---
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

### [Software Quality Assurance Tools and Tips for Developers](https://yomu.fyi/post/software-quality-assurance-tools-and-tips-for-developers.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Kerry Beetge
- Published: Sep 29, 2026
- Type: Explainer

Software quality assurance (SQA) has evolved from a final gate to a continuous process integrated throughout the SDLC. The 2025 Stack Overflow Developer Survey shows 84% of developers use AI tools, increasing the need for tighter quality reviews. Shift-left analysis moves checks earlier, catching defects when cheapest and easiest to fix, and poor software quality costs the US at least $2.41 trillion annually. Essential SQA tools include static code analyzers, unit testing frameworks, integration testing tools, performance testers, and security scanners, but no single tool covers every aspect, so a layered toolkit is vital. A balance of automated and manual code review remains important, and best practices include automating repetitive checks, integrating testing into CI/CD, monitoring technical debt, and treating security as integral.


### [ClickHouse expands collaboration with Microsoft, bringing Fabric integration, deeper OneLake interoperability, and enterprise deployment flexibility](https://yomu.fyi/post/clickhouse-expands-collaboration-with-microsoft-bringing-fabric-integr.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 29, 2026
- Type: Announcement

ClickHouse announced a significant expansion of its strategic collaboration with Microsoft at The European Microsoft Fabric + SQL Community Conference, including the public preview of a native ClickHouse workload for Microsoft Fabric that brings sub-second analytical query latency to OneLake. The company also made generally available the ability to read Apache Iceberg tables from OneLake using OneLake Table APIs, and introduced a public preview of writing Iceberg tables back to OneLake. These capabilities eliminate data duplication and ingestion steps, allowing teams to keep OneLake as the governed single source of truth while leveraging ClickHouse’s speed. ClickHouse Bring Your Own Cloud (BYOC) is now available in the Microsoft Marketplace, enabling customers to run ClickHouse Cloud inside their own Azure tenant with full control over IAM, networking, and encryption. The integrations aim to serve real-time agentic workloads, interactive dashboards, and observability without separate cluster provisioning or ETL pipelines.


### [ClickHouse Workload for Microsoft Fabric: sub-second analytics on OneLake, now in public preview](https://yomu.fyi/post/clickhouse-workload-for-microsoft-fabric-sub-second-analytics-on-onela.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 29, 2026
- Type: Announcement

The ClickHouse workload for Microsoft Fabric brings ClickHouse as a native acceleration layer for OneLake, now in public preview. It provisions dedicated ClickHouse Cloud services on Azure via the Fabric Extensibility Toolkit, using a ClickHouse item in the workspace. Users sync OneLake tables through ClickPipes as point-in-time snapshots with no ingestion charges. The embedded SQL console and external tools (Power BI, Copilot Studio, GitHub Copilot) can then query billions of rows with sub-second response times at high concurrency. Planned future features include write-back to OneLake, continuous sync, permission sync, and deeper Power BI integration.


### [ClickHouse now writes Apache Iceberg tables to Microsoft OneLake](https://yomu.fyi/post/clickhouse-now-writes-apache-iceberg-tables-to-microsoft-onelake.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 29, 2026
- Type: Announcement

ClickHouse previously wrote Iceberg tables to object storage without catalog registration, limiting their discoverability. Now it writes Iceberg tables directly to Microsoft OneLake, registering in the catalog via Iceberg REST Table APIs and securing with OneLake Security. Use cases include AI agents monitoring order data and publishing real-time anomaly detection results to OneLake, and applications writing directly via ClickHouse without a separate ingestion layer. The connection uses the DataLakeCatalog engine with catalog\_type=‘onelake’, warehouse parameter combining workspace and lakehouse IDs, and Microsoft Entra ID service principal authentication; writing uses standard INSERT INTO syntax with backtick-quoted namespace.table names. OneLake automatically syncs Iceberg and Delta Lake formats, provides governance with lineage tracking and data protection, and its security APIs are opening to third-party engines; this is the first catalog to support writes from ClickHouse, with more catalogs planned.


