Nice list I think it’s about time-especially with all the expected openweights-to update my list of recommendations
LLMS
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High expectations for Claude model despite recent setbacks
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We’re all doing our best Hopefully I’ll make local inference the easy by-default within the next 3 years (also why I am raising haha)
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AI Researchers Debate LLM Capabilities: Correlation vs. Causation
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if you’re using Ollama switch to llama.cpp if you’re using OpenClaw switch to Hermes these are basics at this point
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Exploring Interesting Approaches to Infinite Memory and Context in AI
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r/hardwareswap subreddit snd discord server
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Self-Distillation Degrades LLM Reasoning Capabilities
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"Why Does Self-Distillation (Sometimes) Degrade the Reasoning Capability of LLMs?" Self-distillation can make LLMs look smarter by producing shorter, more confident reasoning traces, but in math it often takes out the model's uncertainty and self-correction signals. This can
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Understanding AI Knowledge Cutoff Limitations and Implications
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ask your AI about "knowledge cut-off"
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Sonnet 4.6 disappoints expectations compared to competitors
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Would be disappointing, no? I don't see people being that impressed with Sonnet 4.6 – not that it is bad but in line with others
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LLMs Show Dramatic Performance Improvement on USAMO
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Performance of LLMs in USAMO 2026 vs 2025 Insane jump in just 1 year.
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Reasoning Models Struggle Controlling Chains of Thought
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Reasoning models struggle to control their chains of thought, and that’s good https://
buff.ly/5zkGrQO
#AI #MachineLearning #DeepLearning #LLMs #DataScience