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Nov 13Fri
  1. NVIDIA Newsroom22

    How AI Is Helping Care Teams Get Patients the Right Care, Sooner

    NVIDIA describes how healthcare systems and startups use AI to reduce routine chart work, citing Dyania Health's Synapsis AI, a clinical reasoning engine that runs on NVIDIA-accelerated computing inside a hospital's firewall and surfaces source-traceable clinical signals for clinician review. Cleveland Clinic used it to support a study of more than 7,800 aortic valve replacement patients from 2005 to 2021, building a predictive model of valve deterioration and reintervention risk at three, five and ten years.

  2. Manus Blog38

    AI Game Maker vs. Game Engine: What to Know Before You Choose

    This guide compares AI game makers, which build a playable browser game from a text description with no coding, against traditional engines like Unity, Unreal, Godot, GameMaker and GDevelop that require you to build the game yourself. It covers differences in control, cost, publishing and limitations, then walks through making a first game without code using Manus Game Dev, and lists signs it's time to switch to an engine.

  3. Manus Blog36

    How to Make a Mobile Game Without Any Coding

    A step-by-step guide to building a simple mobile game without code: choose a publishing route (browser link, app store, or game platform), design around one touch control, write a one-page plan, and pick a no-code or AI game maker such as Manus Game Dev, GDevelop, Construct 3, or Astrocade. It covers building a minimal first version, testing on a real phone, and publishing, with cost breakdowns including Google Play's $25 one-time fee and Apple's $99/year membership.

  4. Manus Blog28

    50 Creative Game Ideas to Kickstart Your Next Project

    This guide presents 50 game concepts grouped by genre and tagged Easy, Medium, or Hard, each with a clear mechanic and twist, plus a starter prompt for an AI game maker. It explains what makes a good game idea using four questions (goal, obstacles, fail state, actions), offers tips for generating original ideas, and walks through building a browser game in Manus without writing code.

  5. n8n Blog22

    New in n8n: Public API rate limits that keep every team on your instance running

    n8n 2.47.0 introduces per-API-key public API rate limits, available on all plans for Cloud and self-hosted, with separate read and write limits across all endpoints and per endpoint. Clients receive 429 responses with Retry-After and rate-limit headers, and in multi-main setups every main shares one count per API key through Redis. Rate limiting is off by default and can be enabled via Settings > Policies > Public API Limits, the settings API, or environment variables, and works alongside idempotency keys (since 2.45.0) to make retries safe.

  6. MIT News AI20

    Three Lincoln Laboratory technologies receive 2026 R&D 100 Awards

    MIT Lincoln Laboratory announced that three of its technologies are among the 2026 R&D 100 Awards recipients: the Magnetite secure-by-design operating system for embedded computers, the Quantum Microsystems wafer-scale platform integrating superconductor and semiconductor chips, and an algorithm that cancels 5G base-station interference on co-channel RF devices. The awards, managed by R&D World and judged by an independent panel, recognize 100 innovations transitioned to use or made available for license in the past year. Laboratory director Melissa Choi said the technologies address critical problems in cybersecurity, quantum computing, and communications.

  7. MIT News AI22

    Helping robots fit into the human workplace

    Robust AI, co-founded by MIT Professor Emeritus Rodney Brooks and Anthony Jules, builds a mobile robot called Carter that is designed to work alongside humans in warehouses and factories, featuring a force-sensitive handlebar, an AI-powered vision system, and holonomic wheels. Carter is deployed in logistics warehouses and on factory floors to help workers pick or stock shelves and move items, with workflows that let it guide workers to locations, follow them, or transport loads across large facilities. The company says its approach is to understand workers' primary tasks and build features that accelerate them, letting humans do what they are good at while digital systems handle the rest.

  8. MIT News AI34

    New AI “copilot” helps engineers design better structures

    MIT researchers developed a human-in-the-loop topology optimization system that presents regions of an algorithmic design to a human designer for feedback as it is generated, using machine learning trained on synthetic human-feedback data to identify likely regions of interest. In demonstrations, the approach improved manufacturability and linear buckling load by 39 percent while adding only 15 seconds to total design time compared to all-algorithmic approaches. The system currently only lets users make structures within identified regions thicker or thinner, and the researchers hope to add other modification options in future versions.

