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November 13Friday
  1. Hugging Face Blog58

    llama.cpp Releases v1.0.0, Stabilizing Downstream APIs with Semantic Versioning Rules

    llama.cpp has released v1.0.0, and ggml has also released v1.0.0. Going forward, both projects will indicate incompatible changes to the public C API and ABI through major version number changes. The project team states that as local AI and privacy needs grow and downstream applications increase, stable library interfaces have become critical, and the boundaries for breaking changes will be stricter in the future.

  2. OpenClaw Blog20

    OpenClaw Brings Claw Hack Hackathon and ClawCon to Shenzhen

    OpenClaw is bringing its three-day Claw Hack hackathon and ClawCon conference to Shenzhen on December 4–6, 2026, following its Shanghai event. Claw Hack spans software and hardware tracks, with M5Stack, Seeed Studio's reBot Arm B601 and Pamir hardware, and applications close November 25. ClawCon runs December 6 with talks on open-source AI, open-weight models and running agents locally and in the cloud.

  3. Databricks Blog36

    Bright Machines uses Debezium and Iceberg to give customers live dashboards from air-gapped factories

    Bright Machines now gives customers live, governed dashboards of factory data from air-gapped plants without opening any inbound access. It captures PostgreSQL changes at the edge with Debezium, writes them as Apache Iceberg, pushes them out over one auditable outbound path, and reads them in place in Databricks via Unity Catalog federation. The first customer-visible dashboard went live about two months after kickoff, with committed rows queryable in one to two minutes and dashboards refreshing every ten minutes.

  4. AWS Machine Learning Blog58

    AWS releases SGLang-Neuron, enabling deployment of frontier models on Trainium with the existing SGLang interface

    Amazon announced the public availability of SGLang-Neuron, an out-of-tree plugin that runs SGLang on AWS Trainium through an AWS Deep Learning Container, released by RadixArk with Annapurna Labs. Users keep the same SGLang server interface and generation APIs, while the plugin supplies Trainium-specific models, attention, MoE and a graph runner built on native PyTorch and NKI kernels compiled with torch.compile. It also supports PD disaggregation, transferring the prefill KV cache to a decode server via NIXL over EFA, and the walkthrough serves openai/gpt-oss-120b on a Trainium3 EC2 instance with the pytorch-inference-sglang-neuronx container.

  5. AWS Machine Learning Blog66

    AWS updates AgentCore runtime with 15x faster session starts and 4x more compute

    AWS announced updates to Amazon Bedrock AgentCore runtime, its managed compute layer for agents, with sessions now starting up to 15x faster, scaling to 4x more compute (up to 8 vCPUs and 32 GB, with container images up to 10 GB) and running up to 68% more efficiently, while each session stays in its own hardware-isolated Firecracker MicroVM. Sessions start in under 2 seconds regardless of image size or concurrency, idle agents scale to zero, and agents can mount and save work to a filesystem such as an Amazon S3 bucket with FUSE support so data stays inside the trust boundary. The runtime is available today in AWS Regions across North America, South America, Europe, and Asia Pacific.

    Why it matters: AgentCore runtime 的新版本把会话启动、算力上限和内存计费一起调整，为需要长时间运行的智能体提供了隔离与成本之间的新取舍。

  6. AWS Machine Learning Blog40

    AWS opens catalog of 23 reusable generative AI assets from its Innovation Center

    AWS has published a catalog of 23 production-ready, open source generative AI assets from its Generative AI Innovation Center, now part of AWS Frontier AI Engineering & Services. The building blocks cover RAG pipelines, document processors, agent scaffolding and evaluation frameworks, and AWS says they can cut development from months to weeks. DoorDash reported a 50x increase in testing capacity and Ricoh a 7-fold throughput increase after building on the assets.

