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How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation Memory in Python
The tutorial builds a Python FastAPI voice service that preserves caller context, preferences, and action history across separate inbound calls using Twilio Conversation Memory. It connects a Twilio phone number to a /voice webhook and /ws WebSocket through Conversation Relay, then uses an asynchronous OpenAI client with gpt-4o-mini to generate streamed responses. A memory service retrieves stored information by caller number, while each completed call is summarized and written back to the configured memory store. The example uses an auto-repair assistant whose system prompt incorporates prior context before the next call, and the test procedure checks that a second call recalls details from the first. The setup requires Python 3.11 or later, Twilio and OpenAI credentials, a memory-store and configuration ID, and ngrok for exposing local webhooks.
How to Connect Your Twilio Agent to External APIs in Node.js
Voice agents can converse, but they need external APIs to retrieve live information or take actions such as checking inventory or booking appointments. This tutorial builds a Node.js voice agent with Twilio Conversation Relay, Express, WebSockets, and the OpenAI Node.js SDK. A Conversation Relay WebSocket handler collects voice prompts, maintains conversation messages, streams model output back to the call, and defines a get_cat_fact tool that fetches JSON from the unauthenticated Cat Facts REST API. The service returns the fetched fact or an error message, while TwiML connects Twilio's voice-capable number to the WebSocket endpoint. Testing uses Node.js 18+, an OpenAI API key, ngrok, and a POST /voice webhook; the stated result is that callers can ask for a cat fact and hear the API response.
How to Orchestrate Multi-Call Conversations with an LLM and Twilio Conversation in Node.js Memory
Disconnected voice calls can force callers to repeat information, creating friction for AI phone agents. It builds a Node.js Express service that preserves caller context, preferences, and action history across separate inbound calls using Twilio Conversation Memory. The implementation uses Express and express-ws for HTTP and WebSocket handling, the OpenAI SDK with gpt-4o-mini for streamed responses and summaries, and axios for Twilio’s Conversation Memory REST API; a memory store ID and other credentials are configured through environment variables. The sample application connects Twilio’s Conversation Relay to an auto-repair assistant, retrieves prior context for recognized callers, and logs a summary when a call ends. Testing involves two calls from the same phone number to verify that the second call recalls details from the first.
Italy's email open tracking pixels rules are changing
Italy’s data protection authority, the Garante per la protezione dei dati personali, has issued guidelines governing tracking pixels in email communications. The guidance says recipients must be told about tracking pixels regardless of the email’s purpose or sender, and that prior consent is required for many uses. Senders must comply by October 28, 2026; existing subscribers who signed up before April 29, 2026 need an opt-out opportunity, while subscribers from April 29 onward require prior, explicit consent for open tracking pixels. The guidance also includes complexities, including exemptions for some email types and different rules for opaque pixels, so senders are advised to review opt-in processes with legal or privacy teams. The change follows France’s earlier rules and may foreshadow similar action elsewhere in Europe.
Harbor: Stripe’s AI-assisted prototyping tool
Harbor addresses a gap in Stripe’s AI-assisted workflows: designers needed dynamic information to assess stripe.com designs, but could not always justify a full engineering prototype. Built initially by one engineer in about 40 days, it lets designers describe an idea and use Stripe design systems and realistic mock data to generate shareable, browser-based prototypes without understanding code. Its agent writes a multi-file source tree, while Drydock compiles and renders React and TypeScript in the viewer’s browser; the agent can inspect pages, interact with elements, switch viewports, and iterate before publishing a single snapshot. Since May 2026, more than 3,000 Stripes have created 12,000 prototypes, and Harbor now integrates with other AI tools through MCP and Drydock’s standalone rendering contract, although live data and prototype-scoped state remain future goals.
