---
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

### [Database Branching in Postgres: Git](https://yomu.fyi/post/database-branching-in-postgres-git.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Susan Pierce
- Published: Sep 18, 2026
- Type: Problem & solution

Shared staging databases often drift as schemas diverge, test data accumulates, and developers avoid creating isolated environments because traditional pg\_dump copies are slow and expensive. Databricks Lakebase Postgres addresses this with database branching, which creates fully isolated environments from a parent’s exact schema and data at a point in time. Branches use copy-on-write: they share underlying versioned storage until a branch changes, then record only new data, while each branch has independent compute and a separate connection string. Idle branches scale compute to zero, and storage cost tracks changes rather than the full database size. This supports per-developer, per-pull-request, per-test-run, and recovery workflows, including point-in-time branching within the restore window, with the source describing branch creation through the console, CLI, or API in under a minute.


### [Memory scaling for AI agents](https://yomu.fyi/post/memory-scaling-for-ai-agents.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: The Databricks AI Research Team
- Published: Sep 18, 2026
- Type: Explainer

Memory scaling is proposed as an agent’s performance improving when it accumulates past conversations, feedback, interaction trajectories, and business context in persistent external memory. Unlike model-parameter or inference-time scaling, it relies on selective retrieval to supply relevant information without filling long context windows. In MemAlign experiments on Databricks Genie Spaces, adding labeled examples raised test scores from near zero to 70%, about 5% above an expert-curated baseline, while average reasoning steps fell from about 20 to 5; filtered user logs produced similarly strong gains, exceeding 50% after 62 records. Production use requires storage and retrieval for episodic and semantic memories with organizational and user-level scoping, plus controls that preserve permissions and sensitivity labels.


### [How a global fintech scaled coding agent traffic with Dedicated Model Inference](https://yomu.fyi/post/how-a-global-fintech-scaled-coding-agent-traffic-with-dedicated-model.md)
- Company: [Together AI](https://yomu.fyi/company/together-ai.md)
- Published: Sep 18, 2026
- Type: Problem & solution

A global fintech runs its coding assistant on GLM 5.2 through Together’s Dedicated Model Inference (DMI), but spiky engineering-hours traffic outpaced static capacity planning. DMI gives the customer’s teams direct control over endpoint provisioning, scaling, configuration, model changes, and performance data through APIs, UI, and CLI. The workload is peak-load and relatively low-TPS, with ISL p50/p90/p95 of roughly 81K/163K/178K tokens and RPS p50/p90/p95 of 3/6/7, so concurrency headroom mattered more than raw throughput; the deployment used dozens of B200s, multiple replicas, and 256K context. When a reshaped endpoint later reached near-100% prefill capacity, requests queued for one to three minutes and decode throughput fell to roughly 5 tokens per second, the team restored cache-session-aware routing and widened max-inflight-per-worker, pushing the change same-day with zero downtime.


### [Building custom financial reconciliation workflows with Stripe](https://yomu.fyi/post/building-custom-financial-reconciliation-workflows-with-stripe.md)
- Company: [Stripe](https://yomu.fyi/company/stripe.md)
- Author: Maia Robinson
- Published: Sep 18, 2026
- Type: Tutorial

The post examines how enterprises can replace SFTP-based financial reconciliation with Stripe’s reporting APIs. Stripe’s Reports API v2 and Query Run API provide SQL-based report customization, consistent schemas, API-key permissioning, audit trails, and event-driven completion notifications instead of fixed files and polling schedules. Using fictional retailer Galtee, the examples run payout queries across multiple Stripe accounts with an Organization secret key, joining transfers with balance\_transactions and using a succeeded webhook to trigger retrieval. Further queries join charges to add presentment amounts and exchange rates, then use aggregation and a common table expression to itemize IC+ fees; the conclusion is that this supports an on-demand pipeline that evolves with business requirements.


