Master Any #LLM
by @ingliguori #GenerativeAI #ArtificialIntelligence #MachineLearning #MI
PROMPT ENGINEERING
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Master Any LLM with Ingliguori’s Guide
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Personal LLM Knowledge Bases: The Most Effective Learning Method
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Building and maintaining personal LLM knowledge bases might be the most effective way of learning things with LLMs. Consuming/summarizing things off the chat box does not really seem to work.
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Learn Claude Code: New Engineering Resource Repository
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repo link → https://
markdown.engineering/learn-claude-c
ode/
… I wasn’t able to identify the authors but hats off to them! If anyone knows him/her/them, please share. -
Grok video games will be incredible with new AI features
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Grok video games are going to be incredible Kiri (@Kyrannio) Be sure to update your Grok app, the new Imagine 'Quality' mode is absolutely insane. It really responds exceptionally well to specific camera terminology and feels far more stylized. I love it! Amazing work from xAI once more :). Prompt: a gorgeous Kodachrome film still of Santa Barbara, cinematic and hyperrealistic, 2020s, motion blur, anti aliasing, lens distortion — https://nitter.net/Kyrannio/status/2039945898999124427#m
→ View original post on X — @akshat_world, 2026-04-03 08:07 UTC
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Prompt forces action, not just planning
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What this prompt does that normal "help me make a plan" doesn't: Most people ask Claude "how do I achieve my goal?" and get a list of
reasonable steps organized by logic. This prompt doesn't organize your steps. It forces you to move on them today. It kills every reason for -
Napoleon Planner for Overthinking Decisions
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Steal this mega prompt to turn Claude into your personal Napoleon Rapid Execution Planner: Just describe your goal, project, or decision you've been overthinking, delaying, or
circling without moving on. Watch it strip away every reason for hesitation and rebuild your entire -
Stanford’s Free AI Course: Transformers and Large Language Models
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If you’re serious about AI, this is worth your attention. Stanford has just released its course CME 295: Transformers & Large Language Models in full on YouTube. What stands out to me is the level of clarity and structure. This isn’t another surface-level overview. It’s the actual curriculum used to teach how modern AI systems work. This will help you move from using AI to understanding it. 📚 𝗧𝗼𝗽𝗶𝗰𝘀 𝗰𝗼𝘃𝗲𝗿𝗲𝗱 𝗶𝗻𝗰𝗹𝘂𝗱𝗲: • How Transformers actually work (tokenization, attention, embeddings) • Decoding strategies & MoEs • LLM finetuning (LoRA, RLHF, supervised) • Evaluation techniques (LLM-as-a-judge) • Optimization tricks (RoPE, quantization, approximations) • Reasoning & scaling • Agentic workflows (RAG, tool calling) 🎥 Watch these now: – Lecture 1: zurl.co/F0QR5 – Lecture 2: zurl.co/hG5lp – Lecture 3: zurl.co/PnKrW – Lecture 4: zurl.co/XCZoE – Lecture 5: zurl.co/GWlYI – Lecture 6: zurl.co/zGqqQ – Lecture 7: zurl.co/T06NM – Lecture 8: zurl.co/Un42q – Lecture 9: zurl.co/rR3YL For 2026, consider setting aside 2–3 hours each week to go through these lectures. If you’re working in AI whether on infrastructure, agents, or applications, this is a foundational resource worth your time. It’s a simple way to build depth where it matters most. #AI #LLMs #Transformers #Stanford #GenAI
→ View original post on X — @pascal_bornet, 2026-04-03 05:00 UTC
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New Book on Agentic Architectural Patterns for Multi-Agent Systems
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5- release from @PacktDataML at http://
amzn.to/3MaHy8T "Agentic Architectural Patterns for Building Multi-Agent Systems: Proven design patterns and practices for GenAI, agents, RAG, LLMOps, and enterprise-scale AI systems" Contents:
GenAI in the Enterprise: Landscape, -
Outcome-first prompting changes AI results forever
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Most people will scroll past this article. The ones who read it will permanently change how they prompt every AI tool they use. Outcome-first prompting is the single shift that separates people getting generic answers from people getting finished deliverables. Same models.
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Knowledge Base System with Interactive Tools for Podcast Research
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Same, I have a similar setup. A mix of Obsidian, Cursor (for md), and vibe-coded web terminals as front-end. Since I do a podcast, the number/diversity of research interests is very large. But the knowledge-base approach has been working great. For answers, I often have it generate dynamic html (with js) that allows me to sort/filter data and to tinker with visualizations interactively. Another useful thing is I have the system generate a temporary focused mini-knowledge-base for a particular topic that I then load into an LLM for voice-mode interaction on a long 7-10 mile run. So it becomes an interactive podcast while I run, where I ask it questions and listen to the answers to learn more. Anyway, heading out for a run now, thanks for the write-up 👊
→ View original post on X — @lexfridman, 2026-04-02 23:06 UTC