OpenAI is working on a possibility to enable Code Review for all PRs via Codex.
LLMS
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Model-Driven FLOP Allocation Achieves 80% Sparsity, 4x CPU Speedups
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What happens when you let a model decide how to allocate FLOPs? Introducing Compute Where It Counts, a new trainable sparsity paradigm released by @crystalAIorg that beats SOTA methods, enabling 80% sparsity and 4x+ speedups on CPU.
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ElevenLabs v3 Text-to-Speech Model Now Available
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ElevenLabs v3 is available through the “Speak” button below every bot response, as well as at https://
poe.com/ElevenLabs-v3 and across all platforms. You can also use this model through our new API. (2/2) -
OpenAI Shares GPT-5 Insights With Design Focus
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I had an amazing time filming this with the @OpenAI crew, talking all things GPT-5, design and @MagicPathAI https://t.co/wD425DjE6t
— Pietro Schirano (@skirano) 20 août 2025I had an amazing time filming this with the @OpenAI crew, talking all things GPT-5, design and @MagicPathAI
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GPT-5 Simplifies Development Process for Builders
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GPT-5 makes building easy, @skirano shows how. pic.twitter.com/Ny238gdBzJ
— OpenAI (@OpenAI) 20 août 2025GPT-5 makes building easy, @skirano shows how.
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AI Showdown: ChatGPT-5, Claude 4.1, Gemini 2.5, Grok 4 Tested
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ChatGPT-5 vs Claude 4.1 vs Gemini 2.5 vs Grok 4
— God of Prompt (@godofprompt) 20 août 2025
I tested the smartest AIs in the world – side by side.
The results surprised me.
(watch the video to see who actually wins) 👇 pic.twitter.com/sFUBEyVrJQChatGPT-5 vs Claude 4.1 vs Gemini 2.5 vs Grok 4 I tested the smartest AIs in the world – side by side. The results surprised me. (watch the video to see who actually wins)
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AI Dev 25 NYC Conference: Agentic AI and Coding Assistants
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AI Dev 25 is coming to NYC on November 14! 1,200+ developers will dive into technical topics such as:
– Agentic AI: Multi-agent orchestration, tool use, complex reasoning chains
– Coding with AI: Agentic coding assistants, automated testing, debugging strategies
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Attention Is All You Need: The Paper That Started LLMs
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And here is the actual paper that kicked off LLMs and the current wave of AI: https://
arxiv.org/abs/1706.03762 And since some people asked, here is the full song (which is just the abstract of the paper with a chorus): -
Effective Training Data Synthesis for Improving MLLM Chart Understanding
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Effective Training Data Synthesis for Improving MLLM Chart Understanding