The Daily Index — Wednesday, November 12, 2025 · TLDR Index

HEADLINES & LAUNCHES

TLDR AI

TLDR AI

INTRODUCING SCRIBE V2 REALTIME

Scribe v2 Realtime is a low-latency Speech to Text model that delivers live transcription in under 150 milliseconds. It supports over 90 languages and is specifically built for agentic use cases. The model achieves 93.5% accuracy across 30 commonly used European and Asian languages. Scribe v2 Realtime can be accessed through ElevenLabs' API or directly within ElevenLabs Agents.

elevenlabs.io · 3 min

TLDR AI

YANN LECUN REPORTEDLY PLANNING TO LEAVE META

Meta's chief AI scientist, Yann LeCun, is reportedly preparing to leave the company to start his own venture focused on world models. His departure comes amid a broader AI reorganization at Meta, including the formation of Meta Superintelligence Labs, and internal tensions following shifts in AI development strategy.

techcrunch.com · 5 min

TLDR AI

BYTEDANCE UNVEILS CHINA'S MOST AFFORDABLE AI CODING AGENT AT JUST US$1.30 A MONTH

The Doubao-Seed-Code model is a coding agent priced at 40 yuan a month, with a special launch price of 9.9 yuan (US$1.30) for the first month. The new model set a state-of-the-art record on the SWE-Bench Verified test. It supports popular development tools like veCLI, Cursor, and Cline, and is compatible with Anthropic's API. The model can process up to 256,000 words per query and can handle complex codebases.

scmp.com · 2 min

TLDR AI

BUILD TIMES FOR GIGAWATT-SCALE DATA CENTERS CAN BE 2 YEARS OR LESS

Gigawatt-scale AI data centers are massive undertakings that require extensive permitting, construction, and power infrastructure. Many hyperscalers have concrete plans to build data centers at this scale in two years or less. The time from starting construction to achieving one gigawatt of total facility power ranges from 1 to 3.6 years. The first gigawatt-scale datacenters are expected to come online in early 2026.

epoch.ai · 3 min

TLDR AI

HOW TO TRAIN AN LLM: PART 1

This post walks through how a researcher set up basic pre-training infrastructure and trained a 1B Llama 3-style model on 8xH100s. The resulting model is far from state-of-the-art, but it provides a clear working implementation that can be abstracted further.

omkaark.com · 27 min

TLDR AI

RL ENVIRONMENTS AND THE HIERARCHY OF AGENTIC CAPABILITIES

Researchers dropped nine frontier AI models into a simulated workplace environment and gave them 150 jobs to do. Most of the AIs were barely coherent, and even the best lacked common sense. The experiment showed how models must acquire more fundamental capabilities before we can even begin to discuss how well they perform common-sense reasoning in real environments. Finding out whether common sense reasoning is a set of identifiable, trainable sub-skills or an emergent property of large-scale real-world training will help shape the next stage of AI development.

surgehq.ai · 23 min

TLDR AI

WHY SUDOKU VARIANTS REMAIN A GRAND CHALLENGE IN AI REASONING

Modified versions of Sudoku test an AI's creativity and ability to strategize around rulesets that don't appear in its training data, unlike fixed-rule games like Chess or Go. GPT-5 is the first LLM to solve a 9x9 variant by simultaneously coordinating four constraint types (standard rules, region sum lines, XV pairs, and Roman numeral cages), but it still only succeeded 20% of the time.

pub.sakana.ai · 15 min

TLDR AI

FROM WORDS TO WORLDS: SPATIAL INTELLIGENCE IS AI'S NEXT FRONTIER

Current LLMs are wordsmiths in the dark—eloquent but inexperienced. They lack a core cognitive function of humans: spatial intelligence. "World models" solve this by generating geometrically and physically consistent simulated worlds, processing multimodal inputs like images and gestures, and predicting next states based on actions. If successful, the technology would be a step change for robotics, video models, and drug discovery.

drfeifei.substack.com · 18 min

TLDR AI

report

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ibm.com