Leonid Bugaev

The recent agent-to-agent protocol more than just a technological solution, it is attempt to "fix" colapsing SaaS economy and API market, and moving to value based pricing.

Over time, I see public API usage dropping off, but total API usage skyrocketing – as these agents will make a ton of calls behind the scenes. MCPs are still in a technical adoption phase, but they will evolve with more user‑friendly experiences.

The recent agent-to-agent protocol more than just a technological solution, it is attempt to "fix" colapsing SaaS economy and API market, and moving to value based pricing.

Why would they do that? Because most SaaS vendors want total control over their products and how they’re used. With these “shadow” MCP integrations, they lose the ability to track usage or enforce pricing.

The recent agent-to-agent protocol more than just a technological solution, it is attempt to "fix" colapsing SaaS economy and API market, and moving to value based pricing.

Google’s new A2A protocol helps address this issue. From a pure technology angle, I like it. You can schedule tasks, define “skills,” and basically treat these agents like virtual employees. It’s probably the direction the industry is heading.

The recent agent-to-agent protocol more than just a technological solution, it is attempt to "fix" colapsing SaaS economy and API market, and moving to value based pricing.

Historically, API usage wasn’t a big threat, because sure, you could export data, crunch numbers, or build some custom scripts, but replicating a polished user interface or full product experience was too much hassle.

The recent agent-to-agent protocol more than just a technological solution, it is attempt to "fix" colapsing SaaS economy and API market, and moving to value based pricing.

First of all, the recent agent‑to‑agent (A2A) protocol that Google introduced isn’t really some massive tech breakthrough in itself. It’s more like an attempt to fix the very shaky SaaS economy, which is getting hammered by “shadow API usage.”

It's been over two years since the new cursor was released. We now have juniors who've only programmed with AI, never experiencing coding without it. Would you hire these juniors today? Do you think they can become capable engineers?

With advanced AI tools, anyone in a company—not just product managers but people in virtually any role—could handle tasks traditionally done by coding juniors. Does it then still make sense to hire dedicated coding juniors?

It's been over two years since the new cursor was released. We now have juniors who've only programmed with AI, never experiencing coding without it. Would you hire these juniors today? Do you think they can become capable engineers?

But a key issue remains: how can we ensure juniors still acquire foundational classical engineering knowledge, especially as these traditional skills are becoming less commonly practiced yet remain important?

It's been over two years since the new cursor was released. We now have juniors who've only programmed with AI, never experiencing coding without it. Would you hire these juniors today? Do you think they can become capable engineers?

This shift could work for simpler apps, but complex enterprise systems might suffer from increasing complexity and technical debt unless AI also learns to manage complexity automatically.

It's been over two years since the new cursor was released. We now have juniors who've only programmed with AI, never experiencing coding without it. Would you hire these juniors today? Do you think they can become capable engineers?

Even with AI, having strong architectural sense and intuition—engineering taste—is crucial. But will this AI-native generation develop that taste? More importantly, will traditional engineering taste even matter soon?

It's been over two years since the new cursor was released. We now have juniors who've only programmed with AI, never experiencing coding without it. Would you hire these juniors today? Do you think they can become capable engineers?

It's always tempting to chase the hype—another flashy AI code editor, another trendy framework. But true innovation often hides in the quieter spaces, the "plumbing." These foundational tools quietly enable everything else to run smoothly and grow. 🧵

Probe is open-source and genuinely understands your code. It lets you query your codebase instantly, right on your local machine, without any extra indexing or complex setup. Use it as a simple CLI, built-in AI chat, or integrate it directly within your favorite IDE. buff.ly/X1T9dhr

It's always tempting to chase the hype—another flashy AI code editor, another trendy framework. But true innovation often hides in the quieter spaces, the "plumbing." These foundational tools quietly enable everything else to run smoothly and grow. 🧵

Instead of dreaming about future super-smart AI models that may or may not arrive, we can already do much better with today's resources. If you've ever lost hours tracing through code just to understand a single function or chase down a hidden dependency, you know this frustration.

It's always tempting to chase the hype—another flashy AI code editor, another trendy framework. But true innovation often hides in the quieter spaces, the "plumbing." These foundational tools quietly enable everything else to run smoothly and grow. 🧵

I've spent much of my career in these hidden layers, working on things that don't usually grab headlines. I once built a JSON parser so fast it set new benchmarks. I've crafted load-testing tools that prevented outages, and I've designed utilities specifically to track down those production bugs.