### [Auth0 Agent Gateway: The Identity Control Plane for Customer-Facing AI](https://yomu.fyi/post/auth0-agent-gateway-the-identity-control-plane-for-customer-facing-ai.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: Andrés Aguiar
- Published: Sep 18, 2026
- Type: Announcement

Auth0 Agent Gateway is announced in beta as a Customer Identity and Access Management (CIAM) control plane for governing product-native, customer-facing AI agents in multi-tenant SaaS applications. It addresses a trust model involving organizations, delegated user access, customer consent, independently owned connected accounts, and cross-tenant data boundaries, rather than workforce-only identity. Positioned between an agent and its tools, the gateway evaluates each call using agent, user, and organization context; it uses Organizations, Agents as Principals, Token Vault, token exchange, tenant logs, and administrative controls. The described capabilities include short-lived agent-only tokens, user-scoped credentials, runtime tool authorization, audit logging with optional SIEM streaming, and an agent kill switch. Auth0 is seeking product and engineering teams to join the beta.


### [Move and Scale Faster with FGA Permissions Index](https://yomu.fyi/post/move-and-scale-faster-with-fga-permissions-index.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Author: AI Is Reshaping Authorization in the B2B SaaS Landscape
- Published: Sep 18, 2026
- Type: Announcement

Auth0 FGA Permissions Index is announced in Early Access as a way for B2B SaaS applications to enforce permissions while handling expanding AI-agent activity and complex relationship graphs. Customers select relations used for search and filtering, typically high-cardinality relations such as can\_view, and the index precomputes the access those relations imply. It streams the resulting data into the customer’s database alongside application data, allowing indexed permission checks through SQL JOINs instead of repeated authorization API calls or runtime graph traversal. Computation moves to relationship-write time, while relations outside the index continue to be evaluated by FGA in real time. The announcement positions this design as a path to consistently low-latency filtering at enterprise scale, with permission decisions described as precomputed, traceable, and compliant.


### [Optimize Your Identity Flows with Auth0 Identity Conversion Suite](https://yomu.fyi/post/optimize-your-identity-flows-with-auth0-identity-conversion-suite.md)
- Company: [Auth0](https://yomu.fyi/company/auth0.md)
- Published: Sep 18, 2026
- Type: Announcement

Auth0 Identity Conversion Suite addresses identity friction across the visitor-to-user journey, where forced account creation and complicated checkout are cited as contributors to abandonment. It combines Anonymous Sessions, which uses OAuth 2.0 access tokens to track pre-authentication activity and transfer session data into an authenticated profile, with Experiment Center, which supports controlled authentication-flow tests. Visitors can be assigned to percentage- or segment-based variants, kept in the same variant during their journey, and measured through exposure, success-rate, and time-to-auth events across supported Auth0 surfaces. The Early Access release is available to Enterprise-plan Auth0 customers and is presented as a way to improve personalization, onboarding, guest checkout, and authentication decisions without changing application APIs or code.


### [Dynamically Scaled Activation Steering](https://yomu.fyi/post/dynamically-scaled-activation-steering.md)
- Company: [Apple](https://yomu.fyi/company/apple.md)
- Published: Sep 18, 2026
- Type: Explainer

Activation steering has emerged as a way to guide generative models toward desired behaviors, but uniform interventions can reduce performance when steering is unnecessary. DSAS, or Dynamically Scaled Activation Steering, addresses this by separating when to steer from how to steer and adapting intervention strength across layers and inputs. At generation time, it computes context-dependent scaling factors that modify existing steering transformations, and it can also be jointly optimized end-to-end with the steering function. Combined with existing methods, DSAS improves the reported Pareto front between toxicity mitigation and utility preservation, while adding minimal computational overhead; experiments also apply it to a text-to-image diffusion model for modulating specific concepts and show token-level interpretability through the strength and location of steering.


