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
45 essays and 5 issues of AI Pulse & Data Waves, mirrored in full.
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…
The backlash against personified AI is predictable. Anthropomorphization is dangerous. Users form inappropriate attachments. We're creating fake relationships. The critics have valid concerns but wrong…
People who understand logic and philosophy get more value from LLMs. This isn't about being smart. It's about having tools to structure thinking that LLMs can amplify.
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 in AI/ML and backend technologies. What sets him apart is his readiness to take on any challenge, his curiosity, and a calm approach that lets him understand a project's full context before proposing the right solution.”
“Danial's real strength was turning abstract ideas into working systems fast. I could throw anything at him and trust he'd deliver — not perfection, but 80–90% solutions fast enough to keep us moving.”
“His breadth is remarkable — machine learning, production engineering, computational design. He can design high-level solutions and fix nitty-gritty details. Any employer would be lucky to have him.”
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.