Every post, summarised

Jun 17, 2026

• Databricks

  Payment Fraud Detection: How Banks and Businesses Stop Fraudulent Transactions

  Payment fraud detection is presented as a real-time practice for identifying and blocking unauthorized transactions amid evolving card-not-present, account takeover, authorised push payment, friendly, and card-testing attacks. Modern systems combine predefined rules, machine learning, behavioral analytics, device fingerprints, geolocation, transaction history, and velocity signals to calculate risk scores within milliseconds, declining or routing high-risk payments to review. Rules are fast and interpretable but require continual tuning, while machine-learning and behavioral models can identify complex or user-specific patterns that static thresholds miss. The source also emphasizes prevention controls such as MFA, tokenization, 3D Secure, and device binding, alongside unified real-time data infrastructure that supports cross-channel signals, continuous retraining, and deployment into payment authorization flows.

• Databricks

  What is an AI Agent Harness?

  An AI agent harness is the software infrastructure around a large language model (LLM) that lets it act on tasks rather than only answer prompts. The model reasons and chooses actions, while the harness connects tools, execution environments, memory, external systems and safety controls. Many agents follow a reason–act–observe loop: the model proposes an action, the harness executes it, captures the result and returns that result as context for the next decision. Production harnesses typically include system prompts, tool execution, sandboxes, filesystems and durable storage, context management, feedback and self-verification, guardrails, human-in-the-loop approvals, and observability; their design increasingly shapes reliability as model capabilities converge.

• Clickhouse

  GCP Pub/Sub connector for ClickPipes is now in Private Preview

  ClickHouse has announced a GCP Pub/Sub connector for ClickPipes, now in Private Preview, to stream GCP infrastructure logs, application events, and operational signals into ClickHouse Cloud in real time. Previously, routing data from Pub/Sub into ClickHouse required GCS or a Dataflow pipeline; the connector instead manages subscription lifecycle, schema inference, and message delivery after a topic and target table are configured. It supports JSON, Avro, and Protobuf, Pub/Sub Schema Registry integration, automatic decompression, attribute-based filtering, and start positions of latest, earliest, or a specific timestamp. ClickPipes supports horizontal and vertical scaling, configurable replicas, automatic retries, built-in metrics and logs, and programmatic management through OpenAPI and the ClickHouse Terraform Provider. The connector is available upon request and free during Private Preview, with access offered through a waitlist or a ClickHouse Cloud account manager.

• Databricks

  Databricks and NVIDIA: Building for the Agentic Era

  Databricks and NVIDIA describe a full-stack partnership for enterprise AI, spanning governed model training, inference, agent infrastructure, and industry-specific AI. Databricks AI Runtime brings NVIDIA Hopper GPUs and Quantum InfiniBand to governed data for distributed training, with Blackwell preparation, Free Edition GPU support, and planned NGC container support. Model Serving uses NVIDIA hardware and Triton Inference Server for low-latency, high-throughput production inference, while NVIDIA Vera targets agent harnesses, tool calls, analytics, and multi-step orchestration with up to 3x faster SQL queries and 80% faster agentic performance. The collaboration also places NVIDIA Agent Toolkit on Databricks Apps, integrates GPU debugging through Genie Code, and brings frameworks such as BioNeMo, Parabricks, cuOpt, and Isaac Sim to Databricks workflows. The stated vision is an end-to-end stack in which GPUs handle models and purpose-built CPUs handle surrounding agentic workloads, with governance supplied by the Databricks platform.

• Databricks

  Data Warehouse Modernization: Roadmap, Architecture, and Services

  Data warehouse modernization addresses legacy systems that cannot scale efficiently with growing data volumes, real-time analytics, machine learning, and self-service access. The proposed roadmap spans two to four years for large estates, moving from assessment and architecture design through high-impact workload migration, governance embedding, and optimization rather than relying on a risky big-bang cutover. Its target architecture favors a lakehouse or enhanced cloud data warehouse, with open formats such as Apache Iceberg or Delta Lake, separate compute and storage, and Bronze, Silver, and Gold layers supporting incremental ELT and lineage. The source says modernization can reduce infrastructure maintenance costs by 30–50%, compress query latency from hours to seconds, and halve redundant ETL pipelines, while also improving governance for sensitive data and enabling BI, machine learning, and generative AI workloads on a shared foundation.

