Michel

Business Development Executive | AI & Automation | Cybersecurity | Software Engineering

[Michel @MichelRajkovaca]

I help businesses and tech partners with AI automation, custom software and cybersecurity.

If you’ve got a project your team can’t handle in-house, or need extra delivery capacity, feel free to reach out.

https://t.co/eYheN7lcTp

[Michel @MichelRajkovaca]

The best automation opportunities usually aren’t hidden.

They’re the tasks people are doing manually every single day because “that’s just how we’ve always done it.”

[Michel @MichelRajkovaca]

@claudiamiclea 60+ Dev shop. You could say we are building anything and everything business' ask for :)

[Claudia @claudiamiclea]

Let’s connect 🤝

What are you building right now?

Apps.
SaaS.
AI.
Startups.
Side projects.
Whatever you’re shipping.

Drop it below 👇

I want to meet more people who are actually building, shipping, and figuring shit out along the way.

[Michel @MichelRajkovaca]

Cybersecurity shouldn’t start after something goes wrong

[Michel @MichelRajkovaca]

A company having an internal development team doesn’t mean they have no need for outside technical support.

Sometimes it means the opposite.

The more software they ship, the more integrations, security testing and specialist work starts showing up

[Marcelo Emmerich @EmmerichMarcelo]

X is better when your timeline is full of people actually building things 🚀

AI builders 🤖
SaaS founders 🧱
Frontend devs 🎨
Backend devs ⚙️
DevOps people 🛠️
Product minds 🧠
Indie hackers 🧪
Freelancers 🌍

People shipping in public 📣

If that’s your world, let’s connect 👋

[Michel @MichelRajkovaca]

@EmmerichMarcelo Our team of 60+ Devs definitely ship :)

[Mujeeb Ahmed @hey_mujeebahmed]

The “best LLM” is starting to feel like the wrong question.

I spent some time exploring Martian’s AI Frontier, and a few things stood out:

• No single model dominates across every task
• Real-world cost can look very different from quoted token pricing
• Reliability matters too - repeated runs can produce very different outcomes

The interesting part is that combining models can sometimes give you a better quality/cost tradeoff than relying on one model.

The research behind it was selected for an oral presentation at ICLR 2026. Worth exploring the data yourself: https://t.co/HIiZeIwyZx

[Joe Dakwa @golanger85]

https://t.co/IPIdTukfwo

[Michel @MichelRajkovaca]

@Av1dlive AI is making the build side easier, but the real edge still seems to be picking the right business problem in the first place.

[Michel @MichelRajkovaca]

@madebythomasai @GJarrosson The interesting part is getting agents to work reliably through the same messy interfaces humans already use. That’s where a lot of automation projects still fall apart.

[Michel @MichelRajkovaca]

@rohit4verse The interesting part is how many of the best AI opportunities are still buried inside boring business workflows nobody talks about publicly.

[Michel @MichelRajkovaca]

The best AI opportunities usually don’t start with “we need AI.”

They start with “why are we still doing this manually?”

[Michel @MichelRajkovaca]

@MicroConf The gap seems to be shifting from “should we use AI?” to “how do we actually connect it to the systems the company already uses?”

[Michel @MichelRajkovaca]

@COTInetwork Working with a 60+ person team building AI agents, workflow automation and custom enterprise software. Always interested in seeing what other teams are building around real business use cases.

[Michel @MichelRajkovaca]

@EmmerichMarcelo Hey Marcelo, I’m on the business development side for a 60+ person tech team working across AI automation, custom software and cybersecurity. Always good to connect with founders and builders in the space.

[Joe Dakwa @golanger85]

There is a problem with the bounty space. It’s a big problem. 👇

[Joe Dakwa @golanger85]

💰 Researcher Hacked Google Using AI and Earned $500,000 Bug Bounty

Source: https://t.co/I84gDR5dFD…

A security researcher known as brutecat has disclosed how an AI-driven fuzzing pipeline uncovered more than $500,000 in vulnerabilities across Google's infrastructure in under three months, exposing systemic access-control failures hidden inside roughly 1,500 APIs.

The researcher began by targeting��Google's discovery documents machine-readable API specifications, similar to Swagger docs, that list all available endpoints, parameters, and methods. While these documents are publicly available for APIs like the YouTube Data API, many exist for internal Google APIs and require valid API keys to access.