I was reading this recent @OpenAI article about Gartner naming OpenAI a Leader in enterprise AI coding agents: https://openai.com/index/gartner....

"Software development is becoming more agentic." This is a good summary of what is happening right now. We are moving from AI that helps you write faster, to AI that can take over tasks (actions, use tools, make changes, run tests, and bring the work back for human review). That is a very different behavior.
The article gives Cisco as an example. They used Codex for a big part of their AI Defense platform and reduced delivery time from several quarters to a few weeks.

There are so many new AI agent platforms ( @Wordware @Lindy @CrewAI @zapier and so on) that I'm finding myself curious how everyone is using them.

What AI agents are you using in production? What do they do? Are they working and reliable? What would make them better? Are they replacing roles? Augmenting existing ones?

Everyone s been talking about AI Agents over the past year.

But the real leverage doesn t come from the agent itself. It comes from the context you provide:

I have been looking at what is actually getting traction on Product Hunt right now. The pattern is clear.

In March, OpenClaw products dominated the leaderboard. Anything with "Claw" in the name got votes. That was a land grab new space, everyone rushing in to claim a spot .

In April, that stopped. The "just build an agent" strategy stopped working. What replaced it? Products that do specific tasks inside workflows you already have .

Here is what the data shows.

For a long time, most AI conversations were about answers.

Could a chatbot write a better email? Summarize a document? Explain a concept? Even when the output was wrong, the result was usually a suggestion that a person could accept, reject, or ignore.

Agents change that dynamic a bit.

An AI agent might update a CRM, approve a workflow, modify a database record, send a message to a customer, or trigger actions across multiple systems. At that point, the conversation is no longer just about the quality of the answer. It's also about the consequences of the action.

A few years ago, AI at work meant smarter autocomplete or a chatbot on your website.

Now, I m seeing something different.

Companies are experimenting with AI agents as teammates - not just tools. They schedule meetings, file reports, analyze data, and even handle parts of customer support.

With AI agents and assistants are becoming more advanced, we're seeing them handle everything from scheduling meetings to managing entire workflows.

But here s the big question would you fully trust an AI to run tasks autonomously, or would you prefer keeping an eye on things just in case?

Greg Isenberg (https://x.com/gregisenberg/statu...) asked what every SaaS tool looks like if it was built purely for agents.

That question stuck with me because I think collaboration is one of the biggest categories that needs to be rebuilt.

Most tools today are still human-native:

• Slack is for humans messaging humans

• Linear is for humans managing work

• Notion/Obsidian are for humans organizing knowledge

• agents are still trapped in terminals, browser tabs, and private chats

Scrolling through today's launches, it feels like half of them are AI agents now applying to jobs for you, editing your videos, handling outreach, monitoring other agents. Some of it genuinely saves hours. Some of it I still want to keep my hands on.

So I'm curious where everyone draws the line.

With the advent of clawdbots, it's as if we've all lost our inhibitions and "put our lives completely in their hands."

I'm all for delegating work, but not giving them too much personal/sensitive stuff to handle.

Last week Garry Tan (CEO of Y Combinator) shared his entire Claude Code setup on GitHub and called it "god mode."

He's sleeping 4 hours a night. Running 10 AI workers across 3 projects simultaneously. And openly saying he rebuilt a startup that once took $10M and 10 people. Alone, with agents.

AI agents are everywhere right now.

They help with launch copy, visuals, outreach, follow-ups basically most of what used to take days can now be done in hours.

But I keep thinking about something. Are AI agents actually improving product discovery on Product Hunt or are they just making launches look more polished?

Yes, AI speeds things up. You can test messaging faster, create better assets, prepare more efficiently.

Yesterday went through this Tweet by Greg Isenberg.

There is an app called "rent a human."

Today, I m doing a slightly more relaxed and bizarre corner.

The internet is full of things that are either amusing or scary, but mostly things that capture something outside the norm (and over time, even these weird things tend to become normalised).

I was recently talking with a group of founders, and we went around sharing tools we're using now. Posting my notes for our community here - would love to know what else people are using!

Voice AI toolkit:

- Vapi

I ve been hunting and playing around with AI agents for a while now, and while the progress is impressive, I keep running into things that make me think: Why can t it just do this already?

What s one thing you wish agents could do today that they just can t (yet)?

@OpenAI just dropped a 34-page guide on how to build intelligent AI agents. It s full of great ideas but could be hard to navigate if you're non-technical. I made a simple, no-code breakdown of only what you need to know here and how to apply these ideas into the no-code AI agents & multi-agent workflows you build on @MindPal here: https://mindpal.space/blog/opena...