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Strengthen customer loyalty and employee engagement with Vouchers for Uber Eats
Uber for Business is introducing Vouchers for Uber Eats, a meal-benefit feature intended to help organisations engage customers, guests, and employees during changing work and event routines. Businesses can create single or bulk voucher campaigns, choose coverage amounts, and apply controls for spending per order, order counts, dates, times, and other parameters. Recipients claim vouchers through an Uber account and redeem them at checkout, while administrators can track claims, meals ordered, total spending, and costs within a set time frame. The post presents uses including feeding teams returning to work, supporting remote events, treating clients, welcoming guests, and recognising employees, with no-contact delivery and access to 400,000 restaurant partners in over 6,000 cities cited as supporting options. It also says average delivery time is under 30 minutes, based on internal Uber data from 2018.
Uber for Business
Uber for Business is a centralized platform that helps organizations manage rides, meal deliveries, incentives, and related programs. Since 2014, it has brought Uber’s technology to organizations across industries, and the page says more than 200,000 companies use it worldwide, including more than half of the Fortune 500. Administrators can create travel and meal programs, set policies, spending limits, approved locations, and user access, while employees and clients use the Uber and Uber Eats apps. Dashboards provide spending, compliance, reporting, ride status, costs, and emissions data, with expense integrations such as Navan. Use cases include airport and event rides, subsidized commutes, courtesy transportation, executive travel, individual or group meals, and customer incentives.
Operationalizing AI for public sector fraud prevention
Public-sector agencies face fraud methods including synthetic identities, deepfake-enhanced documentation, and personalized social engineering, while legacy controls remain fragmented and difficult to scale. The fictional Services Bureau demonstrates an operating model that combines Databricks Apps, Lakebase, Unity Catalog, Delta Sharing, Agent Bricks, and AI/BI Genie in a single fraud-operations environment. Governed data lands in Delta tables; Unity Catalog applies attribute-based access control, masks PII by role, and provides lineage, while agents connect live lakehouse queries, agency policies, and external fraud signals through MCP. Analysts review evidence and recommendations, then approve, override, or escalate cases, keeping human judgment central. The described workflow turns weeks of manual investigation into a day, supports dashboards and conversational SQL, and is presented as making fraud decisions faster, more secure, transparent, and defensible.
From months to minutes: Building real-time clinical data pipelines with natural language
Healthcare data teams often spend months integrating EHR systems, normalizing HL7, CCD, and X12, and routing data through intermediary storage before analytics, creating latency and maintenance burden. Databricks and Redox describe a pipeline model that combines Redox MCP Server, natural-language prompts, and Databricks Zerobus Ingest to build integrations inside Databricks and stream clinical data directly into Unity Catalog managed tables. The post says Zerobus provides subsecond latency, while the MCP Server identifies environments, suggests workflow steps, executes integration tasks, and surfaces validation signals such as logs and performance summaries. A demonstrated workflow retrieved a recent patient admission as structured data with a plain-language summary, and Redox writeback can return AI outputs to EHRs for point-of-care action. The same foundation is presented as enabling real-time use cases and Redox Agents built with Databricks Genie Spaces.
AI is the answer to the sales growth-without-headcount problem
Sales leaders have treated pipeline growth as constrained by headcount, making a 30% increase appear to require 30% more people. The post presents Intercom’s use of Fin, its Customer Agent, for inbound sales as a separate pipeline source rather than merely an efficiency layer for SDRs, with its own metrics, targets, and owner. Fin engages inbound prospects instantly, qualifies and routes high-intent visitors, and keeps lower-intent prospects warm, while SDRs focus on phone qualification, relationship-building, and helping trial users realize value. Drawing on customer-service implementation, the operating model includes dedicated AI ownership and continuous optimization against meetings, pipeline, and revenue. The conclusion is that AI can raise the pipeline ceiling above current headcount capacity, although the constraint shifts to how well the Agent understands the product, customers, and demand qualification.