### [coSTAR: How we ship AI agents at Databricks fast, without breaking things](https://yomu.fyi/post/costar-how-we-ship-ai-agents-at-databricks-fast-without-breaking-thing.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Alkis Polyzotis
- Published: Sep 18, 2026
- Type: Explainer

Databricks describes coSTAR, a methodology for testing and iteratively improving long-running, stochastic AI agents without relying on manual review for every change. It addresses nondeterministic outputs, slow executions, cascading failures, and subjective quality by treating scenarios as fixtures, MLflow traces as a reusable harness, and judges as an automated test suite. Its two coupled STAR loops align judges with human experts using a Golden Set, then use those judges to refine the agent until it passes the scenarios. Agentic judges selectively explore traces with tools instead of receiving entire, potentially thousands-line executions, while persisted traces allow rescoring without rerunning agents. The approach can also reuse judges for production monitoring, but remains limited by manually authored scenarios, judge-calibration costs, coverage gaps, overfitting, and difficult root-cause attribution.


### [Business Analytics Tools: A Complete Guide for Data](https://yomu.fyi/post/business-analytics-tools-a-complete-guide-for-data.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

Business analytics tools have evolved beyond static reporting as organizations seek explanations, forecasts, and immediate actions. This guide categorizes visualization and dashboard platforms, self-service analytics, statistical tools, spreadsheets, and SQL-based query tools, then examines how AI adds natural-language querying, anomaly detection, summaries, and forecasting. It argues that inconsistent, duplicated, or differently governed data can undermine even sophisticated dashboards. Lakehouse architecture combines data-lake scalability with warehouse governance, performance, and SQL compatibility, enabling tools such as Tableau, Power BI, and Looker to use broader, fresher datasets alongside analytics and AI workloads. Reported examples include 94% faster BI queries at Anker Innovations, 98% faster query performance at AnyClip, and consolidation across 120+ global analysts at JLL.


### [MLOps Frameworks: A Complete Guide to Tools and Platforms for Production ML](https://yomu.fyi/post/mlops-frameworks-a-complete-guide-to-tools-and-platforms-for-productio.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

The guide explains how MLOps frameworks close the gap between notebook experimentation and reliable, scalable machine-learning production by applying automation, version control, and continuous delivery across the lifecycle. It describes experiment tracking, model registries, workflow orchestration, feature stores, deployment and serving, and monitoring as the capabilities needed to make training reproducible, prevent training-serving skew, automate pipelines, and detect degradation after release. MLflow is presented as a modular open-source option with Tracking, Model Registry, Models, and Projects, supporting run history, lifecycle stages, standardized packaging, and reproducible execution across environments. Kubeflow suits Kubernetes-standardized organizations and large distributed GPU workloads, while Metaflow, cloud-native services, and unified platforms such as Databricks are positioned as alternatives according to developer experience, cloud alignment, or operational preferences.


### [What is Machine Learning? How It Works, Types and Use Cases](https://yomu.fyi/post/what-is-machine-learning-how-it-works-types-and-use-cases.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Databricks Staff
- Published: Sep 18, 2026
- Type: Explainer

Machine learning (ML) is a subset of artificial intelligence that learns statistical patterns from data to make predictions or decisions without hard-coded rules. The guide distinguishes ML from generative AI, which creates new content, and from deep learning, a specialized ML approach using multilayered neural networks that learns features automatically and typically requires more data and computing power. It describes a workflow of preparing and splitting data, training models by comparing predictions with actual outcomes, and adjusting parameters to reduce error, illustrated with image classification and customer-churn scoring. It also covers supervised, unsupervised, and reinforcement learning, applications including computer vision and natural language processing, newer architectures such as transformers and Mamba, and MLOps practices for deployment, monitoring, and retraining.


