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AI-powered multicluster management: Querying fleet health with OpenShift Lightspeed and Red Hat Advanced Cluster Management
Managing modern large-scale Kubernetes fleets across distributed clusters can make troubleshooting slow because administrators must build manual search queries and sift scattered outputs. The post presents a Technology Preview MCP server for Red Hat Advanced Cluster Management that translates natural-language prompts into structured searches across managed clusters, deployed with Helm in the hub cluster. OpenShift Lightspeed is configured through its OLSConfig custom resource to call the MCP endpoint, use a Kubernetes service-account token for internal authorization, and optionally skip approval for read-only searches. The examples cover finding clusters below OpenShift 4.21, locating pods in a specified cluster and namespace, and identifying an unhealthy VM across the fleet; one result finds dev-vm in an ErrorUnschedulable condition and provides diagnostic guidance.
Stop wasting GPU allocation in Kubernetes with GPU-pruner
GPU-pruner addresses idle GPU allocation in Kubernetes by identifying workloads whose hardware activity remains below a configured threshold, rather than relying on browser sessions, network traffic, or pod lifetime. It queries Prometheus for NVIDIA Data Center GPU Manager metrics, including engine activity and GPU utilization, and treats peak performance below 1% throughout a default 35-minute observation window as idle. After tracing an idle pod through ownerReferences or KServe labels, it scales the parent resource to zero, preserving workload metadata for later restoration. Slack notifications, grace periods, lookback checks, and dry-run mode provide safeguards, while Prometheus label handling must preserve the workload namespace and pod identity in DCGM telemetry.
How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify in PHP
The tutorial shows how to combine Twilio Lookup and Verify in a PHP sign-up flow that screens phone numbers before sending an OTP. It starts with Line Type Intelligence to accept mobile numbers, optionally checks reachability with Line Status, and uses Identity Match to compare submitted first and last names against authoritative sources without revealing underlying data. If those checks pass, the application sends a six-digit verification code and validates it through a Verify service. The provided implementation uses PHP 8.5, Slim, Twig, Monolog, and the Twilio REST client, with environment flags controlling optional checks and logs recorded in data/app.log. Testing covers mobile, toll-free, VoIP, and name-mismatch cases, while the pricing discussion recommends ordering cheaper checks before more expensive Identity Match and Verify calls.
Your customers don't hate AI — they hate you lying about it
Twilio research reports a major gap between brands’ and consumers’ accounts of AI-agent disclosure: 81% of brands say agents identify themselves immediately, while only 22% of consumers report that experience. Teams may treat a system-prompt line, widget label, or one-time channel message as disclosure, even though customers can miss it or encounter a different experience on SMS or voice. Recommended practice includes naming “AI agent” at the first turn, repeating disclosure across channels, explaining accessible data and decision logic, and offering an immediate route to a person. It also recommends checking transcripts rather than trusting configuration, using conversation analysis to verify whether disclosure fired. Research cited says 76% expect identification at the start, 49% want data-access transparency, and 54% trust AI more when its logic is explained.
How to Confirm Phone Number Ownership with Lookup Identity Match
The tutorial shows how to use Twilio Lookup Identity Match to confirm that a person is linked to a submitted phone number and reduce sign-up fraud. It describes a zero-knowledge match against authoritative sources such as mobile carrier records, with possible use during sign-up, login, account changes, or checkout. The implementation starts from an international telephone input project, collects first and last names alongside an E.164 phone number, and passes them to the Lookup API with the identity_match field; the response exposes per-field statuses and a summary score. The sample JavaScript treats the check as successful when the phone is valid and both names are exact or high partial matches, then displays the result, while the post also notes approval requirements outside the US, test parameters, and alternative Lookup packages.
Build a Video API with Twilio Programmable Video, Node.js, TypeScript, and Express
This tutorial builds an Express API for creating and managing Twilio Programmable Video rooms with Node.js and TypeScript. It sets up a project with Express, TypeScript 5, ts-node, node-dev, dotenv, and the Twilio Node.js helper library, while requiring Node.js 20 or 22 LTS. Twilio account credentials are stored in environment variables loaded from .env, which is excluded through .gitignore. The TypeScript configuration compiles strict CommonJS source from src into build and targets ES5; routes call the Rooms API to create rooms and complete them by changing status to completed, disconnecting connected participants. The result is a server-side room-management foundation, with access-token issuance identified as the next step for browser or mobile participants.
How to Approve Real Users and Block Fake Accounts at Sign Up with Lookup and Verify
Fake accounts and signup fraud can undermine onboarding, so the tutorial builds a Node.js flow that collects a user's name and phone number. It combines Twilio Lookup and Verify through four progressively intense checks: line type, line status, identity match, and SMS OTP possession. The implementation blocks landline, nonfixed VoIP, toll-free, and pager numbers, checks submitted first and last names against authoritative sources using exact_match or high_partial_match, and sends an OTP only after the lookup steps pass. Line status is optional in the example because access is in Private Beta, while the project uses Express, EJS, and environment variables for its Node.js setup. It also demonstrates expected outcomes for mobile, blocked, and mismatched inputs, then notes that pricing varies by country.
