🤖 AI Weekly Recap (Week 15)

The model can execute a staggering 1,700 tool calls and steps in a single run without suffering from "strategy drift" or forgetting its original goal.

It officially crushed the SWE-Bench Pro coding benchmark with a score of 58.4, dethroning heavyweights like OpenAI's GPT-5.4 and Anthropic's Claude Opus 4.6.

In a wild 8-hour test, GLM-5.1 was tasked with building a Linux-style desktop environment from scratch. It autonomously coded a file browser, terminal, text editor, system monitor, and functional games, iteratively testing and polishing the UI until it was complete.

It functions as its own R&D department. The model can write code, compile it, run it in a live Docker container, analyze where it bottlenecked, and autonomously rewrite its own architecture to fix the problem.

The lineup includes four model sizes: Effective 2B (E2B) and 4B (E4B) for mobile and IoT devices, alongside a highly efficient 26B Mixture-of-Experts (MoE) and a 31B Dense model for workstations.

All models natively support agentic workflows right out of the box, featuring built-in function calling, structured JSON output, and system instructions.

The smaller edge models (E2B and E4B) are multimodal powerhouses, capable of natively processing image, video, and audio inputs locally on smartphones, Raspberry Pis, or Jetson Orin Nanos.

Despite their highly efficient footprint, the 31B model currently ranks #3 worldwide among open models on the Arena AI Leaderboard, while the 26B MoE ranks #6, outperforming competitors up to 20 times their size.

Clones voices from seconds of audio and generates speech across 23 languages using 5 different specialized TTS engines (including Qwen3-TTS and Chatterbox Turbo).

Supports paralinguistic tags, allowing you to synthesize expressive speech with inline emotions like [laugh], [sigh], [gasp], or [clear throat].

Features a multi-track "Stories" timeline editor for composing complex narratives, conversations, and podcasts directly in the app.

Includes a built-in "pedalboard" with 8 real-time audio effects like reverb, pitch shift, and compression to polish your output.

The new lab was created after Zuckerberg reportedly grew frustrated with Llama lagging behind ChatGPT and Claude, prompting Meta to invest a staggering $14.3 billion for a 49% stake in Scale AI.

Muse Spark is available now on the web and the Meta AI app, and it specifically excels at visual STEM questions, coding mini-games, and troubleshooting physical hardware.

An upcoming "Contemplating" mode will use multiple parallel AI agents collaborating on the same problem to solve complex reasoning tasks quickly without massive latency spikes.

Because users must log in with a Facebook or Instagram account, privacy concerns are already swirling around how Meta might use personal social data to feed this new "personal superintelligence."

Before generating anything, it reads your framework, component library, and CSS tokens so the output perfectly matches your actual stack.

generate_design: Creates production-ready UI straight from a text prompt.

refine_design: Lets you adjust layouts and colors using natural language.

Works seamlessly across Cursor, Codex, VS Code, and Windsurf.

Connects to your environment with just one command.

Replaces standard RAG by compiling knowledge once and keeping it current, rather than re-discovering fragments and starting over on every single query.

When you drop a raw source (article, paper, transcript) into a folder, the AI reads it, writes a summary, updates entity pages, flags contradictions, and builds cross-references automatically.

One source can update 10 to 15 wiki pages simultaneously, meaning your explorations compound into a smarter knowledge base over time.

Highly versatile use cases: tracking personal goals, evolving a research thesis over months, building book/fan wikis, or maintaining business docs from Slack threads and meeting notes.