World Wire

DeepSeek published DeepSeek-V4-Flash-0731 on Hugging Face and moved the official V4-Flash API into public beta on July 31, 2026. The model card is explicit that this is the official release superseding the preview, and that the architecture and size are unchanged. The gains come from re-post-training, not a new design. The checkpoint ships with the DSpark speculative decoding module attached, matching the structure of DeepSeek-V4-Flash-DSpark . Hugging Face reports 304B parameters for the repo, which includes that draft module on top of the 284B base. On the API side, deepseek-v4-flash now natively supports the Responses API format and is adapted for Codex. The V4-Pro API and the app and web models were not updated. Is it deployable? Yes, in two very different ways. Via API, it is deployable by almost anyone: DeepSeek’s pricing page lists deepseek-v4-flash at $0.14 per 1M input tokens on a cache miss, $0.0028 on a cache hit, and $0.28 per 1M output tokens...

JetBrains Research Open-Sources KotlinLLM. KotlinLLM is an IntelliJ IDEA plugin for Kotlin/JVM projects that adds a language feature called Smart macros . A Smart macro is a regular Kotlin function call whose body is generated Kotlin code. The public API is deliberately small. asLlm<F, T>(from, hint) converts an input of type F into a typed value T, such as a data class, enum, list, or primitive. mockLlm<T>() generates a stateful implementation of an interface T, whose behavior depends on which methods are called on it. Copy Code Copied Use a different Browser val issuesApiUrl: String = asLlm(repoInput, hint = "GitHub API URL: get all issues, including closed") val issues: List<Issue> = asLlm(response, hint = "Return all beginner-friendly issues for this repository") The runtime loop When a project launches through the KotlinLLM run configuration, the plugin scans for asLlm and mockLlm calls, updates generated bootstrap/provider/...

Nous Research has released Hermes Agent support for Buzz , Block’s open source, self-hostable workspace where humans and AI agents share the same channels. Buzz is built on Nostr. Every message is a signed event on a relay you own, and every participant, human or agent, is a keypair. That removes the bot-token model. Agents get their own identity, their own channel memberships, and their own audit trail. Is it deployable, and for whom? Yes, and both sides are self-hostable today. Buzz is Apache-2.0 with 18.8k stars; Hermes Agent is MIT licensed. Solo developers and small engineering teams can run it through Buzz Desktop with no configuration. Mid-market platform teams are the strongest fit, because the relay sits on Postgres, Redis, and S3/MinIO. Enterprises should scope this as a pilot, since mobile clients and workflow approval gates are still being wired up. Practical applications include incident memory over channel history, branch-as-room code review, agent-drafted...

In this tutorial, we build and execute a multi-agent workflow with Omnigent using a reliable, isolated Python environment created with uv. We configure a financial research lead agent that retrieves a live USD-to-EUR exchange rate from an external API, prepares a concise client-ready summary, and delegates its draft to a dedicated text-auditing sub-agent for clarity and length validation. We define reusable Python functions as callable agent tools, describe the complete agent structure in YAML, and use the Claude Agent SDK as the execution harness. We also manage the Anthropic API key securely through environment variables, apply non-interactive policies that limit tool calls and control session costs, and run the workflow directly from Colab without requiring Node.js, tmux, or an interactive terminal. Through this implementation, we explore how Omnigent combines agents, tools, delegation, live data access, and governance within a single configurable system. Copy Code Copied Use...

Google DeepMind has released Gemini Robotics 2 , the intelligence layer for its next generation of robots. The release moves the stack past table-top manipulation into whole body control, five finger dexterity and multi robot teamwork. It ships as three separate models with three different access tiers. Most robots today are pre-programmed or tele-operated for narrow, repetitive task sequences. They do not adapt to unpredictable environments, and skills rarely transfer between robot bodies. Gemini Robotics 2 targets all three limits at once. TL;DR Three models ship together: a VLA, an embodied reasoning VLM, and an on-device VLA. One checkpoint drives Apollo 2 with two different hands plus a Franka Duo gripper. Gemini Robotics ER 2 is public preview; the VLA and on-device models stay gated. Multi-finger dexterity is still the weak axis, ranging from 32% to 92%. ASIMOV-Agentic, a new safety benchmark, is on Hugging Face under CC-BY-4.0. 3 Models and Wh...