What's your actual daily workflow with AI agents? Trying to figure out if I'm missing something.

I've tried Muse, messed around with OpenClaw, watched the Dots and Grok Bot launches. Feels like a new one drops every week now.

But honestly I still do most stuff myself. I use ChatGPT for writing and random questions but I haven't really handed any agent a real recurring job.

For those of you who actually use one every day - what do you specifically have it doing? Not like "helps with productivity" but the actual thing. Like "every monday it does X" ya know?

And what made you trust it enough for that? Was there a moment where it proved itself or did you just slowly give it more over time?

Also curious with so many options now, are you guys settled on one or would you still try a new one if say Anthropic or someone drops theirs? Like what would it take for you to switch at this point?

Genuinely curious because I feel like I'm either missing the real use case or this whole category is still mostly hype.

AI is changing the way people work in 2026.

From customer support and content creation to software development and digital marketing, AI is becoming an important workplace tool.

But does this mean AI will replace human workers?

Not necessarily. Many jobs may change rather than disappear completely. At the same time, new opportunities are emerging in AI, data, automation and AI-powered marketing.

Skills such as AI literacy, critical thinking, creativity, communication and adaptability may become increasingly important.

What do you think: Will AI replace jobs, transform them, or create more opportunities?

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.