I think there's a fair reaction. OTOH, when using LLMs with APL, which is an extremely efficient and well-designed language, AI is hasn't been able to create any useful code at all for me so far. So their conclusions may be correct anyway…
LLMS
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Reasoning versus Pattern Matching: Causal and Correlative Models
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To make it very short: reasoning generates causal models of the data, pattern matching uses associative/correlative models of the data.
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MiniMax Models Now Available on Abacus ChatLLM Platform
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MiniMax is coming to ChatLLM today You can now use Kimi, Opus or Flash on Abacus Claw
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Three AI Types Explained: From Prediction to Agentic Intelligence
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Simple way to explain AI types Traditional AI = predict/classify/detect anomalies Generative AI = create content + automate knowledge work (incl. RAG) Agentic AI = agents that use tools/APIs + orchestrate tasks end-to-end We’re moving from predict → create →
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Language Models Struggle with Esoteric Programming Languages
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All models struggle in this benchmark because languages are: Brainfuck, Whitespace, Unlambda, Shakespeare. 😅
— Alex J. Champandard 🌱 (@alexjc) 19 mars 2026
If you actually pick a useful but still esoteric language like Joy, the frontier models do great (they *can* reason), but the open source ones struggle (they memorize). https://t.co/E5Mozy0yEBAll models struggle in this benchmark because languages are: Brainfuck, Whitespace, Unlambda, Shakespeare. If you actually pick a useful but still esoteric language like Joy, the frontier models do great (they *can* reason), but the open source ones struggle (they memorize).
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4-bit Quantization Fixes Tool Calling Performance Issues
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Dan found that the 2-bit quantization broke tool calling but upgrading to 4-bit (at 4.36 tokens/second) got that working
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Global AI Token Consumption Reaches 25000 Per Person Daily
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25,000 daily tokens per human alive today – this is about the amount of tokens we are processing today globally
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KVCache Quantization Discussed: Model Quantization Preferred Over KVCache Quantization
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NY friends building agents! reach out to @p_valfre and the @denieddotdev to learn about securing your agents and openclaw's from taking dangerous actions they'll be in town to speak at the MCP Dev Summit (Apr 2-3)
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The 7 Layers of Agentic AI Stack Architecture
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The 7 Layers of #AgenticAI Stack
by @ingliguori #LLM #GenerativeAI #ArtificialIntelligence #MachineLearning -
LLM Coding Benchmarks Questioned Real World Performance
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These kind of claims never pass the sniff test. Benchmarks can be cheated, but if it worked 0-11% of the time on real tasks (which are not part of benchmarks) nobody would ever use LLMs for coding. https://t.co/zp1qpQjf3P
— Peter Gostev (@petergostev) 19 mars 2026These kind of claims never pass the sniff test. Benchmarks can be cheated, but if it worked 0-11% of the time on real tasks (which are not part of benchmarks) nobody would ever use LLMs for coding.