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INTRODUCING / AN INDEPENDENT LEARNING INITIATIVE

Project Bedrock

Deeper foundations. Further possibilities.

A three-month initiative to deepen our mathematical understanding of AI through close reading, first-hand derivation, open-source practice and community dialogue. Build understanding through practice. Let real questions lead the next step.

Mathematical structures and research pages rise from a solid foundation.
A foundation for understanding that keeps growing.
Authorized Launched A joint initiative by LANCER and CIE
03focused months
04connected workstreams
∞a continuing conversation

THE FOUNDATION

Go to the roots of understanding.

Project Bedrock is a three-month initiative for deep study and practical work in AI. Starting with the mathematics behind large models, it connects the history of deep learning, close reading, derivation and open-source practice. Sharing through X and Xiaohongshu will provide a continuing record of ideas, questions and feedback. We aim to build stronger foundations through work we carry out ourselves, find meaningful problems through community dialogue, and develop representative open-source contributions.

Launch preparations are complete. LANCER is examining the feasibility of the learning and practical directions, working together with CIE and other collaborators. Mathematical study, close reading and hands-on building will run throughout, producing understanding that can be explained, revisited and developed further.

  1. 01

    Mathematical understanding

    Return to definitions. Understand the relationships.

  2. 02

    Reading and derivation

    Read the reasoning. Work through it yourself.

  3. 03

    Open-source practice

    Put understanding into working code.

  4. 04

    Community dialogue

    Let shared work lead to better questions.

Questions and feedback return to the next round of learning.

LEARN · DERIVE · BUILD · SHARE

Connect understanding through practice.

01

Mathematical understanding

Return to definitions. Understand the relationships.

Study the mathematical foundations through concrete questions about large models, deep learning and neural networks. Examine how assumptions support a derivation, how representations shape learning and which conditions a conclusion requires, building the ability to explain and assess methods.

02

Reading and derivation

Read the reasoning. Work through it yourself.

Connect the history of deep learning, foundational papers and current research, recording how questions and methods evolved. Work through important formulas and use notes and small computations to check understanding, leaving knowledge that can be revisited and discussed.

03

Open-source practice

Put understanding into working code.

Study how existing open-source projects connect theory, implementation and evaluation. Build and improve around practical problems, working toward representative projects of our own with clear documentation, development records and inspectable results.

04

Community dialogue

Let shared work lead to better questions.

Plan to share knowledge, paper readings, progress and practical experience through X and Xiaohongshu. Bring questions from readers and developers back into study and implementation, continuing the accounts and their public record beyond the intensive phase.

A research workbench connects paper, mathematical surfaces, implementation and an open network.
Reading raises questions. Derivation deepens understanding. Building tests it. Dialogue brings feedback.

THREE MONTHS · A LASTING FOUNDATION

A three-month plan

The following is a planned path. Scope and pace will evolve with feasibility work and practical progress.

  1. MONTH 01

    Establish the foundations

    Map the mathematical foundations and central questions, connect the history of deep learning with representative papers, and establish a reading and derivation path with an initial set of notes and open questions.

  2. MONTH 02

    Deepen through practice

    Turn key derivations into small implementations, study open-source projects in depth, and record the differences between theory and engineering. Use practical difficulties and community feedback to identify directions worth pursuing.

  3. MONTH 03

    Shape useful contributions

    Prepare learning and practical work for public use, advance representative open-source projects, and develop their documentation. Use feedback to define the next questions and carry the work forward through continued sharing and maintenance.

BUILT TO BE SHARED

Let learning become a shared contribution.

We plan to establish an X account and share knowledge, paper readings, practical experience and progress alongside Xiaohongshu. Real questions from the community will help identify directions to investigate and improve. The accounts and sharing will continue beyond the three-month intensive phase.

Planned public outputs

  • Mathematical and derivation notes that retain definitions, assumptions, intermediate steps and open questions.
  • Paper readings and historical notes connecting research motivations, the development of methods and present questions.
  • Practical and open-source work, including shareable implementations, documentation, comparisons and improvements.
  • Community posts and question records that develop with new understanding, experience, feedback and directions for further work.
XAccount in preparationXiaohongshuKnowledge & progress

PROGRAMME UPDATES

Progress, in the open

This is the continuing record of Project Bedrock: study and derivation notes, paper readings, open-source practice, and questions and progress emerging from community dialogue.

THE WORK BEGINS

Build deeper. Share what you learn.

Project Bedrock is underway. We will continue to document progress in reading, derivation and practice, and publish access to the associated accounts and open-source work as they become available. A concrete question is a welcome place to begin a conversation.