DEV Community: Abasiono Alexander

My Final year project Writing

Abasiono Alexander — Sun, 15 Nov 2026 20:38:56 +0000

Nobody really tells you what it takes to write a final-year research project. 🎓

When I started my Mathematics and Statistics project on Modelling Queuing Systems and Customer Wait Times Using Markov Chains and Simulation, I thought the hardest part would be the mathematics.

I soon realised that research goes beyond equations, calculations, and writing chapters.
It means working with data that may not be perfect, questioning your assumptions, revising your methodology, checking your calculations, and ensuring that your objectives, results, and conclusions all align.

One of my biggest lessons? You cannot force your data to support a conclusion simply because it fits your original plan.

I also learned that AI tools can help with research and writing, but you must still verify the information, understand your analysis, and take responsibility for your work.

A good research project is not just about submitting five chapters. It is about understanding the problem, explaining your findings, acknowledging limitations, and defending your decisions.

I'm still learning, but the process is teaching me patience, critical thinking, and intellectual honesty.

To every final-year student: keep going. Research is a process of learning, questioning, and improving.

For those who have completed their projects, what was the biggest lesson you learned?

FinalYearProject #Research #MathematicsAndStatistics #DataAnalysis #AcademicWriting

I just discovered Hermes Agent. Here's why an open agent already has me thinking.

Abasiono Alexander — Fri, 29 May 2026 10:52:26 +0000

A first-timer's take — I haven't built with it yet, but the idea alone is worth talking about.

Full honesty up front: I'm hearing about Hermes Agent for the first time, and I haven't run a single line of it yet. So this isn't a war-stories post. It's the thing that happens before the war-stories —the moment an idea grabs you and you want to think out loud about it.

Here's what grabbed me.

We talk about "agents" like the hard part is intelligence. But the more I read, the more I think the hard part is trust and access. Most capable agent systems live behind a wall (you get the output, never the wiring). You can't see why it picked a tool, can't tweak how it plans, can't fix it when it breaks. You're renting someone else's reasoning.

An open agent flips that. And even as someone who's only read the docs, I can already feel the difference just looking at the entry point:

# The whole appeal in a few lines: I can SEE the loop.
from hermes import Agent

agent = Agent(
    tools=[search, calculator, file_reader],  # swap these freely
    planner="react",                          # and inspect how it plans
)

response = agent.run("Summarize this repo and flag any risky dependencies")

# What I actually care about isn't just `response` —
# it's that I can trace every step it took to get there.
for step in response.trace:
    print(step.thought, step.action, step.result)

(Rough sketch from the docs — I haven't run it, so treat it as illustration, not gospel.)

That last loop is the part that got me. With most agents, response is all you get. Here, the reasoning is inspectable. I can watch it think.

Here's what I keep coming back to:

A closed agent improves when its owner decides to improve it. An open one improves every time anyone does.

That compounding feels like the real story of AI development over the next few years — more than any single benchmark score.

So that's where I am: zero experience, one idea I can't stop turning over. My plan is to build something tiny, let it fail in front of me, and read the failure. I'll post what happens.

If you've actually used it — what should a complete beginner know before their first run?

#opensource #ai #agents `#beginners

Hello 👋🏽 everyone 🤠

Abasiono Alexander — Sat, 18 Apr 2026 06:48:37 +0000