• Ramp

  You're Spending Too Much on AI. You're Also Using Too Little.

  The post argues that a large AI bill does not show excessive use: companies can overspend on routine work while using too little AI where advanced models could create value. It proposes measuring work in atomic tasks—such as invoices coded or pull requests reviewed—rather than tokens, with cost defined by tasks attempted and value by successful tasks. The operating model uses defaults that pair routine work with the cheapest model meeting quality benchmarks, medium reasoning, and flexible latency, while escalating ambiguous, high-stakes work to frontier models at higher effort. It also recommends attributing spend by provider, product, team, and workflow, finding concentrated costs, benchmarking repeated tasks, repricing after model releases, and centralizing controls in one gateway. The conclusion is that efficiency should be treated as an engineering achievement, so cheaper routine execution funds ambition on rare tasks where extra intelligence can materially change outcomes.

• Clickhouse

  The open ecosystem around ClickHouse

  ClickHouse’s open ecosystem spans clients, dashboards, ingestion pipelines, projects built on its engine, and agents that query it, with 2,600+ contributors, 240,000+ commits, and hundreds of millions of downloads cited. It describes open standards including OpenTelemetry for observability, Model Context Protocol (MCP) for AI tooling, and Iceberg and Delta Lake for lakehouse data, plus Iceberg REST catalogs, Apache Polaris, and Unity Catalog. The surrounding stack includes ClickStack, chDB, Langfuse, LibreChat, official language clients, a Kubernetes operator, Postgres bridges such as PeerDB and pg_clickhouse, and integrations with Airflow, dbt, Grafana, Fivetran, and Sigma. Specific updates include full Iceberg v2 support, Iceberg writes in 25.8, Paimon in 25.10, chDB v4’s DataStore API, and a Python client v1.0 with native async and 4x faster cold starts. The post frames this openness as supporting flexible tooling and community-led growth, reporting more than 200 integrations while noting that the ecosystem’s next direction remains unknown.

• Stack Overflow

  AI agents are a confused deputy with the keys to your kingdom

  Attackers recently compromised over twenty thousand Instagram accounts by manipulating Meta's AI support assistant to rebind recovery email addresses without verifying account ownership. This incident illustrates the classic confused deputy security problem, where a privileged process is persuaded by an unprivileged user to perform unauthorized operations. Because large language model interfaces operate purely on natural language and cannot distinguish instructions from data, the model itself cannot serve as an authorization boundary. Securing AI agents requires verifying caller identity through external policy checks against authenticated sessions rather than relying on chat context or prompt engineering. Teams must enforce least privilege with short-lived scoped credentials, place irreversible actions behind hard policy gates or human approvals, and maintain audit trails of agent actions.

• Databricks

  The Partner Well-Architected Framework: What's New and What's Next

  The Partner Well-Architected Framework (PWAF) has moved partner guidance from static PDFs to AI-ready guidance spanning Built-On, Connected, and Data Collaboration architectures. It combines an architecture center with technical standards, patterns, and instrumentation guidance intended to align integrations with partner-engineering validation while making adoption and DBU impact measurable. Since February, Databricks has added a Dev Kit containing 15+ tested AI-developed skills, new or refreshed guidance for Clean Rooms, software-defined storage, Marketplace apps, Genie, Lakebase, and MCP server onboarding, and an open-source Firefly Analytics reference implementation. The framework is positioned as an evolving, agent-compatible way to shift routine integration work to AI tools, freeing partner and Databricks engineers to focus on complex architecture; more patterns, skills, reference implementations, and demos are planned.

• Databricks

  What’s coming next to Free Edition

  Databricks is expanding Free Edition, launched twelve months ago to help people learn modern data and AI, after providing more than $10M in credits to over 500,000 learners. The five additions are Genie Code, serverless GPUs subject to availability, Lakebase, Agent Bricks, and Lakeflow Designer. Genie Code can generate, run, interpret, and refine code autonomously for tasks such as dataset analysis, pipeline cleaning, and visualization, while GPUs support deep-learning workloads including training, fine-tuning, inference, and large-dataset processing. Lakebase adds a fully managed Postgres-compatible database for data apps and AI agents; Agent Bricks supplies composable tools, memory, orchestration, and evaluation; and Lakeflow Designer enables visual pipeline construction. Together, these additions are presented as a complete free toolkit covering data engineering, analytics, machine learning, app development, and AI agents, allowing users to build end-to-end data and AI projects in Free Edition.

