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Nov 6Fri
  1. Databricks Blog22

    Which Data Collaboration Tool: OpenSharing, Clean Rooms, Marketplace

    This Databricks blog compares three data-sharing methods: OpenSharing, which gives recipients direct read-only access to live assets such as tables, views, notebooks and AI models; Clean Rooms, where up to ten parties combine data and run only mutually approved code without exposing full underlying datasets; and Marketplace, a public storefront for discovering and publishing data and AI products. It advises choosing based on the relationship with the recipient and notes the tools are not mutually exclusive.

  2. NVIDIA Technical Blog22

    Five Ways NVIDIA Co-Design Maximizes the Value of Every Byte in the AI Factory

    NVIDIA outlines five co-design approaches for maximizing the value of every byte in an AI factory, where high bandwidth memory (HBM) is described as the fastest, most expensive and most contested memory tier. The piece argues that bytes spent on redundant weight copies, idle sessions' KV cache or recomputed prefixes are bytes not producing tokens, shifting the question from how much memory a GPU has to how much useful work each byte delivers.

  3. Manus Blog38

    How we built a data agent with Manus to help every team make better decisions

    Manus's four-person data team built a data agent on Manus that more than 40 people now use weekly, with roughly 2–3 questions per person on average and heavy users asking a dozen or more a day. The agent connects directly to detailed data and relies on a knowledge base of metric definitions and table relationships, with access split by topic and approval requests automated when users need data they can't see. The team says it took about a week to reach a first working version and that Manus handles the agent infrastructure, letting them focus on data and business context.

Nov 5Thu
  1. Databricks Blog36

    Push-based CDC into Unity Catalog with Zerobus Ingest and Debezium

    Debezium Server 3.7 ships an open-source Zerobus Ingest sink, built by Databricks with NVIDIA's contribution of envelope mode, that pushes change events directly into Unity Catalog Delta tables without a Kafka cluster between capture and the lakehouse. Envelope mode lands each full Debezium change event in a fixed nine-column table with source position and an idempotency key, and the sink waits for Zerobus to confirm durability before Debezium advances its offset, giving at-least-once delivery. Landed events can then be shaped into Type-1 current-state tables via AUTO CDC sequenced by source LSN, or Type-2 history tables.

  2. Browser Use Blog38

    Computer Use vs Browser Use: How AI Agents Should Control Software

    This article compares computer-use agents, which operate a whole desktop from screenshots by moving the mouse and keyboard, with browser-use agents, which drive a real browser over the Chrome DevTools Protocol by reading page structure and acting on elements. It recommends browser use for web tasks because it is more precise, scriptable and parallelizable, and computer use for steps that leave the browser, such as desktop apps or OS dialogs. It also notes that many workflows need both approaches combined.

  3. ElevenLabs Blog22

    Fix speech to text accuracy problems with a smarter workflow

    This guide explains that speech to text accuracy depends on audio input quality, speaker dynamics, and lexical and contextual issues, and that fixes should target the input, processing, or output stage of the pipeline. It recommends recording at 16 kHz or higher in lossless formats with microphones four to six inches from the speaker, using voice isolation, diarization or multichannel recording, and keyterm prompting, and measuring accuracy with word error rate. It also notes that ElevenAPI offers Scribe v2 with keyterm prompting, multichannel transcription, and speaker diarization.

  5. AWS Machine Learning Blog38

    Use decision models with Amazon Bedrock AgentCore Evaluations

    This post explains how to connect decision models—Jev from TypeSafe and the open-source Strands Decider 2B—to Amazon Bedrock AgentCore Evaluations through code-based evaluators running in AWS Lambda, converting evaluator input into decision model requests and probabilities back into evaluation results. It describes noul, choice, and score question types, positions decision models as a calibrated screening layer alongside rather than a replacement for built-in LLM-as-a-judge evaluators, and outlines a five-step process for validating a decision-model evaluator against labeled traffic. A full code sample in the AgentCore samples GitHub repository deploys an HR Assistant agent evaluated by deterministic checks, built-in evaluators, and decision-model evaluators.

  6. ElevenLabs Blog22

    AI voice agent pricing: What it costs for your business

    AI voice agent pricing combines three line items: the platform that runs the agent, the LLM that powers each conversation, and the telephony provider that connects calls. Using ElevenAgents pricing as an example, every paid tier works out to $0.08 per platform minute, with LLM and telephony billed separately at cost, and plans ranging from a free tier with 15 minutes per month to custom Enterprise pricing. The guide also covers concurrency limits, ROI calculation against staffing costs, hidden costs, and ways to reduce spend.