November 12Thursday
  1. vLLM Blog60

    vLLM adds day-0 support for Thinking Machines' Inkling 2 Small

    vLLM supports Thinking Machines' Inkling 2 Small on day 0, a multimodal text-and-vision MoE with 594B total and 17B active parameters and up to 1M tokens of context. The integration adds kernel fusion with PDL, sequence parallelism for prefill that cuts an 8K-token prefill step by 22% at TP4 and 26% at TP8, and a Gluon relative attention decode kernel that drops a batch-1 decode step at 100K context from 15.1 ms to 4.8 ms. vLLM reports up to 630 TPS/user at TP4 on GB200 with acceptance length 3.5, and also supports LoRA and RL via SkyRL.

    Why it matters: vLLM 在发布当天即支持 Inkling 2 Small，并给出内核融合、序列并行和 Gluon 解码内核的具体优化数据，可供部署大 MoE 模型的团队参考。

  2. vLLM Blog64

    vLLM-Omni benchmarks MiniMax H3-Image on Vera Rubin: 0.87s 4K generation and 6,115 images/hour

    vLLM-Omni brought MiniMax H3-Image to NVIDIA Vera Rubin and measured 0.87-second median latency for native 4096×4096 text-to-image on four GPUs, plus 6,115 images/hour in a separate throughput configuration. H3-Image is an open-weight text-to-image and editing model that pairs a Qwen3-VL encoder with a 33.1B dense DiT and an image VAE, handling both tasks in six sampling steps with up to 16 reference images. The team reports MXFP8 DiT GEMMs, Ulysses SP4, AdaLN caching and a VAE decoding change that cut decode time from 2.635s to 0.213s, and lists recipes for AMD, Intel, Huawei Ascend and Moore Threads hardware.

    Why it matters: vLLM-Omni's benchmark shows MiniMax H3-Image running on Vera Rubin at subsecond 4K latency, with per-hardware configurations and a cost model for serving economics.

  3. Mastra Blog40

    Mastra Agents Private Model Gateways for Self-Hosted Models

    Mastra added private model gateways that let agents route requests to self-hosted open-weight models, referenced by a prefix like private/ollama/gpt-oss:20b. Self-hosting keeps sensitive data inside the network and replaces per-token costs with infrastructure costs, and setting AUTO_BLOCK_EXTERNAL_PROVIDERS=true hides external providers in Studio. The feature requires @mastra/core@1.82.0 or later.

November 11Wednesday
  1. Modal Blog58

    Modal joins gVisor maintainers as Google donates the project to CNCF

    Modal is one of three companies, alongside Ant Group and Tines, joining gVisor's maintainers as Google donates the project and its trademarks to CNCF under a maintainer-led governance model. Modal says it launches upwards of 40 million gVisor containers a day and has contributed upstream since 2023, including nvproxy GPU support, an RDMA device proxy called rdmaproxy, and tmpfs and memory-allocator optimizations. The company says it is working on multi-GPU memory snapshots and GPUDirect Storage support for gVisor.

  2. vLLM Blog58

    vLLM adds encoder disaggregation for multimodal serving with EPD

    vLLM has expanded Encoder/Prefill/Decode (EPD) disaggregation for multimodal serving, adding metadata-only requests, a dedicated encoder execution path, remote embedding transfer via NIXL and Mooncake, cross-encoder output reuse, and orchestration through vLLM Router, SMG, and llm-d. Core capabilities including MiMo and XPU support ship in vLLM v0.32.0, while metadata-only support for DeepSeek V4.1 Flash is available on main via #62802. In a DeepSeek V4.1 Flash test on GB200, EPD delivered 6.6–9.5% higher per-GPU throughput and 44–47% lower P90 TTFT than an eight-GPU PD baseline at concurrency 64–128, but had lower per-GPU throughput at concurrency 16.

