From Electronics to Local AI — Sami M.’s Developer Journey
Developer Stories — CoderLegion
From childhood curiosity about computer hardware to robotics, artificial intelligence, multi-agent systems, and today's local LLMs, Sami M.'s journey has always been driven by one thing: understanding how intelligent systems work.
Sami M. is an Electronics Engineer and PhD in Artificial Intelligence (2014). Today, his work and experiments focus heavily on AI, local AI, LLMs, and Ollama, with a particular interest in understanding what these technologies can actually do in practical, real-world situations.
You can find Sami on CoderLegion.
His recent CoderLegion work includes an experiment comparing Qwen3 4B, 8B and 14B for writing correction using 60 local Ollama responses on Windows.
But the story behind that experiment started much earlier.
It Started With an Intel 286
Sami's interest in electronics began when he was very young.
He remembers being fascinated by the Intel 286 and by the idea that a processor and electronic components could somehow make his favourite game, Gorillas under MS-DOS, come to life.
He didn't just want to use the computer.
He wanted to understand what was happening inside it.
That curiosity naturally pushed him toward electronics and engineering.
His move toward artificial intelligence came later through robotics.
One of his early projects involved autonomous trajectory following with a Pioneer 3-DX mobile robot. From there, his interests expanded into more ambitious work involving collaborative multi-agent systems and task-allocation mechanisms based on auction algorithms.
The combination of electronics and AI still feels like a natural continuation of that original curiosity.
From Robotics to Artificial Intelligence
What attracted Sami to AI was ultimately a question about intelligence itself.
He became interested in biological intelligence and biological neural networks, and then in how some of those ideas could inspire artificial systems.
His early work included robotics, computer vision, and mobile-robot path planning.
Classical graph-search techniques such as Dijkstra's algorithm and heuristic search methods such as A* were part of that journey.
Over time, his interest moved toward systems that could do more than execute a fixed algorithm.
He became increasingly interested in systems that could:
- Perceive a situation
- Reason about it
- Make decisions
- Interact with other intelligent systems
That path eventually led to his PhD in Artificial Intelligence, completed in 2014.
How AI Has Changed
The AI landscape Sami entered during his academic journey is very different from the one developers work with today.
One of the biggest changes he notices is the move from highly task-specific systems toward models capable of representing much richer relationships between language, context, and meaning.
He is careful about the word "understanding."
He doesn't claim that an LLM understands meaning exactly as a human does.
But the semantic behaviour these models can demonstrate is still remarkable to him.
Earlier AI systems often required intelligence to be explicitly designed through rules, features, decision logic, or algorithms tailored to a particular problem.
Today, much richer behaviour can emerge from representations learned in high-dimensional spaces.
And one development particularly interests him:
local AI.
Why Local AI?
For Sami, one of the strongest attractions of local AI is control over information.
When AI runs locally, sensitive or private information can remain on his own machine.
That matters when working with technical documents, private information, or other material that he may not want to send to an external service.
It also gives him more freedom to design the architecture around his own requirements.
RAG is one example.
Sami has experimented with large collections of training videos. Instead of manually watching hundreds of hours of material, he can:
- Transcribe the content
- Divide it into useful segments
- Generate embeddings
- Retrieve relevant passages
- Interact with that knowledge through a local model
This turns a huge collection of information into something he can query directly.
For Sami, local AI isn't simply about running a model without an internet connection.
It's about privacy, control, architecture, and practical usefulness.
Experimenting With Qwen and Ollama
Sami's recent CoderLegion article, Qwen3 4B vs 8B vs 14B for Writing Correction: 60 Local Ollama Responses on Windows, came from a very practical need.
He writes technical reports and emails, sometimes in very different communication styles.
Constantly switching between correction, improvement, explanation, and tone adjustment was taking time.
So he wanted something that could:
- Correct genuine mistakes
- Suggest improvements when useful
- Work with text from different Windows applications
- Allow him to keep his original wording when preferred
- Explain corrections in a language he understands well
Instead of simply assuming that the largest model would perform best, Sami built a reusable benchmark framework to test different local models under the same conditions.
The results reinforced an important lesson:
Model size alone isn't enough.
In his tests, a larger model did not necessarily improve the main correction result, while the difference in execution time could be significant.
Benchmarks vs. Real-World Testing
Sami sees published benchmarks as useful.
They can help create a shortlist.
But when it comes to deciding whether a model is actually useful, he prefers testing it against his own real-world requirements.
His writing-correction experiment also connects to something personal.
English isn't his first language, and he doesn't have much time to study English separately from his engineering work.
So he thought:
Why not learn while already working?
He already needed to write reports and emails in English.
By using AI to correct mistakes, suggest improvements, and explain corrections, he could learn while completing work he already needed to do.
He doesn't claim this is a formally validated language-learning method.
But personally, he has found it useful.
For Sami, the important point is simple:
A benchmark can tell you something about a model. Your own problem tells you whether that model is useful.
Human First
Sami's approach to AI can be summed up simply:
Human first.
He uses AI extensively, but he doesn't want AI making important decisions for him.
