The AI conversation
Everything AI.
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Discover what’s new, share what you’re building, and discuss where AI is headed.
Talking about my project.
Hello everyone I am Anannth Roy I am currently working on an AI powered desk robot. That's all for now more news about it will be posted soon....
How I see AI in future.
From my perspective as an active explorer and curious user, the future looks awesome. AI will become more conversational, more multimodal, and just more intertwined into everyday life. Like, it'll understand images, voice, maybe even real-time scenes you show it.
Hello Everyone
vezosdigitals.comThis is my first post here i am looking forward to connect with all the users
Would You Let an AI Agent Control Your Computer While You Sleep? 🤖
Imagine going to sleep and telling your AI agent: "Build my website, fix the bugs, reply to my emails, research competitors, and prepare tomorrow's work. Don't wake me up unless absolutely necessary." You wake up the next morning. Everything is done. But there's a catch. The agent might have sent the wrong email, deleted an important file, made a bad decision, or misunderstood what you actually wanted. So here's the question: How much autonomy should AI agents have? Would you give an AI agent: 💻 Full access to your computer and files? 📧 Permission to send emails without your approval? 💳 Permission to spend money within a budget? 🚀 Permission to make decisions and execute tasks while you're offline? Or should humans approve every important action? My take: The future isn't AI that asks permission for every click. It's AI that knows which decisions it can make independently—and when it absolutely needs a human. But getting that balance right is going to be difficult. What's your take? Would you trust an AI agent to work autonomously for 8 hours while you sleep? Why or why not?
New open-weight model release roundup
example.comShort summary of this week's open-weight releases and what changed in licensing.
Notes on long-context retrieval failures
Reproduced a few long-context results locally. Recall degrades sharply once distractor density goes up, even well inside the advertised window.
What actually broke when you put an agent in production?
Everyone shows the happy path demo. I am more interested in the failure modes: tool loops, silent retries, cost blowups. What did you have to add before it was reliable?
Evals are becoming the real bottleneck
Capability jumps are easy to announce and hard to verify. Without shared evals we are all benchmarking on vibes.
How do you review code written by a coding agent?
Diff size is the problem. Reviewing 800 lines you did not write is slower than writing 200 yourself. Curious what workflows people settled on.