  7. AWS Machine Learning Blog38

    How Indegene is modernizing and scaling enterprise data quality in life sciences

    Indegene built an LLM-powered Data Quality Agent within its NEXT Unified Data Platform on AWS, using Amazon Bedrock, Strands Agents, and Amazon Bedrock AgentCore to generate context-aware data quality rules, validate them, and produce SQL and Python code under human-in-the-loop governance. The agent draws on internal enterprise and external reference knowledge bases via RAG to recommend technical and life sciences-specific business rules. Indegene reports the agent reduced rule definition and implementation effort from about 30 hours to about 4 hours per dataset and cut new table onboarding from 2–5 working days to 2–4 hours.

  8. AWS Machine Learning Blog58

    Four layers of context engineering for production AI agents: How Deep Insight applies them in working code

    This post presents a four-layer context engineering approach for production AI agents—architectural isolation via multi-agent design, prompt-based output control, filesystem-based context externalization, and independent verification with safeguards—illustrated through Deep Insight, an open source multi-agent data analysis solution built on the Strands Agents SDK and Amazon Bedrock AgentCore. Reported results include roughly 95 percent fewer tokens added to context per script from a combined write-and-execute tool and roughly 97 percent smaller system prompts from on-demand skill loading. It is Part 2 of a three-part series.

  9. AWS Machine Learning Blog38

    How U Mobile built agentic telco operations with Amazon Bedrock AgentCore

    U Mobile's Information Systems Division, working with AWS and AWS Partner Axrail, built AiDaH, an AI-Driven Agentic Helpdesk on Amazon Bedrock AgentCore, which now handles over 150 documented operational scenarios across six business divisions. According to U Mobile's internal measurements, monthly trouble ticket volumes fell by up to 84.5 percent in provisioning and network tickets, 81.6 percent in general inquiries, and 48.7 percent in billing and charging between January and September 2026. The post details AiDaH's six-layer architecture, its security foundation including a Well-Architected Security Review of the Landing Zone, and the spec-driven development approach using Kiro.

  10. NVIDIA Technical Blog36

    Use NVIDIA NeMo to Turn Agent Failures into Tests and Harness Fixes

    NVIDIA describes using NeMo to convert production evidence into prioritized issues and custom evaluations for improving agent prompts, tools, models, memory, skills, and harness code. The post notes that generic benchmark scores may not explain domain agent failures, such as choosing the wrong tool, following the wrong workflow branch, missing relevant context, or using an unnecessarily expensive model.

Nov 4Wed
  1. Lovable Blog50

    How we built Lovable, and why we keep rebuilding it

    Lovable's founder recounts how the platform evolved from the 2023 GPT Engineer project into a product where users have created more than 70 million projects, repeatedly reinventing its agent design—from complex async subagents back to a predictable tool chain, then to "knowledge files" with retrieval instead of fine-tuning. He describes stateful sandboxes that keep development servers running for faster edit-check cycles and a framework called Camille for forking and merging entire running computers. He closes with three open problems: automating the full software lifecycle, making all types of software seamless to build, and building a better software stack.

  2. ElevenLabs Blog18

    What is call transcription? A practical business guide

    Call transcription converts phone or video calls into written text, typically via AI speech-to-text models, either after recording or in real time. Businesses use transcripts for searchable records, quality assurance and coaching, compliance documentation, and CRM logging. Legality depends on jurisdiction, with some regions requiring one-party consent and others all-party consent.

  3. Google Developers Blog58

    Cost per task: Using cheap classifiers to route across Gemini models and thinking levels

    The article argues that cost per successful task, not price per token, governs production economics for coding agents, and reports a 50-question auto-graded benchmark routing tasks with TypeSafe's Jev classifier across Gemini configurations. A frozen regex and prompt-length heuristic scored 93.8% on 16 development questions but fell 23.2 points to 70.6% on 34 held-out tasks, while Jev agreed with hand labels on all 34 and 48 of 50 overall; capping maxOutputTokens at 300 saved nothing on easy tasks and broke 7 of 25 hard ones. A router sending tasks to Flash-Lite first and escalating Jev-flagged complex ones to Flash on low thinking solved all 50, as did Flash on low thinking alone, and the article notes caching favors keeping long context-heavy turns on one model.

  4. Meta Engineering34

    F3 Lightning: How Meta Accelerates Ads ML Features to Production at Scale

    Meta introduces F3 Lightning, an infrastructure layer on top of its Facebook Feature Framework (F3) that decouples feature releases from shared execution so each of the 20,000+ features in an ads ranking request can be compiled, validated, deployed, and rolled back independently. To preserve F3's efficiency, it uses stable computation identifiers, a single shared execution plan, one shared memory buffer per request, and dense index mapping for direct array access. Meta reports comparable extraction features reach production about 3.6x faster than prior F3 with no increase in infrastructure compute cost.