### [Big improvements to Seer Agent](https://yomu.fyi/post/big-improvements-to-seer-agent.md)
- Company: [Sentry](https://yomu.fyi/company/sentry.md)
- Author: Peter McCarron
- Published: Sep 29, 2026
- Type: Announcement

Seer Agent, Sentry’s AI debugging assistant, now performs write actions through the Sentry API, such as setting up custom dashboards, creating monitors and alerts, and assigning, resolving, and archiving issues. It defaults to the user’s permission level and explicitly requests permission for tasks outside its write scope, with permissions not persisting beyond a single chat. The agent can also incorporate infrastructure data from external monitoring platforms like Datadog and Google Cloud Platform via Seer Connectors, using API keys instead of OAuth. This allows Seer Agent to diagnose issues by connecting front-end and back-end data beyond Sentry’s native scope. These updates evolve Seer Agent from a conversational rubber duck into a proactive debugging assistant that can act on behalf of users.


### [How to Augment Voice Calls with Twilio Intelligence](https://yomu.fyi/post/how-to-augment-voice-calls-with-twilio-intelligence.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Matthew Setter
- Published: Sep 29, 2026
- Type: Tutorial

This tutorial shows PHP developers how to augment a Twilio voice AI agent with Conversation Intelligence, Conversation Orchestrator, and Conversation Memory. The goal is to retrieve a short summary of each customer call, analyze the caller’s sentiment, and check whether the agent followed set guidelines. The information is persisted to a SQLite database for later use. The tutorial builds on a previous voice agent tutorial and requires a Twilio account, a voice-capable phone number, and ngrok to expose the local server. It walks through setting up Conversation Orchestrator, Conversation Intelligence, and the application database, then testing the flow with example questions. The post also mentions the Conversations API (V2) for retrieving call summaries and sentiment analysis.


### [86% of brands think their AI works. Only 51% of customers agree.](https://yomu.fyi/post/86-of-brands-think-their-ai-works-only-51-of-customers-agree.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Sep 29, 2026
- Type: Problem & solution

A Twilio research report identifies a 35-point satisfaction gap: 86% of brands believe their AI customer service satisfies customers, but only 51% of consumers agree. The discrepancy arises because companies measure metrics such as conversation completion surveys and containment rates, which systematically exclude abandoned or unresolved interactions. 71% of consumers abandon a conversation when the AI fails to recognize them, yet those exits rarely appear in satisfaction data. 49% of consumers say AI never resolved their issue. To close the gap, Twilio recommends tracking abandonment by conversation stage, resolution of the underlying issue across sessions, repeat contact within seven days, containment split by outcome, and escalation quality.


### [Integrating Claude Code with Auth0 APIs](https://yomu.fyi/post/integrating-claude-code-with-auth0-apis.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Manish Hatwalne
- Published: Sep 29, 2026
- Type: Tutorial

This tutorial explains how to integrate Claude Code with Auth0’s Management API using the Auth0 MCP Server, enabling AI-driven authentication configuration management from the terminal. The setup uses the OAuth 2.0 Device Authorization Flow to obtain scoped access tokens stored in the OS keychain, avoiding static secrets that risk exposure in source control. Developers initialize the MCP server with explicit scopes like read:clients or update:actions, then register it with Claude Code via the claude mcp add command, with credentials never stored in plain text. A CLAUDE.md file provides project context and constraints, guiding the agent to respect environment-specific configurations and security policies such as never modifying production callback URLs without confirmation. The tutorial demonstrates refactoring JWT validation code in a single terminal session, with human-in-the-loop confirmation ensuring safe agentic changes and all auth-critical paths undergoing manual PR review.


