The REMEMBR app icon: a cream rounded square with a mark of concentric orange arcs resembling a fingerprint.

MRP Thesis Defense

REMEMBR

Building an accessible journaling application, built without a programming background.

Raafi Islam Master of Digital Media · Toronto Metropolitan University

Dhaka, 2018

I recognized the face.I couldn't place the name.

The interaction was brief and uncomfortable. I made my excuses and left. What stayed with me was not the moment itself. It was realizing I had no way back to that person: no record of how I knew them, who introduced us, or what we had talked about.

A Recurring Pattern

It did not happen once.

It kept happening, until I started avoiding situations where it might come up again.

Epilepsy

Affects memory encoding directly. My own diagnosis, and the reason this pattern was not random.

Monthly support sessions

The same frustration, from other people, again and again. Existing tools are not built for how memory works when it is unreliable.

The Gap

Calendars store names. They do not store the reason you'd remember someone.

REMEMBR grew out of that gap: a journaling application that organizes entries around people rather than dates, and surfaces them through any connected context. A shared location. A mutual contact. An event.

Research Questions

Two questions drive this MRP

  1. RQ1

    How can a non-programmer use generative AI to design and build a functional software application, and what specific competencies does that process require?

  2. RQ2

    What are the practical limits, recurring failure modes, and design trade-offs of AI as the primary development tool, and how were they navigated?

Thesis

Generative AI has created a genuinely new pathway for non-programmers to build production-grade software. It is not frictionless.

It demands a distinct skill set, rooted in design, editorial, and project-management practice, not programming. The skills are real. So are the limits.

Methodology and Positionality

How this MRP was studied: the evidence it draws on, and what first-person, practice-based research can and cannot claim.

Evidence Base

Two kinds of primary evidence

AI conversation transcripts

ChatGPT sessions, October 2025 to April 2026, plus nine Claude Code session transcripts, April to June 2026: the full sequence of directives, AI responses, and corrections.

Versioned application files

Two distinct versioning sequences: fifteen named files from the early build, and a separate series from the first five days of the Claude Code phase.

Positionality

Designer, director, and subject

I did not begin with a research framework. The process was driven by personal experience and iterative making: trying things, observing what happened. This resembles what design researchers call research through practice (Frayling, 1993): knowledge generated by the act of making, not by observing from outside it.

What this makes possible

Access to design intent no external observer could reconstruct. I knew, at every moment, what gap existed between the output and the goal.

What it cannot guarantee

Full objectivity about my own experience, addressed by grounding claims in dated transcripts, not memory alone.

Limitations

What this study cannot claim

A single case

This cannot produce statistical claims about non-programmer AI-assisted development as a general phenomenon.

No external verification

Findings are corroborated against dated transcripts, but the interpretation of that evidence remains mine.

Theory found after the fact

The Actor-Network Theory connection was recognized in retrospect, not tested as a hypothesis.

What it can produce: a detailed, internally consistent account of one instance of this process, precise enough to generate propositions later research can test.

REMEMBR

The artifact this MRP produced, and the accessibility logic behind every decision in it.

The Artifact

A people-first journal, not a dated diary

Users build profiles for the people in their lives, linked to places, events, and shared contacts. Remember any one connected thing, and you should be able to find the person.

Built for

Epilepsy · Prosopagnosia · Traumatic brain injury · ADHD

8 months
Oct 2025, Jun 2026
13,000+
Lines. Single-file HTML, CSS, JS
0
Lines written by hand

Open the live app

REMEMBR home screen on a dark background. A greeting reads Good morning, Raafi. Below it a Your People row shows circular portraits of Adam, Olivia, Daniel and Sara, then prompt cards reading Who did you meet today? and Siobhan's birthday in 2 days.

Design Principle

Accessibility is the baseline, not a feature

Governed throughout by WCAG 2.1 AA, Ontario's AODA, and W3C COGA guidance for cognitive accessibility. Every setting in this panel is user-controlled, not auto-detected.

Perceivable
Atkinson Hyperlegible type. A five-phase color journey ending in verified AA-contrast sky-teal.
Operable
44 by 44 pixel touch targets, reduced-motion setting, no photosensitive strobing.
Understandable
Easy Mode: one plain-language question at a time, full feature parity.
Robust
ARIA labeling, full keyboard navigation, screen-reader optimization.
REMEMBR's Accessibility settings panel. Labelled toggles for Easy Mode, High Contrast, Reduce Motion and Haptic Feedback, a Text Size control set to 100 percent, and a Voice and dictation language selector.

Signature Feature

The Connection Web

A force-directed graph where people, places, memories, and events are equally weighted nodes. Enter from any point, a mutual friend, an event, and trace outward to the person you are trying to reach.

Structurally resonant with Actor-Network Theory (Latour, 2005): a connection recognized only in retrospect, after the interface was already built from lived experience rather than from theory.

