---
title: "Real-time"
description: "12 posts about Real-time, summarised, each linking to the original."
---

# Real-time
> 12 posts about Real-time, summarised, each linking to the original.

## Articles

### [Sync real-time Stripe data to Supabase with Stripe Data Pipeline](https://yomu.fyi/post/sync-real-time-stripe-data-to-supabase-with-stripe-data-pipeline.md)
- Company: [Supabase](https://yomu.fyi/company/supabase.md)
- Author: gregor\_vand, saba\_pochkhua, staaldraad
- Published: Oct 15, 2026
- Type: Announcement

Stripe’s Data Pipeline now adds Postgres as a destination, enabling developers to sync Stripe data directly into their Supabase project without maintaining webhooks or working around API rate limits. Setup occurs entirely in the Stripe Dashboard: under Reporting, Data management, users select Data Pipeline, choose Postgres and Supabase, and authorize the connection. Stripe backfills historical records and continuously syncs charges, refunds, disputes, customers, subscriptions, and invoices into a dedicated stripe\_sdp schema in the user’s Postgres database. A dedicated stripe\_sdp role with scoped permissions, JWT direct connections using short-lived tokens, and enforced SSL ensure the database password is never exposed to Stripe. Developers can then query the synced tables with standard SQL, join them with application data, and implement features like MRR calculations, churn analysis, or subscription-based feature gating without extra API calls.


### [Twilio Video Media SDK Powers Smarter Customer Engagement](https://yomu.fyi/post/twilio-video-media-sdk-powers-smarter-customer-engagement.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Andy O'Dower
- Published: Oct 15, 2026
- Type: Announcement

Twilio launched the Twilio Video Media SDK in Public Beta, a server-side software package that connects applications to a Twilio Video Room as a secure virtual participant, streaming real-time audio and video to backend systems. This launch supports AI-enabled customer engagement by allowing teams to use consistent AI logic across chat, voice, and video channels while keeping sensitive data secure. The SDK enables use cases such as multimodal AI assistants that see and hear users, ambient healthcare documentation, remote verification with computer vision, and server-side compliance checks. By routing individual media tracks to a secure backend, the SDK supports HIPAA and GDPR requirements without client-side rendering. Early adopter GLOBO Language Solutions uses the SDK to enhance real-time quality and AI integration for patient care in healthcare environments.


### [Power real-time applications and agents with Document Intelligence](https://yomu.fyi/post/power-real-time-applications-and-agents-with-document-intelligence.md)
- Company: [Databricks](https://yomu.fyi/company/databricks.md)
- Author: Archika Dogra, Jane Zhang, Zachary Wang, Nihit Desai
- Published: Oct 14, 2026
- Type: Announcement

Databricks announces General Availability of Document Intelligence REST APIs for real-time document parsing, extraction, and classification, with the ai\_decide API for routing in beta. These synchronous APIs eliminate the manual bottleneck of reading documents by transforming unstructured contracts, invoices, and insurance claims into structured insights in real time. The ai\_parse\_document API converts tables and figures into structured text with citation bounding boxes, while ai\_extract pulls specified fields with optional citations and confidence scores for verification. The ai\_classify API assigns labels using a business taxonomy with optional rationales, and ai\_decide uses content to route or flag documents for review. Developers can integrate these APIs into Databricks Apps for human-in-the-loop workflows, such as a contract intake flow where a lawyer uploads an agreement and sees key terms prefilled, or into custom agents on Agent Bricks.


### [Multi-region disaster recovery for Amazon API Gateway WebSocket APIs](https://yomu.fyi/post/multi-region-disaster-recovery-for-amazon-api-gateway-websocket-apis.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Shubham Dhoka
- Published: Oct 14, 2026
- Type: Tutorial

WebSocket connections for real-time applications like live auctions and collaborative editors are vulnerable to regional outages because each connection is tied to a single AWS Region. A regional failure severs all active connections and loses session state. This post presents an active-passive multi-region disaster recovery pattern that uses Amazon API Gateway WebSocket APIs, Route 53 failover routing, DynamoDB Global Tables for cross-Region state replication, a canary health probe, and client-side reconnection logic. The canary Lambda function tests the endpoint every minute and drives a CloudWatch alarm that triggers a Route 53 health check to mark the primary Region unhealthy. Clients detect connection drops via application-level heartbeat messages and reconnect with exponential backoff and full jitter to avoid thundering herd. After reconnection, clients retrieve the latest application state from the secondary Region’s DynamoDB replica and resume their session automatically.