### [The making of the Yelp Assistant UI](https://yomu.fyi/post/the-making-of-the-yelp-assistant-ui.md)
- Company: [Yelp](https://yomu.fyi/company/yelp.md)
- Author: Daniel Andrade Groppe, Services Tech Lead
- Published: Sep 29, 2026
- Type: Explainer

Yelp Assistant is the first large-scale consumer LLM product at Yelp, connecting users with local professionals via chat. To iterate quickly across iOS, Android, and web while avoiding long app-store update cycles, the team chose a hybrid architecture: the top bar and input bar are native, while the chat content area is server-driven using Yelp’s CHAOS framework. The view and data are separated — the client fetches a CHAOS view template once, then conversation data hydrates that template on each turn. The backend sends MessageEvent objects, quick replies, and client actions (such as DisableChat) to control the UI. This hybrid approach enabled rapid iteration, but required building new CHAOS components for text input and network requests, and posed cross-platform consistency challenges.


### [Introducing IdentiFlows.dev: A Visual Reference for Identity Flows](https://yomu.fyi/post/introducing-identiflows-dev-a-visual-reference-for-identity-flows.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Andrea Chiarelli
- Published: Sep 29, 2026
- Type: Announcement

Developers often struggle to quickly find accurate sequence diagrams for OAuth 2.0 and OpenID Connect flows. IdentiFlows.dev is a new website that provides a visual reference for common identity protocols, organized into four categories. Each flow page includes an introduction explaining the flow’s purpose, a sequence diagram showing the full exchange, and a step-by-step breakdown of each stage, with an interactive mode to walk through the steps. The initial release covers eight flows, including OAuth 2.0 Authorization Code with PKCE, the recommended login flow for most modern apps, SAML 2.0 SP-Initiated SSO, DPoP-Bound Access Token requests, and OpenID Connect Discovery. The project is an early release with plans to add more flows like Client Credentials, Device Authorization, and WebAuthn, and the author invites feedback to guide development.


### [The Intelligent Data Platform for the AI Era](https://yomu.fyi/post/the-intelligent-data-platform-for-the-ai-era.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 29, 2026
- Type: Announcement

MongoDB announced the largest platform expansion yet, including MongoDB 9.0, Atlas Infinite, and Atlas Agent Engine. MongoDB 9.0 delivers up to 2x higher throughput, 35% faster reads, and 30% faster updates over version 8.0. Atlas Infinite is a new deployment option that allows storage and compute to scale independently, with time to scale more than 96% faster and each shard holding 10x more storage. Atlas Agent Engine provides built-in memory, retrieval, identity, and governance for production agents, operating on the same data platform as operational data. The company argues that as AI moves from generating answers to taking actions, the data layer must provide live, fresh, and retrievable operational data rather than separate AI infrastructure.


### [Your Database Just Became Your AI Control Point](https://yomu.fyi/post/your-database-just-became-your-ai-control-point.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 29, 2026
- Type: Explainer

Agentic AI transforms the database from a passive record store into an active control point that serves real-time context and enforces policy at the moment an agent acts. According to IDC’s April 2026 survey of 715 organizations, 30% already run agentic AI in production and another 50% have established spending plans. Real-time data delivery is now baseline: 64.6% of enterprise use cases require real-time or near-real-time delivery, and 91.2% of streaming scenarios need end-to-end latency under 200 milliseconds. IDC identifies four requirements for agentic AI: a real-time operational foundation for unstructured data, elastic scale for bursty agent traffic, accurate always-current retrieval via integrated vector databases, and a governed runtime with durable memory and identity-bound policy. By 2029, IDC predicts 60% of enterprise data platforms will unify transactional and analytical workloads in hybrid processing architectures to power agentic AI.


### [Manufacturing data and AI: Connecting the product value chain](https://yomu.fyi/post/manufacturing-data-and-ai-connecting-the-product-value-chain.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Dr. Philip Laserstein, Dr. Max Köhler
- Published: Sep 28, 2026
- Type: Explainer

Manufacturing data is fragmented across systems such as MES, SCADA, QMS, ERP, and PLM, so answering a cross-stage question like whether a scrap spike stems from a supplier lot, machine setup, or process drift requires manual tickets, exports, and reconciliation. Databricks connects the product value chain by pooling or federating these sources using Lakehouse Federation and zero-copy Open Sharing, with Unity Catalog governing both mirrored and federated data under a single permission model and lineage. Lakeflow orchestrates the refinement of raw data into trusted Bronze, Silver, and Gold tables, while Genie and Agent Bricks enable natural-language queries grounded in governed business semantics. A quality engineer can thus ask about a defect’s history across plants and get answers that join MES, supplier, and QMS records without migrating every system. The result is that traceability becomes a query, transforming weeks of manual investigation into minutes of analysis across the entire value chain.


