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Turn Your Latest Observations Into Timely Weather Decisions With NVIDIA Earth-2
Weather-sensitive industries can now turn real-time observations into timely decisions using the AI data assimilation tools in NVIDIA Earth-2. The tutorial covers two techniques: Score-Based Data Assimilation (SDA) for constraining diffusion models with point observations, and HealDA for assimilating disparate datasets into a consistent global state. SDA works within downscaling models like CorrDiff and forecasting models like StormCast, guiding denoising steps toward observations without retraining. A concrete example showed SDA reducing wind-speed RMSE by 54% at held-out stations when applied to CorrDiff downscaling over Europe. Earth2Studio provides a unified interface across gridded, conventional, and satellite data sources, making it easy to connect proprietary observations to weather pipelines.
Introducing Our New Dropbox API Documentation
Dropbox has launched a modernized API documentation site at https://docs.dropboxapi.com, rebuilt to make integration faster and more intuitive. The new documentation features interactive API testing that lets developers set parameters, issue API calls, and view real responses from the Dropbox API directly in their browser without switching tools. An embedded AI assistant offers instant help with endpoint questions, troubleshooting, and code examples, while the documentation integrates with MCP (Model Context Protocol) so AI tools like Claude Code and Cursor can access up-to-date API information. Through the MCP server at https://docs.dropboxapi.com/_mcp/server, developers can connect their AI assistants to answer questions, explore the API, and assist with integrations using information directly from the docs. The update delivers a clean, responsive, searchable interface that brings endpoints, types, schemas, and related documentation together, with improved navigation, and provides comprehensive information including detailed error responses, type definitions, and full request/response schemas.
Pruning LLMs Like a Physicist: Block Removal as an Ising Optimization Problem
Removing entire transformer blocks is a cheap way to speed up large language models, yielding predictable inference gains and stacking with quantization and other compression methods. The challenge is that block choices interact, making it a combinatorial problem rather than a simple ranking task. The authors reformulate block selection as a constrained binary optimization (CBO) problem, mapping it onto an Ising glass, a disordered spin system with all-to-all interactions and a fixed number of removed blocks. They note that mean-field methods, which treat each block independently, are inadequate for this interacting system. Block removal composes with quantization, low-rank compression, width pruning, and knowledge-distillation-based healing, fitting into a larger pipeline rather than competing with it. The paper is titled ‘LLM Compression by Block Removal with Constrained Binary Optimization.’
Comparison of Cluster Autoscaler and KEDA MachineSet Autoscaling on Red Hat OpenShift
This article compares two autoscaling approaches for Red Hat OpenShift compute infrastructure: the built-in Cluster Autoscaler and the KEDA-driven MachineSet Autoscaler. The key difference is the trigger mechanism: Cluster Autoscaler reacts to Pending pods, while KEDA scales based on external metrics like PromQL queries. KEDA triggers faster, scaling within about 15 minutes versus 24 minutes for Cluster Autoscaler, because it acts on metric thresholds before pods become unschedulable. Cluster Autoscaler offers built-in scale-down safety and zero configuration, while KEDA provides fine-grained, metric-driven control but requires the Custom Metrics Autoscaler operator. The choice depends on workload predictability, with Cluster Autoscaler suited for general-purpose workloads and KEDA for dedicated pools with predictable metric patterns. The two autoscalers cannot coexist on the same MachineSet, as they would fight over replica counts.
Scaling localnet user-defined networks (CUDNs) in Red Hat OpenShift to 3,500 networks
Migrating from legacy virtualization with flat VLAN-based tenant networks to Red Hat OpenShift Virtualization often requires re-architecting network topology, creating friction. Red Hat OpenShift supports localnet user-defined networks (CUDNs) to preserve existing VLAN infrastructure while modernizing. Red Hat tested localnet CUDNs at scale, successfully managing up to 3,500 localnet CUDNs hosting over 3,500 active virtual machines. OVN-Kubernetes achieved 100% SSH connectivity, near-line-speed data-path performance over 100GbE interfaces without Geneve tunnel overhead, and stable control plane resource usage. Resource peaks reached approximately 5.5 CPU cores per worker node and 10 CPU cores on control plane nodes during bulk creation, then returned to low baselines.
AI threat modeling shouldn't be a bottleneck, so we open sourced ours
The post announces the open-sourcing of agentic-threat-modeling, an AI-assisted threat-modeling module for Claude Code, Cursor, and Gemini CLI, built to address the bottleneck of security teams reviewing AI-generated code. It runs on Lola, an open source package manager for AI contexts, and profiles likely attackers, generates narrative attack scenarios grounded in actual code, and checks them against six structured threat modeling frameworks (STRIDE, PASTA, LINDDUN, attack trees, and more). The module ties every finding to a specific file and line of code, as shown in a real-world example against a file upload endpoint. It’s designed to produce structured output for AI-assisted triage, though that pipeline isn’t built yet. The team open sourced it because the problem isn’t unique to them, and it’s available in RedHatProductSecurity/agentic-threat-modeling and LobsterTrap/lola.