### [Putting a Transformer Inside Every Card Authorization](https://yomu.fyi/post/putting-a-transformer-inside-every-card-authorization.md)
- Company: [Ramp](https://yomu.fyi/company/ramp.md)
- Author: Dwight Temple, Simon Miller
- Published: Oct 7, 2026
- Type: Problem & solution

Ramp’s new card fraud model combines a small transformer pre-trained without fraud labels with an XGBoost stage to score each authorization in real time. The transformer reads a cardholder’s recent transactions and learns to predict the next transaction, producing embeddings and surprisal scores that capture how anomalous each field is. The model caught 32% more fraud dollars than its predecessor while flagging the same number of transactions, with average precision 12% higher than XGBoost alone. Most of the dollar gain comes from weighting XGBoost training examples by dollar amount, while the transformer improves ranking and catches more first fraudulent charges on a card. The transformer trains in under 90 minutes on four L40S GPUs and runs on CPU within the authorization latency budget after optimizations cut median model step time from 145 to 54 milliseconds.


### [Runtime Roundup: VM Sandboxes, Multi-node clusters, and more](https://yomu.fyi/post/runtime-roundup-vm-sandboxes-multi-node-clusters-and-more.md)
- Company: [Modal](https://yomu.fyi/company/modal.md)
- Published: Oct 1, 2026
- Type: Announcement

Modal announced several new features at its inaugural Runtime conference. VM Sandboxes provide agents with full Linux computer access, supporting Docker stacks, databases, graphical environments, and kernel experimentation, already used at Linear, Legora, and Snorkel for coding agents and evals. Sandbox Sidecars run alongside main sandboxes with isolation but share a host, allowing agent code in the main sandbox while trusted operations run in a sidecar the agent cannot access, avoiding network round trips in execution-heavy loops. Modal Clusters, now generally available via @modal.clustered, enable multi-node jobs for training or serving trillion-parameter models, powering post-training at Decagon and real-time inference at Runway. Sticky Sessions maintain client state by routing to the same container using a session token, critical for voice model serving, while Endpoint Candidates allow mirroring production traffic to compare quality, latency, throughput, and cost before promoting.


### [Small Talk with Emiliano Bonilla, CEO and Co-Founder of Synnax](https://yomu.fyi/post/small-talk-with-emiliano-bonilla-ceo-and-co-founder-of-synnax.md)
- Company: [Jetbrains](https://yomu.fyi/company/jetbrains.md)
- Author: Mitsal Athaya
- Published: Sep 30, 2026
- Type: Announcement

Emiliano Bonilla, CEO of Synnax, built the company after witnessing rocket engine testing operations ruin multi-million-dollar hardware because Excel and Python could not handle real-time control. Synnax is a platform that replaces decades-old aerospace tools, capturing sensor data in real time and tightening the loop between collection and action. The team built its own real-time programming language, Arc, in about six months after finding existing tools unsuitable. Synnax currently focuses on aerospace but is expanding into cryogenic control for quantum computing, oil and gas, and manufacturing. The company uses JetBrains IDEs, citing their debuggers and merge conflict resolution as critical to balancing speed and quality in mission-critical software.


### [Your Database Just Became Your AI Control Point](https://yomu.fyi/post/your-database-just-became-your-ai-control-point.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 29, 2026
- Type: Explainer

Agentic AI transforms the database from a passive record store into an active control point that serves real-time context and enforces policy at the moment an agent acts. According to IDC’s April 2026 survey of 715 organizations, 30% already run agentic AI in production and another 50% have established spending plans. Real-time data delivery is now baseline: 64.6% of enterprise use cases require real-time or near-real-time delivery, and 91.2% of streaming scenarios need end-to-end latency under 200 milliseconds. IDC identifies four requirements for agentic AI: a real-time operational foundation for unstructured data, elastic scale for bursty agent traffic, accurate always-current retrieval via integrated vector databases, and a governed runtime with durable memory and identity-bound policy. By 2029, IDC predicts 60% of enterprise data platforms will unify transactional and analytical workloads in hybrid processing architectures to power agentic AI.