  3. Meta Engineering Blog58

    Meta explains TLX, a hardware-native GPU compiler extension to Triton for production ML

    Meta detailed TLX (Triton Low-Level Extensions), an extension to Triton that adds explicit warp-group and cluster-level orchestration while keeping Triton's tensor programming model. TLX is already running in production across Meta, with 20-25% end-to-end MFU on thousands of NVIDIA Blackwell GPUs for GEM training, up to two-thirds lower compute-intensive net latency for RecSys inference, and a distributed grouped GEMM kernel deployed in large-scale LLM pretraining. Meta says TLX targets NVIDIA Hopper and Blackwell plus AMD Mi300 and Mi350x, and is working on more architectures, automatic warp-group inference and a torch.compile backend.

  4. NVIDIA Blog60

    NVIDIA Contributes OpenShell Agent Runtime to CNCF and Adds DSX Kubernetes Tools

    NVIDIA is contributing OpenShell, its open source runtime for securing and governing autonomous agents, to the Cloud Native Computing Foundation as a Sandbox project, and is also contributing to Google-led Agent Substrate. NVIDIA announced three new DSX AI Factory Platform components: NVIDIA Kubernetes Engine and NVIDIA Kubernetes Recipes, now in public preview, and NVIDIA Snapshot, which restores checkpointed replicas in seconds without model reload or warmup. Crusoe tested GLM-5.3 on Crusoe Cloud and cut workload ready time from roughly eight minutes to 31 seconds, while Saturn Cloud reduced vLLM startup from between two and 8.5 minutes to six and 34 seconds.

    Why it matters: NVIDIA contributes its agent security runtime OpenShell to CNCF and adds Kubernetes tooling for GPU AI factories, with early Snapshot results cutting inference startup from minutes to seconds.

November 10Tuesday
  1. LangChain Blog59

    How Stripe built Kai on Deep Agents in one week

    Stripe built Kai, its company-wide Knowledge AI Platform, on LangChain's open-source Deep Agents harness, with one engineer shipping the initial version in a single week. The stack layers a Stripe-specific harness and configuration layer over Deep Agents, using filesystem middleware backed by S3, sandbox middleware for code execution, summarization middleware for long sessions, and skill-based dynamic tool loading across 500+ internal MCP tools and 1,000+ skills. Kai grew from 296 to over 5,000 users in roughly four weeks and is now used weekly by 83% of Stripe across more than 60k sessions.

  2. Meta Engineering Blog40

    Meta extends LLDB with vendor-neutral accelerator plugin architecture for CPU-GPU debugging

    Meta has built a vendor-neutral accelerator plugin architecture in lldb-server that lets LLDB debug applications spanning CPUs and GPUs in a single session. The design represents the CPU and GPU as separate LLDB targets, each with its own architecture, threads, registers and address space, while a plugin translates vendor device debugging APIs into LLDB concepts. GPU support activates only when the app uses a supported GPU runtime, so CPU-only debugging keeps the existing native path without added overhead.

  3. Goodfire Research62

    Goodfire studies Qwen3.6-35B-A3B neurons as an interpretable basis

    Goodfire Research published a study arguing that individual MLP neurons in Qwen3.6-35B-A3B are more interpretable than assumed, based on sparsification, attribution tools and research agents. Ablating the 92.2% weakest MoE neuron activations per forward pass raised language model loss by only 2.9%, and 69% of neuron poles had a single mode responsible for at least 90% of their causal effect. Natural-language neuron descriptions let a judge model predict firing with 95% accuracy against random text and recover 73% of ablation damage through simulation.

    Why it matters: Goodfire's study of Qwen3.6-35B-A3B neurons argues that strong activations alone carry most of the model's computation, offering a concrete alternative to feature-dictionary interpretability.

