800 million tokens per month. Free. Someone just combined the free tiers of dozens of AI models into a single API. You input your API keys and it automatically manages: • routing between models
• rate limits
• fallback when a provider hits the limit
• load balancing
AI
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Service pools free tiers of dozens of AI models into one API
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Research shows more ‘thinking’ time does not always improve AI answers
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The assumption that "more thinking = better answers" was never tested until now. This paper tested it. The answer is no. For operators: if your AI output feels overengineered, bloated, or keeps second-guessing itself, the model isn't being thorough. It's losing confidence.
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Impact of Output Length on LLM Reasoning Accuracy
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The most useful finding for anyone using AI daily: When they capped reasoning at 60% of the model's natural length, it maintained 97% of peak accuracy. Longer natural outputs also correlated with lower accuracy. 71.9% accuracy under 4K tokens. 44.7% accuracy above 12K. The
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Analysis of LLM reasoning performance relative to token length
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It gets worse for simple questions. Easy problems hit the overthinking zone at 2,000 tokens. Hard problems don't hit it until 8,000. Translation: the simpler the question, the faster the model starts hurting its own answer by thinking longer. Optimal reasoning length varies
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Analysis of LLM Self-Correction Failures
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67.5% of these flips are genuine overthinking. The model explicitly reconsiders a correct answer, says "wait, let me double-check," and then replaces it with a wrong one. Not a glitch. Not hallucination. The model second-guesses itself into failure. The tell? Phrases like
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Impact of Reasoning Budgets on LLM Prompt Engineering
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Here's what it means for prompt engineering: They tracked individual answers across 32 reasoning budgets from 500 to 16,000 tokens. At ~7,000 tokens, something flips. The model starts abandoning correct answers MORE often than it finds new ones. They call it "negative flips."
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New research suggests longer chain-of-thought reasoning can degrade AI performance
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RIP "think step by step." Nanjing University and Baidu just published a paper that proves longer AI reasoning actively flips correct answers to wrong ones, and the implications are brutal for every prompt engineer using chain-of-thought.
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Google I/O: Gemini 3.5 Flash Model Launch
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GOOGLE I/O : We are getting Gemini 3.5 Flash today! > GEMINI
> GEMINI
> GEMINI
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NVIDIA and Oxford Research Propose Non-Backpropagation Learning for Neural Networks
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NVIDIA just proved AI doesn't need backpropagation to learn anymore. Training giant neural networks has meant one thing for a decade. You run backpropagation, compute gradients, and update weights with calculus. A new paper from NVIDIA and Oxford breaks that assumption.
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Discussion on LLM runtimes and coding benchmarks
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Yes, of course. The Hermes + Codex runtime swap looks promising for shell/patch workflows, but those "~5% coding gains" are still just Teknium's reply-thread anecdotes until we have proper evals.