Imran Hassanali presents a comprehensive benchmark comparing "Code Mode"—where an LLM generates complete code to execute tools—against traditional function calling for tool interactions. The results demonstrate that Code Mode is significantly more efficient, achieving 60% faster execution, 68% fewer tokens used, and an 87.5% reduction in API round trips while maintaining equal accuracy compared to regular agents.
- Evaluated using eight realistic business scenarios, including multi-client invoicing and expense tracking.
- Code Mode advantage scales with task complexity; high-complexity tasks saw up to a 79.2% speedup.
- The benchmark tested both Claude 3 Haiku (showing major efficiency gains) and Gemini 2.0 Flash Experimental.
- Secure code execution is implemented via RestrictedPython, preventing filesystem and network access during the sandbox phase.
- Evaluated using eight realistic business scenarios, including multi-client invoicing and expense tracking.
- Code Mode advantage scales with task complexity; high-complexity tasks saw up to a 79.2% speedup.
- The benchmark tested both Claude 3 Haiku (showing major efficiency gains) and Gemini 2.0 Flash Experimental.
- Secure code execution is implemented via RestrictedPython, preventing filesystem and network access during the sandbox phase.
Wes Steyn writes that making an AI agent production-ready requires addressing observability, governance, deployment, and evaluation through a shared agent factory pattern. By defining the agent once in a single factory, developers can deploy the same core logic across different hosts—such as interactive consoles, hosted services on Foundry, or automated eval runners—while applying specific security constraints like disabling shell access for cloud environments.
- Observability is achieved via OpenTelemetry to track model turns, tool calls, and token usage.
- Microsoft Purview can be integrated via middleware to screen prompts and responses against organizational policies.
- Hosted agents in Foundry automatically handle telemetry configuration through environment variables.
- Local evaluations use simple functions for quick checks, while hosted evals provide model-graded quality scores like relevance and coherence.
- Observability is achieved via OpenTelemetry to track model turns, tool calls, and token usage.
- Microsoft Purview can be integrated via middleware to screen prompts and responses against organizational policies.
- Hosted agents in Foundry automatically handle telemetry configuration through environment variables.
- Local evaluations use simple functions for quick checks, while hosted evals provide model-graded quality scores like relevance and coherence.
- **Inference** – Platforms and engines for running models, plus user interfaces.
- **Models** – LLMs (general, coding, multimodal, image, audio), model providers, and specific model highlights.
- **RAG** – Retrieval-Augmented Generation tools.
- **Safeguards** – Safety and content filtering.
- **Agents & Tools** – Agent frameworks, Model Context Protocol, coding agents, computer/browser automation, memory management, and testing/evaluation.
- **Research, Training & Fine-tuning** – Security, sandboxing, and model development.
- **Hardware** – Local hardware options.
- **Tutorials** – Guides covering models, prompt/context engineering, inference, agents, and RAG.
- **Communities** – Places to connect and share knowledge.
- **Models** – LLMs (general, coding, multimodal, image, audio), model providers, and specific model highlights.
- **RAG** – Retrieval-Augmented Generation tools.
- **Safeguards** – Safety and content filtering.
- **Agents & Tools** – Agent frameworks, Model Context Protocol, coding agents, computer/browser automation, memory management, and testing/evaluation.
- **Research, Training & Fine-tuning** – Security, sandboxing, and model development.
- **Hardware** – Local hardware options.
- **Tutorials** – Guides covering models, prompt/context engineering, inference, agents, and RAG.
- **Communities** – Places to connect and share knowledge.
Alibaba has open-sourced Qwen-UI-Agent, a GUI agent foundation model that operates across mobile, desktop, web, and deep-search environments on real hardware rather than relying on simulation. It achieves top benchmark results: 82.1% on MobileWorld, 79.5% on OSWorld-Verified, and first on WebArena. It also introduces MobileWorld-Real, a 400+ task benchmark on 100+ phones and 150+ apps, with a 92.2% success rate.
- Supports command-line execution alongside standard GUI operations and batches multiple actions into a single decision step to shorten trajectories.
- Built-in safety layer refuses illegal or high-risk requests outright and pauses at sensitive operations (payments, data deletion, privacy grants) for explicit user confirmation.
- Trained via online reinforcement learning on trajectories exceeding 100 steps, paired with adaptive curriculum learning to progressively tackle longer tasks.
- Supports command-line execution alongside standard GUI operations and batches multiple actions into a single decision step to shorten trajectories.
- Built-in safety layer refuses illegal or high-risk requests outright and pauses at sensitive operations (payments, data deletion, privacy grants) for explicit user confirmation.
- Trained via online reinforcement learning on trajectories exceeding 100 steps, paired with adaptive curriculum learning to progressively tackle longer tasks.
Nous Research has bundled Bot Mode as default in Hermes Agent v0.20.3 for Hermes Desktop, replacing the single-agent session list with a roster of named bots that each have their own chat, memory, skills and pinned model for multi-agent workflows on the desktop.
