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Google's Agents CLI, first launched at Cloud Next '26, now ships with an Extension System as of version 1.5.0. This allows developers to override existing commands or add new ones without forking the tool. Version 1.8.0 includes a first-party LangChain extension, enabling a full agent development lifecycle (scaffold, run, deploy, evaluate) with LangChain agents. It also facilitates custom policies, like an SBOM scan wrapping the deploy command.

This is a welcome evolution for agent development, especially for teams working across multiple frameworks or needing to enforce enterprise-specific governance. If you've been avoiding Agents CLI due to its initial ADK opinionation, this update makes it far more versatile. The limitation is that while the system is flexible, building complex custom extensions for unique policies will still require internal development effort.

more: https://developers.googleblog.com/powering-the-agent-development-lifecycle-with-any-framework-from-adk-to-langchain-and-beyond/

IBM released granite-tsfm 0.3.11, a Python package that bundles four pre-trained time-series models into a single ensemble interface. Calling QuantileEnsembleForecaster.from_pretrained downloads the member checkpoints from Hugging Face and returns both point forecasts and quantile outputs without requiring prior training on your data. The default recipe pools forecasts in probability space using fixed equal weights.

It is worth testing if you need zero-shot probabilistic forecasting across mixed industrial or retail signals and want to skip the overhead of training model weights. The catch is that the first run takes extra time to download all four underlying architectures at runtime, and the package is not built for custom streaming pipelines. You should stick to a single tuned model if you already have clean data and well-defined domains.

more: https://huggingface.co/blog/ibm-research/granite-timeseries-ensemble-r1

my other side: ivo.blogspot.com

Ovis is a PWA and browser interface for the herdr agent manager, letting you monitor and reply to Claude Code, Codex, and other CLI agent sessions from your phone or desktop. It gives you a chat-to-terminal toggle, push alerts, a mobile keyboard modifier bar, and voice dictation running over a Tailscale connection. The installer requires macOS or glibc Linux, Bun 1.4+, Node 18+, and herdr 0.9.0+.

It is worth setting up if you already run herdr and want to manage long-running local coding sessions away from your desk without dealing with raw SSH. The catch is its strict environment requirements and the security footprint of exposing local agent terminals via Tailscale serve. Skip it if you do not use herdr or prefer managing tmux sessions manually.

more: https://github.com/devswha/ovis

my other side: ivo.blogspot.com

Hugging Face's guide covers system-level performance analysis for AWS Trainium using TorchNeuron and the NeuronProfiler API. It walks through setting up NeuronConfig, capturing device and runtime traces, and analyzing an RMSNorm implementation using Neuron Explorer. The post shows how to wire standard PyTorch profiler steps into the PrivateUse1 backend without modifying core model code.

It is worth reading if you are deploying models on Trainium and need to move past guesswork to trace actual hardware bottlenecks. The catch is that it only covers the mechanics of capturing a trace for a single tiny operation rather than diagnosing a full production model. If you are not targeting AWS silicon, you can safely skip it.

more: https://huggingface.co/blog/amazon/profiling-torchneuron-part-1

my other side: ivo.blogspot.com

FairStress is a 300‑scenario benchmark spanning 29 domains, hosted on Hugging Face, designed to measure how LLMs respond to demographic changes and adversarial prefixes. It integrates directly into Olmo‑eval, so you can run it on any open‑weight model or GPT‑style API with a single command. The dataset includes explicit, implicit, and no‑identity versions of each scenario to test subtle bias.

If you’re evaluating model fairness beyond surface decisions, FairStress gives a more nuanced view than standard parity checks. It does not cover equity‑based fairness or long‑term impact analysis, and its adversarial prefixes are manually curated, so you’ll need to supplement it for deeper coverage. It’s best suited for research labs or teams with evaluation pipelines, not for quick production testing.

more: https://huggingface.co/blog/allenai/fairstress

my other side: ivo.blogspot.com

Project Maya is a local inference engine for massive Mixture-of-Experts models like GLM-5.3-Flash and DeepSeek-V4.1. It offloads inactive experts to system RAM and NVMe SSDs, allowing 748B parameter models to run on single GPUs or multi-GPU setups. The API is compatible with OpenAI and Anthropic standards, supporting tool calls and concurrent requests.

This is worth trying if you have high-end local hardware and need private, low-latency LLM access without cloud costs. The catch is the steep hardware floor: expect to use 100GB+ of combined VRAM and RAM for usable performance. It is not a solution for standard consumer laptops or low-memory systems.

more: https://github.com/mw00/project-maya

my other side: ivo.blogspot.com