Introducing NVIDIA Nemotron 3 Nano Omni: Long-Context Multimodal Intelligence for Documents, Audio and Video Agents
NVIDIA Nemotron 3 Nano Omni is introduced as an omni-modal understanding model for document analysis, multiple-image reasoning, automatic speech recognition, long audio-video understanding, agentic computer use, and general reasoning. It extends the Nemotron multimodal line from vision-language processing to text, image, video, and audio, targeting long, dense inputs such as 100-plus-page documents and mixed audio-visual evidence. Its unified encoder-projector-decoder combines a Nemotron 3 Nano 30B-A3B hybrid Mamba-Transformer MoE backbone with C-RADIOv4-H and Parakeet-TDT-0.6B-v2 encoders; dynamic visual resolution, Conv3D tubelets, and Efficient Video Sampling support multimodal context compression. The post reports leading or top results across document, video, audio-video, voice, and ASR benchmarks, alongside up to 9x higher throughput and 2.9x single-stream reasoning speed on multimodal use cases, and provides BF16, FP8, and NVFP4 checkpoints.
Agentic data engineering with Genie Code and Lakeflow
Genie Code is presented as a natural-language assistant for developing, orchestrating, deploying, and debugging data pipelines and jobs. It uses pipeline and job context, including code, configuration, run results, Unity Catalog metadata, lineage, popularity, and code samples, to help engineers discover datasets and understand data flows. Engineers can describe pipelines or jobs, and Genie Code can generate Spark Declarative Pipelines with Bronze, Silver, and Gold layers, sources, transformations, data quality expectations, and outputs, then configure orchestration, schedules, dependencies, Auto Loader, AutoCDC flows, and Declarative Automation Bundles. It also analyzes failures and unexpected row-count or schema changes, proposes cross-file updates with reviewable diffs, and supports extensions through custom instructions, agent skills, and MCP servers. The stated result is faster development and guided debugging while workflows remain aligned with Unity Catalog governance, performance, and data quality standards; future plans include background failure response and cluster right-sizing.
Securely send first-party conversion signals with Snapchat Conversions API on Databricks Marketplace
Snapchat Conversions API is now available as a Databricks Marketplace listing that lets teams activate first-party conversion signals directly from governed Lakehouse data. The included pre-built notebook reads gold-layer tables, batches web, app, and offline events, and supports deduplication with Snap Pixel, allowing server-side signals without double-counting. Because it runs in the Databricks environment, Unity Catalog preserves governance, lineage, security, and access controls while teams control what data is shared and when. The post says fuller server-side coverage can raise Event Match Quality, while citing that advertisers who improved their EQS from “Poor” to “Good” saw 26% higher ROAS and 49% lower CPI. It also describes fresher custom audiences and Lookalikes plus more complete deduplicated measurement.
Connecting Agents to Decisions
Palantir’s Ontology is presented as a decision-centric architecture for human-agent operations, intended to represent how enterprises make and execute decisions rather than only storing or analyzing data. It combines data, logic, action, and security in a shared, dynamic representation that can connect operational data, computational processes, workflow execution, and policy controls. The data layer includes structured, streaming, edge, unstructured, imagery, and decision data, with decision lineage recording the data version, application, and decision context; logic binding connects heterogeneous business, modeling, planning, and optimization assets for human and agent workflows. The architecture exposes approved actions to agents with configurable human review and guardrails, while capturing decisions as learning material for improving workflows and agentic memory. It concludes that a real-time ontology can anchor AI in enterprise conditions while controlling where automation is allowed.
Introducing the OSSCAR Index
Supabase has launched the OSSCAR Index, a quarterly ranking of fastest-growing open source organizations, with the site, data, and scoring code released as open source. It addresses the limits of rankings based on accumulated stars, downloads, or contributors by measuring community growth within two divisions: Emerging organizations below 1,000 GitHub stars at quarter-start and Scaling organizations at or above that threshold. Scores combine normalized net new stars, unique contributors, and npm, PyPI, and Cargo downloads using an L² norm, with minimum thresholds and no penalty for missing or declining signals. The Q1 2026 edition identifies Openclaw as a breakout project, places AI-agent projects prominently in Emerging, and reports global representation; quarterly updates, expanded package coverage, an RFC process, and historical rankings are planned.