• Databricks

  Becoming the most comprehensive data & AI ecosystem on earth

  Databricks describes a year of expanding its partner ecosystem for independent software vendors and data providers, responding to requests for stronger go-to-market support, technical guidance, and Marketplace transactions. It says it introduced partner tiering, the Partner Well-Architected Framework, a Marketplace Commit Drawdown pilot, and the ability to list Databricks Apps and Genie Agents. Under the pilot, customers with a Universal Commit can submit invoices for eligible solutions that run on or share data to Databricks; validated purchases reduce their commitment obligation, while Databricks compensates its sales team. OpenSharing is presented as an open-source, vendor-agnostic protocol for cross-cloud, cross-platform sharing of structured and unstructured data, volumes, agent skills, AI models, and semantics. The post says Apps adoption has grown fivefold since last year's summit, with more than 5,000 accounts running Apps in production weekly, and that more than 20,000 customers rely on Databricks.

• Databricks

  Design Beautiful Dashboards in AI/BI

  This guide presents dashboard design as a way to apply brand identity consistently while helping viewers interpret business data and trust the result. It recommends choosing a 3-, 4-, or 6-column subdivision of AI/BI’s 12-column grid according to audience needs, then using F- or Z-pattern hierarchy and unequal widget sizes to prioritize information. The visual system combines readable sans-serif or local fonts, high-contrast text, neutral backgrounds, a 60-30-10 color allocation, and distinct interactive and visualization palettes. For the Wanderbricks example, the final five-color palette is #15AFDD, #2375A8, #52A870, #C85070, and #C89930, tested in light and dark modes and with color-blindness simulations; workspace themes extend the resulting styling across dashboards.

• Databricks

  What’s new in Genie Code at Data + AI Summit 2026

  At Data + AI Summit 2026, Databricks announced expansions to Genie Code for complex, agentic data and ML work. The changes include a full-page command center for managing concurrent threads and assets, upgrades across production ML engineering, and scheduled tasks that run prompts while users are away. For ML workflows, Genie Code uses Databricks production expertise and Genie Ontology, integrates with MLflow and Model Serving, and can move GPU jobs to AI Runtime while using workspace environment features. It can write features, coordinate edits, run and debug code, compare candidates, inspect endpoint health, and diagnose issues, with users deciding what to keep. Scheduled tasks are described as coming soon, creating reviewable threads from prompts and optional Databricks assets.

• Databricks

  What’s new in Databricks Data + AI Platform security and compliance at Data + AI Summit 2026

  At Data + AI Summit 2026, Databricks announced security and compliance capabilities for scaling Genie, Lakebase, serverless workloads, and AI-powered applications without relying solely on manual provisioning, static network controls, or siloed compliance programs. Automatic Identity Management (AIM) for Microsoft Entra ID is generally available on AWS and Google Cloud, AIM for Okta is in Public Preview, and Context-Based Ingress is in Public Preview across all three clouds for policies based on network source, identity, and access scope. Private Network Gateway, in Private Preview on Azure Databricks, provides one secure connection from serverless workloads to private networks, while expanded Private Link support extends to Lakebase and other services. Compliance additions include Azure Serverless coverage, HITRUST across AWS, Azure, and Google Cloud, ISMAP on Azure and AWS, expanded AWS GovCloud availability, and planned FedRAMP High support on Azure Commercial.

• Clickhouse

  92x faster queries: How Open Electricity uses ClickHouse to track Australia’s energy transition in real time

  Open Electricity uses ClickHouse to serve live Australian energy data through a public platform and API spanning roughly one billion records. Migrating from Postgres and TimescaleDB addressed queries that could take more than five minutes, reducing a benchmark to just over three seconds and to 60 milliseconds with a materialized view. The new setup keeps heavy relational workloads in Postgres while ClickHouse handles analytics, using ReplacingMergeTree base tables and more than 20 materialized views for different aggregation patterns. It also uses substantially less hardware than the previous configuration: four cores, four GB RAM, and 200 GB disk versus 16 cores, 32 GB RAM, and 1.6 TB, while serving data added daily and API users.