Instead, he uses it to:
- Challenge an idea
- Show him another perspective
- Point out something he may have missed
- Save time on repetitive work
- Explore possibilities
Sometimes he will have several exchanges with an AI system before deciding whether an idea is worth keeping.
But the final judgment remains his.
"For me, the best use of AI is not to replace human expertise, but to make that expertise more effective."
That philosophy is particularly relevant as AI becomes increasingly integrated into everyday development work.
The Challenge of Multi-Agent Robotics
One of the most challenging technical areas Sami has worked on is heterogeneous multi-agent robotics.
Imagine a humanoid robot and a tracked mobile robot working together on the same task.
They may have completely different:
- Ways of moving
- Sensors
- Architectures
- Physical capabilities
The challenge isn't simply getting them to communicate.
The harder questions are:
Who should do what?
When should they do it?
How can the system take advantage of the fact that the agents are different?
Sami worked with dynamic task-allocation approaches, including auction-based mechanisms where tasks can be allocated according to the capabilities and current state of different agents.
It is a problem of making different intelligent systems cooperate even though they don't necessarily experience or interact with the world in the same way.
Choosing AI Without Following the Hype
The AI ecosystem changes incredibly quickly.
New models and tools appear constantly.
Sami tries not to choose a model simply because it is fashionable or because everyone is talking about it.
He starts with the problem.
He asks:
- What exactly am I trying to achieve?
- What level of quality do I need?
- What latency is acceptable?
- How much memory do I have?
- Do I need privacy?
- Does it need to work offline?
- Do I need RAG?
- Do I need structured output?
Once those constraints are clear, he tests.
A smaller model with good context and a well-defined task can sometimes be more useful than a massive model used with noisy input or without a clear understanding of the problem.
But Sami doesn't treat that as a universal rule.
He prefers to measure.
"For me, specifications come before trends."
What Comes Next for Local LLMs?
Sami doesn't think the future of local LLMs is simply about making models bigger.
He believes efficiency will be just as important:
- Better architectures
- Improved quantization
- Better memory management
- Greater efficiency with fewer parameters
One example from his own experiments illustrates this.
For his particular test set, Qwen3 4B matched Qwen2.5 7B on the primary complete-case writing-correction metric he was measuring.
That doesn't mean the two models have the same overall reasoning capabilities.
It means that for that particular workload, the larger parameter count didn't improve the main result he was measuring.
That is precisely why he prefers experimentation over assumptions.
He expects increasingly capable local models to continue running on ordinary consumer hardware, and that is one of the developments he finds most exciting.
Electronics Still Shapes How He Thinks About AI
Despite spending much of his time working with software and AI, Sami hasn't forgotten the physical layer underneath it all.
His electronics background influences the way he evaluates AI systems.
He naturally thinks about:
- GPU architecture
- Memory
- Data movement
- Computational cost
- Latency
- Energy consumption
- Responsiveness
- Efficiency
A model isn't just an abstract collection of parameters.
It has to fit into memory.
Data has to move through the system.
The hardware has limits.
Energy and latency matter.
That physical view of computing is one reason local AI interests him so much.
His Advice to Developers Exploring Local AI
Sami's advice is straightforward:
Start with the problem, not with the model.
His suggested approach is:
- Define one clear use case.
- Decide what a good result actually looks like.
- Choose a reasonably small model.
- Run it locally using something accessible such as Ollama.
- Create representative test cases.
- Measure the results.
- Only then consider moving to a larger model.
And if the application involves RAG or external knowledge, spend time cleaning and structuring the data.
Good context can make a significant difference.
For Sami, a well-defined problem, a suitable model, and good-quality input can often matter more than simply choosing the largest model available.
Sami joined CoderLegion in September 2026 and has already become active in the AI, Dev Tools, and Python communities.
What he appreciates most is the technical relevance of the discussions.
He has received questions about the methodology behind his experiments and discussions about the technical choices he made.
As an engineer, he says he is much more comfortable discussing the substance of a project than promoting himself.
That makes a technically focused community a natural fit.
As CoderLegion grows, he hopes it maintains that technical spirit:
People sharing what they tested, what worked, what didn't work, and why.
For Sami, that kind of discussion is more useful than visibility for its own sake.
Looking Ahead
One of Sami's current research interests is radio-spectrum management.
He is exploring how computational methods, optimization, and AI can support problems such as:
- Spectrum sharing
- Interference analysis
- More efficient use of limited spectral resources
But even here, his philosophy remains the same.
He doesn't want to add AI simply because AI is available.
He prefers to first understand the engineering problem and then identify where AI can provide measurable value.
That philosophy connects his past and present work.
From the Intel 286 that first sparked his curiosity, to robotics, artificial intelligence, multi-agent systems, and today's local LLMs, the technologies have changed dramatically.
The curiosity hasn't.
About Sami M.
Sami M. is an Electronics Engineer and holds a PhD in Artificial Intelligence (2014). His interests and work span electronics, robotics, artificial intelligence, multi-agent systems, local AI, LLMs, and practical experimentation with AI tools such as Ollama.
Follow Sami on CoderLegion.
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