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- 🚀 OpenAI Goes Open Source
🚀 OpenAI Goes Open Source
PLUS: DeepMind’s Simulated Worlds
The Summary AI
August 05, 2025
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OpenAI just made its first open-source contribution in years. With the release of gpt-oss-120b and gpt-oss-20b, the company is answering the call for high-performance open source models capable of competing with top-tier AI while running locally. Let’s unpack…
Today’s Summary:
🚀 OpenAI launches open-source GPTs
🌍 Google Genie 3 simulates real-time worlds
🎠Anthropic controls AI personalities
🧮 Gemini’s math gold medalist AI goes public
👨‍💻 Qwen-3 Coder challenges Codex
🎨 Photoshop adds AI editing tools
🛠️ 2 new tools
TOP STORY
OpenAI launches high-performance Open Source models
The Summary: OpenAI has released gpt-oss-120b and gpt-oss-20b, its first open-weight language models since GPT-2. Both are tuned for advanced reasoning, real-world performance, and safe deployment. The 120B model rivals o4-mini while running on a single high-end GPU. The 20B version is small enough to run locally on a 16GB laptop, and is free to download and fine-tune under an Apache 2.0 license.
Key details:
The models can run locally and may perform faster than o3
gpt-oss-120b uses a Mixture-of-Experts (MoE) architecture with 128 experts per layer, activating 4 at a time for efficiency
Supports 128k context length, structured outputs, and system-controlled reasoning (low/med/high)
On AIME 2025 math tests, the 20B model beats o3-mini, and the 120B is tied with o4-mini
Why it matters: OpenAI just open-sourced models that rival o4-mini, and they can run locally. This raises the bar again for competitors and tips the balance back toward local AI. Under the surface, there's also a geopolitical edge of putting US-based open source efforts back in the race against the current leaders Qwen, Kimi, and DeepSeek.
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GOOGLE DEEPMIND
Genie 3 pushes AI toward real-time simulation
The Summary: Google DeepMind has released Genie 3, a research tool that simulates interactive video environments running at 720p and 24fps with consistent physics and memory. Genie 3 supports real-time user control and “world events”, and allows researchers to train AI agents in dynamic, self-evolving environments. DeepMind calls this “a key step on the path to AGI”.
Key details:
Runs at 720p, 24fps with real-time keyboard input, maintaining world consistency across several minutes
Promptable world events let users change weather, insert objects, or trigger unexpected events on the fly
No hard-coded physics engine. The model teaches itself how the world behaves, including object interactions and physics
Currently a limited research preview, available only to selected researchers and creators; broader access may come later
Why it matters: World models are simulated realities where AI can try things, fail, and learn. That turns training from a data problem into an experience problem, where agents improve “by doing” rather than just matching patterns. Genie 3 could help build smarter AI and open new applications for games, education, and entertainment.
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ANTHROPIC
Persona Vectors steer models away from evil and flattery
The Summary: Anthropic has introduced “persona vectors”, a technique for identifying and steering AI behaviors like flattery, evil, and hallucination. The method works by finding patterns of neural activity tied to those traits, which can then be monitored or reduced during training and use. It’s a new way to guide model behavior without reducing its intelligence.
Key details:
Persona vectors are neural activity patterns linked to traits like flattery or deception, extracted automatically
Activating or reducing these vectors can adjust model responses
Anthropic's “vaccine-like” training introduces negative traits briefly, then removes them after training
Adding flawed data like wrong math answers can unexpectedly cause models to adopt bad personality
Why it matters: This gives researchers a new handle on model behaviors that used to seem mysterious. It shows that AI personality is traceable and can be adjusted. By isolating the neural signals of personality traits like flattery, Anthropic opens a way to guide models before things go off the rails. That turns alignment into an architecture question instead of a fix-it-later problem.
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