How to Connect Your Twilio Agent to External APIs in Python
Voice agents can converse, but need external APIs to retrieve live data or take actions such as checking inventory and booking appointments. This tutorial builds a Python and FastAPI voice agent with Twilio Conversation Relay, OpenAI tool calling, and an unauthenticated Cat Facts REST API. A FastAPI webhook returns TwiML that connects callers to a websocket, while Conversation Relay messages are converted into conversation history and streamed responses. When the model requests a cat fact, the application executes the tool, sends its result back to OpenAI, and streams the follow-up answer to the caller. The setup uses environment variables for the OpenAI key, httpx for asynchronous external requests, and ngrok to expose local webhooks; the completed test lets a caller ask for and receive a cat fact.
Engineering Real-Time Mobile Personalization Across iOS, Android, React Native, and Flutter
Building real-time mobile personalization across iOS, Android, React Native, and Flutter required one architecture to handle different rendering models, lifecycles, identity flows, consent requirements, and release cycles without disrupting native app experiences. The team standardized schemas, event semantics, identity behavior, fallback handling, and SDK APIs, using bridge layers so React Native and Flutter could access native iOS and Android capabilities. Developers instrument apps once with constrained content zones and approved native components, while marketers configure templates, targeting, and content through Salesforce UI; metadata is published via a global CDN and rendered dynamically by the SDK. Data 360 performs server-side identity resolution and decisioning, limiting unnecessary raw profile storage on devices. An early customer implemented the system in two months and generated more than 150 million decisions within three weeks, while QR-code previews enabled testing against live profiles without app recompilation.
Health Plans: Your BI Tells You MLR Moved. Can Your AI Tell You Why?
Health plan finance teams can see when medical loss ratio (MLR) moves above budget, but the variance alone does not explain which business areas, costs, populations, or providers caused it. Conversational AI is presented as a way for finance leaders to ask follow-up questions across claims, membership, provider, contract, clinical, quality, risk, and financial data without repeatedly commissioning reports. Its usefulness depends on explicit payer context: MLR combines premiums with medical-care components such as pharmacy rebates, IBNR, recoveries, risk adjustment transfers, and provider settlements, while cohort definitions and metric logic vary. Abacus supplies normalized, connected healthcare data and payer-specific business context; Databricks provides governed enterprise data and AI capabilities for conversational analytical experiences. Together, they are described as creating a reusable foundation for tracing executive signals into operational questions across MLR and other payer domains, while leaving action judgment to the organization.
Unify your marketing data with Lakeflow Connect
Lakeflow Connect addresses the fragmentation of Customer 360 data across ad platforms, analytics tools, CRMs, and support systems. Its fully managed native connectors ingest sources through a UI or API, incrementally landing governed tables in Unity Catalog without infrastructure to provision, manage, or scale. Connectors for Google Ads, Meta Ads, TikTok Ads, Reddit Ads, and LinkedIn Ads provide pre-built or custom reports with spend, impressions, clicks, and conversions, while OAuth flows simplify setup for some platforms. The post also describes HubSpot, Salesforce, and other journey-wide connectors, plus the Ad-Genie accelerator, which transforms bronze data into silver and gold tables for AI/BI dashboards and Genie agents. Customer examples report reduced engineering overhead and faster ingestion.
From Signals to Context: Lessons from Lavaredo Ultra Trail
The title “From Signals to Context: Lessons from Lavaredo Ultra Trail” presents a subject involving signals, context, and lessons associated with Lavaredo Ultra Trail. The available page content consists of Uber navigation and promotional text, including links for products, advertising, driving and delivery, Uber Eats, business, health, higher education, transit, engineering, community support, news, and Uber.com. It identifies Uber Blog as the surrounding publication area, but supplies no body text explaining what the signals are, how context is produced, or which lessons were drawn from the trail event. Consequently, no technical approach, implementation detail, measurement, result, or conclusion can be stated from the available content without adding information beyond the displayed page.