Formatting an entire 25 million line codebase overnight: the rubyfmt story
Stripe’s 25-million-line Ruby codebase needed a true autoformatter; before rubyfmt, no industry-wide Ruby formatter existed, and earlier tools had crashed on its files. rubyfmt began as a zero-configuration open-source project motivated by formatting disputes, Ruby’s slow startup, and the uncertainty faced by engineers arriving from other languages. To meet a 100ms target, it initially used a hand-authored C launcher and later moved formatting into Rust, with ripper parse trees first passing through JSON and eventually being deserialized directly from Ruby VALUE objects. Stripe adopted per-file opt-in, expanded coverage cautiously, and formatted the codebase on a Saturday in 2024; the excerpt reports that all 42-million-line Ruby codebase is now formatted. In 2025, migrating from ripper to Prism removed the need to link a Ruby VM and made rubyfmt smaller and dramatically faster.
How leading tech companies are killing the builder's tax with Lakebase
The post argues that AI-native applications face a “builder’s tax” because operational databases and lakehouse analytics are separated by CDC, ETL/ELT, and reverse ETL pipelines. This architecture can leave AI systems with stale data while duplicating governance and shifting engineering effort toward orchestration and failure management. It presents Lakebase, a fully managed serverless Postgres engine integrated with the Databricks Data + AI Platform, as a shared operational foundation where applications, agents, analytics, and governance use the same data. The proposed pattern combines a lakehouse intelligence layer, a low-latency Lakebase execution layer, and a continuous learning loop, with pgvector supporting agent memory and semantic search. Reported customer examples include faster feature onboarding, reduced on-call disruption, higher agent throughput, and consolidated data estates, though the results vary by organization.
Putting Fairness in the Driver’s Seat
Uber describes its deactivation principles and safeguards for drivers and couriers, motivated by the stress of losing app access and the need for decisions that are fair, accurate, and transparent. Most access issues are temporary, while permanent deactivations are described as rare and intended to protect platform safety; users should receive notice, relevant details, and an opportunity to respond whenever possible. The updated process lets drivers view eligible reports, submit context including Record My Ride audio or video, and have reports reviewed alongside their information, while systems filter suspected fraudulent or false claims. The Review Center explains deactivation reasons and appeal options, supports uploads, and sends every appeal to a real person, with additional measures including excluding low ratings caused by factors outside a driver's control, limiting trip categories when appropriate, and covering third-party testing for disputed impairment claims.
From Clicks to Conversions: Architecting Shopping Conversion Candidate Generation at Pinterest
Pinterest built a dedicated shopping candidate generation model to optimize for lower-funnel purchase conversions rather than relying strictly on engagement retrieval models. Because offsite conversion events are sparse and delayed, engineers trained a single multi-surface two-tower model using conversion data supplemented by duration-weighted click engagement and unengaged ad impressions as hard negatives. The two-tower retrieval architecture incorporates a parallel DCN v2 and MLP layer structure that allows simultaneous learning from the same inputs without information bottlenecks. Later revisions transitioned the system from a multi-head loss structure to a unified single-head multi-task architecture paired with an advertiser-level objective. Deployed across more than 600 million monthly active users, the system increased shopping conversion volume by 2.3% and boosted return on ad spend by 3.1% for United States shopping campaigns.
Powering KPMG UK Audit's AI future with Databricks
KPMG UK is evolving its Audit data platform with Databricks to support AI-enabled analytics while preserving governance, rigour, and professional standards. The program converges structured data, advanced analytics, and AI on a unified cloud-native Lakehouse, with Databricks SQL replacing core SQL Server workloads and Delta underpinning the platform. Lakebridge assessed migration complexity, while Databricks-hosted large language models including Claude Sonnet and Genie Code helped convert T-SQL, refactor stored procedures, modularize queries, and suggest Delta-based optimizations under engineer review. This reduced refactoring time by around 60% and enabled modernization of more than 400 scripts and stored procedures in roughly three months. Databricks SQL Serverless provides elastic compute for spiky workloads, while Genie offers traceable, version-controlled SQL and Delta Sharing supports governed data exchange.