  5. AWS Machine Learning Blog38

    Building ultra-low-latency voice AI applications on AWS

    This AWS post explains how Cartesia, an AWS Partner, uses State Space Models (SSMs) and AWS infrastructure to deliver sub-100ms text-to-speech (Sonic) and speech-to-text (Ink) at global scale. It details two deployment patterns for Sonic: an Amazon SageMaker AI model package via AWS Marketplace running in-VPC, and self-managed containers on EC2 GPU instances, and describes Cartesia's voice models operating inside Amazon Connect.

  6. AWS Machine Learning Blog34

    Build a semantic layer for agentic AI with EDB Postgres AI and Amazon Bedrock AgentCore

    This post shows how to build a semantic layer for agentic AI using EDB Postgres AI's AIDB extension, where business meaning lives in Postgres COMMENT statements, join paths come from foreign keys, and governance uses GRANT and row-level security through the built-in MCP endpoint. A Strands Agents agent on Amazon Bedrock AgentCore queries the layer via AgentCore Gateway and an ALB to answer customer 360 questions, with two Postgres roles receiving different results for the same prompt based on column-level grants. AIDB also captures OpenTelemetry traces for each SQL execution as rows in the aidb_otel schema, queryable with ordinary SQL.

  7. AWS Machine Learning Blog22

    How Gen Digital built Norton Revamp’s AI-curated News Engine with Amazon Bedrock AgentCore Web Search

    Gen Digital describes how its Norton Revamp professional reputation platform, launched in March 2026, uses Web Search on Amazon Bedrock AgentCore as a managed, MCP-compliant connector to ground personalized news feeds, content suggestions and goal recommendations in cited, real-time web results. The post details an event-driven architecture using Amazon Nova Lite, Bedrock and an AI Learning System for ranking, plus reported results including 99.9 percent uptime and 3x faster feature implementation.

  8. AWS Machine Learning Blog36

    ServiceNow on-behalf-of token exchange with Bedrock AgentCore Gateway

    This post explains how to configure Amazon Bedrock AgentCore Gateway for an on-behalf-of (OBO) token exchange with Microsoft Entra ID so an AI agent can call the ServiceNow REST API while preserving the calling user's identity. It covers creating Entra application registrations for the agent and a ServiceNow API connector, configuring a ServiceNow inbound OAuth integration with an OIDC provider, and setting up the gateway with MCP version 2025-11-25, an outbound OAuth client using the JWT authorization grant, and a target with the token exchange grant (scope plus requested_token_use: on_behalf_of). The post states that native OBO support removes the need for custom AWS Lambda interceptors to perform token exchanges.

  9. AWS Machine Learning Blog25

    Bring structured and unstructured data together with Amazon Quick

    This AWS post walks through connecting an Amazon S3 bucket as a knowledge base, enriching a Product_Sales.csv dataset with semantic descriptions, and combining both in a Space that a custom chat agent queries through a single conversational interface. The agent uses text-to-SQL for structured questions, cites sources across both data types, and can generate PowerPoint, Excel, and Word artifacts from chat prompts. It also covers prerequisites, sharing, best practices, security considerations, and cleanup.

Nov 3Tue
  1. Hugging Face Blog58

    Transformers Model Integration Contracts

    Transformers introduces Model Integration Contracts (MICs), which pair a shrunk fixture model built from a real checkpoint's configuration with a runnable usage path and human-reviewed baselines, run on cheap CPU runners in PRs. Replaying frozen MICs over five months of commits showed a small UMT5 fixture flagged a decoder-attends-to-future-tokens regression from July 30 to September 28, 2026, which a forward pass alone would have missed. The tmic CLI and agents help draft contracts, and the MIC CI job currently reports failures without blocking PRs.

  4. NVIDIA Technical Blog25

    Keep Distributed Training Running with Automated Workload Recovery

    As distributed AI training scales, failures across hardware and software dependencies increasingly disrupt training progress, making fast, reliable recovery essential. The material notes that a stalled training rank can trigger an NCCL timeout, a GPU node can stop responding to the scheduler, or a straggler can slow the entire job, and that each disruption is manageable on its own.

  5. ElevenLabs Blog20

    How are synthetic voices created for natural speech?