Python Workers are now generally available
Cloudflare announced general availability of Python Workers, making Python a first-class language on its developer platform. The runtime now encapsulates type conversion so developers use Cloudflare bindings like D1 and Workers AI directly, without the previously required JavaScript glue code. Popular frameworks FastAPI, Django, and Flask run inside Workers via built-in ASGI and WSCI connectors that translate JavaScript requests into standard Python structures. A custom socket syscall implementation enables TCP connections for database drivers, unlocking Hyperdrive integration with PostgreSQL and MySQL, previously blocked by WebAssembly sandbox limits. Example projects include streaming from Bluesky Jetstream via Durable Objects, building an MCP server with the official Python MCP package, and creating a RAG system with Vectorize, with documentation now including Python examples alongside TypeScript.
Inside Petal: Building the World’s First Petabit-Class Transoceanic Subsea Cable
Meta announced Petal, the first transoceanic subsea cable to deliver petabit capacity, connecting France and the United States over 7,000 km and expected to enter service in 2029. It will be the first subsea cable system to deploy multi-core fiber technology at scale, using 2-core fiber in a 24 fiber-pair system to achieve 1 Pbps. The cable overcomes the Shannon Limit by adopting spatial division multiplexing with multi-core fiber, requiring ultra-pure synthetic silica for low attenuation and counter-propagation to minimize crosstalk. Petal’s repeaters use a single-body 96 amp design with a Fan-In/Fan-Out interface to transition between 2-core and single-core fiber for amplification, staying within existing 18 kV power feeding limits. Built with NEC, Sumitomo Electric, and Orange, Petal represents the largest generational capacity increase of any repeatered subsea system.
Building Sentry's Laravel AI Integration
Sentry’s Laravel AI integration, built during an internal Agent Hackweek, provides Agent Tracing for Laravel AI agents. The author used Claude for coding, which initially produced poor generic helper code. Laravel AI emits lifecycle events for agents and tool calls, but not for individual LLM invocations. To capture these, the integration matches outgoing HTTP requests to active invocations using provider URL prefixes, associating with the most recently started invocation if multiple match. Enabling the integration requires only updating to sentry-laravel 4.27, with no Sentry-specific code. Traces appear in Sentry’s Explore > Agents section, showing conversations, tool calls, and their inputs and outputs.
Supporting a DPDPA-Compliant Ecosystem in India with MongoDB
India’s Digital Personal Data Protection Act (DPDPA), made law in 2023 and enforced in 2025, requires organizations to implement technical controls for consent, access, security, retention, and deletion of personal data. Traditional relational architectures often fragment customer information across multiple tables and services, making it difficult to track consent or respond to correction and deletion requests. MongoDB’s capabilities, including its flexible document model, change streams, queryable encryption, and search, can help consolidate privacy-relevant data and context. The document model allows storing consent state, personal data, and security controls together, reducing fragmentation and simplifying auditability. MongoDB Atlas and Enterprise Advanced offer deployment options for fully managed or self-hosted environments to support privacy-by-design architectures.
Sending SMS with an Agentic AI using Twilio and Hermes Agent
The author, a Twilio employee, built a personal agentic AI using Hermes Agent and connected it to Twilio SMS to enable phone-based interaction. They chose SMS over a custom Android app because it works out of the box on any phone and globally, making the agent accessible to anyone with the phone number, including non-technical users. The author wired up a toll-free 800 number and tested the setup by sending a text message from a grocery store parking lot, receiving a reply from the agent. The experiment demonstrated that agentic AI can be integrated with legacy protocols like SMS to provide realtime chat capabilities. The author also noted the importance of reading email provider terms of service if creating a new account for the agent.
More data won't make your AI smarter (here's what will)
Twilio research found that 99% of companies report something blocks them from understanding customers, with 48% citing connecting data across channels as the top obstacle. The problem isn’t data collection, which has succeeded, but the missing data activation layer that makes context usable in the eight seconds an AI agent has before a customer gives up. Twilio’s solution involves three elements: continuity across channels, real-time profile freshness, and retrieval of relevant history mid-conversation. Twilio Conversations treats touchpoints as a single lifecycle, Conversation Memory syncs new details in real time, and Conversation Intelligence surfaces approved documents alongside history. Twilio recommends starting small by picking one handoff where customers repeat themselves and making context survive it, rather than feeding the AI more data.
Automating Twilio Auth Token Detection and Rotation with TruffleHog, Tines, and Slack
Hardcoding Twilio Auth Tokens in source code or exposing them in plaintext leaves communication systems vulnerable to account takeover, toll fraud, and unauthorized exploitation. This tutorial presents an automated detection and rotation pipeline using TruffleHog for secret scanning, Tines for security workflow automation, and Slack for user interaction. TruffleHog scans local directories or remote Git repositories for Twilio secrets, performs live verification via API requests, and outputs results in JSON. Tines ingests detection events through a webhook, parses the results, and triggers a Slack notification with interactive buttons for acknowledgment, scheduling remediation, or immediate rotation. Upon rotation, Tines calls the Twilio Account API to generate a new secondary auth token, displays it in a Slack modal for secure copy, and eventually promotes it to primary, while the old token is revoked.