### [Lakebase Search: State-of-the-art full text and vector search for Postgres](https://yomu.fyi/post/lakebase-search-state-of-the-art-full-text-and-vector-search-for-postg.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Zhou Sun, Jinjing Zhou, Keming Yang, Usamoi Cui, Pranav Aurora
- Published: Sep 28, 2026
- Type: Announcement

Traditional OLTP systems like Postgres face challenges meeting the low-latency, high-accuracy search demands of AI agents, often requiring standalone search engines with ETL pipelines. Lakebase introduces lakebase\_vector and lakebase\_text, two extensions that bring scalable approximate nearest neighbor search and BM25 full-text search directly into Lakebase Postgres. lakebase\_vector overcomes pgvector’s memory-bound HNSW index by using hierarchical IVF clustering and binary quantization, achieving 71-millisecond P99 latency at 97 percent recall on the 100-million-vector LAION benchmark. The extensions decouple storage from compute for pay-per-use scaling, enable fast parallel index builds, and support native hybrid search that fuses vector similarity with keyword relevance in a single SQL query. Early adopters like Conexiom reduced infrastructure costs by three times while achieving five times higher throughput, and the service is generally available on AWS and Azure.


### [How Databricks rolls out frontier models to 14,000 employees on Day 1](https://yomu.fyi/post/how-databricks-rolls-out-frontier-models-to-14-000-employees-on-day-1.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: The Databricks AI Product and Engineering Team
- Published: Sep 28, 2026
- Type: Problem & solution

Databricks provides employees Day 1 access to new AI models using Unity Gateway for governance and cost management. The company uses a three-step pipeline: immediate experimental availability, per-user budget constraints, and data-driven promotion decisions. Unity Gateway CLI distributes model configurations to employee laptops, tagging new models as experimental. Per-user budgets include a monthly maximum, daily runaway limit, quality frontier budget, and experimental budget to control costs. Within three days of the Opus 5.5 and GPT-6 Sol launches, Databricks gathered benchmark, user feedback, and cost data to confirm they were on the efficiency frontier and promoted them, with Opus 5.5 showing a 29% cost reduction per session over Opus 4.8 and GPT-6 Sol showing a 48% reduction over GPT-5.6 Sol.


### [Engineering Multi-Agent AI Teams That Build and Test Themselves](https://yomu.fyi/post/engineering-multi-agent-ai-teams-that-build-and-test-themselves.md)
- Company: [Salesforce](https://yomu.fyi/company/salesforce.md)
- Author: Scott Nyberg
- Published: Sep 28, 2026
- Type: Problem & solution

Salesforce Marketing Cloud’s AI Delivery Lead Sohini Arya and team built Agent Designer, a system that automates the creation and testing of multi-agent AI teams. The orchestrator cannot write agent files itself; instead, seven specialist agents analyze architecture, failure modes, and compliance before the orchestrator synthesizes findings for human review. The system transforms an engineer’s request into a tested multi-agent team in 15-30 minutes, down from 2-4 hours manually, and helped approximately 200 engineers adopt the manager-of-agents model. Governance challenges included enforcing write-scope boundaries via prompts, reconciling independently produced design conflicts, and applying topology-specific budget formulas that account for flat, swarm, or team structures. Verifier agents return structured PASS/WARN/FAIL blocks, and self-healing is limited to two fix attempts and a $3 ceiling to prevent runaway repairs, with the orchestrator’s 500-turn budget divided across analysis, approval, generation, testing, and retries.


