Carl Franzen writes that DeepSeek has launched V4.1-Flash, a model featuring a 552-billion-parameter mixture-of-experts backbone designed to drastically reduce costs for long-context workflows through specialized caching and architecture. The model offers extremely low off-peak rates of $0.003 per million cached input tokens, making it highly competitive against frontier models like GPT-5.6 Sol and Claude Opus 5 when used in repetitive agentic loops. While its total parameter count has increased significantly compared to previous versions, its Causal Encoder-Decoder architecture aims to minimize compute requirements during the prefill stage of inference.
- V4.1-Flash utilizes a Causal Encoder-Decoder architecture that activates 8 billion parameters during prefill and 16 billion during decoding.
- The model features an extremely high context window of up to 1 million tokens.
- DeepSeek's technical report highlights the use of FP4 KV caching, which reduces global KV cache size by approximately one-quarter compared to its predecessor.
- Off-peak hours for lower pricing are scheduled from Monday through Friday, specifically between 01:00–04:00 UTC and 06:00–10:00 UTC.
- V4.1-Flash utilizes a Causal Encoder-Decoder architecture that activates 8 billion parameters during prefill and 16 billion during decoding.
- The model features an extremely high context window of up to 1 million tokens.
- DeepSeek's technical report highlights the use of FP4 KV caching, which reduces global KV cache size by approximately one-quarter compared to its predecessor.
- Off-peak hours for lower pricing are scheduled from Monday through Friday, specifically between 01:00–04:00 UTC and 06:00–10:00 UTC.
Pedro Cuenca writes Meta released Muse Glimmer-30B, a local, open-source multimodal model distilled from its larger Muse architecture. Designed for agentic workflows, it combines a 28B text decoder with a 2B vision encoder, supporting image, video, and multimodal tool calling out of the box. The release includes immediate compatibility with major inference frameworks like transformers, llama.cpp, and vLLM, alongside built-in speculative decoding for faster generation.
- Features a hybrid attention pattern alternating between three sliding window layers and one full attention layer.
- Incorporates a DFlash block-diffusion drafter to accelerate structured text generation like coding.
- Supports fine-tuning via TRL with practical minimums ranging from one to eight H100 GPUs depending on the method.
- Demonstrates autonomous agent capabilities such as self-quantization, self-deployment, and hardware-specific optimization.
- Features a hybrid attention pattern alternating between three sliding window layers and one full attention layer.
- Incorporates a DFlash block-diffusion drafter to accelerate structured text generation like coding.
- Supports fine-tuning via TRL with practical minimums ranging from one to eight H100 GPUs depending on the method.
- Demonstrates autonomous agent capabilities such as self-quantization, self-deployment, and hardware-specific optimization.
This article explains how to distinguish agentic workflows from autonomous agents by focusing on ownership of control flow. It argues that the primary distinction is a spectrum between predictability and autonomy, rather than just whether a large language model is involved. The author breaks down four key stages of system design:
* Deterministic workflows where humans define fixed sequences in advance
* Orchestrated workflows where models choose from predefined branches
* Reactive agents that use reasoning to decide actions at runtime via patterns like ReAct
* Autonomous multi-agent systems where agents act as nested tools for one another
While fully autonomous systems offer high flexibility, the piece concludes that production environments typically favor hybrid architectures that combine structured workflows with targeted autonomy.
* Deterministic workflows where humans define fixed sequences in advance
* Orchestrated workflows where models choose from predefined branches
* Reactive agents that use reasoning to decide actions at runtime via patterns like ReAct
* Autonomous multi-agent systems where agents act as nested tools for one another
While fully autonomous systems offer high flexibility, the piece concludes that production environments typically favor hybrid architectures that combine structured workflows with targeted autonomy.
ZeroID is a new open-source identity and credentialing platform designed specifically to address the attribution challenges in agentic workflows. It provides a verifiable delegation chain using RFC 8693 token exchange, ensuring that when orchestrator agents spawn sub-agents, every action remains traceable back to the original authorizing principal while maintaining strict permission boundaries.
Key features and details:
- Implements verifiable delegation chains for multi-agent systems
- Supports real-time revocation via OpenID Shared Signals Framework (SSF) and CAEP
- Offers SDKs for Python, TypeScript, and Rust
- Integrates with frameworks like LangGraph, CrewAI, and Strands
- Provides a containerized deployment model backed by PostgreSQL
Key features and details:
- Implements verifiable delegation chains for multi-agent systems
- Supports real-time revocation via OpenID Shared Signals Framework (SSF) and CAEP
- Offers SDKs for Python, TypeScript, and Rust
- Integrates with frameworks like LangGraph, CrewAI, and Strands
- Provides a containerized deployment model backed by PostgreSQL
This article explains how to implement function calling with Google’s Gemma 3 27B model. It covers the concept of function calling, the step‑by‑step workflow, and provides a practical example using a Python `convert` function to turn $200,000 into EUR. The post walks through prompting Gemma, parsing its `tool_code` output, executing the function with `eval`, and returning a friendly final response. It also demonstrates how to set up the Google‑GenAI SDK, create a chat session, and extract tool calls. The discussion highlights Gemma’s multilingual, multimodal, and agentic capabilities, making it suitable for real‑world AI assistants that need to interact with external APIs and tools.
GitHub Agentic Workflows are built with isolation, constrained outputs, and comprehensive logging. Learn how our threat model and security architecture help teams run agents safely in GitHub Actions.
This post explains how we built Agentic Workflows with security in mind from day one, starting with the threat model and the security architecture that it needs. It details the defense in depth approach using substrate, configuration, and planning layers, emphasizing zero-secret agents through isolation and careful exposure of host resources. It also highlights the staging and vetting of all writes using safe outputs, and comprehensive logging for observability and future information-flow controls.
This post explains how we built Agentic Workflows with security in mind from day one, starting with the threat model and the security architecture that it needs. It details the defense in depth approach using substrate, configuration, and planning layers, emphasizing zero-secret agents through isolation and careful exposure of host resources. It also highlights the staging and vetting of all writes using safe outputs, and comprehensive logging for observability and future information-flow controls.
