Show HN: Change my website if the agents let you
Article URL: https://actera.dev/playground Comments URL: https://news.ycombinator.com/item?id=50378545 Points: 1 # Comments: 0
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Show HN: Edgepad – Turn an Android phone into a trackpad for your laptop
Article URL: https://github.com/akshit-bansal11/edgepad Comments URL: https://news.ycombinator.com/item?id=50374433 Points: 1 # Comments: 1
Show HN: Homotope, vectorize images to SVG in the browser, MIT engine on NPM
Article URL: https://github.com/stevenroman91/homotope-engine Comments URL: https://news.ycombinator.com/item?id=50370248 Points: 1 # Comments: 0
Show HN: Multiplayer context – a brainpot if you will
Collective intelligence by shared context Comments URL: https://news.ycombinator.com/item?id=50372201 Points: 1 # Comments: 0
Show HN: OldTunes – iTunes 4.9 for modern macOS (With GEISS)
Article URL: https://github.com/rib3ye/oldTunes Comments URL: https://news.ycombinator.com/item?id=50380614 Points: 1 # Comments: 0
Use decision models with Amazon Bedrock AgentCore Evaluations
Some agent-evaluation questions are narrow and repeatable, and decision models answer exactly that kind of question. This post shows how to connect two decision models, Jev and Strands Decider, to Amazon Bedrock AgentCore Evaluations through code-based evaluators, and how to validate a decision-model check before you gate a release on it.
A thousand fewer paths to root
OpenAI built an autonomous pipeline that found, validated, and generated patches for 948 local privilege-escalation vulnerabilities in the Linux kernel.
From Visual Matching to Textual Conditioning: Cross-Domain Few-Shot Segmentation via Reference Inversion
arXiv:2611.04084v1 Announce Type: new Abstract: Cross-Domain Few-Shot Segmentation (CD-FSS) aims to segment objects from unseen domains using only a few annotated references. Recent Segment Anything Model (SAM)-based methods have advanced CD-FSS, yet their reference-target visual matching is highly sensitive to target appearance variations. SAM3 introduces text-conditioned segmentation, enabling natural-language descriptions to guide segmentation across target images, but directly using the category name as the condition still yields unstable performance under severe domain shifts. We propose Reference Inversion for SAM3 (RI-SAM3) that leverages SAM3's large-scale vision-language pre-training to learn suitable textual tokens for each target category. RI-SAM3 comprises three core designs: (i) Bi-level Reference Inversion that learns complementary tokens before and after the frozen text encoder: the former remain anchored in SAM3's text space while the latter represent the category more flexibly; (ii) Layer-adaptive Textual Conditioning combines these tokens differently as object queries evolve across decoder layers; and (iii) Presence-aware Confidence Supervision that jointly supervises query confidence and target presence for reliable predictions. With SAM3 frozen, RI-SAM3 optimizes around 12K parameters only per category and it efficiently segments each target in a single forward pass with reusable textual conditioning without per-sample reference-target visual matching. Extensive experiments over multiple widely adopted benchmarks show that RI-SAM3 achieves superior performance consistently in CD-FSS tasks, together with strong robustness, efficiency, and generalization.
GC-MoE: Overlapping Expert-Parallel Token Dispatch with FP8/FP4 Grouped GEMM via CUDA Green Contexts
arXiv:2611.04222v1 Announce Type: new Abstract: Under expert parallelism, mixture-of-experts (MoE) layers pay all-to-all token dispatch, grouped GEMMs, and a combine step in every layer. In decode each rank holds only 1-256 tokens: dispatch latency and weight traffic dominate while tensor-core utilization is inherently low. In fused operators such as DeepGEMM's Mega MoE, dispatch and computation overlap only by sharing every SM: dispatch warps compete with TMA/MMA and epilogue warps for registers, shared memory, and issue slots; every phase shares one CTA resource budget, synchronization spans the whole SM array, and every replay ends with a workspace-clean pass and a cross-rank barrier. We present GC-MoE, an open-source FP8/FP4 MoE inference operator for NVIDIA Hopper and Blackwell GPUs that uses CUDA Green Contexts to split the device into a small communication partition and a large compute partition. Each role gets its own kernel, resource budget, and synchronization domain, so dispatch and GEMM tune independently. A three-node CUDA Graph (four on Hopper with fused shared experts) launches the dispatch and grouped-GEMM kernels as concurrent roots; a token-ring handshake starts computation on the first filled ring blocks, and double-buffered receive counters remove the trailing cross-rank barrier between replays. The compute partition runs operand-swapped tcgen05 block-scaled MMA on Blackwell and register-A WGMMA with in-register FP4 decoding on Hopper, with fused SwiGLU, requantization, and optional shared experts. On 8 H20 GPUs GC-MoE accelerates DeepSeek-V4.1-Flash decode by 1.188x on average on both the routed and shared-expert suites (peaks 1.296x and 1.353x); on 8 B300 GPUs it accelerates GLM-5.2 and DeepSeek-V4.1-Flash by 1.106x (1.123x with fused shared experts, peak 1.209x), beating Mega MoE at all 54 measured points at matched accuracy. GC-MoE reserves only 20 of 148 SMs on B300 for dispatch.
