Researchers propose a new definition of "good explanations" for AI outputs, emphasizing counterfactual reasoning and the role of an individual's prior beliefs. The study highlights why explaining large language model (LLM) outputs remains particularly challenging despite the growing need for AI transparency. The findings aim to improve explainability in AI systems to support broader adoption.

Researchers propose a definition of a good explanation that draws on counterfactual reasoning while also accounting for the listener’s prior beliefs. They argue this framework has important implications for AI explainability and helps clarify why producing satisfying explanations for large language model outputs is especially difficult.

Kuala Lumpur-based Respond.io raised $62.5 million in Series B funding led by Camber Partners, with participation from Endeavor Catalyst and existing investors. The AI-powered customer messaging platform says it has reached $35 million in annual recurring revenue and plans to use the funding for hiring, organic growth and acquisitions.

Malaysia-based Respond.io, an AI-powered messaging platform for businesses, has raised $62.5 million in a Series B funding round to fuel growth and acquisitions. The company, which automates customer conversations across messaging apps like WhatsApp and Instagram, reports $35 million in annual recurring revenue and processes 2 billion messages quarterly. Respond.io plans to expand its AI-driven customer engagement tools while targeting strategic acquisitions.

datasette-agent 0.3a0 adds an execute_write_sql tool that can request user approval before making database changes while respecting user permissions. The release also updates the chat terminal mode to handle approvals and adds options such as --root, --yes and --unsafe, enabling direct database modifications through chat prompts when allowed.

Datasette-agent 0.3a0 introduces a new tool allowing users to execute SQL write operations with built-in approval prompts and permission checks. The update enhances the chat terminal mode with options for auto-approval and root access, enabling direct database modifications through conversational commands. Additional improvements include plain text alternatives for HTML displays in the CLI.

OpenJarvis, an open-source framework for building local-first personal AI agents, is now available with built-in Ollama support. Developed by Stanford’s Hazy Research and Scaling Intelligence labs, it runs models on users’ own hardware by default while offering optional cloud use and tracking energy, cost, latency, and accuracy. Version 1.0 includes ready-to-run agent presets for tasks such as morning briefings, research across local files and the web, and local coding.

OpenJarvis, an open-source framework for building personal AI agents that run locally on your own hardware, is now available with built-in support for Ollama. Developed by Stanford researchers, it prioritizes local processing to reduce energy use, costs, and latency while keeping cloud access optional. Users can install it on macOS, Windows, or Linux and choose from pre-built agents for tasks like morning briefings, research, or coding.

Google DeepMind introduced DiffusionGemma, an experimental open text-generation model that uses diffusion to generate blocks of text in parallel rather than token by token. Released under an Apache 2.0 license, the 26B Mixture of Experts model is designed for speed-critical local workflows and can deliver up to 4x faster inference on dedicated GPUs, though traditional autoregressive Gemma models remain preferred for high-quality production use.

Google DeepMind unveiled DiffusionGemma, an open experimental model that accelerates text generation up to four times faster on dedicated GPUs by generating entire blocks of text simultaneously instead of word-by-word. Designed for speed-critical local workflows like real-time editing and interactive applications, it trades some quality for performance but enables new use cases such as non-linear text generation. The model is released under an Apache 2.0 license and targets researchers and developers optimizing for low-latency, local inference.

Hugging Face’s second PyTorch profiling post examines how nn.Linear maps to matrix multiplication plus bias addition, then extends the analysis to a three-layer MLP with activation. It walks through profiler traces, torch.compile behavior, kernel layouts, and fused Triton or hand-tuned kernels to show how MLP performance can be optimized on an NVIDIA A100 GPU.

The second part of a PyTorch profiling series explores how a basic `nn.Linear` layer operates under the hood, breaking down its matrix multiplication and addition steps. It then builds a fused Multilayer Perceptron (MLP) block by stacking three linear layers with activations, analyzing performance improvements through kernel fusion and `torch.compile`. The post includes hands-on scripts and traces to demonstrate optimizations on NVIDIA A100 GPUs.