    Synthetic voices are built by training neural AI models on large datasets of real human speech, then generating audio from text or cloning a specific speaker from recorded samples. Modern systems sound natural because they replicate prosody and interpret context rather than stitching together individual words or syllables. Voices can also be designed from a text description alone, without any recording.

  6. AWS Machine Learning Blog38

    How Circle K accelerated DevOps incident triage to under 15 minutes with Amazon Bedrock

    Circle K built an agentic AI triage system on Amazon Bedrock that investigates Grafana alerts across AWS resources, OpenSearch logs, and GitHub code, then posts a structured root-cause summary to Slack. Initially built on Amazon Bedrock Flows, the system was migrated to a single agent on Amazon Bedrock AgentCore Runtime with AgentCore Gateway brokering data source access. Circle K reports mean triage time dropped from 45–60 minutes to under 15 minutes, a 67–75 percent reduction based on internal incident-response metrics.

  7. AWS Machine Learning Blog40

    Agentic engineering intelligence with Amazon Nova Multimodal Embeddings and Amazon Bedrock AgentCore

    This post describes a multi-agent engineering assistant built on Amazon Bedrock AgentCore, Amazon Nova Multimodal Embeddings, and the open source Strands Agents SDK that assembles evidence for aerospace part substitution decisions. A planner routes queries to Catalog, Evaluation, and Knowledge specialists, with deterministic Python tolerance-rule scoring, Nova Multimodal Embeddings for multimodal retrieval and photo cosine similarity, and a supervisor that synthesizes results only when multiple specialists contribute. The engineer remains the decision-maker, and the system drafts justification memos and Integration Impact Assessments with audit trail entries aligned with AS9100 sections 8.4 and 8.5.6.

  8. AWS Machine Learning Blog36

    Build efficient agents with knowledge distillation on Amazon SageMaker AI

    This post explains how to use sequence-level knowledge distillation (SeqKD) to fine-tune a smaller shopping agent on Amazon SageMaker AI, using a frontier teacher (GLM-5.3 on Amazon Bedrock) to generate multi-step tool-use trajectories that are filtered by ShoppingBench's programmatic scorer and used for serverless supervised fine-tuning of a Qwen3.5-4B student. The distilled student reached an average Absolute Success Rate of 0.541 on 324 held-out ShoppingBench problems, versus 0.297 for the base model and 0.540 for the greedy teacher, while IFEval general instruction-following dropped roughly 6 to 8 points. The post also covers per-step trajectory encoding, LoRA training configuration, a low-cost model-evaluation pre-check, and serving the base model and student adapter as two inference components on one real-time endpoint.

  9. AWS Machine Learning Blog34

    Prepare web content for Amazon Bedrock Knowledge Bases with Crawl4AI

    This post explains when to use the Amazon Bedrock Web Crawler connector versus a custom Crawl4AI-based preparation step that writes Markdown and metadata sidecars to Amazon S3 for ingestion into Amazon Bedrock Knowledge Bases. It walks through the sample AWS CDK workflow—scheduled Fargate crawling, S3 upload, and ingestion job—and covers chunking strategies, metadata filtering for retrieval validation, and responsible AI and security considerations.

Nov 2Mon
  1. Trail of Bits Blog64

    Backporting security fixes at scale with Codex

    Trail of Bits describes using Codex with GPT-5.6 to backport security fixes into stable Linux kernel branches and distribution packages, reporting that by October 29 the project had delivered fixes for 149 distinct CVEs into stable kernel releases plus several Fedora and Ubuntu package fixes. The workflow has agents identify missing fixes, adapt patches, assemble validation evidence, run tests and prepare submission packets while engineers review and submit them, cutting preparation and review of a straightforward backport to about 30 minutes and a more involved case to around three hours. The post also presents counts of unfixed CVEs in stable kernel branches and in Debian 13 and Ubuntu 26.04, noting that regression testing and maintainer acceptance remain essential.

    Why it matters: It offers a concrete account of AI-assisted security backporting, including a described workflow, effort estimates and CVE backlog figures for stable Linux software.

Nov 1Sun
  1. OpenRouter Announcements60

    Build an AI Code Reviewer with OpenRouter

    This guide presents a 233-line Python script (review.py) that uses the OpenRouter Responses API with the Files API, Shell server tool, and Apply Patch server tool to run changed files in a hosted Linux sandbox, confirm bugs by executing checks, and return fixes as V4A patches. It covers setup, a five-bug sample repository, a Git pre-push hook, and a GitHub Actions workflow, with benchmark results across four models (e.g., Claude Sonnet 5.5 fixing all five bugs at $0.0447 and 60 seconds). The script writes a report to .git/ai-review.json and exits 1 when a finding meets the --block-at severity threshold.

