Thinking Machines in talks to raise $1B at $40B valuation

🚀 RELEASES & LAUNCHES

• ampixa releases sanoTTS, a 294K-parameter multilingual TTS stack
  ampixa released sanoTTS, a tiny multilingual text-to-speech stack for microcontrollers.
  • The stack targets embedded developers and browser users needing local text-to-speech.
  • sanoTTS offers 11 voices across 6 languages, with models ranging from 294K to 2.2M parameters.
  • Its smallest model is 337 KB when quantized to int8 and is designed to run on a $3 microcontroller with 512 KB of SRAM.
  • A 1.5M-parameter model reports SCOREQ 4.13, UTMOS 4.10, and about 2% WER on Whisper.
  • The project includes WebAssembly support through the sanotts-web npm package and recipes for adding languages and voices.
  Sources 📄 Source: github.com · 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA

🔬 RESEARCH

• Runtime MoE routing tweak cuts Qwen reasoning tokens by 8.5%
  A runtime MoE routing tweak cut Qwen 3.6 reasoning tokens by 8.5%.

  • For builders of sparse MoE reasoning models, without retraining or fine-tuning.
  • The method activates extra experts only in late transformer layers, using a linear decay for their weights.
  • On Qwen 3.6 35B-A3B across 714 MMLU-Pro questions, mean reasoning tokens fell 8.5%.
  • The model reached the same correct answers through shorter reasoning trajectories.
  Sources 📄 Source: zenodo.org · 📎 Coverage: reddit.com · 👁 via r/LocalLLaMA

• Mol-JEPA brings multimodal JEPA learning to molecular drug discovery
  Researchers introduced Mol-JEPA, a multimodal molecular foundation model.

  • For drug-discovery researchers and molecular machine-learning developers.
  • The model learns from molecular structures, cellular phenotypes, binding affinities, ADMET profiles, quantum-chemistry simulations and other data.
  • Mol-JEPA masks entire modalities and predicts their latent representations with a Transformer-based predictor.
  • The SIGReg regularizer targets representation collapse without chemically invalid molecular augmentations.
  • Its learned representations outperform baselines across multiple molecular benchmarks, according to the paper.
  Sources 📄 Source: arxiv.org · 📎 Coverage: flogrammer.github.io · 👁 via r/MachineLearning

• 📄 Source for Google releases TimesFM-3 for zero-shot multivariate forecasting — github.com

💰 BUSINESS & FUNDING

• Thinking Machines in talks to raise $1B at $40B valuation
  Thinking Machines is reportedly seeking $1 billion at a $40 billion valuation.
  • Businesses using Thinking Machines’ Tinker platform to customize AI models with proprietary data.
  • Accel is reportedly discussing leading a $1 billion funding round at a valuation of at least $40 billion.
  • The startup’s annual revenue run rate exceeds $100 million, while its open-weight Inkling model is free and Tinker charges usage-based compute fees.
  • The funding would support model training, computing infrastructure and hiring; the proposed valuation is below the $50 billion level reportedly sought previously.
  Sources 📎 Coverage: techcrunch.com · 👁 via TechCrunch AI