PRJ-04SYSTEM CASELIVESAAS · READING DISCOVERY

Bookcrumb

PERSONAL READING DISCOVERY AND ROUTE SYSTEM

A recommendation product that builds personal reading routes from mood, interests, pace and reading history.

NAR-01 / CASE SPINE

Before the technical detail, read the problem, decision and evidence as one narrative chain.

01Problem
Mood, theme, pace, profile and history signals had to become one recommendation context that remains consistent across routes, notes and membership state.
02Decision
Model recommendation as route context rather than a single-book score
03System architecture
Recommendation architecture
04Build
Mood / theme / pace context model
05Delivery
Bookcrumb is live with discovery, routes, notes, memory and paid membership combined into one reading experience.
06Evidence
Live · bookcrumb.app
07Learning
In recommendation products, context design matters more than the model call.

STATUS: Live
TYPE: SaaS · Reading discovery
ROLE: Product and systems architecture · Recommendation context
PLATFORMS: Web · PWA

OUT-VERIFY / CASE / BUSINESS IMPACTPRJ-04

What was the problem, what changed, what is different now?

Before the technology details, this shows the transformation: the original bottleneck, the intervention, and the outcome that can be verified today.

OUT-01

PROBLEM

Mood, theme, pace, profile and history signals had to become one recommendation context that remains consistent across routes, notes and membership state.

OUT-02

INTERVENTION

I modeled persistent reader profile and current intent as separate inputs, then built a flow that produces a Reading Route through a Recommendation Context.

OUT-03

OUTCOME / CURRENT STATE

Bookcrumb is live with discovery, routes, notes, memory and paid membership combined into one reading experience.

PROOF / LIVE SURFACE / DISTRIBUTION

EVD-01 / Live product: bookcrumb.app
EVD-02 / Transformation: Flat book list → contextual reading route
EVD-03 / Product outcome: Discovery · routes · notes · memory
EVD-04 / Monetization layer: Paddle entitlement flow

ROI, revenue lift or percentages are shown only when a verifiable source exists. Otherwise the proof is the working product, distribution, system outcome and sourceable scope — never invented metrics.

NS-01 / NORTH STAR / MEASUREMENT CONTRACTactivation

METRIC

Reading route start rate

DEFINITION

Share of users who create a valid Recommendation Context and then save / start the generated Reading Route, moving into the actual reading flow.

OBSERVATION CHAIN

Recommendation Context completed → Reading Route generated → route saved / started.

MEASUREMENT SOURCE

Reading Route lifecycle events; the analytics vendor and real event names must be verified before publishing a baseline.

BASELINE / NOT-PUBLISHED

No verified baseline is published for this case yet; no value is invented and no numerical claim is made before the source is verified.

CTX-01

Context

A flat discovery list does not explain why a book fits the reader now. Bookcrumb treats recommendation as an ordered route with memory.

PRB-01

Problem

Mood, theme, pace, profile and history signals had to become one recommendation context that remains consistent across routes, notes and membership state.

ROL-01

My role

Product and systems architecture
Recommendation context
Monetization

SOL-01

Solution

I modeled persistent reader profile and current intent as separate inputs, then built a flow that produces a Reading Route through a Recommendation Context.

BLD-01

What was built

Mood / theme / pace context model
Reading routes and route stops
Reading memory and notes layer
AI-ready provider architecture
Paddle monetization and entitlement flow
PWA and Cloudflare-compatible worker build

ARC-01

System architecture

Recommendation architecture

SYS-TRACE READY

STK-01

Technical stack

FRONTEND

React 19 · TypeScript · TanStack Start · TanStack Router · TanStack Query · Tailwind CSS 4

BACKEND / DATA

Supabase

AI / BILLING

AI SDK · OpenAI-compatible provider layer · Paddle

RUNTIME

Vite · Nitro · Cloudflare-compatible worker build · PWA

DEC-01

Important engineering decisions

Model recommendation as route context rather than a single-book score
Keep the AI provider layer separate from the product domain
Feed reading history back into future recommendation context

CODE-01

Code / architecture excerpts

Reading route modelTS

01type ReadingRoute = {
02  mood: Mood;
03  pace: ReadingPace;
04  themes: Theme[];
05  stops: BookStop[];
06  memoryContext: ReaderMemory;
07};

OUT-01

Result / current state

Bookcrumb is live with discovery, routes, notes, memory and paid membership combined into one reading experience.

LRN-01

Engineering takeaways

In recommendation products, context design matters more than the model call.
User memory should be an input to the next decision, not just an archive.

OUT-LIVE / VISIT PRODUCT