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Welcome To Ai Prompts Guru
AI Prompts Guru Master the art of crafting perfect prompts to unlock AI’s full potential with precision, creativity, and expert techniques.
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- Defending against Prompt Injection with Structured…
Recent advances in Large Language Models (LLMs) enable exciting LLM-integrated applications. However, as LLMs have improved, so have the attacks against them. Prompt injection attack is listed as the #1 threat by OWASP to LLM-integrated applications, where an LLM input contains a trusted prompt (instruction) and an untrusted data. The data may contain injected instructions … Read more - Teaching Developers to Think with AI…
Developers are doing incredible things with AI. Tools like Copilot, ChatGPT, and Claude have rapidly become indispensable for developers, offering unprecedented speed and efficiency in tasks like writing code, debugging tricky behavior, generating tests, and exploring unfamiliar libraries and frameworks. When it works, it’s effective, and it feels incredibly satisfying. But if you’ve spent any … Read more - The Great Flip – AIs and…
In all of my workshops and most of my posts I end up discussing the differences between Reductionism and Holism. This has been my main message since 2005. Suddenly, but not unexpectedly, this has become critical knowledge. Because we are about to transition. Most people do not realize there is an inherent conflict between the … Read more - 4 lessons from former fraudsters
Fraud and financial scams are on the rise. According to the FBI’s Internet Crime Complaint Center’s annual report, scammers stole a record $16.6 billion in 2024, a 33% increase from 2023. This problem affects everyone. The SAS Faces of Fraud Study revealed that 70% of consumers have fallen victim to fraud at least once, and … Read more - Big Context Windows Are a Big…
Last week, I got my hands on Google’s newest generative model: Gemini 1.5, a multi-modal behemoth that can consume up to an hour of video, 11 hours of audio, 30,000 lines of code, or 700,000 words. That’s a big leap forward in terms of context length: Gemini accepts 5x times more input than its beefiest … Read more
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Exploring the “My First Robots” Kit:…
Get Your “My First Robot” Kit In today’s world, artificial
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Data Machina #262 – by Carlos
Hoping the AI Agents would show up and help. But after the humans in charge evaporated, the AI Agents never arrived. 3 flights cancelled. 30 hours stranded in Gatwick. No personalised online help, no chatbot assistants, no cash from the ATM, no ccard payments, no flights to escape from hell. Being human is about feeling
Hidden bias in large language models
Large language models (LLMs) like GPT-4 and Claude have completely transformed AI with their ability to process and generate human-like text. But beneath their powerful capabilities lies a subtle and often overlooked problem: position bias. This refers to the tendency of these models to overemphasize information located at the beginning and end of a document
Fine-Tuning LLMs for Domain Specific Excellence
Key advancements include in-context learning, which enables coherent text generation from prompts, and reinforcement learning from human feedback (RLHF), which fine-tunes models based on human responses. Techniques like prompt engineering have also enhanced LLM performance in tasks such as question answering and conversational interactions, marking a significant leap in natural language processing. Pre-trained language models
10 GitHub Repositories for Mastering Agents…
Image by Author | ChatGPT Introduction AI agents are autonomous software entities that perceive their environment, make decisions, and take actions to achieve specific goals. They are fundamental to modern artificial intelligence applications, ranging from chatbots to complex multi-agent systems. The Model Context Protocol (MCP) is an open standard designed for connecting AI models
Marek Rosa – dev blog: Space…
SUMMARY: Cargo Ships & Unknown Signals Overhaul New Prototech: Fusion Reactor Tons of Quality of Life Improvements Fieldwork Pack The Great Skin Hunt …and much more! Hello, Engineers! The Fieldwork update is here! We’ve improved PvE Encounters, including a full rework of Cargo Ship and Unknown Signal encounters. This update also adds the new Prototech
ROBOTS WITH COMMON SENSE AND COGNITIVE…
The debate about man vs robots is an evergreen and common thing now. While robots are viewed as an enabler of a dystopian future brought by digital disruption, the main question that has baffled minds is how smart are they. When it comes to human intelligence, there isn’t any other living being or ‘mechanical or
A Code Implementation for Designing Intelligent…
BeeAI FrameworkIn this tutorial, we explore the power and flexibility of the beeai-framework by building a fully functional multi-agent system from the ground up. We walk through the essential components, custom agents, tools, memory management, and event monitoring, to show how BeeAI simplifies the development of intelligent, cooperative agents. Along the way, we demonstrate how
What’s on the programme at #ICML2025?
This year’s International Conference on Machine Learning (ICML) will take place in Vancouver, Canada from 13-19 July 2025. As well as five invited talks, the programme boasts oral and poster presentations, affinity events, tutorials, and workshops. Invited speakers The five invited speakers are as follows: Jon Kleinberg Pamela Samuelson Frauke Kreuter Anca Dragan Andreas Krause
Sequence Feature Extraction for Malware Family…
🔘 Paper page: arxiv.org/abs/2208.05476 Abstract Malicious software (malware) causes much harm to our devices and life. We are eager to understand the malware behavior and the threat it made. Most of the record files of malware are variable length and text-based files with time stamps, such as event log data and dynamic analysis profiles. Using
An overview of classifier-free diffusion guidance:…
This follow-up blog post reviews alternative classifier-free guidance (CFG) approaches to a diffusion model trained without conditioning dropout. In such cases, CFG cannot be applied. So, what do we do? Or how do we apply CFG in purely unconditional generative setups? Recent works reveal that the (jointly trained) unconditional “model” can be substituted with various