    Why it matters: It offers a concrete, reproducible implementation with cost and latency benchmarks, plus practical notes on API pitfalls, useful for developers building automated code review.

  2. Databricks Blog50

    Lakebase branching for CI/CD: lessons from 100 PR databases

    Snorkel AI replaced ~20-minute Aurora database restores for each PR environment with Databricks Lakebase copy-on-write branches, provisioning a writable production-like database in under 60 seconds. A DMS-replicated main branch stays within about 2 minutes of production Aurora, and each PR branch is created via API, given a TTL, and deleted when the PR closes. The design targets about 100 concurrent PR branches on a ~2.5 TB dataset, with per-branch compute scaling to zero and service principals using short-lived OAuth tokens for CI.

  3. Browser Use Blog38

    How to Track Competitor Prices with Python

    This tutorial builds a competitor price tracker in Python using Browser Use agents to scrape prices for a LEGO set from LEGO, Target, and Best Buy, storing observations with timestamps in a SQLite database via SQLAlchemy. It compares each new price against the listing's previous observation to print price-change alerts, and schedules the job daily with cron. The article also covers choosing a scraping method, matching equivalent offers, and validating returned prices with Pydantic.

Oct 31Sat
  1. OpenRouter Announcements62

    How to Build Your Own Always-On AI Agent

    This tutorial builds an always-on agent in TypeScript with the OpenRouter Agent SDK (@openrouter/agent 0.11.0) that checks Hacker News on a schedule, writes a digest of new relevant stories, remembers seen story IDs across restarts, and caps each run at six steps or five cents. Each check cost $0.0003 to $0.0012 with Claude Haiku 5.5 in the author's runs on October 13, 2026, roughly $0.86 to $3.46 a month at one check every 15 minutes. It also covers pausing a run for human approval before a tool posts to a team channel, resuming after a crash, and deploying on a VPS or scheduled job.

    Why it matters: It offers a concrete, cost-measured walkthrough of the practical pieces—scheduling, memory, spend limits, and approval—that separate a demo agent from one that runs unattended.

  2. AWS Machine Learning Blog36

    Managing Amazon Bedrock costs end to end: A playbook for FinOps and platform teams

    This AWS post lays out a five-stage, looped operating model for Amazon Bedrock cost management: attribute every dollar to an owner via IAM principal and session tags in CUR 2.0, make every request observable through model invocation logging and stamped request metadata, enforce budgets at one of three tiers (monitor-only, IAM deny on breach, or synchronous gateway blocking), optimize platform defaults such as intelligent prompt routing and prompt caching, and operate a scorecard with a review cadence. It notes that agentic workloads issue many invocations per task and scale token consumption with autonomy and context size rather than user interactions, and it addresses the shared-role attribution problem in LLM gateways. FinOps leads the attribution and operate stages, platform engineering leads observability, enforcement, and optimization, and the review is joint.

  3. AWS Machine Learning Blog50

    Preview web apps live as your coding agent builds them with Amazon Bedrock AgentCore runtime

    Amazon Bedrock AgentCore runtime Session endpoints give each agent session an authenticated URL that forwards HTTP and WebSocket traffic to the ports you allow via allowedPorts, so a Vite site running in the session can be viewed in a browser as the agent edits it. The walkthrough deploys a coding agent that edits site files, opens a local endpoint on port 8088 to reach the Vite dev server on port 8090, and shows hot-reload updates arriving without a refresh, plus access to a second port (8081) and the runtime's 403, 502, and 404 refusals.

  4. NVIDIA Technical Blog25

    Build a Validated Kubernetes Runtime for NVIDIA Dynamo

    The article addresses the need for a repeatable path from model configuration to a working inference endpoint when deploying NVIDIA Dynamo, a distributed inference framework that coordinates model serving across GPU workers, on Kubernetes. It notes that deployment is only a first step, since a successful model deployment also requires GPU drivers, resource allocation, scheduling, networking, and monitoring to all work.

  5. ElevenLabs Blog20

    Warm transfer vs cold transfer: choosing the right call

    This article compares warm transfers, where the receiving party gets the caller's context before connecting, with cold (blind) transfers, which pass no advance context, and describes a middle "tepid" transfer that dials the destination without a conference or live introduction. It recommends warm transfers for complex, sensitive or high-value calls and cold transfers for simple, high-volume routing, and details how ElevenAgents' transfer_to_number tool supports Conference, Blind and SIP REFER transfer types, including client_message and agent_message parameters and no-answer fallbacks.