For Teams – Tools & craft – Notion
The page presents a collection of Notion content about teams, tools, workplace practices, and the use of AI in organizational work. Its entries cover Custom Agents for automating repetitive busywork, teamwork skills, launch orchestration, and the changing role of team size. Several pieces focus on disconnected company knowledge, describing Notion AI as a way to bring information together and turn knowledge into action, while other entries examine AI operating systems and workflows. The page also includes survey-based perspectives from Notion, including research involving more than 3,000 global knowledge workers, 85-plus interviews, and 650 European business leaders, alongside customer stories from companies such as Merge, Ramp, Chronicle, Toyota, Affirm, and Mercury.
ClickHouse at Google Cloud Next '26
ClickHouse announced several product updates and integrations designed to deepen native compatibility with Google Cloud. Migration of ClickHouse Cloud to Google Axion ARM-based processors demonstrated a 30 to 55 percent query speed increase and reduced data load times from 64 to 26 seconds on ClickBench benchmarks. An integration with Google Lakehouse Runtime Catalog enables querying Iceberg tables on Google Cloud Storage via the DataLakeCatalog engine without data movement. Furthermore, Bring Your Own Cloud reached general availability on Google Cloud Marketplace, and ClickPipes added an unordered mode using Pub/Sub notifications for GCS ingestion. Google Antigravity integrated ClickHouse through an MCP server to translate natural language into SQL and generate interactive React charts.
Inside one of the first production deployments of Lakebase: LangGuard's agentic workflow governance engine
LangGuard is presented as a runtime enforcement layer for enterprise agentic workflows, monitoring actions, decisions, tools, credentials, and intent across connected systems. Its GRAIL data fabric records multidimensional trace data, builds a live knowledge graph, and evaluates allow/deny/modify decisions against policy before tools, datasets, or models are accessed. The deployment uses Databricks Lakebase as the operational system of record, relying on PostgreSQL, serverless autoscaling, scale-to-zero, compute-storage disaggregation, hot-data caching, and copy-on-write branching for policy testing. LangGuard chose this architecture to handle bursty trace writes and low-latency enforcement reads without provisioning for peak demand, while keeping operational data available to Databricks analytics and AI capabilities without additional ETL. The stated next step is predictive governance: training behavioral models on historical traces to flag anomalous agent behavior before a policy violation.
How to build scalable web apps with OpenAI's Privacy Filter
The article presents three web applications built around OpenAI’s open-source Privacy Filter, a PII detector that labels eight categories in one forward pass over a 128k-token context. Document Privacy Explorer extracts PDF or DOCX text and returns category-labeled spans, while Image Anonymizer combines Tesseract word boxes with character offsets to produce editable pixel redaction rectangles. SmartRedact Paste replaces detected spans with category placeholders, stores public and token-gated views, and evicts expired pastes in a daemon thread. Across the apps, gradio.Server’s @server.api routes model work through Gradio’s queue and gradio_client, while plain FastAPI routes serve custom HTML, views, and lightweight lookups. The implementations keep browser-side filtering, canvas edits, and PNG export out of server round-trips, and the paste service is about 200 lines including storage.
The next generation of Databricks Genie
The next generation of Databricks Genie expands the assistant beyond individual Genie Spaces, combining structured and unstructured enterprise data in a unified chat experience. It reuses logic from certified Genie Spaces, governed dashboards, and Databricks Apps, with metadata-based routing that prioritizes higher-trust sources, while new reasoning models and agent architecture handle questions spanning multiple domains. Built-in connectors for Google Drive and SharePoint, plus MCP support, let Genie access knowledge stores and take actions; Unity Catalog AI Gateway manages these connections. The experience replaces Databricks One as an account-level global home, adding domains, custom URLs, unified login, automated identity management, and governance through Unity Catalog. Native iOS and Android apps extend access to dashboards, apps, and chat beyond desktop environments.
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