• Hugging Face

  From the Hugging Face Hub to robot hardware with Strands Agents and LeRobot

  Strands Robots integrates Hugging Face’s LeRobot stack with Strands Agents to connect dataset recording, policy execution, simulation, hardware deployment, and fleet coordination in one agent loop. Its integration keeps LeRobot responsible for hardware drivers, calibration, recording, and dataset handling, while Strands supplies AgentTools, a shared Robot abstraction, policy providers, and a Zenoh peer mesh; simulation and hardware use the same LeRobotDataset format. The walkthrough records demonstrations in MuJoCo, optionally pushes them to the Hub, runs policies through a common interface, and switches from simulation to a physical SO-101 with mode="real". The companion script and notebook run the default simulation workflow on a laptop without hardware, a GPU, or Hugging Face credentials, although the default Mock policy produces structurally valid but behaviorally unhelpful random demonstrations.

• Databricks

  Building an open ecosystem for AI governance with Unity AI Gateway

  Databricks announced the Unity AI Gateway partner ecosystem, extending enterprise AI governance beyond models to runtime interactions among models, agents, MCP servers, skills, and AI tools. Built on Unity Catalog, the gateway lets organizations apply policies, monitor activity, manage spend, and govern AI across providers and frameworks, while integrating security, identity, and governance products they already use. The announcement groups the integrations into runtime AI security, observability and guardrails; agent identity and access governance; and AI observability and risk monitoring. Named integrations include Alice, CrowdStrike Falcon AI Detection and Response, Cyera, HiddenLayer, Netskope, Noma Security, Obsidian Security, Openlayer, Okta, Ping Identity, SailPoint, and Saviynt, with described capabilities including prompt-injection detection, data-loss prevention, agent discovery, authorization, and lifecycle governance.

• Hugging Face

  GLM-5.2: Built for Long-Horizon Tasks

  GLM-5.2 is presented as a flagship model for long-horizon tasks, extending GLM-5.1’s capability to a solid 1M-token context for sustained engineering work. It was trained extensively on 1M-context coding-agent scenarios involving large-scale implementation, automated research, performance optimization, and complex debugging, and adds effort-level controls to balance capability, latency, and computational cost. Its architecture uses IndexShare, sharing one lightweight indexer across every four transformer layers and reducing per-token FLOPs by 2.9× at 1M context; MTP changes for speculative decoding increase acceptance length by up to 20%. On reported long-horizon and standard coding benchmarks, GLM-5.2 is described as the highest-ranked open-source model: it trails Opus 4.8 by 1% on FrontierSWE, ranks second only to Opus 4.8 on PostTrainBench, and scores 81.0 on Terminal-Bench 2.1 versus GLM-5.1’s 63.5.

• Databricks

  What’s New in the AI Platform: Agents for ML Engineering, Our Deep Learning Platform, and New Capabilities for Real-Time ML

  The announcement presents three additions to the Databricks AI Platform: Genie Code support for ML engineering, AI Runtime’s serverless GPU environment, and expanded real-time ML capabilities. Genie Code integrates with Unity Catalog, Feature Store, training, serving, monitoring, and MLflow, assisting with feature engineering, model training, deployment, evaluation, and production operations. AI Runtime provides on-demand serverless NVIDIA A10 and H100 GPUs, supports high-performance multinode training with RDMA and high-performance data loading, and adds Lakeflow Jobs, DABs, MLflow, and Unity Catalog integration. For real-time ML, the platform adds declarative feature engineering, streaming features, online feature serving on Lakebase, and enhanced Model Serving targeting 300K+ QPS with under 10ms p99 latency overhead. Reported customer examples include faster workflows, lower infrastructure costs, and production scaling beyond 100K QPS.

• Databricks

  Introducing the Agentic CDP: A New Species of CDP for a New Era of Agents

  Traditional customer data platforms (CDPs) were built for human-managed, batch-based campaigns, but the post argues that agentic buying requires millisecond speed, hyper-personalization, and richer context. It contrasts the familiar Golden Record with Golden Context, which combines customer data with current business goals and the history and outcomes of prior decisions. The proposed Agentic CDP uses “Infinity Campaigns,” always-on engagement loops that use LLMs and agents to adapt messaging, timing, and channels for individuals. It is also embedded in the data foundation, bringing customer, business, and decision context together under existing governance, and is designed for agents and humans from the outset. Databricks presents CustomerLake as an implementation of these principles for its platform.