No more bad prompts. They just launched a free skill for Claude that writes the perfect prompt for any AI on the first try. No retries.
No credits wasted. It's called Prompt Master and it works with:
→ ChatGPT
→ Claude
→ Midjourney
→ Cursor
→ ElevenLabs
→ and 13 more
LLMS
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Claude launches ‘Prompt Master’ skill to generate perfect prompts
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GPT-5.5 Reinforcement Learning Scaling Across Model Sizes
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GPT-5.5 by Reasoning Effort: I've asked it in Codex to create a physics-based visualisation of RL cycles for different sized models (70b, 1t, 10t), to demonstrate how the amount of RL you can do differs by model size.
— Peter Gostev (@petergostev) 26 avril 2026
My assessment of each:
– Low: weird slop
– Medium: kinda… pic.twitter.com/6YCNqPyzcRGPT-5.5 by Reasoning Effort: I've asked it in Codex to create a physics-based visualisation of RL cycles for different sized models (70b, 1t, 10t), to demonstrate how the amount of RL you can do differs by model size. My assessment of each: – Low: weird slop – Medium: kinda
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ChatGPT Hallucinations: How Often Does It Fabricate Facts?
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Have you ever caught ChatGPT making up a fact you knew was wrong?
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Grounding: Why Perplexity Cites Sources, ChatGPT Sometimes Doesn’t
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Grounding is why Perplexity always cites a source, and ChatGPT sometimes doesn't.
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Stanford Meta-Harness: Open Source Framework Optimizes AI Scaffolding
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Stanford just turned the Meta-Harness paper into open source code. It's a framework that automatically optimizes the scaffolding around a fixed base model. Think memory, retrieval, and context decisions. The approach is unusually direct. A coding agent acts as the proposer.
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Grounding: How Document Upload Improves AI Accuracy
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Ever uploaded a document to ChatGPT and asked a question about it? The answer you got came from grounding. When you ask a model a question without any file, it answers from memory.
Whatever it learned during training. Sometimes right, sometimes made up. Grounding forces the -
Unified Framework Controls Large Language Models Without Breaking
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Can you control a large language model without breaking its brain? Zhejiang University and Alibaba Group researchers just showed how. They unify all model control methods (fine-tuning, LoRA, activation edits) into one single framework, separating effects into "preference"
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Daily LLMs and AI Agents Knowledge Sharing
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Found this helpful? Like and repost to share the knowledge. Follow me → @datachaz for your daily dose of LLMs and AI agents.
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Tolaria: Free Mac App for AI Knowledge Building
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@Karpathy
's LLM wiki concept just became a real Mac app. It is called Tolaria, a free desktop app for Mac and Linux. The goal is simple: give humans and AI agents a shared, native environment to build knowledge. It writes like a modern block editor, but saves everything as -
Build Full Stack Apps with GPT 5.5 and Opus 4.7
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Build full stack apps using GPT 5.5 Thinking and Opus 4.7 Just use English language and kick start your business on Abacus AI Agent!