Looks like that's the search tool usage instructions – they still don't share tool prompts in their published system prompts which continues to be frustrating
LLMS
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Asking source of $13/day from Claude cost docs
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Where did you get that $13/day number from? I found this page of the Claude docs but it's advising enterprise customers how much to budget when paying full API price for their team member https://
code.claude.com/docs/en/costs -
Scalable Voice Agent Design with Amazon Nova Sonic and BedRock
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Scalable Voice Agent Design with Amazon Nova Sonic with Amazon BedRock: Multi-Agent, Tools, and Session Segmentation! #BigData #Analytics #DataScience #AI #MachineLearning #NLProc #LLM #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang
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LangSmith Engine: The Agent of Agents
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LangSmith Engine: The agent's agent. pic.twitter.com/0oBp3rM2AX
— LangChain (@LangChain) 16 juin 2026LangSmith Engine: The Agent of Agents
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Traces and Evals: From Debugging to Continuous Improvement Loop
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Traces show the inputs, model calls, tool calls, outputs, and final action. Evals turn those learnings into a way to test whether the next version is better. This is how teams move from manual debugging to a continuous improvement loop. Join @hwchase17 for a deep dive on June
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The real clue: every sentence has the same length
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3/ The real clue is that every sentence has the same length. Look at how people actually write. A long thought that builds and meanders for a while. Then a short one. A fragment. Then a line that continues well past where a model would have
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Erasing AI markers doesn’t change detection
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2/ Long dashes. The word "delve". A typo slipped in on purpose. People erase all that to hide AI and it doesn't change anything. It wasn't that part that betrayed your writing.
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Rhythm betrays AI: how to fix it
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Everyone can tell when you used AI to write. It's not the words. It's the rhythm. Here's how to fix it:
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AI betrayed by the uniformity of its writing
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1/ AI gets caught because its writing can be predictable.
Detectors measure how uniformly the text flows. Same sentence length, same shape, line after line. This uniformity is what indicates it. -
3B Model VibeThinker-3B Nears State-of-the-Art on Reasoning
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Crazy: A 3B model is now reaching highly competitive results on verifiable reasoning tasks. VibeThinker-3B scores 94.3 on AIME26, 80.2 Pass@1 on LiveCodeBench v6, and 96.1% on unseen LeetCode contests. The gains appear to come primarily from post-training on top of