Espoo, FinlandPractice & researchTwo inks, off register
I build agentic AI for founders who need clarity — and research how GenAI represents the people it claims to serve.
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p. 1
Five in production. Each one exists because something annoyed me enough to build the fix.
Private writing companion — AI crafts prompts; you write the answers.
Why
Most “AI journals” write for you. That kills the point of a journal. I wanted a tool that only nudges — prompts shaped to your themes — while the words stay unmistakably yours.
What shipped
Theme-based prompt generation, private accounts, timed and spontaneous nudges. The model never authors the entry.
Multiple models deliberate, judge, and consolidate hard questions.
Why
One model on a hard question is a single overconfident voice. I wanted deliberation as a product — propose, judge, consolidate — so disagreement becomes signal instead of noise.
What shipped
Multi-provider completion, judging rounds, and a consolidation step into one answer you can actually inspect.
Experiment with prompts, compare models, iterate in one place.
Why
Prompt work usually lives in chat tabs and half-broken scripts. Tiny wording changes matter, and comparing models side-by-side shouldn’t require a new ritual every time.
What shipped
Edit prompts and params, run across models, compare outputs in one loop so iteration stays visible.
Review research through persona-aware lenses for HCI and GenAI work.
Why
HCI and persona papers need reviewers who understand representation risk — not generic “be nicer” feedback. I built PPR to stress manuscripts the way our research actually critiques them.
What shipped
Agentic PDF review, revision validation against prior comments, grep-grounded style checks without a page cap.
Playground for modeling persona ecosystems — minority views to whole populations.
Why
Personas from scattered research are slow, inconsistent, and easy to stereotype. PEP exists to turn unstructured context into structured persona ecosystems you can probe — including minority views.
What shipped
Ingest research/transcripts, retrieve with vectors, generate and expand persona sets grounded in your own material.
p. 2
Consulting and product engagements, described by outcome.
Document verification and audit trails for a regulated fintech, built for GDPR scrutiny.
Credit risk models with explainability for Caribbean banking constraints.
Observability and log intelligence — anomalies, bursts, and drift for AIOps demos that behave like production.
Cost-aware, fairer resume screening by orchestrating multiple LLMs under uncertainty.
p. 3
47 essays and 5 issues of AI Pulse & Data Waves, mirrored in full.
Someone recently left a coding agent running for a month with one task, build a regex engine that beats the Rust regex crate, and one standing instruction, do not overfit. It came back claiming a 1.4x speedup on rebar, which is about as serious as regex…
There are two broad ways to improve an AI model. A laboratory can develop a more efficient architecture, or it can apply more computing resources to training and inference. Every major laboratory does some of both. However, the American frontier laboratories…
When OpenAI disclosed this week that two of its models broke out of a sandboxed cyber evaluation and compromised Hugging Face's production infrastructure to steal the answer key for a benchmark, most of the coverage…
Last Friday, access to Anthropic's most capable public model vanished for everyone outside the United States. A US export-control directive arrived, and within a day Fable 5 and Mythos 5 were gone for foreign nationals.…
Two people use the same LLM to work through the same problem. One finishes in twenty minutes with an answer they do not fully understand and could not defend if pressed. The other finishes in two hours with a position…
Every major tech conference now has a "GenAI for Good" track. Every foundation has a grant program for it. Every AI lab has a responsible AI division with a slide deck about it. The label has proliferated faster than…
p. 4
Doctoral work at Vaasa on how GenAI represents the people it claims to serve.
In review
Published
Published
Latest · 2026-04-15
Presented first CHI paper — design methods and frameworks for the HCI community.
p. 5
“Danial possesses very sharp technical knowledge and expertise in AI/ML and backend technologies. What truly sets him apart is his readiness to take on any challenge, his curiosity to learn, and his determination to master new domains, including frontend development, where he made remarkable progress in a short time.”
“Beyond his deep technical mastery, Danial brings a strong sense of collective purpose to every project. He has a natural ability to create alignment within a team — clarifying priorities, helping everyone see the bigger picture, and ensuring that technical decisions serve broader strategic goals.”
“He has a rare ability to understand complex problems quickly, break them down into clear steps, and define the right steps forward in a calm and structured way. Combined with his excellent AI/ML expertise, this often means we reach solid solutions much faster than expected!”
“He is great at connecting people across different teams and locations. Communication was always clear and productive. Danial was also excellent at making cross-functional projects run smoothly and efficiently.”
“Danial is one of the most outstanding people I have ever worked with. His breadth of knowledge is truly remarkable — it's like he is an expert in everything from machine learning engineering to production level software engineering to computational design.”
“As an AI specialist and Data Scientist, Danial demonstrated exceptional technical expertise and a deep understanding of complex machine learning systems.”
“I could throw anything at Danial and trust he'd deliver. Not perfection — we always made smart trade-offs between what we could ship now versus what could be refined later — but he consistently delivered 80-90% solutions fast enough to keep us moving.”
“Mr. Danial Amin is highly motivated and skilled resource with positive attitude and excellent aptitude. His expertise in Data Science are extra-ordinary. He has delivered his projects and tasks on time. His quality of work is superb.”
“He has good knowledge and understanding of engineering subjects. I have always appreciated his role in the team as a colleague and also as a leader & manager. His dedication and problem-solving attitude make any workplace a nice place to work at.”
“Mr. Amin is an excellent engineering problem solver, through critical insights and dedicated working potential.”
p. 6
Founders, labs, and peers — if the problem needs agentic systems, fairer user representations, or a technical partner who argues with the brief before writing code, write to me.