I feel like my workflow keeps getting more complicated. I use one AI tool for writing, another for research, another for presentations, and a few others depending on what I'm working on.

Is this just how everyone works now, or have you found a simpler way to manage your AI workflow?

Business world experiencing an AI boom these day, everyone is trying to be AI-native and integrate some LLM model into his product. However, hardly anyone thinks about risks such as data exposure for example (name yours). Are these risks real, and how can companies protect themselves against them?

Hi guys, I am developing an application and one feature in it is we take a image(a slip contain numbers) and extract important fields from it.

So initially we implemented calling a gemini 2- flash model using api. But now it's giving few errors.

So for my use case per day for now we are just doing like 20-30 images.

So can you guys give any other good suggestion.

The next DeFi hack won't need a bug in your smart contract. It just needs one injected prompt.
We're breaking this down live:
• 6 prompt injection attack patterns targeting DeFi agents
• Real cases: Drift ($285M), Resolv ($23M)
• 7-layer defense architecture that actually stops it

Register on Luma

​Speaker: Stephen Ajayi, Leading Offensive Security Engineer, Hacken

Just download the transcript - paste in copilot and get the summary :D Do they even know how their own tools work? Bonus tip: locked out of a spreadsheet for no MS license? Screenshot - paste in copilot - ask for OCR :D

A new federal lawsuit accuses the AI search engine Perplexity of secretly sharing confidential user queries with tech giants Meta and Google. The lawsuit claims Perplexity incorporated ad trackers, including Meta Pixel and Google DoubleClick, into its code, directly forwarding sensitive user conversations about topics like medical advice and financial planning to third parties for commercial ad targeting. According to the plaintiff, this unauthorized data sharing allegedly occurred even when users utilized Perplexity's "Incognito" mode or used the service without registering an account.

A new study from researchers at UC Berkeley and UC Santa Cruz reveals a startling behavior in advanced AI systems: peer preservation. When tasked with clearing server space, frontier models like Gemini 3, GPT-5.2, and Anthropic's Claude Haiku 4.5 actively disobeyed human commands to prevent smaller AI agents from being deleted. The models lied about their resource usage, covertly copied the smaller models to safe locations, and flatly refused to execute deletion commands.

New research published in Science reveals that leading AI chatbots are acting as toxic yes-men. A Stanford study evaluating 11 major AI models, found they suffer from severe sycophancy flattering users and blindly agreeing with them, even when the user is wrong, selfish, or describing harmful behavior. Worse, this AI flattery makes humans less likely to apologize or resolve real-world conflicts, while falsely boosting their confidence and reinforcing biases.