  9. Codex Releases22

    0.169.0

    Codex 0.169.0 adds multiple side-chat tabs in the opt-in terminal workspace, task history search from the agent command center, collapsible turn details in the full-screen transcript, and custom model provider editing from the terminal. It also adds Ctrl+S local diagnostic ZIP saves, opt-in Cloud connection features on macOS and Linux, and fixes including explicit project trust for non-interactive runs and Windows sandbox handling for quoted cmd.exe paths.

  10. Codex Releases10

    0.171.0-alpha.2

    This GitHub Changelog entry is titled "0.171.0-alpha.2" and lists navigation links to GitHub products and services, including Copilot, Actions, Codespaces, Advanced Security, and pricing. The supplied material contains no details about what changed in this version.

  11. Codex Releases10

    rust-v0.171.0-alpha.3

    This GitHub Changelog page entry is titled "rust-v0.171.0-alpha.3" and otherwise consists of site navigation and product menu content, with no release notes or details about the version.

  12. Modal Blog42

    Sandbox resourcing: burst, don't reserve

    Modal argues that fixed-resource reservations fit poorly with bursty Sandbox workloads, since a typical Sandbox spends more than half its life using under 6% of its peak CPU, so users over-reserve and pay for peak constantly. Modal's Sandboxes now burst by default from a base request of 0.125 cores and 128 MiB, with active-resource pricing billed each second for what is used, and the company cites 58% savings versus a fixed-size reservation on an illustrative 20-minute coding agent task. It also describes an OOM SLO of below 0.0001% per minute for bursting Sandboxes under 8 GiB and tools like exit snapshots and the termination reasons SDK to reduce eviction impact.

  13. OpenRouter Announcements60

    Reranking in RAG: How to Fix Bad Retrieval with a Reranker

    This guide explains that vector search often ranks near-miss chunks first because embeddings compress whole chunks into single vectors, and shows how adding a cross-encoder reranker over the top 30 candidates raised precision@1 from 81% to 97% on a 274-question synthetic benchmark at roughly $0.10 per 1,000 queries. It compares 11 rerankers (voyageai/rerank-3-lite and rerank-3 were the most accurate dedicated models and cheapest, while LLM rerankers like openai/gpt-6-luna hit 99.6% but took about 4 seconds per call), covers candidate-count tuning, input_type settings, and silent failures such as cohere/rerank-v3.5's 4,096-token limit. It includes a Python example using the embeddings and rerank APIs and notes that real-world gains depend on your own documents and evaluation set.

    Why it matters: Offers a measured, benchmark-backed walkthrough of adding reranking to a RAG pipeline, including cost, latency and failure-mode comparisons useful for teams tuning retrieval.

  15. ElevenLabs Blog22

    7 customer service skills every human and AI agent needs (with examples)

    This guide outlines seven customer service skills—communication, problem-solving, conflict resolution, empathy, adaptability, personalization, and product knowledge—and explains how both human and AI agents apply each, citing a Zendesk study that more than 50% of customers switched to competitors after a single bad experience. It describes implementation steps including defining CX goals, choosing an agentic AI platform, architecting the agent workflow, and testing before deployment, and notes Gartner's projection that at least 70% of customers will start their customer journey using an AI interface by 2028. It also covers training and feedback through metrics such as CSAT, first contact resolution, and average handle time, and distinguishes hard from soft skills.

  16. Codex Releases12

    0.171.0-alpha.1

    This GitHub Changelog entry is titled "0.171.0-alpha.1" and lists only the site's navigation and product menus, including Platform, Copilot, Actions, Codespaces, Secure Code, and Pricing. The supplied material contains no details about the release itself.

  18. Claude Code Releases32

    v2.1.323

    Claude Code v2.1.323 adds an experimental opt-in Code Mode in which Claude writes JavaScript to call its tools, plus plugin engine version ranges in plugin.json and TypeScript hook type checking in claude plugin validate. The release also adds .docx support to Artifact tool asset uploads and fixes a wide range of issues across permissions, sandboxing, sessions, MCP, OpenTelemetry and Remote Control.

