolution for the CIO engine, and WebRTC support for JVM.


### [Classic Engine Feature EOL: SSR, Desktop SSO, Device Trust](https://yomu.fyi/post/classic-engine-feature-eol-ssr-desktop-sso-device-trust.md)
- Company: [Okta](https://yomu.fyi/company/okta.md)
- Author: Vanshika Pandey
- Published: Sep 18, 2026
- Type: Announcement

Okta will end support for Self-Service Registration, Desktop Single Sign-On using Integrated Windows Authentication (IWA), and Device Trust on Classic Engine on March 5, 2027. After that deadline, the capabilities will be turned off unless organizations upgrade to Okta Identity Engine, and Okta will no longer address related bugs, security vulnerabilities, or broken flows. The migration paths are Profile Enrollment Policy for self-service registration, Agentless Desktop Single Sign-On or Okta FastPass for desktop authentication, and management attestation with Okta Verify for mobile device trust. Identity Engine adds passwordless authentication, device assurance, phishing-resistant multifactor authentication, identity verification, zero-trust capabilities, and a newer policy framework, with the upgrade included at no additional licensing cost. Organizations are advised to assess feature usage, test in non-production, and schedule production migration before the hard deadline.


### [Modernizing the Trade Lifecycle With Governed Data and AI](https://yomu.fyi/post/modernizing-the-trade-lifecycle-with-governed-data-and-ai.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Kim Hatton, Andrea DeSosa
- Published: Sep 18, 2026
- Type: Explainer

Capital-markets firms are revisiting the trade lifecycle as data volumes grow, AI moves toward production, settlement cycles shorten, and scrutiny increases. Fragmented data across research, trading, risk, operations, and surveillance can slow decision-making, complicate execution analysis, limit risk visibility, and hinder reconstruction of events. The proposed response starts with decisions such as execution-cost divergence, shock impacts, and exception or settlement-fail rates, then identifies the data domains needed for a few high-value workflows rather than migrating everything at once. It emphasizes a governed, reusable foundation connecting real-time and historical data, analytics, and AI, with access controls, lineage, traceability, and evaluation. Databricks components named include Unity Catalog for governance, discovery, and lineage, Agent Bricks for domain-specific agents, and AI/BI for governed exploration; the stated conclusion is that this operating-model approach can support faster decisions, resilience, and scalable use cases.


### [Database for AI Agents: 5 Evaluation Criteria](https://yomu.fyi/post/database-for-ai-agents-5-evaluation-criteria.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

The post defines a database for AI agents as infrastructure for short-term, episodic, procedural, and operational memory, rather than merely a place to retrieve documents. It proposes five evaluation criteria for moving agent workloads from prototypes to production: per-agent branch isolation, serverless scale-to-zero, hybrid search, ACID guarantees, and unified platform access. The technical rationale is that agents perform repeated reads and writes, resume tasks, share state concurrently, and need current data across vector, keyword, and metadata retrieval; zero-copy branches, sub-second wakeups, and single-query hybrid search address specific operational constraints. It concludes that a database missing more than one minimum bar becomes a production risk, while standard Postgres alone supplies ACID but not branching, scale-to-zero, or unified operational and analytical access.


### [Building a knowledge assistant over code](https://yomu.fyi/post/building-a-knowledge-assistant-over-code.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Daniel Liden
- Published: Sep 18, 2026
- Type: Benchmark

Building a knowledge assistant over code requires more careful chunking than indexing ordinary business documents, because code has nested structure, cross-file dependencies, and multiple file formats. The comparison built three Databricks Knowledge Assistants over the Casper’s Kitchens repository: fixed-size character chunks, LangChain language-aware splitting, and AST-based splitting with structure and metadata headers. The AST approach used ASTChunk to preserve units such as functions, classes, or statement blocks and prepend file paths plus class/function hierarchies to chunks. MLflow GenAI evaluation ran 46 questions with retrieval sufficiency, groundedness, and custom answer-correctness judges; AST chunking achieved 89% retrieval sufficiency and 70% fully correct answers, versus 85% and 59% for the naive baseline. The advantage was strongest for disambiguating structurally similar code, while value lookups and complete definition retrieval were comparable across methods.