LiquidCP: Decoupling Attention Tasks from Communication and Kernel Boundaries in Context Parallelism
arXiv:2611.04401v1 Announce Type: new Abstract: Context parallelism (CP) partitions long sequences across GPUs for LLM training and inference. For variable-length training and full prefill, attention-balanced placement can leave token loads uneven, while ring execution repeats overhead at each computation and communication step. In stage-serialized decode CP (DCP), long-running tasks delay dependent work and leave substantial GPU hardware idle. We propose LiquidCP, which unifies training and inference through a common model of Transfer, Compute, and Reduction tasks with tile-level dependencies, decoupling attention tasks from communication and kernel boundaries. For training and full prefill, MegaRing incorporates BuddyRing placement to balance attention and token loads, and coalesces ready KV regions during forward attention to amortize overhead. For decoding and mixed batches, MegaDCP selects history-KV splits by jointly modeling the completion of attention and its dependent reductions. A shared persistent GPU-resident engine executes both designs. Compared with the fastest baseline per configuration on eight H100 GPUs, MegaRing achieves geometric-mean core-attention speedups of $1.23\times$ forward and $1.18\times$ backward, and accelerates a Transformer layer by $1.08$--$1.16\times$. MegaDCP achieves geometric-mean core-attention speedups of $1.20\times$ for decode-only and $1.35\times$ for mixed batches. In vLLM serving with KV prefix cache, it achieves an average time-to-first-token speedup of $1.22\times$.
Show HN: Alternative to Nvidia Auto-Ontology
This repo provides an alternative to the ontology compiling capabilities of NVIDA Auto-Ontology.The main difference is that it builds the ontology from most of your existing data assets (transformation code, dashboards, docs), instead of building it from your warehouse and semantic layer only.It consists in skills and python scripts that ingest the metadata and docs your team already has, and compile them into YAML and Markdown files structured in a file tree that an agent can use as context to query your data.When your existing asset contradict, or reveal a gap in the business context around your data, it writes down questions for you and your team instead of guessing a plausible meaning.It runs locally in any coding agent. Comments URL: https://news.ycombinator.com/item?id=50363667 Points: 1 # Comments: 0
Show HN: TubeHaus – Open-source YouTube app for Android, desktop and TV
Article URL: https://github.com/biolend83/tubehaus Comments URL: https://news.ycombinator.com/item?id=50363507 Points: 1 # Comments: 0
v1.19.0 - Trace Your Workflows, Layer by Layer
Dify 1.19.0 enhances observability with nested workflow tracing, allowing users to follow execution steps inside Workflow Tools, inspect LLM calls, and maintain context across nested invocations. The release also enables portable app packages via App packages, improves Agent file uploads and previews, introduces Skill Hub automation through CLI commands, and upgrades the bundled plugin daemon to version 0.6.14-local. Key changes include support for format v2 app packages, enhanced security, improved editing workflows, and updated environment variables and API endpoints.
vLLM Delivers Day-0 Support for Kimi K3.1 Pro
vLLM supports Kimi K3.1 Pro on day 0: sparse attention on a KDA hybrid, a packed KV cache with FP8 or NVFP4 latents and MXFP4 index keys, token-sharded prefill, decode context parallelism, and fused low-latency decode.
FlatCake: A Flat KVCache Storage Architecture for LLM Serving
arXiv:2610.38355v1 Announce Type: new Abstract: KV cache reuse accelerates LLM serving, but existing systems manage host DRAM and NVMe SSD as a tiered hierarchy where DRAM acts as an intermediate cache. Production LLM workloads break both premises of this hierarchy. First, bimodal reuse defeats DRAM temporal filtering, since workloads are dominated either by one-shot blocks with long reuse distances or by multi-gigabyte agentic sessions that exceed DRAM capacity, putting SSD on the critical read path. Second, DRAM staging and eviction-ordered layout bottleneck SSD restoration. Holding host staging buffers for in-flight prefixes causes contention under load that aborts prefetches, while incremental eviction scatters a session's blocks across the SSD and prevents large sequential reads. We p
Wabi reels in $55M for its consumer-focused AI assistant
Wabi Inc., the developer of an artificial intelligence assistant for consumers, today announced that it has raised $55 million in funding. IVP led the seed round with contributions from Amazon Alexa Fund, Andreessen Horowitz and Forerunner. They were joined by a group of angel investors that included Salesforce Inc. CEO Marc Benioff and Shopify Inc. […] The post Wabi reels in $55M for its consumer-focused AI assistant appeared first on SiliconANGLE.
Specifically Designed for End-to-End Large Models: XHEART Unveils the X7 Ultra Intelligent Driving Chip, Featuring China’s First Mass-Produced Armv9 Automotive‑Grade CPU
On October 29, XHEART unveiled its next-generation intelligent driving large‑model chip, the X7 Ultra. In response to the industry trend of shifting from multiple small models to “end-to-end large models,” the company adheres to the philosophy of “large models define the chip.” Starting from the fundamental requirements of real‑world mass‑production models, it reverse‑engineered and redesigned the computing architecture, storage solutions, and data flow mechanisms, while also leaving ample room for future model evolution. The article further discusses core computing power and architectural design.
How to build evidence-grounded agent memory with OpenWiki and Graphs
LangChain used OpenWiki, its open-source wiki compiler, and an evidence graph to build a persistent memory layer that preserves knowledge for agents to reuse across tasks. Learn how.
GitHub intelligence puts your code, context, and history to work
Agents should understand more than your codebase—they should understand how your team works. Your preferences, repository conventions, skills, and custom instructions guide how changes should be made, while code in… The post GitHub intelligence puts your code, context, and history to work appeared first on The GitHub Blog.
NVIDIA RTX Spark Windows Laptops Gain New Models, Agent Tools and Games
NVIDIA RTX Spark powers thin and light laptops built for everything that developers, creators and gamers do. RTX Spark laptops are now available from ASUS, Dell, HP, Lenovo, Microsoft and MSI. These powerful systems combine an NVIDIA Blackwell RTX GPU and an NVIDIA Grace CPU in a single superchip with up to 128GB of unified […]