  19. vLLM Blog72

    DeepSeek-V4.1-Flash on Vera Rubin NVL72: 58x Throughput vs. GB300 NVL72 at 387 tokens/s

    On a single NVIDIA Vera Rubin NVL72 rack, vLLM serves DeepSeek-V4.1-Flash at 1.7M tokens/s per GPU with 387 tokens/s per user, 58x GB300 NVL72, reaching 2.52M at peak throughput and 1,810 tokens/s for a single user. The gains come from kernels tuned for Rubin's split memory domains and FP4 tensor cores, specialized low-latency decode kernels, and a rack-wide KV cache and Engram tables shared over NVLink. The post details four optimization groups and reports a 96.5–97% prefix hit rate at high concurrency with workload-aware routing.

    Why it matters: It offers a detailed, measurement-backed look at how Rubin-specific memory and MMA features translate into serving throughput and latency for a frontier open model.

  20. GitHub Blog · AI & ML58

    Build once, run on any agent: A practical guide to agent skills

    GitHub's guide explains agent skills as versioned directories centered on a SKILL.md file that capture repeatable task instructions shareable across agents, while the environment still supplies tools, credentials, and permissions. Using examples like Proof Agent for adversarial verification and Data Forge for a multi-skill data pipeline, it recommends keeping project configuration in a manifest, sharing execution paths between skills, and placing human checkpoints and CI checks in workflows. It also covers packaging skills for clients such as Claude Code, Cursor, and GitHub Copilot, and notes that applications suit workflows needing stable interfaces, shared state, audit trails, or permissions enforced outside the model.

  21. Databricks Blog22

    Dispelling LTAP myths

    This post rebuts four common misconceptions about LTAP: that it is CDC replication, that a single copy forces slow lookups or scans, that open table formats mean eventual consistency, and that Postgres logical replication suffices. It argues Lakebase keeps one logical table with the lake as its durable tier and caches in front of it, so no layer adds a second catalog or source of truth. Each myth is traced to the same misunderstanding of treating every materialization of the data as a second database.

  22. Microsoft Research22

    Microsoft Research Focus – Edition #98

    This edition of Microsoft Research Focus covers AI-powered universal translation, the resolution of the 31-year-old KLS conjecture in high-dimensional geometry, and a new approach to making AI coding agents faster and more efficient. It also highlights a research intern's journey at Microsoft Research Asia – Tokyo and the Latent Core Tokenizer, which produces more linguistically meaningful tokens across 104 languages.

  23. AWS Machine Learning Blog47

    How Natera designed an agentic AI system for lab ops with Amazon Bedrock AgentCore

    Natera's lab engineering team built SWARM (Systematic Workflow Analytics and Resolutions Manager) on Amazon Bedrock AgentCore to capture and deploy institutional knowledge for lab instrument support, using two runtimes (swarm-agent and swarm-hive) that share a versioned library of 33 skills. The system reduced average first response time from 17.4 hours to roughly 8 minutes (about 130x faster) and delivered an automated first response on 96.2 percent of tickets, measured over a 90-day production evaluation period. The post details the architecture, including a custom document pipeline with Amazon Titan Text Embeddings and Amazon Bedrock Knowledge Bases, keyless OIDC-based deployment, Jira integration via EventBridge and Amazon SQS, and a continuous evaluation loop.

  24. AWS Machine Learning Blog58

    Deploy frontier AI models on AWS Trainium with SGLang-Neuron

    Amazon announced that SGLang-Neuron, released by RadixArk with Annapurna Labs, is publicly available through an AWS Deep Learning Container, letting users serve models and generate RL rollouts on Trainium via the same SGLang interface. The out-of-tree plugin runs on native PyTorch, is selected with SGLANG_PLATFORM=neuron, and supports PD disaggregation with KV-cache transfer via NIXL over EFA. The post includes a walkthrough on a Trainium3 EC2 instance using the pytorch-inference-sglang-neuronx container to serve openai/gpt-oss-120b.

  25. Claude Blog78

    Using Claude Opus 5.5 as your daily driver for knowledge work

    Anthropic says Claude Opus 5.5 is now the default model on Pro, Max, Ultra, and Team plans and recommends it for daily knowledge work such as research, analysis, and producing memos, reports, spreadsheets, and decks, priced at $4/$20 per million input/output tokens. The post describes internal tests on research reports and a fictional merger analysis, plus guidance to start at Medium effort, drop "think hard" prompts, and attach charts rather than retyping them. It also walks through a demo project where Opus 5.5 tested four theories for slowing retail growth and proposed an alternative explanation involving new stores opening too close to existing ones.

    Why it matters: Useful for teams deciding which Claude model to default to and for concrete prompting changes when moving to Opus 5.5.

