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Holy sh*t… nobody’s talking about JSON prompting but it’s the cheat code to unlock God-tier outputs from GPT-4, Claude, and Gemini. Here’s how it works (with copy-paste templates): 👇
I often rant about how 99% of attention is about to be LLM attention instead of human attention. What does a research paper look like for an LLM instead of a human? It’s definitely not a pdf. There is huge space for an extremely valuable “research app” that figures this out.
I often rant about how 99% of attention is about to be LLM attention instead of human attention. What does a research paper look like for an LLM instead of a human? It’s definitely not a pdf. There is huge space for an extremely valuable “research app” that figures this out.
#ArrestDhoni. @grok why this is trending ?
RT to help Simon raise awareness of prompt injection attacks in LLMs. Feels a bit like the wild west of early computing, with computer viruses (now = malicious prompts hiding in web data/tools), and not well developed defenses (antivirus, or a lot more developed kernel/user…
RT to help Simon raise awareness of prompt injection attacks in LLMs. Feels a bit like the wild west of early computing, with computer viruses (now = malicious prompts hiding in web data/tools), and not well developed defenses (antivirus, or a lot more developed kernel/user…
If you use "AI agents" (LLMs that call tools) you need to be aware of the Lethal Trifecta Any time you combine access to private data with exposure to untrusted content and the ability to externally communicate an attacker can trick the system into stealing your data!
Transformer vs. Mixture of Experts in LLMs, clearly explained (with visuals):
The most dangerous person in tech isn't Elon or Altman. It's a 28-year-old who built the training system for OpenAI. Now Zuckerberg just gave him $15 BILLION to join Meta's "superintelligence" lab. Here's why every tech CEO is terrified of what's coming: 🧵
Self-attention in LLMs, explained as a directed graph:
Self-attention in LLMs, explained as a directed graph: https://t.co/1vtjS1wNs1
(1/5) We’ve never enjoyed watching people chop Llamas into tiny pieces. So, we’re excited to be releasing our Low-Latency-Llama Megakernel! We run the whole forward pass in single kernel. Megakernels are faster & more humane. Here’s how to treat your Llamas ethically: (Joint…
Let's fine-tune Qwen 3 (100% locally):
I'm surprised that DeepSeek isn't part of everyone's daily life yet. It's capable of doing whatever you want and more… Here are 15 useful, crazy and absurd prompts that will prove it to you:
Google presents Learn-by-interact A Data-Centric Framework for Self-Adaptive Agents in Realistic Environments
MAPS guides different LLMs to generate more effective test cases. MAPS automatically generates tailored prompts for different LLMs to improve test case generation quality, achieving better code coverage through diversity-guided optimization and failure-driven learning. Original…
LlamaRestTest uses LLMs, fine-tuning, and quantization to improve REST API testing effectiveness and efficiency. It dynamically refines API requests based on server feedback. ----- Original Problem 🤖: → Current REST API testing tools struggle with complex parameter formats…
This paper compares LLMs with traditional machine learning models for text classification, examining the impact of prompting techniques. ----- Methods in this Paper 💡: → This paper evaluates various LLMs (Llama2, Llama3, Mistral, GPT-4, etc.) against traditional machine…

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