If you've worked in software long enough, you've probably lived through the situation where you write a ticket, or explain a feature in a meeting, and then a week later you look at the result and think: this is technically related to what I said, but it is not what I meant at all. Nobody considers that surprising when humans are involved. We shrug, we sigh, we clarify, we fix it.
But LLMs took it a notch even further, coders have started morphing into LLM prompters today, that is primarily how software is getting produced. They still must baby sit these LLMs presently, reviewing and testing the code thoroughly before pushing it to the repo for CI/CD. A few more years and even that may not be needed as the more enhanced LLM capabilities like "reasoning", "context determination", "illumination", etc. (maybe even "engineering"!) would have become part of gpt-9
Agentic AI has become a hot topic amongst software developers in recent months. As usage of LLMs has become increasingly popular, many developers are switching to agentic AI services to build projects. One point of contention with agentic AI is that productivity is always limited without proper context. Solid context is crucial in streamlining agentic AI tools, as it guards against things like hallucinations and inefficiencies with software development.
I've spent 15+ years building across multiple tech ventures and cultures - starting in Vietnam, sharpening my craft in Japan and Singapore, then expanding to the U.S., Australia and Europe. Each stop taught me how different ecosystems turn constraints into capability: how to ship products under pressure, build companies from zero, grow talent pipelines and lead teams through the hardest execution challenges.
Peirce believed abduction was the starting point of thought. The origin of all insight. It's how new ideas enter the room. It's what lets a product team hypothesize why a metric dropped. What lets a designer anticipate confusion before it happens. What lets a researcher frame the right question - not just analyze the data.
Building this platform required solving extraordinarily complex engineering challenges. We're not just sending emails - we're orchestrating an entire ecosystem of domain management, inbox creation, deliverability optimization, and AI-driven decision making across multiple legacy systems with limited APIs.
95% of developers now rely on AI‑assisted tooling to accelerate their workflows and catch critical bugs before they ship, indicating a significant shift towards AI solutions in software development.