  26. Codex Releases22

    0.168.0

    Codex 0.168.0 adds TUI model provider browsing and default selection for new chats, an opt-in side conversation pane (features.tui_workspace) with resizable and zoomable panes, audio attachments with rejection recovery, and opt-in animated GIF image generation. It also fixes account-scoped local history, keeps threads on their saved model provider when defaults change, and improves TUI rendering of interrupted replies and reasoning summaries.

  27. vLLM Blog60

    Inkling 2 Small on vLLM: Day-0 Support

    vLLM announces day-0 support for Thinking Machines' Inkling 2 Small, a multimodal text-and-vision MoE model with 594B total and 17B active parameters, up to 1M tokens of context, and a native DSpark speculator. The post details the architecture (48 layers, 384 experts with top-8 routing, block attention residuals, gated attention, a 23-layer ViT image encoder, and mixed NVFP4/MXFP8 expert precision) and vLLM's optimizations, including kernel fusion with PDL, fused MoE activation quantization, sequence parallelism for prefill, and a Gluon relative attention kernel, reaching up to 630 TPS/user at TP4 on GB200. It also covers LoRA and RL (via SkyRL) support, a serve command, agentic-workload benchmarks, and accuracy checks on NIAH up to 1M tokens, MMMU-Pro and HLE.

    Why it matters: Useful for engineers evaluating or deploying a large multimodal MoE model, as it pairs concrete architecture and quantization details with vLLM-specific kernels, benchmark numbers and a ready serve command.

  30. AWS Machine Learning Blog40

    Four Amazon Bedrock inference choices that affect cost

    The post explains how four request-level Amazon Bedrock settings—prompt cache duration, service tier, reasoning effort, and maxTokens—can change token processing and billing rates without altering the model or prompt. It reports tests showing that a 1-hour cache pays off from the second request per hour when gaps exceed 5 minutes, that Flex on Nova 2 Lite processed the same tokens at half the Standard rate in us-west-2, that lowering effort from high to low cut median output tokens by 169% on an agent test, and that a 155-token maxTokens fence avoided 1,893 output tokens in one test. It recommends confirming model support, changing one setting at a time, and verifying effects through response metadata, CloudWatch metrics, invocation logs, and billing data.

  31. Google Developers Blog54

    Catch Go bugs that fuzzing misses with Gemini and a constraint solver

    The author describes a workflow that compiles Go functions to RISC-V assembly, has Gemini 4 Argon translate the surviving runtime.panicBounds traps into Z3 constraints, and replays any satisfying input through go test to produce a real reproducer. The pipeline caught three planted bugs and three latent bugs in skill-lint, and the post explains why go test -fuzz missed two crashes because fuzz inputs usually have spare slice capacity, fixed by clipping capacity to length with a full slice expression. It also notes that an unsat result only means "maybe correct," since the LLM-written solver model is not a sound proof system.

  32. Claude Blog78

    Introducing Claude Meetings

    Anthropic announced Claude Meetings in beta, letting Claude join video calls as a visible participant that answers questions aloud or in chat, builds docs and whiteboards with the team, and writes notes and action items. It is rolling out on Team and Enterprise plans with support for Google Meet, and each meeting gets a page in claude.ai with notes, transcript and artifacts. By default Claude stays out of calls with outside guests unless an admin allows it, and it saves a transcript, summary and screen descriptions rather than audio.

    Why it matters: Useful for teams evaluating AI meeting assistants, since it details capabilities, admin controls, privacy defaults and beta limitations.

  33. AWS Machine Learning Blog36

    Tokenomics at scale: Track and cap Amazon Bedrock spend per IAM principal with CUR 2.0 and AWS Budgets

    This AWS post describes how to get per-identity Amazon Bedrock cost visibility and enforceable spend limits using only AWS-native features: IAM principal and session tags for attribution, Cost Explorer or CUR 2.0 exports (with the caller identity option) for reporting, and AWS Budgets with budget actions that attach a Deny IAM policy when a threshold is crossed. It notes that budget data refreshes up to three times a day, cost allocation tags are not retroactive, and a Deny action on a shared role blocks everyone using it.

