New context engineering challenges in late 2026
LangChain CEO Harrison Chase shares insights into the biggest harness engineering challenges with the emergence of proactive agents.
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Prepare web content for Amazon Bedrock Knowledge Bases with Crawl4AI
This post shows how to use Crawl4AI, an open source web crawler, as a content preparation layer for Amazon Bedrock Knowledge Bases, handling JavaScript rendering, selective extraction, and custom metadata enrichment before web content reaches an Amazon S3 data source.
AI Programming Tutor Challenges Low-End Android Phones! No Server Required—Python and SQL Run Directly in the Browser
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ggml : add batched tensor copy API (#30881) * ggml : add generic batched tensor copy API Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * llama : use batched tensor copy API in context io Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * ggml : add batched tensor copy API Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * llama : batch device io tensor copies Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * ggml : add CUDA support for batched tensor APIs Assisted-by: pi:llama.cpp/ggml-org/DeepSeek-V4-Flash-Vision-Exp-GGUF (Mac Studio, M2 Ultra, 192GB) * ggml : remove async tensor_n APIs a
[AINews] Building AIE CODE: The top Coding Agent Conference
Mistral’s open-weight release, frontier monitoring, and the day’s AI Twitter and Reddit recaps.
How to Build Proactive, Always-On Agents with the Schedules SDK in Managed Deep Agents
Most proactive agents run on a simple cron. Learn what scheduled runs unlock, from memory to smart triage, and how to add them to Managed Deep Agents.
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.
[AINews] Anthropic ends Coding Interviews: "Hunger beats tenure"
Since coding is solved, why code for the job?
How Anthropic ships Claude with Claude: Separating the Brain from the Hands
Katelyn Lesse and Angela Jiang on Opus 5.5, the Claude Platform, and the engineering behind AI agents.
How We Built Our Design Agent
How LangChain built Content Studio, an open-source design agent in Slack that makes on-brand videos, graphics and decks, and the harness that powers it.
[AINews] Anthropic files for $2T IPO in 2 weeks: 1400% growth rate, 48% of revenue through cloud partners, 85% margin, $1B op profit in Q2, $134B cash in bank and $10B lifetime burn
The S-1, the numbers, and the reactions.
MANTA: Unlocking DAG Flexibility in Asynchronous BFT
arXiv:2610.34419v1 Announce Type: new Abstract: DAG-based BFT protocols marked a shift in consensus design by replacing single-leader proposal paths with concurrent block proposals linked through a shared graph, where references encode consensus evidence. However, under heterogeneous wide-area networks (WANs), existing designs suffer from prolonged rounds and degraded performance due to straggler effects. The root cause is rigid round advancement: replicas move in synchronized rounds, waiting for enough references from the previous round before advancing. We propose a flexible DAG growth framework for asynchronous BFT consensus. Our key observation is that while reference quorums are necessary for safety and liveness, they need not block every round advancement. Instead, the connectivity r
When and Why to Use TMA: A Workload-Grounded Characterization of Tensor Memory Acceleration on NVIDIA Hopper GPUs
arXiv:2610.34907v1 Announce Type: new Abstract: The Tensor Memory Accelerator (TMA), introduced on NVIDIA Hopper, is used pervasively in production GPU kernels yet its usage is conspicuously selective: expert kernel developers in CUTLASS, FlashAttention-3, and FlashInfer apply TMA to some tensor operands within a kernel but fall back to cp.async, direct global loads, or distributed shared memory (DSM) for others. Vendor documentation describes the API, and recent microbenchmarking work characterizes TMA throughput in isolation, but neither answers the question practitioners actually face: given a memory operand in a kernel, should it use TMA, and if so, with or without cluster multicast? We address this gap with a workload-grounded characterization study on H100. We catalog the TMA, multic
Async-RBench: Benchmarking Asynchronous Task Execution in LLM Agents
arXiv:2610.32429v1 Announce Type: new Abstract: Large language model (LLM) agents increasingly execute tasks asynchronously: a main agent continues working while its subagents run, and the task state may change before their results arrive. The main agent must decide which work to update or preserve and which checks to repeat, while avoiding prohibited changes. Final task success alone does not reveal whether these results were handled correctly, and natural asynchronous arrivals are hard to reproduce. We introduce Async-RBench, a benchmark for asynchronous task execution in LLM agents, with 200 executable tasks covering eight scenarios. A scenario controller regulates result delivery using predefined task-state conditions, while an Event Response Contract (ERC) specifies what each response
GLP Networking API Specification
arXiv:2610.32583v1 Announce Type: cross Abstract: Multiagent deterministic GLP (madGLP) runs Grassroots Logic Programs (GLP) as agents that communicate solely by asynchronous message passing, and assumes fair delivery: every message sent is eventually delivered. Each agent runs on a person's smartphone, named by the person's public key. A grassroots platform depends on no global resource but the network, so agents must reach one another over Bluetooth Low Energy, on a local network, and across the Internet from behind NAT, via rendezvous servers anyone can run. What madGLP requires of such a network, and what the network requires of the platform, has not been specified. We specify the networking layer beneath madGLP. Anything that can be done in GLP is done in GLP: the layer provides only
Hugging Face Releases Carbon-X, a 14B Open DNA Language Model that Brings DNA and Natural Language Together for the First Time
Hugging Face released Carbon-X, an open 14.8B DNA language model built on Qwen3-14B. It reads raw DNA in chat, answers in English, and averages 66.7% across 5 DNA benchmarks under Apache 2.0. The post Hugging Face Releases Carbon-X, a 14B Open DNA Language Model that Brings DNA and Natural Language Together for the First Time appeared first on MarkTechPost.
Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL
Async GRPO with LoRA across HF Jobs: a bucket, a proxy, and no NCCL
Unlocking asynchronicity in continuous batching
Unlocking asynchronicity in continuous batching