But most users don't really understand what a model is, let alone how to pick the right one for their purposes Not helped by the fact that 4o and o4 are entirely different, I've seen so many people confused by that
GENERATIVE AI
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OpenAI Model Early Access: Honest Review and Performance Analysis
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I was not paid by OpenAI. I was just given access to the model in advance to try it out and share my honest thoughts. Read my full review (find it in my feed). you’ll see that I’m not 100% happy with the model. But it is really great at some things!
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China’s Qwen3 Outpaces GPT-5 with 1M Token Context
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While the discussion continues about whether GPT-5 meets expectations and also (as said in the Reddit AMA by Sam Altman) whether 256k context is enough or not – China enables up to 1m token context with their Qwen3 30b. China is delivering every day.
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400K Context Window: Major LLM Capability Expansion
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What's this? From the playground its 400k 400,000 context window
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Coding AIs That Fix Bugs Instead of Creating Them
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Imagine coding AIs that fix bugs instead of causing more.
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OpenAI doubles GPT-5 rate limits for ChatGPT Plus users
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GPT-5 rollout updates: *We are going to double GPT-5 rate limits for ChatGPT Plus users as we finish rollout. *We will let Plus users choose to continue to use 4o. We will watch usage as we think about how long to offer legacy models for. *GPT-5 will seem smarter starting
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Extracting Training Data Directly from Large Language Models
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FUTURE WORK – direct extraction we're working on directly extracting training data from models using RL and other methods. we'll be presenting our first work on this in COLM, and expect more in this space we may be able to directly extract data from the 120b model.. one day
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Deduplicating Redundant AI-Generated Output Data
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FUTURE WORK – deduplication even though i varied the random seed and used temperature, a lot of the outputs are highly redundant it would be prudent to deduplicate, i bet there are only 100k or fewer mostly-unique examples here
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Describing Text Distribution Differences Between Language Models
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FUTURE WORK – describing differences @ZhongRuiqi has some incredible work on methods for describing the difference between two text distributions *in natural language* we could compare outputs of 20b to the 120b model, or LLAMA, or GPT-5…