### [Google Cloud Use Cases: How Enterprises Run Data & AI on GCP with Databricks](https://yomu.fyi/post/google-cloud-use-cases-how-enterprises-run-data-ai-on-gcp-with-databri.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

The guide presents Google Cloud as a unified platform for enterprise data engineering, analytics, machine learning, and generative AI, with managed services intended to reduce infrastructure and integration work. It focuses on practical architectures, including medallion ETL pipelines that store Bronze, Silver, and Gold Delta Lake tables in Google Cloud Storage and expose Gold data to BigQuery through an optimized connector. For streaming workloads, Cloud Pub/Sub supplies ingestion while Apache Spark Structured Streaming processes continuous micro-batches; together with Delta Lake ACID transactions, this supports exactly-once results despite duplicates or out-of-order messages. The text also describes Cloud Run and Cloud Functions for containerized or event-driven workloads, and identifies retrieval-augmented generation applications on Vertex AI as a common starting point for generative AI. It concludes that integrated service combinations, rather than isolated tools, are the central strategic advantage for organizations building evolving data and AI platforms on Google Cloud.


### [Databricks recognized as a Gartner® Peer Insights™ Customers' Choice for Analytics and BI](https://yomu.fyi/post/databricks-recognized-as-a-gartner-r-peer-insightstm-customers-choice.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Team Databricks
- Published: Sep 18, 2026
- Type: Announcement

Databricks says it was recognized as a Customers’ Choice in Gartner Peer Insights’ Voice of the Customer for Analytics and Business Intelligence Platforms, based on verified reviews from customers using platforms in production. The post reports a 4.8 out of 5 overall rating and 94% willingness to recommend, while noting that the designation reflects customer adoption and overall experience meeting or exceeding market averages. It presents Databricks AI/BI, AI/BI Dashboards, Genie Spaces, and Genie as a unified architecture combining governed live data, AI, business intelligence, and Unity Catalog controls without moving or duplicating datasets. The described use cases include conversational exploration, dashboard and visualization development, consolidating fragmented reporting, and expanding governed analytics access, with the company positioning these capabilities as a foundation for agentic analytics.


### [How Databricks Helps Baseball Teams Gain an Edge with Data & AI](https://yomu.fyi/post/how-databricks-helps-baseball-teams-gain-an-edge-with-data-ai.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Nick Ragonese, Wesley Pasfield
- Published: Sep 18, 2026
- Type: Explainer

Baseball teams can use Databricks to turn Statcast and team-derived pitch-level data into game-day plans for hitters, bullpen changes, pinch-hit choices, advance scouting, and roster decisions. Genie, on Unity Catalog, queries governed Delta tables and reusable Unity Catalog SQL functions for tendencies by count, handedness, pitch type, location, and base-runner state. A Multi-Agent Supervisor built with Agent Bricks and deployed on Model Serving simulates matchup pockets, compares reliever arsenals with hitter profiles, and produces explanations that staff distill into printed decision cards. For advance work, AI Search indexes pitcher and hitter embeddings to find similar arms, while Lakebase Postgres keeps reports, tags, and decisions close to the source data for shared operational memory.


### [What is a Relational Data Model?](https://yomu.fyi/post/what-is-a-relational-data-model.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

The relational data model organizes structured data in tables of rows and columns, with SQL used to access and manage it. Introduced by Edgar F. Codd in 1970, it uses schemas, domains, keys, constraints, and relationships to define and connect data. A relation schema specifies a table's structure, while a relation instance is the data held at a particular moment; primary, candidate, super, foreign, and composite keys support identification and referential integrity, and one-to-one, one-to-many, and many-to-many relationships model associations between tables. Functional dependencies inform normalization, whose 1NF, 2NF, and 3NF stages reduce redundancy and insertion, update, and deletion anomalies, though heavier normalization can require more joins. The model is used in banking, e-commerce, healthcare, education, and enterprise systems, where constraints and ACID transactions help preserve accuracy and reliability.


### [What is Intelligent Document Processing?](https://yomu.fyi/post/what-is-intelligent-document-processing.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

Intelligent document processing (IDP) uses AI to extract, classify, validate, and structure information from PDFs, images, emails, forms, and other structured, semi-structured, or unstructured documents. Its pipeline combines document ingestion and preprocessing with OCR, document classification, NLP, layout analysis, data validation, human review for uncertain results, and delivery into downstream systems such as ERP, CRM, or accounting software. The post explains how embeddings support classification, how continuous learning adapts models to new layouts and corrections, and how metrics such as accuracy, throughput, confidence scores, and model drift support optimization. IDP can reduce manual effort, improve accuracy, lower costs, scale document-heavy workflows, and supply structured data for analytics, automation, AI agents, and retrieval-augmented generation, while high document variability and edge cases still make human-in-the-loop review relevant.