November 9Monday
  1. Preferred Networks News55

    Preferred Networks releases PLaMo 3.0 Fin finance-specialized small language model as open weights

    Preferred Networks released PLaMo 3.0 Fin, a finance-specialized small language model built on the PLaMo 3 NICT pretraining model from joint research with NICT and further trained on Japanese financial data, publishing open weights on Hugging Face under the PLaMo Community License. The model adds step-by-step Reasoning with reasoning traces output in Japanese, plus tool calling for web search, calculation and internal database access, and can run on a single GPU in closed cloud or on-premise environments. PFN says the instruction-following training for Reasoning and tool use was carried out on its own MN-Core 2 AI chip, with code porting completed within a week and training speed comparable to a GPU environment.

November 6Friday
  1. vLLM Blog58

    vLLM migrates its CUDA build to the LibTorch Stable ABI

    vLLM has migrated its CUDA build to the LibTorch Stable ABI, and as of v0.33.0 it publishes LibTorch ABI Stable CUDA wheels, so a new PyTorch wheel can be imported without rebuilding vLLM. The team moved in-tree sources and seven LibTorch-dependent dependencies, including FlashMLA, DeepGEMM and FlashAttention-2/3, onto stable APIs, using torch-abi-audit in CI to confirm no unstable symbols remain. The post notes tradeoffs such as a limited stable API subset and small host-side overhead, and says CPU and ROCm builds are next.

  2. OpenClaw Blog50

    OpenClaw adds self-updating model catalog so new models work without an app update

    OpenClaw rebuilt how it discovers models so new ones like GPT-6.1 Sol appear without shipping a release, pulling lists from a self-updating catalog at catalog.openclaw.ai (rebuilt every four hours, about 1,000 models), each account's own provider model list, and a handshake with a newer Codex on your PATH. The catalog switches live on a running Gateway, and model lists no longer shrink after provider timeouts or catalog swaps.

  3. OpenClaw Blog69

    OpenClaw releases Multiplayer for shared agents and context

    OpenClaw has released Multiplayer, a free and open-source feature that lets multiple people connect to the same OpenClaw agent and workspace, with prompts appearing live across laptops and requests coordinated through a shared queue. In demos at GitHub Universe in San Francisco, two attendees edited the same 3D scene by typing prompts on separate machines without restating context. The OpenClaw Foundation says it has had 50+ people collaborating in the same shared environment and is continuing to expand the feature.

    Why it matters: OpenClaw 把多人共享同一 Agent 与上下文做成免费开源功能，为团队协作中反复重建上下文的问题提供了一种可迁移的做法。

November 5Thursday
  1. Mastra Blog38

    Mastra Launches Custom Harness Program with Embedded Forward-Deployed Engineers

    Mastra launched the Custom Harness Program, in which forward-deployed engineers embed with customer teams to co-build tailored agent harnesses. The program includes observability, evals, custom MCP, human-in-the-loop workflows, auth and RBAC, plus early access to the Mastra Enterprise Platform. Mastra cites Workday, which moved off Amazon Bedrock Agents in 31 days, cut 3,000 lines of code and lowered vector-storage costs by 98%.

November 4Wednesday
  1. Ai2 · Allen Institute for AI38

    Ai2 and Google collaborate on NeuralGCM+ climate models

    Ai2 has launched a climate modeling initiative backed by three-year funding from Google.org and technical support from Google Research to develop NeuralGCM+, an open-source model for faster, more reliable climate projections. The project extends Google Research's NeuralGCM, which combines physical equations for large-scale dynamics with AI for smaller-scale processes like cloud formation and rainfall, to simulate atmosphere and ocean together. Ai2 aims to cut regional temperature and rainfall errors over the past 50 years and release models and frameworks with documented workflows for outside researchers.