### [Why model versioning is not enough for production AI](https://yomu.fyi/post/why-model-versioning-is-not-enough-for-production-ai.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Silu Panda
- Published: Sep 28, 2026
- Type: Problem & solution

Model versioning alone is insufficient for production AI because changes in inputs, preprocessing, prompts, retrieval, tool contracts, and serving settings can alter behavior independently. The post proposes a versioned release manifest that captures all interdependent components, including model revision, prompt revision, retrieval index revision, embedding revision, pipeline revision, and runtime revision covering token limits, batching, and timeouts. This manifest enables atomic release testing and rollback, with an evaluation gate that tests the full retrieval-generation path using deterministic checks and semantic rubrics on a versioned dataset. Practical steps include canary deployments that compare candidate and control signals, with explicit stop conditions and rollback procedures that restore compatible dependencies rather than just older model weights. The post concludes that the smallest useful implementation is a manifest, evaluation job, load test, release-aware traces, and a rehearsed rollback, and asks whether the on-call engineer can identify and restore a compatible known-good version.


### [How to roll out Genie One: A step-by-step enterprise playbook](https://yomu.fyi/post/how-to-roll-out-genie-one-a-step-by-step-enterprise-playbook.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Josh Rosenberg
- Published: Sep 28, 2026
- Type: Tutorial

Genie One is an AI coworker that lets business users ask questions in plain language and receive cited answers grounded in governed data. The post outlines a phased rollout playbook: Phase 0 foundations include choosing one team, defining metric views for 10-20 numbers, creating Pages for concepts, and establishing governance with Unity Catalog. Phase 1 runs a 4-6 week pilot with 5-10 users, scoring Genie against 25-50 real questions and logging every incorrect answer to fix root causes, with exit criteria including accuracy holding steady and users reaching for Genie over their old workflow. Phase 2 expands to 2-3 adjacent domains with shared ontology definitions and a lightweight review workflow. Phase 3 aims for organization-wide adoption with federated ownership, MCP server governance, and embedding Genie in Slack, Teams, Excel, or Google Sheets, with a realistic timeline of 1-2 weeks for foundations to 1-2 quarters for full rollout.


### [Is Your “Human-in-the-Loop” Actually Slowing You Down? Here’s What We Learned](https://yomu.fyi/post/is-your-human-in-the-loop-actually-slowing-you-down-here-s-what-we-lea.md)
- Company: [Stack Overflow](https://yomu.fyi/company/stack-overflow.md)
- Author: Kavin Xavier
- Published: Sep 28, 2026
- Type: Problem & solution

Human-in-the-loop (HITL) frameworks integrate human judgment into automated AI workflows to improve reliability and safety, but without careful design they can create bottlenecks. The post reveals key trade-offs: more automation increases speed and reduces cost but raises risk, while more human oversight boosts accuracy and safety but adds latency and cost. Common failure modes include over-reviewing trivial cases, lacking clear trigger logic, and expecting human reviewers to scale linearly. The proposed solution is a tiered HITL system applying the Pareto principle: automation handles 85% of high-confidence decisions using confidence calibration, with three tiers addressing the remaining cases under 3 ms latency, asynchronous review within hours, and real-time oversight with 30-60 second windows. Success requires careful routing, reviewer tooling, and closing feedback loops across seconds, days, weeks, and quarterly cycles to continuously improve model performance and reduce human involvement over time.


### [Can your Postgres survive a bad query?](https://yomu.fyi/post/can-your-postgres-survive-a-bad-query.md)
- Company: [Clickhouse](https://yomu.fyi/company/clickhouse.md)
- Published: Sep 28, 2026
- Type: Benchmark

Postgres memory tuning is difficult because work\_mem applies per operation, not per query, and parallel workers multiply memory usage without a query-wide cap. Some executor structures, such as those in recursive queries, have no disk-backed fallback and can grow until the backend runs out of memory. A benchmark using a recursive query that exhausts memory tested ClickHouse Managed Postgres, Cloud SQL, PlanetScale, and Amazon RDS on 16 GiB instances. ClickHouse Managed Postgres enforces a memory ceiling at 57% of host RAM, rejecting queries that exceed it, while RDS allows backends to consume nearly all RAM until the host crashes. Cloud SQL and PlanetScale enforce similar caps, but ClickHouse Managed Postgres’s ceiling is lower, a deliberate tradeoff that returns clean query failures instead of forcing every client through crash recovery.


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