Evolving Pinterest’s Embedding Retrieval Platform
Pinterest describes how its Manas distributed search platform is evolving embedding retrieval for a corpus growing toward tens of billions of embeddings. Traditional HNSW indexes require all data in RAM, while single-vector two-tower retrieval limits context-dependent relevance, motivating changes across quantization, SSD-based serving, and multi-embedding retrieval. Manas uses Scalar Quantization and Product Quantization across HNSW and IVF, with SQ preserving over 90% recall while reducing index size, and Linear Scaling plus SIMD cuts per-query computing resources by 10–15%. For disk-based serving, SPANN with PQ is reported as 4.5x faster than plain SPANN and over 3x DiskANN QPS, while a five-billion-embedding production evaluation saved over 40% CPU time with under 5% recall drop. The platform is also adding ColBERT-style late interaction using Sum of MaxSim, with a pilot in progress.
From zero-shot forecast to purchase order with Amazon Bedrock AgentCore
This post presents an inventory automation architecture that moves from zero-shot demand forecasts to validated purchase orders while applying safety stock, minimum order quantities, budgets, and promotional adjustments. It combines Amazon Chronos2, hosted on SageMaker Serverless Inference, with four Claude-powered agents built using the Strands Agents SDK and deployed on Amazon Bedrock AgentCore; deterministic Python tools perform computation and agents handle judgment. Product data and future covariates arrive through CSV files in S3, while business rules remain in JSON, enabling what-if forecasts and onboarding without per-SKU training. In internal testing across 50 SKUs over a four-week horizon, the system reached 12.3% median WAPE, reduced onboarding from 2–3 weeks to under five minutes, and lowered estimated monthly inference cost from about $1,091 to $15. A conditional retry loop surfaces budget violations for user decisions instead of silently truncating orders, with traces and evaluator scores supporting auditability.
Unlock a LUKS root over SSH on Fedora and Red Hat Enterprise Linux
Remote Fedora or Red Hat Enterprise Linux servers with encrypted LUKS roots can stop in the initramfs, waiting for a passphrase at an unattended console. The documented setup installs dracut-sshd from EPEL, adds dracut’s network-manager module, enables rd.neednet=1 and ip=dhcp in GRUB, then rebuilds the initramfs and boot configuration. NetworkManager brings up the initramfs network, while SSH exposes systemd-tty-ask-password-agent; the SSH session closes after unlocking because the root transition destroys the initramfs sshd. The rd.neednet=1 argument creates the neednet flag that gates nm-initrd.service, and the generated connection profile carries into the real root. Initramfs credentials sit unencrypted on /boot, so dedicated host keys, separate known_hosts entries, and physical testing are recommended before relying on remote recovery.
Bringing custom knowledge to agents with AutoRAG
Enterprise LLMs can hallucinate when they lack company-specific vocabulary and documentation, so the guide builds a bank agent grounded in internal data. It configures Red Hat OpenShift AI 3.5 Technology Preview features, enables OGX, AutoRAG, and the MCP catalog, and deploys PostgreSQL, Milvus, and MinIO for metadata, vector embeddings, and artifacts. AutoRAG processes documents, evaluates pipeline configurations against a benchmark dataset, and identifies a preferred setup; the example uses Qwen3-8B and embeddinggemma-300m models. A MariaDB MCP server adds access to customer records, with a patch supplying writable logging storage and a warning that unrestricted ALLOWED_HOSTS is for demo/development only. The resulting Streamlit agent answers a customer-information query and a Gold-card-limit question using RAG context and database tools.
Red Hat edge platforms: Choosing the right one for your use case
Choosing an edge platform requires balancing hardware footprint, workload needs, high availability, and operational complexity; the options range from RHEL with Podman or KVM to OpenShift clusters. The guide organizes Red Hat's offerings into RHEL-based and OpenShift-based deployments, noting that both traditional virtual machines and modern containerized workloads can coexist and that platforms can overlap as requirements grow. It details image-mode RHEL (bootc), transactional updates with rollbacks, efficient over-the-air delivery, offline installation, and dependency pre-resolution for constrained or disconnected sites. RHEL is positioned for static workloads of one to five components, while Red Hat Device Edge adds MicroShift when lightweight Kubernetes is needed. Kubernetes becomes more appropriate for larger microservices or event-driven systems, automated recovery, dynamic workloads, cross-environment scaling, and rolling updates; the series next covers OpenShift topologies.