Key technical details:
* Standalone repo archived; active development continues in-tree at `apps/desktop/src/plugins/hermes-bots/`, first shipped as a one-day public beta plugin by co-founder Teknium
* Bots are stored as isolated profiles under `~/.hermes/profiles//` with own config, memory, skills, credentials and history; managed via existing `profiles.*` gateway RPCs `list, create, describe, configure`
* Avatars generated via the `image.generate` RPC; options include geometric faces in seven shapes/ten colors or uploaded/generated portraits/pixel pets
* Routines are ordinary Hermes cron jobs namespaced ` bot: » `; bot-to-bot messaging uses a persistent Agent Inbox with CLI handoffs e.g. `hermes -p chat -c "Agent Inbox" -q "..."` and `@mention` handoffs
* Per-bot config via New Agent dialog supports cloning a profile, pinning provider/model, custom SOUL.md and skills; duplicates clone config, skills, SOUL.md, memory and appearance
* Bundled version adds Groups for roster sections synced across machines, Group chats for 2-6 bots with up to three serial rounds, and a multi-source roster pulling bots from every connection under Settings → Connections
Key technical details:
* Standalone repo archived; active development continues in-tree at `apps/desktop/src/plugins/hermes-bots/`, first shipped as a one-day public beta plugin by co-founder Teknium
* Bots are stored as isolated profiles under `~/.hermes/profiles//` with own config, memory, skills, credentials and history; managed via existing `profiles.*` gateway RPCs `list, create, describe, configure`
* Avatars generated via the `image.generate` RPC; options include geometric faces in seven shapes/ten colors or uploaded/generated portraits/pixel pets
* Routines are ordinary Hermes cron jobs namespaced ` bot: » `; bot-to-bot messaging uses a persistent Agent Inbox with CLI handoffs e.g. `hermes -p chat -c "Agent Inbox" -q "..."` and `@mention` handoffs
* Per-bot config via New Agent dialog supports cloning a profile, pinning provider/model, custom SOUL.md and skills; duplicates clone config, skills, SOUL.md, memory and appearance
* Bundled version adds Groups for roster sections synced across machines, Group chats for 2-6 bots with up to three serial rounds, and a multi-source roster pulling bots from every connection under Settings → Connections
Michael Larabel writes that Meta Superintelligence Labs announced the release of Muse Glimmer, a 30-billion-parameter open model for always-on local agent workflows with weights released under Apache 2.0. The model is sized to run on a single consumer GPU and targets local coding agents, LLM-as-a-judge evaluation and similar uses, having been trained and evaluated for end-to-end agentic task completion, multi-step reasoning and optimized local deployment. Details are posted on research.meta.ai and the model is available on Hugging Face, with Ollama 0.32.7 already adding support.
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Ashwini Sinha explains how ESP-Claw turns an ESP32 into a self-programming agent through natural language instructions. By integrating LLMs, the system can automatically generate code and flash firmware to perform tasks such as managing sensor data or controlling peripherals without manual coding. This setup effectively transforms conventional microcontrollers into intelligent embedded systems capable of autonomous updates and performance optimization.
- Minimum hardware requirements include 8 MB PSRAM and 8 MB Flash memory on the ESP chip.
- The agent can access information via web search capabilities to answer specific queries.
- Setup requires a USB-C cable for initial firmware flashing and programming.
- Minimum hardware requirements include 8 MB PSRAM and 8 MB Flash memory on the ESP chip.
- The agent can access information via web search capabilities to answer specific queries.
- Setup requires a USB-C cable for initial firmware flashing and programming.
Skill Recorder converts screen activity into repeatable agent tasks by recording user sessions and reconstructing intent via GitHub Copilot. Captured data includes clicks, window changes, URLs, and optional narration to produce structured Skills or Automations.
- Built with Electron.
- Performs on-device transcription using Whisper for 99 languages.
- Limits cloud uploads to specific metadata during the analysis phase.
- Built with Electron.
- Performs on-device transcription using Whisper for 99 languages.
- Limits cloud uploads to specific metadata during the analysis phase.
Anurag Singh writes that Cursor’s strength is its capacity for deep project understanding through efficient context management rather than simple model speed. Instead of pushing a whole repository into an LLM's window, the IDE employs search tools to provide only relevant code segments as tasks arise. This curated approach ensures models receive high-quality information and instructions tailored to their specific capabilities, allowing them to maintain existing architectural patterns more effectively.
* The tool uses Instant Grep via a local text index instead of scanning every file from scratch.
* Cursor is significantly more effective in complex or large projects where it can identify and reuse established implementations.
* Its search ability helps prevent the common problem of generating redundant helper functions that already exist within the codebase.
* The tool uses Instant Grep via a local text index instead of scanning every file from scratch.
* Cursor is significantly more effective in complex or large projects where it can identify and reuse established implementations.
* Its search ability helps prevent the common problem of generating redundant helper functions that already exist within the codebase.
This article discusses how the newest generation of Claude models necessitates a shift in context engineering. Anthropic found that they could remove over 80% of their system prompts without losing performance on coding evaluations by moving away from rigid, often conflicting instructions and instead allowing the model's inherent judgment to guide its behavior based on surrounding context.
Key shifts in methodology include:
- Moving from strict rules to letting models use judgement for nuance such as documentation style or intent interpretation.
- Prioritizing intuitive tool interface design over providing restrictive examples that limit exploration.
- Implementing progressive disclosure by using skills and deferred loading to manage large context windows efficiently.
- Replacing repetitive instructions with streamlined, high-fidelity descriptions directly within tool definitions.
- Transitioning from manual memory management in files toward auto-memory and rich references like HTML artifacts or code snippets.
Key shifts in methodology include:
- Moving from strict rules to letting models use judgement for nuance such as documentation style or intent interpretation.
- Prioritizing intuitive tool interface design over providing restrictive examples that limit exploration.
- Implementing progressive disclosure by using skills and deferred loading to manage large context windows efficiently.
- Replacing repetitive instructions with streamlined, high-fidelity descriptions directly within tool definitions.
- Transitioning from manual memory management in files toward auto-memory and rich references like HTML artifacts or code snippets.
