Agreed! It’s hard to find a task I’m not starting from Codex lately, and it’s only gonna get better from here!
PROMPT ENGINEERING
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MoonDream 3 API Integration Challenges and Usage Options
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MoonDream 3 is impressive, but the API surface is pretty messy right now. There are three ways to use MoonDream 3 right now. Option 1 (Hugging Face Transformers – model download only) You need a Hugging Face token to download the model. It is gated. With some tweaks, this
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Codex AI Model Code Analysis Reliability Discussion
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Ha for sure, I believe you – I had Codex look at some of these and they definitely looked quite sus
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Product Ideas Now Become AI Skills Built in Minutes
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Many of these product ideas are now just “skills” that you can make in minutes..
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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 -
Why Structured Workflows Beat Simple Prompting for LLMs
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Most people are using LLMs wrong. Not because of bad prompts. Because of the wrong *system*. The difference is massive:
→ Prompting = average results
→ Structured workflows = 10x outputs I wrote a deep dive on how to actually use LLMs at a high level. Read here → -
OpenAI Codex Gallery: AI-Powered Code Generation Use Cases
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Link to the Codex gallery:
→ https://
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3-Phase Analysis Method
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1/ Run the full prompt in ChatGPT, Claude, or Grok. 2/ Answer the 4 discovery questions in Phase 1. 3/ Type "continue" after each phase to go deeper. Works for startup strategy, organizational culture issues, market positioning, supply chain breakdowns, product-market fit, and team challenges.
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Claude’s Secret Leverage Point Deconstructor Mode
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BREAKING: Claude has a secret mode called "Donella Meadows Leverage Point Deconstructor." It maps any complex problem as interconnected feedback loops, finds the single point where a tiny change produces massive results, and rebuilds your entire strategy from the structure
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LLMs Excel at Technical Editing Tasks Over Content Generation
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Well, I guess that's because it had a lot of newspapers in it's training corpus .
Joking aside, I think LLMs work best for technical editing tbh. Things like "what sources did I forget to cite", "is my spelling of technical terms consistent" etc.