Helix: The internal tool powering our Shopify app's native migration (2026) - Shopify
Shopify’s internal tool Helix powers the native migration of its Shopify app, which has over 300 screens. The company previously rebuilt the Shop app in just 12 weeks using similar methods. Helix is a set of tools and skills that help LLMs migrate features and screens from the React Native app while following a highly opinionated architecture. Unlike most tools that gather information, create specs, and implement everything at once, Helix aims for consistent, high-quality, and maintainable results. The approach is now being applied to the larger Shopify App after its success with Shop.
Financial Crime Mitigation with MongoDB / Series Overview
Financial crime losses could exceed $2 trillion annually, and legacy systems—fragmented, batch-processed, and transaction-focused—cannot keep pace with modern, AI-powered attacks. MongoDB Atlas offers a unified platform that consolidates entity management, transaction monitoring, behavioral risk scoring, model management, and case management on a single flexible data foundation. Key capabilities include AI-driven entity resolution via Atlas Search’s $rankFusion, network analysis with $graphLookup, and hybrid detection combining rules with vector search. The platform enables real-time intelligence through Change Streams and Time Series Collections, allowing immediate action at the moment of decision instead of retrospective analysis. Organizations can either buy off-the-shelf solutions or build custom platforms on MongoDB, with the build approach offering rapid iteration, freedom from vendor constraints, and competitive differentiation.
Financial Crime Mitigation with MongoDB / Part I: Dynamic Customer Profile
This article is the first in a series on building a financial crime mitigation platform with MongoDB as a unified data platform, focusing on the customer onboarding stage. It outlines a three-step process: capturing prospective customer data into a dynamic profile, running candidate basic checks using hybrid search with the $rankFusion operator, and performing compliance verification via network graphs built with $graphLookup. MongoDB’s flexible data model aggregates structured and unstructured data from various sources, enabling a single customer view. The platform supports vectorized profile representations for similarity searches and clustering, all within one cluster without external search engines. The post concludes by noting that the next part will explore AI-enhanced due diligence processes.
Financial Crime Mitigation with MongoDB / Part III: Behavioral Risk Modeling for Event-Driven Fraud Detection
MongoDB’s document model and event-driven architecture support behavioral risk modeling for fraud detection in financial institutions. The platform combines rule-based policies with probabilistic reasoning, using change streams for real-time event processing and vector search for similarity assessment. MongoDB captures behavioral events like typing patterns and device IP addresses, aggregating them into dynamic customer profiles. Vector search compares incoming transactions against historical patterns, but it does not evaluate numerical data such as transaction amount, which requires traditional risk scoring. This approach reduces false positives and lowers total cost of ownership by minimizing manual intervention, while MongoDB’s flexible schema allows adding risk factors without downtime.
tokenizers v1: encode, decode and scaling, measured
The tokenizers library is being reworked for version 1 with a heavy focus on performance, as tokenization can become a bottleneck that starves models of data when workloads scale. The work draws on ideas from other open-source tokenizers like gigatoken, tiktoken, and kitoken. Key optimizations include using SIMD instructions for UTF-8 text, ensuring merges never cross pre-token boundaries, and reusing scratch buffers to avoid allocations. The release candidate is implemented in the Rust pre-release on crates.io, with the crate split into tk-encode, tk-serialize, tk-convert, and tk-train. Batched model calls and faster decoding with parallel batch processing are also part of the improvements.
Building Secure AI Agents with Microsoft Agent Framework and Auth0: Human-in-the-Loop Approval
The post details how to add secure human-in-the-loop approval to an AI expense agent built with Microsoft Agent Framework and Auth0, completing the four-part series that handles identity, authorization, and action. Because approval is a security-sensitive action requiring the same cryptographic rigor as any authentication event, the agent uses Client-Initiated Backchannel Authentication (CIBA) to push a notification to the manager’s phone instead of accepting chat input. The notification includes a binding message describing the expense by ID, amount, vendor, and context, giving the manager enough information to decide from a lock screen. The agent polls Auth0’s token endpoint until the manager approves or denies, then uses the CIBA-issued access token scoped with approve:expenses to record the decision on a separate Expense API. This design ensures approval decisions are cryptographically bound to the manager’s identity and verifiable by downstream services, completing the secure loop.
RADAR: Catch gray failures with anomaly detection
Gray failures are partial customer-facing problems that can leave health checks green while a subset of users quietly fails. RADAR (Reliability Anomaly Detection, Alerting, and Root-cause analysis) addresses this gap by collecting error counts and distinct affected users, segmented by error code and region, then applying the unsupervised streaming SPOT model. SPOT learns normal behavior from the previous 14 days using a single risk parameter, while downstream stages enrich, filter, deduplicate, and route alerts before providing anomaly details and AI/BI Genie-backed dashboards for investigation. Running RADAR internally reduced incident-discovery time by 95% with over 90% precision and no human needed to spot the pattern. The post also describes a Databricks scaffold that maps metrics, detection, alerting, and root-cause analysis to platform components and can be assembled by an AI agent from a metric, scaffold, and prompt.
Top topics
- Architecture834
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- AI Agents553
- AI470
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