Th

ThinkTotem – turn boring books into engaging conversations

Hacker News

ThinkTotem – turn boring books into engaging conversations

Hi HN, I love reading non-fiction, but I kept catching myself skimming, zoning out, and forgetting most of what I’d “read”. To fix that I built ThinkTotem, a small web app that lets you upload a PDF/EPUB/article/YouTube link and then chat with the material until you actually understand it. What it does - Ingests PDFs, EPUBs, Word docs, plain-text URLs and even YouTube transcripts (≤ 4 MB for now) - Maps key ideas, trims filler and surfaces the essential concepts automatically - Runs an active-recall loop: Socratic questions, explain-it-back prompts, spaced-repetition style refreshers - Tracks mastery so you can skip, revisit or move on Why not just use ChatGPT/Claude? - General LLM chat is open-ended: you have to decide what to ask next. - ThinkTotem is purpose-built for reading: short conversational loops keep attention, progress is visible, and the questions are sequenced to test retention rather than entertain. How it works under the hood - Document is paraphrased, chunked, metadata, summaries and questions are created, and stored in Postgres - Every chat turn includes in the context the most relevant part of the current chapter, the summary of the book and the last messages (no vectorisation of the content needed) - A small policy model classifies each turn (summary vs. question vs. recall) so the chat stays focused. - A spaced-repetition scheduler writes “due” concepts back into the queue and surfaces them at the right time. - User voice is transformed into text and LLM generated text is transformed into speech with OpenAI TTS/STT models - All LLM logic runs server-side (Next.js app); the front end is Next.js with React Server Components. Pricing / openness There’s a free tier (3 uploads, 1 h chat, 50 messages) plus paid plans that just scale limits-no feature gating. Costs - Ingestion (Gemini Flash): ~30 s per 300-page PDF, $0.05–$0.10. - Conversation (OpenAI TTS): ≈$0.40 per user-hour. Nothing is optimised yet; these two items dominate my bill. Privacy I delete originals after processing, just derivative work of it gets stored (e.g. summaries). I never feed your documents into model training and you can purge processed content at any time. What’s missing / known issues - 4 MB per-file cap-larger uploads are on the roadmap - No org accounts yet; “Ultra” plan lets teams share a single login until then - Conversation UX on mobile is still not state of the art (mobile Safari disables audio/mic when not in use for a few seconds, so UX is limited) Ask - Does the active-recall flow feel helpful or annoying? - What would you need in the product to become a daily user? Live demo The app is live at https://thinktotem.com (no credit card needed). I’ll be online all day-happy to answer anything, share infra costs (TTS is crazy expensive), or dive into the ingestion process. Thanks for reading, -Claudio (solo maker, UTC+1)

Share card

Actual performance

1points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: created, para, gemini · Missing: supports, reddit linkedin, podcasting
96%96% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
best fitHighest predicted score across all platforms for this description.
Product HuntOn track for Day 1 leaderboard · Strong signals: claude, model, user · Missing: mac, agents, macos
94%94% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: para · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released
47%47% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
AppSumoMay struggle as an AppSumo deal · Strong signals: plus · Missing: platform, intuitive, reviews
38%38% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: training, active · Missing: arr, mrr, revenue
21%21% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: chat, paid, audio · Missing: web3, crypto, cryptocurrency
0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

Similar products

ThinkTotem
ThinkTotem70%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Turn boring books into engaging conversations

Indie Hackerscommitment-side-project
MyBookChat
MyBookChat68%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Have Conversations with Your Favourite Books.

Indie Hackerscommitment-side-project
No
Not So Boring Newspaper46%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Not So Boring Newspaper

Hacker News4
Do
DomainHacks – no more boring .coms54%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

DomainHacks – no more boring .coms

Hacker News3
Social Cue
Social Cue32%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Find the conversations worth engaging.

Indie Hackers1ai
Bo
BookNomads – borrow or lend books around you55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

BookNomads – borrow or lend books around you

Hacker News2
Bu
Bubblin – Bandcamp for books55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Bubblin – Bandcamp for books

Hacker News138
A
A RottenTomatoes for Books55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

A RottenTomatoes for Books

Hacker News146
Bo
Books for Programmers66%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

Books for Programmers

Hacker News1
St
StumbleUpon for books55%Launch Intel prediction score: how likely this product is to succeed on its source platform, based on its name, tagline, and description.

StumbleUpon for books

Hacker News37