### [What are Analytic Applications?](https://yomu.fyi/post/what-are-analytic-applications.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

Analytic applications are packaged business intelligence systems designed to measure, analyze and improve performance for defined business domains. They combine data integration, modeling, visualization, reporting and workflow support so technical and non-technical users can turn operational data into decisions. Typically, they connect ERP, CRM and supply chain systems with a data warehouse or lakehouse, process historical and real-time data, and deliver dashboards, reports, scorecards or embedded insights through preconfigured schemas, connectors, governance controls and business logic. Unlike general-purpose BI tools built for open-ended exploration, they support standardized, domain-specific workflows such as sales performance management, financial planning, risk assessment and inventory optimization. Organizations can start with descriptive analytics and adopt predictive or prescriptive capabilities as data maturity increases, while choosing standalone or embedded deployment based on workflow needs and data readiness.


### [Building an A/B testing analysis framework for mobile gaming on Databricks](https://yomu.fyi/post/building-an-a-b-testing-analysis-framework-for-mobile-gaming-on-databr.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Sanjay Ashok, Jack Holdsworth, Tingting Wan, Joel Dias, Richard Carr, Monika Kolodziejczyk
- Published: Sep 18, 2026
- Type: Problem & solution

HARDlight needed a more consistent way to analyse mobile game A/B tests as experimentation expanded, because manual workflows and differing statistical approaches slowed decisions and weakened trust. It built a Databricks-native framework that standardises experiment definitions, telemetry, modelling, and governed outputs, with notebooks materialising effect estimates, uncertainties, and segment-level impacts into a unified analytics model. Databricks AI/BI then presents daily refreshed, progressively detailed dashboards, beginning with an LLM-generated summary and extending through KPIs, diagnostics, revenue, engagement, monetisation, and segment analysis. At experiment completion, a frozen snapshot preserves outcomes, decisions, and recommended actions. The framework reduced manual effort by more than eight hours weekly, saves roughly one day per experiment, and enabled a targeted two-times increase in monthly testing capacity without added headcount.


### [Tevogen Bio’s Journey to Streamlining Life](https://yomu.fyi/post/tevogen-bio-s-journey-to-streamlining-life.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Mittul Mehta, Vibhor Nigam
- Published: Sep 18, 2026
- Type: Problem & solution

Tevogen Bio faced drug development costing upwards of $3 billion and requiring 10–12 years; selecting targets for its single-HLA-restricted SARS-COV2 product took 18–24 months of wet-lab work. Tevogen.AI partnered with Microsoft and Databricks to build and curate a multi-terabyte protein-sequence library and parallelize processing for machine-learning training. Databricks Data + AI Platform, Medallion Architecture, Unity Catalog, and distributed computing cut processing from 50 days to 24 hours under governed, fine-grained access controls. The dataset reached 24 million proteins, 16 billion datapoints, ~700 million unique peptides, and ~37 million cross-matched expert articles. An MLOps framework automated training, inference, monitoring, and retention; the alpha PredicTcell model used XGBoost and ESM models and reported 93–97% recall and 38–43% accuracy, while beta PredicTcell and alpha AdapTcell work began.


### [What is a Cloud](https://yomu.fyi/post/what-is-a-cloud.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

A cloud-based database management system stores, manages, and queries data on third-party cloud infrastructure instead of local or on-premises servers. It retains capabilities such as query optimization, security, transactions, and support for relational SQL, NoSQL, data warehouse, and in-memory workloads, while operating across distributed nodes. Organizations can self-manage database software on cloud virtual machines or use DBaaS, where the provider manages the software and infrastructure; connections typically use SQL, APIs, or web consoles. Virtualized resources can scale, and providers may handle patching, backups, maintenance, and failover, while replication across availability zones supports recovery and availability. The comparison with on-premises systems covers ownership, scalability, cost models, maintenance, accessibility, control, compliance, total cost, and emerging capabilities including AI integration, vector search, autonomous operation, multicloud, hybrid, and serverless architectures.


### [Fizz accelerates ecommerce analytics with Databricks SQL](https://yomu.fyi/post/fizz-accelerates-ecommerce-analytics-with-databricks-sql.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Tamás Bácskai, Istvan Viz
- Published: Sep 18, 2026
- Type: Problem & solution

Fizz.hu, an ecommerce marketplace backed by OTP Group, outgrew a Microsoft SQL Server and Power BI setup that relied on daily batch loads and separate virtual-machine Python workloads. The company migrated about 50 tables and several stored procedures to Databricks SQL, recreated core views, and moved to a lakehouse architecture using an MVP-first approach rather than redesigning everything. Completed in three months, the migration switched off SQL Server while keeping Power BI reports running on the new engine. Nightly processing fell from three to four hours to roughly 90 minutes, reports became consistently available by 4:30 a.m., Power BI refreshes dropped about 50%, and gigabyte-scale exports finish in minutes. Databricks now unifies SQL, Python, machine learning, governance work, and Hungarian-language Genie self-service, with about 20 users reclaiming roughly 20% of an analyst’s time.


