Crux — Built to Prepare

Crux turns your syllabus, notes, and past papers into a deadline-aware study map—then tells you the highest-yield thing to learn, practice, or review next. with several features

Comment

Inspiration

Most study tools assume you have weeks. Real students often have hours.

LETS BE HONEST HERE , literally everyone i know from my college start preparing 1-2 days or even 12 hours before exam 😭🥀 , but we dont need a platform whcih builds us roadmap to study which is oriented with the thought we have lots of time to study so a big elaborated roadmap ,

thats why what we are building is for students or anyone with a deadline and a problem in mind we are having custom agentic harness building out perfect roadmaps for the students after testing their capabilities to get them the top marks within 12 hrs

Before an exam, the problem is rarely “not enough content.” It’s decision paralysis: notes in PDFs, slides in folders, past papers in chats, and no clear answer to the only question that matters—

What deserves the next 25 minutes?

Crux was built for that moment. Not another chatbot tutor that invents answers—a deadline-first workspace that reads your materials and turns them into an explainable study plan.

What it does

Crux is a deadline-aware study workspace. The core loop:

  1. Create a workspace with an exam date and constraints
  2. Upload sources — syllabus, lecture slides, notes, past papers (PDF, DOCX, PPTX, TXT, Markdown)
  3. Extract a Study Map — topics, dependencies, and cited source evidence
  4. Calibrate confidence — a fast check of what you already know
  5. Plan a mission — a realistic time-budgeted sequence of learn / practice / review / skip
  6. Study and prove it stuck — source-grounded briefs, questions, and a review desk

Crux is opinionated on purpose. Every screen pushes toward one next action, with evidence you can inspect—not a black box grade prediction.

How we built it

Product stack

  • Next.js 15 + React 19 for the app shell and study surfaces
  • Supabase for Postgres, auth-ready architecture, and private file storage
  • Drizzle ORM for schema and migrations
  • Inngest for async document ingestion and extraction
  • React Flow for the interactive Study Map
  • TanStack Query + Zustand for client state

AI architecture

  • Deterministic topic inference and mission planning stay explainable
  • Optional generation runs through a bounded harness: retrieve evidence → pack context → generate → validate → cite
  • Students can bring their own OpenAI key (Responses API) and select GPT-5.6 for source-grounded study briefs and concept guides

Built with Codex + GPT-5.6

  • Codex accelerated the end-to-end product: ingestion pipeline, roadmap extraction, mission planner, Study Map UI, API routes, and review flows
  • GPT-5.6 is the generation model for study briefs—grounded in uploaded excerpts, not generic textbook regurgitation
  • Key product decisions stayed human: deadline-first UX, citation-first answers, and “next action” clarity over chat-first UI

Challenges we ran into

  • Trust vs. speed — students need a map in minutes, but incomplete uploads can’t pretend to be complete. We surface uncertainty instead of hiding it.
  • Grounding — AI answers must point back to the student’s sources; unconstrained tutoring was a non-goal.
  • Planning under pressure — optimizing for marks-per-minute without feeling harsh or opaque.
  • Real document mess — PDFs, slides, scanned notes, and pasted text all behave differently; extraction + OCR had to stay resilient.

Accomplishments that we're proud of

  • A working spine from upload → extract → map → calibrate → plan → study → practice → review
  • Source-cited topic evidence, not floating flashcards
  • An interactive Study Map that makes dependencies and progress visible
  • A BYOK path so students can use GPT-5.6 without Crux holding their keys in plaintext
  • A product thesis that stayed narrow: ruthless next moves under a deadline

What we learned

  • Under exam pressure, clarity beats features. One obvious next action beats a dashboard of options.
  • Separating retrieval / planning / generation keeps the system honest when the model is wrong.
  • Codex is strongest when the product loop is already clear—then it compresses implementation time without diluting the thesis.
  • Students trust systems that show their work: citations, confidence, and “why this topic now.”

What's next for Crux — Built to Prepare

  • Deeper past-paper pattern clustering (what repeats, what converts to marks)
  • Stronger last-day revision modes (including audio briefs)
  • Collaborative workspaces for study groups
  • Better OCR and image-heavy notes support
  • Tighter GPT-5.6 study modes: trap detection, formula recall, and timed practice sets

Built With

Share this project:

Updates