### [Building the future of security with NAB with Lakewatch](https://yomu.fyi/post/building-the-future-of-security-with-nab-with-lakewatch.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Taylor Kain
- Published: Sep 18, 2026
- Type: Announcement

National Australia Bank (NAB) is working with Databricks as one of five design partners for Lakewatch, an Open Security Lakehouse intended for modern security operations. NAB, part of Australia’s critical infrastructure, ingests more than 30TB of security data daily while confronting AI-enabled threats, expanding digital services, and growing third-party integrations. The partnership brings NAB’s threat-informed cyber-defense experience to a platform designed to unify signals across environments, apply advanced analytics and AI-driven workflows, generate higher-confidence insights, and automate response actions. Lakewatch is presented as an open, composable, AI-native foundation built natively on the Lakehouse for organizations facing constantly evolving threats, supporting enterprise-wide correlation, proactive threat hunting, detection, monitoring, and response at scale.


### [Databricks Announces Lakewatch: New, Agentic SIEM](https://yomu.fyi/post/databricks-announces-lakewatch-new-agentic-siem.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Reynold Xin, Andrew Krioukov, Molly Limaye, Taylor Kain, Keegan Dubbs
- Published: Sep 18, 2026
- Type: Announcement

Databricks announces Lakewatch, an agentic SIEM intended to help organizations defend against increasingly sophisticated, AI-driven attackers. Launching in Private Preview, it unifies security, IT, and business data in a governed lakehouse built on Unity Catalog, while supporting open OCSF formats and storage in customer-controlled cloud object stores. Lakeflow Connect ingests and normalizes major security sources, and Detection-as-Code lets teams define YAML rules with SQL queries or Python notebooks, backtest them against historical data, and deploy through CI/CD. Genie Code and Genie Spaces provide AI-assisted ingestion, detection authoring, rule refinement, investigation, and natural-language threat hunting, while custom models can support anomaly detection, behavioral analytics, and entity risk scoring. The announcement also introduces an Open Security Lakehouse Ecosystem and acquisitions of Antimatter and SiftD.ai; Lakewatch is available in Private Preview, with Adobe and Dropbox among the named customers.


### [How agentic software development will change databases](https://yomu.fyi/post/how-agentic-software-development-will-change-databases.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Ippokratis Pandis, Nikita Shamgunov, Reynold Xin
- Published: Sep 18, 2026
- Type: Explainer

AI agents are changing software development from a slow, linear process into rapid evolutionary experimentation, creating many small, ephemeral applications and databases. The post identifies three resulting database requirements: near-zero marginal cost with elastic growth, native branching for rapid variants, and open ecosystems that agents can use reliably. Lakebase addresses branching through O(1) metadata copy-on-write at the storage layer, so agents can branch production or test state without physical data copying and pay only for experiment compute. Its decoupled, serverless compute can scale in sub-second intervals and down to zero, while standard open Postgres page formats in cloud object storage keep data accessible to external engines and tools. Lakebase telemetry reports about 10 branches per project on average, some nested beyond 500 iterations, and roughly four times as many databases created by agents as by humans.


### [Announcing the 2026 Built](https://yomu.fyi/post/announcing-the-2026-built.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Andrew Ferguson, Steve Sobel, Kevin Rasmussen, David Porter, Joslyn O'Connell
- Published: Sep 18, 2026
- Type: Announcement

Databricks has announced its third annual Built-On Databricks Startup Challenge, inviting early-stage startups that use Databricks as a core part of customer-facing, B2B applications to compete for more than $1M in prizes. Eligible companies must have been founded within the last five years, be incorporated in one of the listed countries, and be no later than Series B if funded. The competition offers a potential Databricks Ventures investment of up to $600,000, up to $500,000 in Databricks product credits across finalists, and access to Databricks resources, workshops, mentorship, and feedback. Judging considers Databricks usage, market potential, and founding team strength and experience; applications close May 22, 2026, with judging in late May and winner announcements during the week of June 15, 2026, at the Databricks Data + AI Summit in San Francisco.