### [Zero downtime patching lakebase part-1 prewarming](https://yomu.fyi/post/zero-downtime-patching-lakebase-part-1-prewarming.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Hans Norheim
- Published: Sep 18, 2026
- Type: Problem & solution

Lakebase addresses the workload disruption caused by planned PostgreSQL maintenance, where a restart clears in-memory buffer and local file caches. Although the database can return in about one second at P99, the source reports a roughly 70% pgbench TPS reduction while caches warm, potentially causing timeouts. Lakebase uses stateless Postgres compute replicas and disaggregated shared storage to prewarm a replacement compute before a scheduled restart. The control plane transfers cached-page information, loads pages from shared storage, and follows relevant WAL from Safekeepers, while the current primary continues serving traffic. After prewarming, Lakebase promotes the new compute with PostgreSQL’s pg\_promote without restarting the server; tests on 10 GB pgbench workloads found nearly instant throughput recovery, especially for read-only queries.


### [Protect Performance and Reduce Surprise Costs with Default Warehouse](https://yomu.fyi/post/protect-performance-and-reduce-surprise-costs-with-default-warehouse.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: JooHo Yeo, Gabriel Dutra Dias
- Published: Sep 18, 2026
- Type: Announcement

Databricks SQL’s generally available Default Warehouse lets administrators choose the SQL warehouse automatically used for ad hoc experiences, addressing manual selection and fallback behavior such as “Last Selected” or alphabetical ordering. A workspace-level setting applies across SQL Editor, Catalog Explorer, AI/BI Dashboards, Alerts, and Genie Spaces, while users can override it or retain “Last Selected”; admin APIs and a Terraform resource support per-user assignment, auditing, and control. The feature supports serverless, pro, and classic SQL warehouses and respects existing governance and access controls, with the setting opt-in and new assets automatically using it. In customer results, Catalog Explorer queries sent to smaller warehouses rose from 77% to 96%, and users running ad hoc SQL across multiple warehouses fell by 15%, while future coverage is planned for MLflow, Lakeflow CLI, and DBSQL MCP.


### [The next era of the open lakehouse: Apache Iceberg™ v3 in Public Preview on Databricks](https://yomu.fyi/post/the-next-era-of-the-open-lakehouse-apache-icebergtm-v3-in-public-previ.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ryan Blue, Daniel Weeks, Jason Reid, Benjamin Mathew, Hao Jiang
- Published: Sep 18, 2026
- Type: Announcement

Databricks has placed support for Apache Iceberg v3 into Public Preview, bringing new table-format capabilities to its open lakehouse. Unity Catalog Managed Iceberg v3 tables support Row Lineage, Deletion Vectors, and the VARIANT type, while interoperability extends these features to foreign Iceberg tables. Row IDs and modification sequence numbers identify changed rows, and lightweight delete files avoid immediate Parquet rewrites, with data manipulation reported as up to 10x faster than copy-on-write. VARIANT stores evolving semi-structured payloads beside relational columns for direct SQL querying, while Unity Catalog provides cross-catalog governance and Delta-Iceberg interoperability without data duplication. Iceberg v3 is available on Databricks Runtime 18.0+ with Unity Catalog enabled; Databricks is also advancing Iceberg v4 metadata proposals.


### [What is Agentic Analytics?](https://yomu.fyi/post/what-is-agentic-analytics.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

Agentic analytics is an approach in which autonomous AI agents continuously monitor data, detect patterns and anomalies, generate contextual insights, and take actions with minimal human intervention. Unlike traditional business intelligence, which relies on people to query dashboards and interpret reports, these systems operate as goal-directed decision loops that can recommend or trigger alerts, forecasts, workflows, and system changes within defined boundaries. Their architecture combines data access, agent orchestration, LLM-based reasoning, a semantic layer, action integrations, and feedback signals. The source identifies faster insight, lower reporting overhead, reduced analytics backlogs, and potentially better forecasting as benefits, while stressing governance, auditability, security, monitoring, and human oversight to control errors and unintended outcomes.


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