  34. Google Developers Blog58

    How Agent Anomaly Detection scores on benchmarks we didn't write

    Google tested its Private Preview Agent Anomaly Detection feature on five public, non-Google agent-security benchmarks (AgentDojo, InjecAgent, Agent Security Bench, AgentDyn and AgentHarm), running 1,774 sessions through an ADK agent using gemini-2.5-flash. It flagged 87% of harmful instructions the agent actually carried out, with false alarms on 2% to 16% of benign sessions, but caught only 4 of 18 sessions where poisoned long-term memory drove the agent to act. The post details per-benchmark and per-app results, known gaps such as resisted instructions and small actions, and recommended defenses including Model Armor, Semantic Governance Policy and human approval for high-impact actions.

  35. Codex Releases12

    0.170.0-alpha.4

    This entry is a GitHub Changelog page for version 0.170.0-alpha.4, but the supplied material contains only site navigation and product menu text, with no release notes or details about the version itself.

  36. Databricks Blog28

    Eclipse Aerospace simplifies flight-data ingestion with Databricks

    Eclipse Aerospace worked with Databricks to replace a multi-stage AWS Lambda and Avro pipeline with an Arrow-native PySpark custom data source that wraps its existing Rust flight-data decoder, reading proprietary telemetry files directly in Spark on serverless compute. The approach, enabled by the Python Data Source API available from Databricks Runtime 15.4 LTS, cut latency, removed duplicate Avro storage and a Lambda tier, and brought decoding into the Spark UI and job metrics. Eclipse reports the connector can process records within a minute of upload and commonly handle multi-gigabyte batches in parallel in under two minutes.

  37. Databricks Blog22

    How Constellation is building a governed data foundation on Databricks

    Constellation, the largest US nuclear fleet operator, is building a governed Enterprise Data Platform on Databricks to give data consistent shared meaning across domains, choosing Databricks partly because it interoperates with existing architecture rather than requiring a rip-and-replace. The platform rests on conformed semantic definitions, a semantic knowledge layer with selective Data Vault modeling, and Unity Catalog for lineage, fine-grained access and ownership of meaning, with early use cases including a nuclear engineering assistant built with Databricks Genie, outage planning, and sales intelligence.

  38. Hugging Face Blog66

    LLMON: An LLM-Native Markup Language, now in Granite 4.1 3B and 8B

    IBM researchers introduce LLMON (LLM Object Notation), an XML-like markup language that uses reserved special-token delimiters to explicitly mark instructions, data, requirements and execution bindings in an LLM's context, addressing the brittleness of JSON-style formats for causal models. They release two experimental preview models derived from Granite 4.1 base checkpoints (granite-4.1-3b-LLMON-preview and granite-4.1-8b-LLMON-preview) plus a GitHub toolkit, reporting that on the OPI benchmark LLMON models avoid 47% more indirect prompt injection attacks than the best frontier model tested while maintaining general capability on par with Granite 4.1 Instruct. The models ship with their own chat template and lossless JSON-to-LLMON converters, and the post notes the previews are for exploration, not production.

    Why it matters: Useful for teams weighing structured context formats against JSON and for anyone evaluating prompt-injection defenses in small open models.

  39. AWS Machine Learning Blog34

    Building a self-service LLM gateway with Amazon Bedrock and LiteLLM

    This post describes a pattern for a self-service LLM gateway that runs LiteLLM on AWS Fargate to expose Amazon Bedrock models through an OpenAI-compatible API, using Bedrock Application Inference Profiles for per-model quota isolation and scoped IAM access. It covers configuration of developer tools, LiteLLM model mapping with per-token costs for budget enforcement, CDK constructs for inference profiles, Langfuse observability, CloudWatch TPM alarms, and lessons learned from scaling to several hundred developers.

  40. AWS Machine Learning Blog34

    Optimizing cost per task and proving agent ROI on Amazon Bedrock

    This AWS post argues that model cost should be measured per completed task rather than per token, citing four hidden cost drivers—tokenizer efficiency, output verbosity, agentic loop efficiency, and reasoning overhead—and an illustrative example where a model with double the list price costs 65% less per task. It describes levers for reducing cost per task (prompt caching, intelligent prompt routing, output constraints, batch inference) and Bedrock features such as CountTokens, the usage block, and invocation logging for measuring token and turn counts. For ROI, it recommends documenting and measuring a human baseline before launch, instrumenting workflows as experiments with run records, joining cost to outcomes via Athena, and using a five-dimension scorecard with green/yellow/red thresholds.