### [Announcing General Availability and Open Sourcing of Unity Catalog Business Semantics](https://yomu.fyi/post/announcing-general-availability-and-open-sourcing-of-unity-catalog-bus.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Can Efeoglu, Sachin Thakur, Richard Tomlinson, Justin Talbot, Daniel Rosenthal, Jasmeet Jaggi
- Published: Sep 18, 2026
- Type: Announcement

Databricks announces the general availability of Unity Catalog Business Semantics, a unified semantic foundation for consistent business definitions across BI dashboards, notebooks, SQL applications, and AI agents. Built into Unity Catalog, it applies existing governance, security, lineage, and access controls to centrally defined Metric Views, whose metadata includes display names, formats, and synonyms. Metric Views are authored declaratively in SQL, compiled and executed deterministically at query time, and can use materializations with automatic pre-aggregation, incremental refresh, intelligent query rewriting, and transparent routing; materialization remains in Preview. The core Metric View implementation is being open sourced in Apache Spark OSS for an upcoming release, with Unity Catalog OSS v0.5 support coming soon, while a point-and-click authoring UI is in Public Preview.


### [How Addepar scales investment workflows with Databricks AI agents](https://yomu.fyi/post/how-addepar-scales-investment-workflows-with-databricks-ai-agents.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Bob Pisani
- Published: Sep 18, 2026
- Type: Explainer

Addepar, a global technology and data platform, manages nearly $9 trillion in assets and moved from older systems to a unified data intelligence platform on Databricks running on AWS. It ingests hundreds of disparate data feeds, standardizes and enriches them, and delivers results through products, APIs and data sharing. Since adoption, pipeline costs fell 60% versus legacy infrastructure, saving more than $2 million, while new pipelines and integrations became five times faster. Addison, a native AI experience embedded in the platform, combines governed portfolio Q&A, proactive insights and action-oriented workflows, using live data and Addepar’s core calculations engine. Addepar is adopting Databricks Agent Bricks, including Supervisor Agent flows with Genie-powered analytics, while Foundation Model Serving and managed MLflow support governed model serving, traceability, evaluation and iteration for human-supervised agentic workflows.


### [Why agentic analytics starts with a well](https://yomu.fyi/post/why-agentic-analytics-starts-with-a-well.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Catherine Brown
- Published: Sep 18, 2026
- Type: Explainer

Agentic analytics increases the need for trustworthy semantics and governance because AI systems must interpret business definitions, trace outputs, and support follow-up questions beyond static dashboards. Nick Eayrs argues that fragmented metrics across BI tools, proprietary semantic layers, and duplicated pipelines erode trust, slow decisions, and increase engineering cost. The proposed foundation is an open, interoperable semantic layer where certified metrics such as revenue, churn, and customer lifetime value are explicitly defined, reusable, and accessible through SQL to BI, AI interfaces, notebooks, and agents. Organizations should establish governed data foundations and a catalog first, certify metrics next, then add AI and evaluate its outputs. Examples include NTT Docomo reducing manual log-analysis time from 66 hours to 6 hours and Net One Systems cutting support response time by 75 percent.


### [Accelerate business insights with Lakeflow Connect, now with a Free Tier](https://yomu.fyi/post/accelerate-business-insights-with-lakeflow-connect-now-with-a-free-tie.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Giselle Goicochea
- Published: Sep 18, 2026
- Type: Announcement

Databricks is introducing a Lakeflow Connect Free Tier to help teams ingest SaaS application and operational database data into the Databricks Data + AI Platform, addressing silos that can leave AI agents without complete enterprise context. Each workspace automatically receives 100 free DBUs per day for managed SaaS and database connectors only, with capacity advertised as up to 100 million records per workspace per day across eligible supported sources. Lakeflow Connect offers 30+ connectors, including SaaS services and databases, through a point-and-click UI and API, with Unity Catalog providing unified governance and end-to-end lineage. The offer uses standard pricing without volume-based premiums or row-based pricing, but actual capacity varies with workload characteristics, record sizes, and source types.


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