Introducing automatic remediation policies with Cloudflare CASB
Cloudflare CASB now supports automatic remediation policies that respond to newly detected SaaS security findings without requiring an administrator to act on each instance. Teams can match policies by vendor, integration or tenant, and finding type, then revoke file-sharing access, send finding details to webhooks, or do both; first-party remediation currently covers Microsoft and Google Workspace file and folder findings. The backend enqueues findings to Cloudflare Queues, uses a Worker to match policy configurations, and runs matched jobs through Cloudflare Workflows, which provides durable execution, retries, and rate-limit backoff. Policies generate administrator-activity and runtime execution logs, while the stated target from detection to completed remediation is five minutes or less; support for Custom Findings is planned for the coming weeks.
AI Functions in ClickHouse: Upgrade your SQL to the AI age
ClickHouse’s beta AI Functions let SQL queries call an LLM or embedding provider directly, keeping classification, extraction, generation, translation, redaction, filtering, embedding, and similarity work alongside stored data. The feature addresses workflows that export rows to separate LLM, orchestration, or vector-database systems and then reload results, with the stated goal of running the full RAG cycle in ClickHouse. Functions make remote HTTP calls per input, or batch embedding inputs, and return native ClickHouse values; in open-source deployments, named collections store provider credentials and endpoint settings. Examples use Hacker News data for topic classification and natural-language filtering, while the guidance emphasizes cheap predicates, quotas, LIMIT tests, prompt-injection defenses, non-determinism, cost, latency, and provider access to data in cleartext after TLS termination.
Debugging our AI search assistant with agent tracing
Sentry’s Search Query Assistant converts natural-language prompts into Sentry Search Syntax, but its non-deterministic output requires evals and debugging when generated queries are wrong. A production issue caused searches on custom numerical attributes to return no events, so the team used Sentry’s AI Conversation view and Agents filters to find the relevant trace, inspect the timeline, and follow tool calls into the backend API. The trace showed that tool results were being incorporated into the system prompt with the field name bug__predictions_count instead of the required tags[bug_predictions_count, number] form. The team added an eval scenario, fixed the prompt generation, verified the corrected query locally and in production, and confirmed the updated prompt and API parameter in the trace. They also instrumented local eval runs to send data to Sentry, allowing debugging before a pull request.
Together AI expands fine-tuning service with more models, live metrics, and finer controls
Together AI is expanding its Fine-Tuning service to cover newer open-weight models and more of the workflow from dataset inspection through model selection. The update adds live training and evaluation metrics through the API, CLI, dashboard, and Python SDK, including curves that can be compared across in-flight jobs and underlying series that can be exported. Training controls now include Expert LoRA for mixture-of-experts layers, early stopping based on validation loss, and arbitrary effective batch sizes through gradient accumulation. The post reports that Expert LoRA recalled up to 89% of 200 invented facts versus 15% for attention-only adapters, while listed LoRA training prices fall by 30% to 70% for selected models, including the gpt-oss series.
Welcoming Julia Zettel, our new Video Producer
Resend announces that Julia Zettel has joined its team as a Video Producer, bringing more than a decade of experience in video and storytelling from work with brands including Zee.Dog and Globo. She will help the company communicate with customers, produce original content, and tell stories beyond the product through video, education, launches, and the people building with Resend. Julia says her background grew from being surrounded by television and music production, while her studies in Social Communication and AV led her toward narrative-driven visual work; she initially collaborated with Resend on editing, then a full producing and filming project before joining full time. She cites Adobe Premiere as her primary tool, Adobe Podcast for noisy or windy recordings, and recommends accepting challenging projects and learning from experienced people alongside online tutorials and AI.
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