How to Confirm Phone Number Ownership with Lookup Identity Match in PHP
The post presents a PHP signup flow that uses Twilio Lookup Identity Match to confirm whether submitted identity details correspond to a phone number and to reduce signup fraud. Identity Match performs a zero-knowledge match against authoritative sources such as mobile carrier records, using supplied fields including names, addresses, national IDs, or dates of birth. The implementation builds on a Slim PHP project, adds Twig form handling and the Twilio REST client, then sends the phone number, first name, and last name through the Lookup API. Registration succeeds only when the number is valid and both names return exact_match or high_partial_match; otherwise, the form displays an instruction to provide information for a phone number the user owns.
Type-Safe Authentication in Rust with Auth0 and Axum
CLI applications cannot use a standard browser redirect OAuth login flow when no browser or redirect URL is available, so the tutorial uses OAuth 2.0 Device Authorization Flow. It builds an end-to-end Rust workspace containing a CLI, a shared auth-lib crate, and an Axum API configured with Auth0. The CLI requests a device code, displays a verification URL and user code, polls for an ID token and access token, then sends the access token as a Bearer credential. The API middleware fetches JWKS, selects the key matching the JWT’s kid, validates RS256 signatures plus issuer and audience claims, and deserializes strongly typed UserClaims. With valid claims, a protected route returns a message and subject; otherwise the extractor returns an unauthorized response.
Previewing the Model Hardware Standard
Anthropic and HHMI Janelia Research Campus are opening a research preview of the Model Hardware Standard (MHS), a shared specification for AI agents to operate programmable laboratory and manufacturing devices safely. MHS replaces bespoke integrations with standardized drivers built around read and write primitives, device discovery, natural-language tags, generated reference files, and control through MCP, a command-line interface, or code files. The model-agnostic standard lets agents coordinate instruments, monitor operating data, adjust parameters in real time, and turn exploratory work into deterministic scripts; early projects covered protein assays, qPCR, microscopy, robotics, dose-response experiments, and quantum-laser stabilization. Reported results include serial dilution experiments running about three times faster and a QuEra laser-lock controller recovering 99.3% of the time, while the preview will inform safety evaluations and guidance before an open-source release.
What is an AI Copilot?
An AI copilot is an assistant embedded in a software application that uses workflow context to offer suggestions, generated content, or approved actions while keeping the human in control. Unlike a standalone chatbot, it combines an LLM with contextual grounding, retrieval-augmented generation (RAG), access to enterprise data, and action layers that can call APIs, execute code, trigger workflows, or update records. The explainer surveys code, productivity, data and analytics, customer-facing, and domain-specific copilots, including uses such as SQL generation, dashboard creation, email drafting, and case summarization. Reported benefits include faster routine work, broader data access, more consistent outputs, and less context switching; a cited GitHub study found Copilot users completed a controlled coding task 55% faster. It also stresses hallucinations, privacy, bias, overreliance, integration complexity, cost, and the requirement for human review, governance, and accountability.
Kentucky defensive driving course
Kentucky regulations require rideshare drivers to complete a two-hour driver safety training course each year, with collision prevention and safe driving habits as its focus. Starting June 25, 2019, drivers cannot receive trip requests in Kentucky through the Uber app until they upload a passing-course certificate to their account. The online course costs $15: drivers begin through the Online Training Center’s “New Partners” option, use the email address and phone number associated with their Uber account, and verify that the confirmation-page username matches the official name on their license. After training, drivers download the certificate or open the certificate page’s printable completion confirmation, then upload it to their Uber account; unfinished training can be resumed through the driver portal’s “My Courses” section.
Beyond answers: New Genie One features to turn insights into action
Genie One’s latest updates target the manual work that follows an AI-generated answer, including accuracy checks, report formatting, sharing, and repeated workflows. The release adds a native macOS desktop app in beta with a global launcher, persistent conversations across web and desktop, and access to documents, skills, scheduled tasks, Genie Agents, and Databricks Apps. Genie One can create editable documents with live visualizations, comments, version history, sharing, PDF export, and external-document editing through MCP writes. Users can also turn conversations into reusable, shareable Genie Agents or distribute read-only chat links containing questions, answers, visualizations, and context. Additional capabilities include ontology snippets, private uploads of supported files, and governed MCP actions, with Unity Catalog, source authentication, and Unity Gateway governing access across assets and connected tools.
How Trackunit turns construction data into decisions with AI
Construction data is fragmented across systems, organizations, equipment types, and formats, limiting the operational context available for decision-making. Trackunit addresses this data-intelligence problem with IrisX, an operating data platform built on Databricks that connects equipment, machine, operator, site, and operational data. Its three-part model—connect, distill, and amplify—combines data engineering, analytics, and AI on an open platform, then delivers governed, construction-specific intelligence through applications, workflows, and AI interfaces. The platform supports natural-language analysis of equipment signals and contextual factors such as maintenance history, operating conditions, location, contracts, project needs, and asset availability. Examples described include battery-management guidance for OEMs, missed-invoice detection for rental companies, and asset redeployment for contractors, with reported value of approximately $3 million and $2 million in two examples.