  2. vLLM Blog73

    Buoyant releases Kinference, an open source inference control plane for vLLM and Kubernetes

    Buoyant announced Kinference, an Apache v2 open source control plane that routes OpenAI- and Anthropic-compatible inference traffic to vLLM workers on Kubernetes, adding token rate limiting, per-tenant usage and cost tracking, KV-cache-aware load balancing, and YAML inference policies. In the project's public benchmarks, Kinference cut median time-to-first-token by 95% and median end-to-end latency by 45% versus standard Kubernetes service load balancing on one workload, and served 7.4x the goodput on shared-prefix and about 3.5x on multiturn in a capacity test. Policies can route simpler prompts to cheaper models, burst overflow to providers such as Bedrock and Databricks, or keep PII-tagged requests inside the cluster, and per-tenant metrics cover token rates, TTFT, ITL and cached versus uncached prompt tokens.

    Why it matters: Buoyant's Kinference adds KV-cache-aware load balancing and YAML inference policies on top of vLLM, with benchmarks claiming large TTFT and goodput gains over standard Kubernetes service routing.

November 2Monday
  1. Zyphra Research58

    Zyphra introduces ZHA hierarchical attention, a sparse method with subquadratic routing

    Zyphra introduced Zyphra Hierarchical Attention (ZHA), a sparse attention method that builds a tree of compressed KV-cache summaries and routes queries top-down through it, giving subquadratic complexity with suitable top-p thresholds. Existing pretrained models can be adapted via a short KL-distillation phase using only a few billion tokens; on Qwen3.6-35B-A3B, ZHA adds 81,920 trainable parameters, about 0.00024% of the base model. On RULER-13 at 256K tokens the wide-tree model scored 96.2% versus 96.0% for dense attention, and the deep six-level tree scored 63.4% on LongBench v2 versus 58.0% for dense while attending to about 1.1% of the KV cache; a GPU roofline model projects 32-35x speedup over dense attention at 10M tokens.

  2. Ai2 · Allen Institute for AI32

    How Olmo 3 earned Nextcloud's highest ethical AI rating

    Ai2's Olmo 3 received Nextcloud's highest Ethical AI rating, green, which requires open-source training and inference software, a self-hostable trained model, and publicly available training data. Nextcloud bundles Olmo 3 with its llm2 local LLM backend, and the 7B variant was chosen so users without 16 or 32 GB of VRAM still get a good out-of-the-box local experience.

November 1Sunday
  1. Sakana AI Blog40

    Sakana AI Symposium: Jürgen Schmidhuber on AI's Past and Future, Japan's Push into Language Models, World Models and Physical AI

    Sakana AI held its "From World Models to Real-World AI" symposium in Tokyo on October 26, 2026, where Chief Scientific Advisor Jürgen Schmidhuber gave a keynote and Sakana AI announced it will develop its own pretrained language model series, Sakana Sawara (tentative name). The first small model, Sawara Small (tentative name), is planned for release in spring 2027 if development proceeds smoothly. More than 400 attendees joined the event, which also featured a conversation between Schmidhuber and CEO David Ha.

October 29Thursday
  1. Meta Newsroom · AI22

    Meta Partners With Five US Universities to Accelerate Medical and Scientific Breakthroughs

    Meta is partnering with five US universities — Caltech, Ohio State's College of Medicine, Louisiana State University, Indiana University and UTHealth Houston — and granting $2 million to support research using its models in medicine, physics, chemistry, biology, computer science and mathematics. Projects include a computer vision system built on Meta's open-weight SAM models to detect tracheostomy tube displacement in infants, multimodal AI to help conversational agents adapt to people with dementia, and open-weight models predicting heart failure risk from routine CT scans and medical records.

October 23Friday
  1. Modal Blog58

    How Modal and Sakana AI served a ~1T-parameter MoE model for the Sakana Namazu API

    Modal's forward deployed engineering team worked with Sakana AI to run Sakana Namazu, a ~1T-parameter MoE model tuned for Japanese business context, on Modal for its public API launch. The team applied selective NVFP4 quantization to routed experts only, keeping attention, the router and shared experts in BF16, and calibrated on Japanese-heavy conversations so all 23,040 experts saw representative traffic. A custom DFlash draft model trained on more than 30 billion raw token IDs raised speculative token acceptance by 25% and cut per-token decode latency by about 35%, while a second drafter trained on roughly 1M real requests lifted average acceptance length from 3.51 to 5.01 and cut p99 latency from 68s to 52s.

