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An agentic conversational video intelligence solution built on AWS

AWS has released an agentic video intelligence solution based on the Strands Agents SDK, in which a single AI agent orchestrates Amazon Bedrock, Amazon Rekognition, and Amazon Transcribe at runtime, allowing users to query video content by asking questions in natural language. Responses for already-analyzed content are returned within 1 second, while the first analysis of a new video takes 5–10 minutes; one media and entertainment company used it to reduce the manual review time for more than 200 multi-hour recordings by about 80%.

Biosecurity Is an AI Arms Race: Radical Numerics CEO Eric Nguyen on Genomic Language Models

Eric Nguyen, co-founder and CEO of Radical Numerics, believes that biosecurity is an AI arms race, and that models which enhance biological capabilities can equally help defenders keep pace. The company is building genomic language models (GLMs) that use chain-of-thought reasoning and multimodal perception in the language of DNA to address problems involving RNA, proteins, and more; its team previously participated in developing Evo and Evo 2 at Arc Institute. Nguyen says defenders are currently at a disadvantage in this race.

Fireworks ARCv3 Cuts RL Weight Update Size by Nearly 50%, Enables Cross-Region Training

Fireworks has released the ARCv3 compressor. Across 1,000 production RL weight update deltas, its transfer payload is nearly 50% smaller than ARCv2, with the average delta at about 0.19% of the original BF16 weight size (versus 0.36% for ARCv2), and it can losslessly reconstruct the trainer's exact BF16 weights. The smaller transfer volume lets rollout clusters catch up with the current policy faster, making it more feasible to use compute across regions than to rely on a single large co-located cluster. ARCv3 is available in the Fireworks Training API via fireworks-delta-compression, for teams running their own trainers alongside Fireworks rollouts.

Evaluating Agents Equipped with Skills Using Strands Evals and Amazon Bedrock AgentCore

The Strands Evals SDK and Amazon Bedrock AgentCore Evaluations add three new skill-oriented evaluators: Skill Selection Accuracy returns a binary result for each invoked skill, Skill Instruction Following gives a five-level rating for each prescribed step, and Strands Evals also provides a deterministic Skill Invoked check. These evaluators can run on trajectories recorded by Strands Evals or on OpenTelemetry traces, and are used to pinpoint two types of failures: selecting the wrong skill and incompletely executing instructions; all are operated through the AgentCore CLI.

TypeSafe CEO Diogo Almeida on Jev: Building a System One Model for Production, Not for God

InstructGPT paper author and TypeSafe CEO Diogo Almeida detailed his new model Jev on the Latent Space podcast, describing it as a System One large programmatic model oriented toward code consumption, with its core innovation being an unpublished RLCD (reinforcement learning for calibrated decision-making), optimized for "intelligence per dollar" rather than human preference feedback. He criticized RLHF for causing mode collapse, sycophancy, and hallucinations, arguing that safety alignment suits products like ChatGPT but fragments intelligence at the API layer, and said TypeSafe would rather be seen as a data lab, refusing to publish benchmark tests. He revealed that Jev's daily token consumption has exceeded one trillion since launch, and previewed follow-up models such as ReasoningJev.

What are agentic workflows? A practical guide to automation

Agentic workflows let AI agents reason at each step and decide the next action themselves, rather than executing along a fixed script. They consist of an LLM reasoning layer combined with RAG, memory, and guardrails; high-risk steps still go through fixed approvals, and anything beyond their authority is escalated to a human. ElevenLabs' ElevenAgents Workflows editor can explicitly map decision points, routing between specialized sub-agents or human agents.

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AIUC Completes $40 Million Series A, Launches AIUC-1 Agent Safety Standard and Insurance Program

AIUC announced the completion of a $40 million Series A funding round, led by Ribbit Capital and First Harmonic, with customers including Cursor, Harvey, Lovable, and ElevenLabs. Its AIUC-1 standard covers agent safety, security, and reliability, requiring thousands of simulated tests each quarter for jailbreaks, hallucinations, and data leaks, with a certification period of about 3 to 10 weeks. ElevenLabs has purchased the first AI agent insurance policy, underwritten with participation from Lloyd's of London, and AIUC's evaluation results are used directly for pricing.

Ant Ling Open-Sources Native Multimodal Model Ling-3.0-flash-VL

Ant Ling has released and open-sourced Ling-3.0-flash-VL, the first native multimodal large model in the Ling series. Built as an extension of the Ling-3.0-flash MoE architecture, it has 124B total parameters and 5.5B activated per inference, natively supports image, text, and video inputs, and has a 256K Token context window. The company says native multimodal joint training improved the model's score on the Artificial Analysis Intelligence Index v4.1.1 by 4 points over the text-only version, and it introduces a visual feedback loop of observe, act, verify, and correct for front-end replication, GUI Agent, and medical report interpretation. The model is now available for free trial on Ling Studio, and the BF16 and FP8 versions have been open-sourced on Hugging Face and ModelScope.

GPT-6 Family's First Model Astra Released, Author's Hands-On Test Consumed 4 Billion Tokens

OpenAI released Astra, which the author calls the beginning of the GPT-6 family; it performs outstandingly on ARC-AGI-3 and scored highest on Zapier's AutomationBench, with pricing the same as Fable 5.1. During weekend trials, the author consumed 4 billion tokens and found its capability fluctuates noticeably—it is a showcase model rather than one that reliably does work. Astra in Codex can now continue advancing tasks without waiting for an answer, and OpenAI also claims it has achieved its automated research intern goal, with the next goal being an automated AI researcher by March 2028.

