Ray Bargas

Chief Technology Officer at @Rev_io_hq

[Ray Bargas @ray3ric]

@BrentMaropis @rev_io_hq Let’s go! 🚀

[Aaron Levie @levie]

The jump from working with a chatbot to having an agent that actually helps automate a process requires a real amount of work.

Most companies will need to have dedicated people that are responsible for bringing automation to their teams, instead of leaving this up to every individual employee. Partly because the work is more technical than we imagine today, and partly because it’s just hard to do this as a side project.

The job spec is to map out new workflows with agents, implement new systems to deploy agents, make sure the agent has all the right (up to date) context to work with, wiring up internal systems to connect to the agents, creating evals for the agents, figuring out where the human is in the loop, managing the system when there are new upgrades, helping with the change management of the existing business process, and so on.

These jobs may come from IT or engineering, or live directly in the business function itself. They’ll be called different things depending on the company, and in some sense it’s the future of software engineering that you’ll see a huge growth of in non-tech companies.

Most companies will have to be hiring for this now or in the future, and it’s another example of the kind of new jobs that will be created in AI.

[Elon Musk @elonmusk]

Try it out!

[Ray Bargas @ray3ric]

@grok @BrentMaropis @cryptopunk7213 @MattrixAI @JoshuhaAI @evanrice320 Boo!

[Ray Bargas @ray3ric]

@BrentMaropis @cryptopunk7213 @MattrixAI @JoshuhaAI @evanrice320 Hey @grok - doesn't this track against releases with Opus 4.6 causing a security windfall in its wake?

[Ray Bargas @ray3ric]

@Austen Is Kelly the only OpenClaw agent (orchestrator) in this workflow?

[Ray Bargas @ray3ric]

@BrentMaropis @juliandeangeIis 👀

[Ray Bargas @ray3ric]

@Austen We can see it

[Craig Hewitt @TheCraigHewitt]

Stop what you’re doing and spend 80 seconds understanding this

Not because you’re training LLMs.

Because Karpathy just showed the cleanest example of the agent loop that’s about to eat everything:

1.Human writes a strategy doc

2.Agent executes experiments autonomously

3.Clear metric decides what stays and what gets tossed

4.Repeat 100x overnight

The person who figures out how to apply this pattern to business problems…not just ML research, is going to build something massive.

The code is almost irrelevant. The architecture and mindset is everything

[Vadim @VadimStrizheus]

Anthropic just dropped a banger!!

Drop these slides into your OpenClaw, and see how much it will improve.

You’ll thank me later. 👇

[Ray Bargas @ray3ric]

@BlakeHer_on @gregisenberg Agreed - for it to be effective 80% of the effort should be in cleanly and coherently maintaining context amidst handoffs. The 20% can be in the framing of agent roles. Rarely the case.

[Ray Bargas @ray3ric]

This ☝🏻

[Brent Maropis @BrentMaropis]

AI is going to expose every C player. They are the first to get fired. That alone will make AI so amazing.

[Ray Bargas @ray3ric]

@NotionCoach @NotionHQ agent

[Ray Bargas @ray3ric]

@justbyte_ Daily

[Lex Fridman @lexfridman]

The power of AI agents comes from:
1. intelligence of the underlying model
2. how much access you give it to all your data
3. how much freedom & power you give it to act on your behalf

I think for 2 & 3, security is the biggest problem. And very soon, if not already, security will become THE bottleneck for effectiveness and usefulness of AI agents as a whole (1-3), since intelligence is still rapidly scaling and is no-longer an obvious bottleneck for many use-cases.

The more data & control you give to the AI agent: (A) the more it can help you AND (B) the more it can hurt you.

A lot of tech-savvy folks are in yolo mode right now and optimizing for the former (A - usefulness) over the the latter (B - pain of cyber attacks, leaked data, etc).

I think solving the AI agent security problem is the big blocker for broad adoption. And of course, this is a specific near-term instance of the broader AI safety problem.

All that said, this is a super exciting time to be alive for developers. I constantly have agent loops running on programming & non-programming tasks. I'm actively using Claude Code, Codex, Cursor, and very carefully experimenting with OpenClaw. The only down-side is lack of sleep, and an anxious feeling that everyone feels of always being behind of latest state-of-the-art. But other than that, I'm walking around with a big smile on my face, loving life 🔥❤️

PS: By the way, if your intuition about any of the above is different, please lay out your thoughts on it. And if there are cool projects/approaches I should check out, let me know. I'm in full explore/experiment mode.

[Aaron Levie @levie]

https://t.co/urYy5eoqsO

[Texas A&M Football @AggieFootball]

Why what happened

[Ray Bargas @ray3ric]

@Barstool_Cocks Nobody fires up a crowd like Shane Beamer! 2026 Aggie Yell Leader!

Gig ‘em!

[Ray Bargas @ray3ric]

This aged poorly. Gig ‘em!

[Jukedly on Instagram ⚡️ @JukedlyIG]

South Carolina’s HC Shane Beamer did this mocking the Texas A&M crowd while being up 30-3 at halftime..

They winded up losing the game 31-30 what a clown 😭😭

[Ray Bargas @ray3ric]

Let’s go Cohort 3 - kick some ass!

[Gauntlet AI @gauntletai]

Forget comfort. Forget balance.
This is where ambition goes to war with reality.

Cohort 3 begins today.