October 22Thursday
  1. Anthropic Newsroom71

    Anthropic donates Agent Skills standard to the Agentic AI Foundation

    Anthropic is donating Agent Skills, the open standard for giving AI agents specialized knowledge and workflows, to the Agentic AI Foundation (AAIF) under the Linux Foundation. More than 70 agent platforms including ChatGPT, Cursor, GitHub Copilot and VS Code have integrated the standard since it was published in December 2025, with 1 million skills published and nearly 280 million installed. The donation covers the Agent Skills name and logo, the GitHub organization and the agentskills.io domain, with engineers from Anthropic, Google, OpenAI and other companies maintaining it as individuals under Lead Maintainers David Soria Parra and Mahesh Murag.

    Why it matters: Anthropic hands its Agent Skills standard to the Linux Foundation's AAIF, the same neutral home as MCP, giving the community a formal say over the spec's roadmap.

October 21Wednesday
  1. Mistral AI48

    Mistral and NVIDIA Partner on AI for Engineering Workflows

    Mistral and NVIDIA are collaborating to build AI-native engineering workflows that pair Mistral Large 4 with NVIDIA CUDA-X libraries and Warp, aiming to speed simulation and enable closed-loop design. The partnership will also produce public benchmarks and open datasets for physics models and agentic AI in CAE and EDA, plus integration with NVIDIA Omniverse for 3D scene generation and SimReady assets. Mistral Large 4, known as Le Chonk, has roughly 1 trillion total parameters and 49 billion active parameters and was trained on 3,800 NVIDIA Grace Blackwell GPUs.

  2. OpenClaw Blog22

    OpenClaw Launches Focus Weeks, Starting With Control UI

    OpenClaw launched Focus Weeks, a program that each week targets one area of its roadmap with a Maintainer on point and invites the community to contribute code, bug reports, testing, documentation and accessibility feedback. The first Focus Week runs October 19–25, 2026 on the Control UI, with Vyctor Brzezowski as Maintainer. A select group of rotating Clawtributors also gets temporary guest access to a shared OpenClaw Team environment to test its multiplayer collaboration features.

October 15Thursday
  1. Modal Blog34

    Augment Your LiveKit Agent with Modal GPUs

    Modal explains how to run LiveKit Agents with open-weights or custom models on its serverless GPUs, using BitHuman's open-weights Expression Video Avatar voiced by Qwen3-TTS to clone Charles Frye. The setup exposes the avatar container's /launch endpoint via a Modal Server and uses Sticky Sessions to keep WebSocket clients on the same container, with GPU snapshots hiding cold starts.

  2. Meta AI Research Blog88

    Meta releases Muse Spark Open, an 816B open-weight multimodal model that runs on two Mac Studios

    Meta released the weights for Muse Spark Open, an open-weight natively multimodal model with 816B total parameters (42B active) and a 1M-token context window, positioned for coding and agentic work. It is the first model in its class to run on a desktop using two Apple M5 Ultra Mac Studios with the MLX framework, and it is available through API partners including Baseten, Fireworks AI, Groq, Modal, Nebius, OpenRouter and Together AI, with weights on Hugging Face, Amazon SageMaker AI, Google Cloud Model Garden and Microsoft Foundry Models. Meta says the model ships with auto-tunable Helion GPU kernels in SGLang, delivering 1.03-3.95x kernel-level speedups on NVIDIA and AMD GPUs and a 3% end-to-end speedup on AMD MI350.

    Why it matters: Meta releases open weights for an 816B-parameter multimodal model that runs on two Mac Studios, giving developers a local option for long-horizon coding and agentic tasks.