Open the live app

The Connection Web in REMEMBR, titled Adam's Web with four connections. Adam sits at the centre of a dark canvas with thin teal lines radiating out to Daniel above, Olivia to the right, Sofia below and Jesse to the left, with a list of the four connected people beneath the graph.

Signature Features

Two more ways in

Associative Search

One field searches every content type at once, without category pre-selection: matching how episodic memory is actually retrieved (Tulving and Thomson, 1973).

REMEMBR search results for the single word coffee. Matching people Priya Nair and Sara Williams appear as cards, followed by a heading reading 13 memories found and memory entries including Full circle: Priya meets Marcus and Coffee catch-up with Adam.

Easy Mode

App-wide progressive disclosure. Every multi-field form becomes a one-question wizard, grounded in cognitive load theory (Sweller, 1988), without reducing functionality.

REMEMBR in Easy Mode. The home screen asks What would you like to do? and offers four large, plainly labelled choices: Add a person, Add a memory, See my people, and My memories.

Signature Features

Designed to remove friction, not add polish

Avatar Creator

For users without photos of everyone they track. Reference standard: Snapchat's Bitmoji. Three rejected rounds before the fourth met the bar.

REMEMBR's avatar creator. A large illustrated face preview sits above a Randomise button, a row of eight selectable skin tone swatches, and a grid of five face shape options with the last one selected.

Social Card Generator

A canvas-based, shareable card acknowledging someone's place in the user's life. "I REMEMBR YOU."

REMEMBR's Share your memory screen. A generated card for Adam Chen, labelled Colleague, reads We met at DG8011 class on Thu, Sep 5, 2024. Sat next to each other on the first day, above the tracked words I REMEMBR YOU.

Findings

What eight months of session transcripts and versioned files reveal about this development model.

Finding 1 of 3

Four competencies, not one line of code

  1. Specification

    Translating design intent into language precise enough for the AI to act on correctly: a continuous discipline, not a single act.

  2. Evaluation

    Judging whether an output meets the standard without reading the code, using behavior, comparison, and testing as proxy signals.

  3. Direction

    Corrective instruction specific enough to close the gap. Naming a reference product outperforms "make it better."

  4. Continuity Management

    Holding a coherent product vision across sessions with a tool that retains nothing between conversations.

Finding 2 of 3

Four failure modes, recurring by structure

  1. Silent Feature Regression

    Confirmed features vanished between sessions with no warning. The AI worked from immediate context, not a persistent inventory.

  2. Persistent Hallucination

    The same factual errors recurred across sessions, for example claiming local-only storage, when REMEMBR uses hybrid cloud sync.

  3. Token Limit Constraints

    Productive windows cut to 15 to 20 minutes before context exhausted; resets could take up to five hours.

  4. Code Token Exhaustion

    At 13,000+ lines, loading the file alone consumed a large share of the context window before any new work began.

Finding 3 of 3

Three practices developed in response

  1. Structured Context Documents

    Dense, machine-readable briefings, 1,200 to 3,500 words, pasted at the start of every session to compensate for zero persistent memory.

  2. Standing Rules

    Every recurring AI error converted into a permanent, explicit constraint carried into every future session: never re-explained, always enforced.

  3. Forced Review and Planning Cycles

    Mandatory token wait periods became a built-in cycle to test live updates, find bugs, and plan the next session on paper.

A Question This Raises

Who owns this, and is the data safe?

REMEMBR handles sensitive personal memories about real people. Ownership and data governance were design requirements from the outset, not an afterthought.

Code ownership

AI outputs directed through sustained human creative choice qualify for copyright protection. Anthropic does not claim ownership over generated code (Anthropic, 2024; U.S. Copyright Office, 2023).

Training data exposure

Development sessions were run with training opt-out enabled. Design decisions and architecture were never exposed to the public model.

Platform choice

Rork was abandoned in part because its free tier made project data public. REMEMBR runs on infrastructure I own and can audit.

Discussion

Answering both research questions

  1. RQ1

    A non-programmer can build production-grade software by developing four specific competencies: design and editorial skills, not programming ones, already held by a population far larger than the one that currently builds software.

  2. RQ2

    The limits cluster into four failure modes, all expressions of one root cause: a stateless tool operating on an incomplete picture of what it has already built. These are manageable through documented, repeatable practice.

Contribution and Next Steps

What this case adds, and where it goes

To the literature

A four-competency taxonomy precise enough to be tested, and a context-document practice that answers the multi-session, stateless-tool problem the existing literature does not engage with.

What comes next

Continued development on the same methodology. User testing beyond the researcher's own account. Production release beyond closed testing.

Ko et al. (2011) named the persistent gap between the people who need software and the people who can build it. REMEMBR is a direct instance of that gap narrowing, not because the technology got smaller, but because the skills it demands are already held by a much larger population than the one that currently builds software.

I REMEMBR YOU

Thank you.

Questions and discussion

Raafi Islam · REMEMBR · Master of Digital Media, Toronto Metropolitan University

REMEMBRMRP Defense

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