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Chromex is a Codex-powered Chrome side-panel assistant, and the new Translation/Live mode extends it from page-aware chat into live speech workflows. The repo says it can capture transcripts and optionally translate audio in real time across YouTube videos, live streams, meetings, presentations, and other audio playing inside Chrome, while keeping the workflow tied to a local native bridge rather than storing secrets in extension storage.
Baidu says ERNIE 5.1 inherits ERNIE 5.0’s foundation while shrinking total parameters to about one-third, active parameters to about one-half, and pre-training compute to roughly 6% of comparable models. It says the model improves agentic tasks, search, reasoning, and creative writing, with strong results on Arena Search.

Markus is an open-source platform for coordinating AI teams with persistent memory, built-in tools, and a responsive web UI. It pitches itself as a full runtime for messy repo work, not just another agent wrapper.
Claude Code 2.1.138 is a maintenance release focused on internal fixes and better command stability. The update aims to cut down on unexpected errors rather than add new user-facing features.
OpenAI’s free webinar walks through using Codex for real work, not just code, with a live demo that builds a daily work brief workflow. It frames Codex as a reviewable task loop for turning scattered notes, messages, docs, and data into usable outputs.
Anthropic’s new interpretability method translates Claude activations into human-readable text, then tries to reconstruct the original activations to see whether the explanation actually preserved signal. The release is aimed at practical auditing, especially for safety-relevant behavior the model may not verbalize.

Caliby is an embedded vector database for AI agents that keeps text, metadata, and ANN indexes in-process instead of pushing teams toward a separate service. The project leans on HNSW, DiskANN, and IVF+PQ, with self-reported gains over pgvector and disk-backed operation as the main pitch.
A LocalLLaMA user hit a `llama-bench` quirk on a DGX-1-style setup: `--device CUDA0,CUDA1` caused the benchmark to sweep GPUs one at a time instead of tensor-splitting a Qwen3.6-27B run across both cards. The thread points toward `-sm row` for tensor parallelism, with `CUDA_VISIBLE_DEVICES` and `-ts 1,1` as the cleaner way to control which GPUs participate.
Ben Goertzel says a SingularityNET researcher reached a 32.58% mean human-normalized score on ARC-AGI-3 using LLMs, procedural world models, and verification. The post is a follow-up on the interactive benchmark, which has been live since March 25, 2026 and still leaves frontier LLMs near zero without heavy scaffolding.

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