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How do you secure large language models from hacking and prompt injection? 🔐 Jeff Crume explains LLM risks like data leaks, jailbreaks, and malicious prompts. Learn how policy engines, proxies, and defense-in-depth can protect generative AI systems from advanced threats. 🚀
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#llm #secureai #aihacking #aicybersecurity
Using Claude Code to build a GitHub Actions workflow
A short demo of using Claude Code to add a new feature to one of my GitHub repositories by implementing a GitHub Actions workflow for me.Full code written by...
TheAgentCompany: Benchmarking LLM Agents on Consequential Real World Tasks
We interact with computers on an everyday basis, be it in everyday life or work, and many aspects of work can be done entirely with access to a computer and the Internet. At the same time, thanks...
Gartner: Over 40% of Agentic AI Projects Will Be Canceled by End 2027
Over 40% of agentic AI projects will be canceled by the end of 2027, due to escalating costs, unclear business value or inadequate risk controls, according to Gartner #GartnerSYM #GartnerIT
Design Patterns for Securing LLM Agents against Prompt Injections
This new paper by 11 authors from organizations including IBM, Invariant Labs, ETH Zurich, Google and Microsoft is an excellent addition to the literature on prompt injection and LLM security. …
Researchers Uncover Hidden Ingredients Behind AI Creativity | Quanta Magazine
Image generators are designed to mimic their training data, so where does their apparent creativity come from? A recent study suggests that it’s an inevitable by-product of their architecture.
6 tactics for fixing your context and shipping better agents. As Karpathy says, building LLM-powered apps means learning to ‘pack the context windows just right’—smartly deploying tools, managing information, and maintaining context hygiene.
Drew Breunig has been publishing some very detailed notes on context engineering recently. In How Long Contexts Fail he described four common patterns for context rot, which he summarizes like …
Agentic Coding: The Future of Software Development with Agents
Armin Ronacher delivers a 37 minute YouTube talk describing his adventures so far with Claude Code and agentic coding methods. A friend called Claude Code catnip for programmers and it …
35% off our evals course: https://bit.ly/evals-ai
Vincent introduces Marimo, a reactive notebook environment. He walks us through the features of Marimo, including interactive and reactive charts and widget integration. Vincent demonstrates how you can use these components to build annotation apps for evals. Vincent also highlights differences between Marimo and traditional Jupyter Notebooks.
Links:
1. Repo w/notebook: https://github.com/koaning/molabel
2. Vincen'ts drawing pad: https://www.amazon.com/Inspiroy-H640P-Graphics-Battery-Free-Sensitivity/dp/B075T6MTJX
3. Vincent's sites: https://koaning.io , and https://calmcode.io/
00:00 Introduction to Data Science Journey
00:27 Exploring the Chick Weight Dataset
00:57 Interactive Data Analysis with Marimo
02:04 Importance of Looking at Data
03:32 Advanced Data Visualization Techniques
05:14 Introduction to Marimo's Unique Features
06:44 Reactive Programming in Marmo
12:50 AI Integration and Custom Rules
15:30 Marimo's Storage and Export Options
27:16 Advanced Visualization and Annotation
37:10 Introduction to Any Widget
37:45 Building Custom Widgets
38:56 Showcasing the Scatter Widget
40:29 Defining Widgets with Any Widget
45:58 Annotation Widgets and Their Uses
52:14 Exploring More Widget Capabilities
01:01:32 Marimo's App Mode and Deployment
01:03:37 Final Thoughts and Future Directions
01:04:45 Q&A and Closing Remarks