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Google has released EmbeddingGemma 2, a sub-1 billion parameter model that maps text, code, images, video, and audio into a shared 768-dimensional space. Integrated into sentence-transformers v6.1.0, the architecture is modular so you can drop unused modality encoders at load time to scale the footprint from 270M parameters up to 740M. It includes support for task-specific prompts and native dimension truncation down to 128 dimensions.

If you are building a local RAG pipeline or cross-modal search tool that needs to handle mixed media without a heavy inference server, this is worth testing. The catch is that matching modalities still requires strict adherence to prompt names and specific token markers, and sub-1B models will inevitably lag behind larger proprietary endpoints on nuanced retrieval tasks. Skip it if your stack is purely text-based and already tuned around existing dense retrievers.

more: https://developers.googleblog.com/embeddinggemma-2-the-developer-guide/

my other side: ivo.blogspot.com

Agent Memory is a runtime that gives AI agents local, file-based persistence by mapping conversations to a directory of Markdown files indexed by SQLite. It functions as a local cache that agents can query via shell commands, allowing them to recall context across sessions without relying on external databases or cloud APIs. The system treats the filesystem as the source of truth, meaning all data remains readable by standard tools like grep even if the index is deleted.

It is worth your time if you prefer local, transparent, and git-managed data over opaque vector database abstractions. However, it requires you to manage a directory-based workflow and configure individual agent schemas, which adds overhead if you just want a plug-and-play solution. It is not for users who prefer managed services or have no interest in maintaining their own data structure.

more: https://github.com/tigerless-labs/agent-memory

my other side: ivo.blogspot.com

Microsoft and Hugging Face released ThinkingBox via OpenEnv to evaluate AI agent reliability across 507 stateful business workflows. Instead of checking generated text or tool calls, it runs agents in isolated Model Context Protocol sessions and checks the resulting database records. Running 12 LLM models across 121,680 trials, the benchmark measures whether an agent can produce the correct backend state twenty times in a row.

It is worth running if you are building autonomous database workflows and need to move past single-run pass rates. The catch is that most current models suffer drastic reliability drops when forced to repeat tasks consistently, retaining as little as eight percent of their pass@1 score. Skip this if you only need conversational assistants or static text generation.

more: https://huggingface.co/blog/microsoft/thinkingbox

my other side: ivo.blogspot.com

The dsh-our-free-model repository provides a zero-dependency plugin for the dsh runtime that exposes models like DeepSeek V4.1 and Kimi K3. Installation requires no API keys, accounts, or configuration, routing requests directly through the OpenCode Zen gateway. It includes a local OpenAI-compatible forwarding port, a usage dashboard, and automatic hot-reloading.

It is worth installing if you already use dsh and want free access to frontier models without managing credits. However, availability depends entirely on your local network's exit IP, and heavily managed desktop bundles may disable the plugin's auto-update features. Skip it if you require guaranteed uptime or enterprise SLAs.

more: https://github.com/Ebony-Vinyl/dsh-our-free-model

my other side: ivo.blogspot.com

Concat is a cross-platform video editor built on a native Rust engine and FFmpeg, positioned as a free alternative to CapCut. It runs locally on macOS, Windows, Linux, and Android, handling auto-captions via Whisper, background removal, and 4K export entirely on your machine without cloud uploads or accounts. It also exposes a JSON-RPC, gRPC, and MCP API for programmatic editing.

It is worth a look if you want a timeline editor that does not require a subscription or telemetry, though it is currently in beta and still has rough edges. Because it relies on local models and GPU compositing, resource usage depends heavily on your hardware, and it lacks the extensive stock asset libraries found in commercial tools.

more: https://github.com/jub0t/concat

my other side: ivo.blogspot.com