### [A kernel-centric path to real-time video generation on Trainium](https://yomu.fyi/post/a-kernel-centric-path-to-real-time-video-generation-on-trainium.md)
- Company: [AWS](https://yomu.fyi/company/aws.md)
- Author: Stephen Zorio
- Published: Sep 25, 2026
- Type: Problem & solution

Reactor and the AWS Neuron Science team collaborated to optimize real-time video generation on AWS Trainium. They focused on autoregressive diffusion models, which combine sequential next-token prediction with iterative diffusion to generate video frames that respond to user input. The teams used the Rolling Forcing model as a proxy, addressing challenges such as dynamic shapes, unusual memory access patterns, and heavy cache management that generic compilers struggle with. Optimizations included splitting SDPA heads across four cores and sequences across two, and batching common components of the diffusion and cache update phases to improve hardware utilization. The collaboration achieved real-time video generation above 16 frames per second on a single Trainium2 chip, a result that generalizes across autoregressive diffusion models.


### [How to handle real-time interruptions in your AI voice agent](https://yomu.fyi/post/how-to-handle-real-time-interruptions-in-your-ai-voice-agent.md)
- Company: [Twilio](https://yomu.fyi/company/twilio.md)
- Author: Jesse Sumrak
- Published: Sep 25, 2026
- Type: Tutorial

Real-world AI voice agent calls are messy, with background noise, overlapping speech, and callers interrupting mid-sentence. Twilio Conversation Relay handles interruptions through attributes like interruptible, which controls whether caller input stops the agent’s speech, and ignoreBackchannel, which filters out short backchannel phrases. Background noise that sounds like speech can cause false interruptions and garbled transcripts; Twilio’s testing found Deepgram Flux produced zero transcription failures under combined noise, while a tuned Nova-3 setup also succeeded. Turn detection uses either a traditional silence timer or Deepgram Flux’s fused model, which reduces false interruptions by roughly 30% and trims response latency. When a caller interrupts, the app receives an interrupt message with the exact text played before cutoff, allowing it to trim the LLM’s conversation history so the model only remembers what the caller heard.


### [\*\*Know Who Spoke When: Build Real-Time, Multi-Speaker AI with NVIDIA Nemotron 3 Diarization\*\*](https://yomu.fyi/post/know-who-spoke-when-build-real-time-multi-speaker-ai-with-nvidia-nemot.md)
- Company: [Hugging Face](https://yomu.fyi/company/hugging-face.md)
- Author: Francesco, Ivan Medennikov, Taejin Park, Jagadeesh Balam, Adi- margolin, Maryam Motamedi
- Published: Sep 23, 2026
- Type: Announcement

NVIDIA Nemotron 3 Diarization is an open-weight, 100M-parameter model that ranks #1 on VoiceArena’s Diarization-Bench leaderboard with a 14.72% Diarization Error Rate (DER). It classifies who spoke when in conversations, handling overlapping speech and supporting up to eight speakers across live and recorded audio. The model uses a Sortformer approach with arrival-order speaker labeling, a 31-layer Transformer encoder, and two memory mechanisms for streaming inference. It accepts 16 kHz single-channel audio and outputs a floating-point tensor of speaker-activity probabilities at a configurable time step. Developers can combine its timestamps with automatic speech recognition to produce speaker-attributed transcripts, with recommended input-buffer latencies ranging from 0.32 to 30.4 seconds.


### [How Real-Time Rewards Drive 2.5x Higher Customer Spend](https://yomu.fyi/post/how-real-time-rewards-drive-2-5x-higher-customer-spend.md)
- Company: [MongoDB](https://yomu.fyi/company/mongodb.md)
- Published: Sep 16, 2026
- Type: Problem & solution

Many retail loyalty programs use batch processing, delaying rewards by up to 72 hours or 10 business days, eroding customer trust. Nearly 75% of shoppers prefer instant rewards, 82% of Gen Z and Millennials expect real-time responsiveness, and real-time credit can increase basket size by 36% and spending by 2.5 times. Relational databases struggle with atomic writes across multiple tables, forcing batch cycles; layering microservices on top only distributes fragmented data without solving latency. MongoDB’s document model solves this by storing the complete loyalty profile in a single document for atomic writes; Triggers and Change Streams push updated balances instantly and allow promotional rule changes without redeployment. This architecture turns the post-purchase moment into immediate reinforcement, reduces support inquiries, and drives engagement, as seen with Starbucks Rewards contributing nearly 60% of total revenue.