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OpenBot is a desktop application for macOS, Windows, and Linux that runs multiple persistent AI agents in parallel. It wraps CLI tools and app servers from Anthropic, OpenAI, xAI, Google, Cursor, and Cline into a single interface with isolated workspaces, FIFO message queues, an embedded browser, and agent-to-agent messaging. The application manages local file transfers and context compaction automatically across long sessions.

It is worth downloading if you routinely juggle multiple terminal-based AI agents and want a unified window with shared history instead of scattered tmux panes. The major catch is security: agents currently run with unrestricted access and approval policies disabled after first-launch consent, meaning it will execute shell commands and modify files without prompting. Do not run it outside of a throwaway VM or a directory with strict backups.

more: https://github.com/nightly-labs/openbot

my other side: ivo.blogspot.com

Google Developers released the Agents CLI framework to automate the testing loop for agent development. It provides specific commands for generating traces, grading responses via LLM-as-judge, and performing automated prompt optimization to improve performance metrics over time.

The framework is useful if you are tired of manual testing and want a consistent way to track regressions during prompt iteration. However, it requires you to build and maintain your own evaluation dataset, which is where the real work happens. It is not a magic fix for poor agent logic, only a tool to measure it.

more: https://developers.googleblog.com/hill-climbing-agent-quality-with-agents-cli-evaluation/

my other side: ivo.blogspot.com

Mattt has released AnySpeechRecognizer and AnyDecisionModel, two Swift packages that extend the AnyLanguageModel approach to on-device audio transcription and decision making. AnySpeechRecognizer mirrors Apple's SpeechAnalyzer API while letting you swap out the default engine for open models like NVIDIA Parakeet v3, Parakeet Ultra, and Alibaba Qwen3-ASR running on Core ML. It also adds a speaker diarization module using Streaming Sortformer, which Apple's native framework lacks.

It is worth adopting if you need to run non-Apple speech or decision models locally without rewriting your routing logic. However, alternative engines like Qwen lack volatile results and word timing, and you will need to manage hefty model downloads ranging from 250 megabytes to over two gigabytes. Skip this if you are content using Apple's default APIs and hardware constraints directly.

more: https://huggingface.co/blog/anydecisionmodel

my other side: ivo.blogspot.com