📆 ThursdAI - Sep 4 - Codex Rises, Anthropic Raises $13B, Nous plays poker, Apple speeds up VLMs &...

Quick Overview

The ThursdAI September 4th episode covered significant AI advancements, including Codex's improved performance with GPT-5, Anthropic's massive $13 billion funding round, the release of Nous Research's Hermes 4 models and Husky Holdem benchmark, Apple's speedy VLMs, and OpenAI's GPT real-time updates.

Key Points: Codex, powered by GPT-5, demonstrates significantly improved performance, with usage reportedly increasing 10x in two weeks, making it a solid coding assistant option. Anthropic secured a substantial $13 billion in Series F funding, valuing the company at $183 billion post-money, indicating strong investor confidence and significant growth. Nous Research released Hermes 4 models (405B and 70B fine-tuned from Llama 3.1, and 14B fine-tuned from Qwen 3), focusing on neutral alignment and demonstrating strong performance on non-traditional benchmarks like EQBench and their new Husky Holdem poker bot evaluation. Apple introduced Fast VLM 7B, a vision encoder noted for its speed, being 85 times faster to first token compared to peers, and is gaining traction on Hugging Face. OpenAI launched GPT real-time to General Availability, offering improvements in speech-to-text models for lower latency and better instruction following, though still not matching text-based LLM intelligence. Google released Embedding Gemma, an open-source embedding model with 300 million parameters, ranking first on the MTB multilingual benchmark and suitable for on-device RAG applications. The open-source landscape saw the release of Tensent's Hunan MT (a 7B machine translation model) and Switzerland's Uppercas 8B and 70B multilingual LLMs trained on 15 trillion tokens across 1,800 languages, both released under the Apache 2.0 license.

Context: This episode of ThursdAI, hosted by Alex Fov, features co-hosts Yan Pelleg and LDJ, with guests Roger Jin and Besh Bves from Nous Research, and Quindla Kramer discussing AI developments. The discussion covers a wide range of topics from coding agents and LLM performance to significant funding rounds and new open-source releases, reflecting the rapid pace of innovation in the AI field.