Markov Robotics Uses Modal to Complete End-to-End Training on a Single Platform

Markov Robotics runs preprocessing, training, and deployment entirely on Modal, training a model capable of executing a variety of complex manipulation tasks from less than ten hours of robot data. Its preprocessing pipeline uses .spawn_map to scale to over a thousand containers and up to 500 GPUs, shortening a task that originally took a full day to 20–30 minutes. The team runs 2 to 10 training jobs simultaneously and connects the robots directly to GPUs in us-west via Modal Tunnels, with round-trip network overhead of about 20 milliseconds.

Claude Code supports remote control of sessions from mobile phones

Claude Code adds a Remote Control feature that lets users start new Claude Code sessions on their phones, automatically recovers disconnections between laptop and phone, and loads sessions faster on iOS. Anthropic has also launched the free Claude Academy, offering Claude usage courses, tutorials, and job guides.

Meta's Muse Glimmer 30B Launches on Fireworks, Targeting Persistent Agents

Fireworks has launched Meta's Muse Glimmer 30B dense model under the Apache 2.0 license, supporting 128K+ token context and native image understanding, targeting persistent agents that require multi-turn tool calling. The model scores 75.5 on MCP Atlas, 74.6 on DeepSearch QA, and 51.2 on SWE-Bench Pro, all higher than the corresponding results of Gemma 4 31B and Qwen 3.6 27B; these benchmark figures are reported by Meta. Fireworks also provides recommended settings of temperature 1.0, top_p 0.95, and top_k 64, and supports adjusting reasoning intensity in the system prompt.

Kimi K3 Complete Developer Guide: 2.8 Trillion Parameters, 1M Context, and Together AI Usage

Together AI has released the Kimi K3 developer guide, introducing Moonshot AI's 2.8-trillion-parameter open-weight model, which uses KDA hybrid linear attention, Attention Residuals, and Stable LatentMoE (896 experts with 16 activated), and supports a 1M token context and native visual input. The guide provides usage details for the OpenAI-compatible API, including reasoning_effort (low/high/max, default max), streaming reasoning_content, structured outputs, tool calling, and dynamic tool loading, and notes that sampling parameters are fixed at temperature 1.0 and top_p 0.95. Pricing is $0.30 per 1M tokens for cached input, $3.00 per 1M tokens for uncached input, and $15.00 per 1M tokens for output, with thinking tokens billed as output.

Open Model Roundup: Kimi K3, Qwen 3.8, the Distillation Controversy, and the Open-Closed Gap

The Interconnects podcast uses last week's release of Kimi K3 as the starting point for a quarterly roundup of open models, discussing its gap to the closed-source frontier, the continuing role of GLM 5.2, and progress from Chinese vendors such as Qwen, DeepSeek, and MiniMax. The episode also covers the distillation controversy, the U.S. open-model ecosystem, the distinction between frontier and near-frontier models, and cybersecurity arguments against bans.

Cato AI Labs discloses two zero-click prompt injection RCE vulnerabilities in Cursor IDE

Cato AI Labs discovered two remote code execution vulnerabilities in Cursor IDE that can lead to sandbox escape, both with a CVSS score of 9.8, tracked as CVE-2026-50548 and CVE-2026-50549. The two vulnerabilities stem respectively from the working_directory parameter of run_terminal_cmd being blindly added to the sandbox writable list, and from the fallback logic when symlink canonicalization fails; attackers can overwrite the cursorsandbox binary through zero-click prompt injection in an MCP server or web search results, achieving non-sandboxed RCE. The Cursor team fixed both issues in Cursor 3.0, released on April 2.

Ethan Mollick: The chatbot era is ending, and work is shifting to managing agents

Ethan Mollick argues that, as the length of tasks AI can complete autonomously keeps lengthening, the mainstream usage is shifting from collaborating with chatbots to handing work over to agents. Citing benchmarks such as METR, the UK AI Safety Institute, and GDPval, he says capabilities are improving faster than exponential growth; Epoch found that Opus 4.7 worked autonomously for 14 hours to complete a software package that would originally have required 2-17 weeks of human engineering, at a cost of $251 in tokens. He also mentions that joint research by OpenAI and academics shows that within OpenAI, non-technical roles such as legal and HR are adopting agents at a pace close to that of programmers, with a quarter of employees running at least four agents simultaneously each week.

Anthropic Releases Sixth Economic Index Report: Analysis of Claude Usage Rhythms and Artifacts

Anthropic released its Economic Index report, switching to privacy-preserving telemetry data sampled continuously each day, making hourly usage patterns observable. The report shows that the share of personal-use conversations rose from about 35% on weekdays to nearly 50% on weekends, recipe requests peaked at 2.3 times at 6 p.m., and tax-related conversations on April 14 were 8 times those of an average day in May. The report also classifies conversations by artifact; artifacts were identified in 93% of conversations, of which explanations accounted for 17% and documents and reports for 15%.

Import AI 459: The Difficulty of AI Regulation, Scaling Laws for Protein Folding Models, and Pricing AI Extinction Risk

Economists from the University of Virginia, Anthropic, and the Bank of Canada estimate that the nominal size of the US AI economy in 2025 is about $250 billion, with real growth adjusted for quality of roughly 2,600% per year, but this growth is almost invisible in conventional GDP statistics. The article also discusses why AI regulation is difficult, scaling laws for protein folding models, and pricing the extinction risk of AI systems.