How Indra unified EV charging data on Databricks
Indra Renewable Technologies consolidated a fragmented EV-charging data estate spanning Cosmos DB, Synapse, Azure Data Lake Storage, Azure Functions, Power BI, and other services onto Databricks. The migration began with a parallel Synapse prototype and expanded to Delta tables, a consolidated fleet pipeline, and greater use of Databricks for operational workloads. A medallion architecture stores raw fleet data in bronze, transforms it with PySpark in silver, and publishes gold tables for reporting and client delivery; one pipeline now serves three clients and improved performance by 60% to 70%. Unity Catalog, serverless SQL warehouses, AI/BI dashboards, and Genie Agents support governed self-service analytics, while reported results include 80% to 90% business cost savings, 90% storage savings, and query latency falling from 24.4 seconds to 3.5 seconds.
Deploy an Open Model from Checkpoint to Inference in Two Commands with NVIDIA TensorRT Model Connect
Deploying open models into native applications can require model-specific conversion, preprocessing, post-processing, and runtime code. NVIDIA TensorRT Model Connect addresses this with reference implementations that turn a Hugging Face model ID or local checkpoint into a deployment bundle containing TensorRT engines and runtime assets, then load it from a native C++ application. Its semantic API exposes task-level inputs and outputs, while its module-level API provides named tensors and individual TensorRT components; TVM FFI can connect custom GPU kernels to targeted pipeline stages. Python is used during preparation, but production inference does not require PyTorch or a Python interpreter. The project uses AI-assisted development, human review, nightly releases, and automated validation, and the post states that supported, validated workloads can deliver faster inference than torch.compile.
How AI-Powered Attacks Led Salesforce to Reinvent Hyperscale DDoS Defense
Salesforce built DREAM (DDoS Response and Mitigation) to defend its distributed cloud against hyperscale, machine-speed DDoS attacks, with a stated goal of responding in seconds while protecting more than 4.5 million domains and 3 million customer orgs at 99.99% availability. The platform targets rapidly evolving application-layer attacks in a shared, multi-tenant environment by combining AI-driven inference with distributed orchestration for telemetry, policy deployment, notifications, retries, and fault-tolerant execution across regions. The team selected Temporal rather than rebuilding distributed-systems primitives, and used AI coding assistants to migrate about 100,000 lines at roughly ten times prior velocity; more than 90% of the rewrite was AI-assisted, with human reviews and test coverage above 95%. Production lessons moved bulk data outside orchestration and bounded workflow history, while DREAM reduced time to first mitigation fivefold.
MAPS: Netflix’s Multimodal Asset Personalization at Scale
Netflix treats artwork and video previews as personalized assets, but ID-based models struggle with newly launched titles and assets that have little interaction history. The systems encode artwork with 768-dimensional CLIP image embeddings and combine those vectors with learned asset IDs, allowing visual preferences to transfer across titles and enabling one model to serve five canvases. Reward-based weighting balances pooled canvas data by the long-term value of interactions, while inverse propensity scoring on randomized exploration traffic supports offline evaluation. For video previews, MediaFM combines visual, audio, and text signals and ranked ahead of SeqCLIP in a linear probe, offline IPS evaluation, and online A/B testing; the Netflix Embedding Store supplies consistent embeddings to production models.
Introducing Claude for Teachers
Claude for Teachers gives verified US K-12 educators free access to premium Claude capabilities, tailored teaching skills, Learning Commons curricula, and academic standards across all 50 states. It is designed to help teachers apply differentiation, mastery-based learning, and small-group instruction despite limited time, resources, and large classes, with an emphasis on protecting time with students. Once verified, users can ask Claude to draft standards-aligned lessons, differentiate materials, analyze de-identified classroom data, and schedule recurring tasks; Claude Code and Cowork support carrying work forward. The offering includes no model training on verified teacher conversations, FERPA-aligned protections, and district and state control over educational-data use. Anthropic is releasing connectors, open-source teaching skills, and an evaluation write-up, while planning a Detroit Public Schools Community District pilot focused on educator wellbeing and practice.
The State of Django 2026: Boring is so back
The fifth annual Django Developers Survey, produced by Django Software Foundation and PyCharm, draws on responses from nearly 3,500 developers in more than 40 countries. It reports that Django’s core remains stable, with PostgreSQL used by 76–79% of respondents across five years, templates around 80%, and 43% already on Django 6.0. At the same time, AI is routine but fragmented: 58% of AI users work with it daily, while 56% use it only for chat and advice and just 27% use autonomous multi-step tasks. The survey also describes rapid consolidation around uv and Ruff, growing adoption of type hints, and a testing and CI/CD base that can verify agent-generated changes, although 19% write no automated tests. Django supports both server-rendered and API/frontend patterns, while its stable foundation lets developers change surrounding tools and workflows without replacing the framework.
Top topics
- Architecture830
- Performance812
- LLMs648
- Machine Learning617
- Open Source561
- AI Agents542
- AI454
- Developer Experience446
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