October 13Tuesday
  1. LiveKit Blog18

    Troubleshooting LiveKit Egress: Common Errors and Fixes

    LiveKit published a troubleshooting guide for Egress, covering common errors such as the 412 "start signal not received" from templates that never call START_RECORDING, 404 participant or track not found within 30 seconds, and 400 upload failures from invalid S3/GCP/Azure credentials. It also addresses layout, webhook, and self-hosted Egress issues, and notes the guidance applies to both the current StartEgress API and the deprecated source-specific APIs.

October 8Thursday
  1. Goodfire Research71

    Goodfire trains and deploys probe-based cyber misuse monitors on Kimi K3 and GLM 5.3

    3 and deployed them on a production inference stack, where a cheap probe filters turns and only suspicious exchanges reach an LLM judge. 5% benign-session interruption rate, costs $65 per million turns without judge reasoning and $185 with it, and adds no measurable throughput loss inside SGLang.

    Why it matters: Goodfire shows a probe-plus-judge cascade can monitor agentic cyber misuse at roughly 50x lower cost with no measurable throughput loss, a concrete recipe for serving open models safely.

October 1Thursday
  1. Modal Blog58

    Modal Clusters 正式可用，通过 @modal.clustered 一行代码获取多节点 RDMA 集群

    Modal announced that Modal Clusters are generally available to all workspaces, letting users request multi-node clusters through the single decorator @modal.clustered. The clusters provide InfiniBand RDMA communication at up to 6.4 Tbps, automatically configured for PyTorch and NCCL, with clusters starting within seconds and billed by the second. Modal also describes a new gang scheduler that places N nodes at once and RDMA support built into the gVisor runtime, with Decagon, 1x and Runway cited as users for trillion-parameter fine-tuning, world model pre-training and multi-node video inference.

September 28Monday
September 25Friday
  1. OpenClaw Blog74

    Microsoft Autopilot is built on OpenClaw, with contributions flowing both ways

    Microsoft introduced Autopilot, a persistent personal agent built on OpenClaw, as part of its new Copilot experience, with a private preview expanding to first customers at the end of this month. Autopilot is the new name for Microsoft Scout, announced earlier this year, and Omar Shahine's team says it is working with OpenClaw and the OpenClaw Foundation on an enterprise-grade runtime. OpenClaw highlights upstream contributions from Microsoft engineers, including policy conformance checks, a native Windows companion, and reliability fixes for background jobs, restarts and duplicate replies.

    Why it matters: Microsoft's Autopilot is built on OpenClaw, and the upstream contributions listed here show how a shared agent runtime improves across platforms.

September 22Tuesday
  1. OpenClaw Blog60

    OpenClaw adds opt-in decision models and a shared Plugin SDK interface

    OpenClaw introduced support for a separately configured decision model that its core and plugins can call through api.runtime.decisions.evaluate in the Plugin SDK, returning a choice, score or probability instead of a chat reply. The feature is opt-in and does not replace the chat model, and the provider packages are still awaiting a supporting release. The TypeSafe adapter supports hosted Jev and a local System One server such as Jared Palmer's Kev, and the evaluation tool is moving into OpenClaw core as decision_evaluate.

    Why it matters: OpenClaw adds an opt-in decision model layer that returns typed answers from application code, with a shared Plugin SDK interface so plugins can use it without per-provider integrations.

September 21Monday
  1. LiveKit Blog38

    Comparing Open Source Frameworks for Realtime Voice and Video AI Agents: LiveKit Agents vs Pipecat

    This guide compares LiveKit Agents and Pipecat, the two open source frameworks it says matter most for building production voice and video AI agents, across architecture, adoption, developer experience and production readiness. LiveKit Agents runs on LiveKit's open source WebRTC and SIP infrastructure and wires the pipeline for you, while Pipecat assumes no transport and has developers assemble the pipeline stage by stage. It also covers where TEN Framework, Dograh, Vision Agents and Vocode fit in.