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Projects using Jev, TypeSafe AI's System One decision model (released 2026-09-15), in investment, trading, and financial-data contexts. Surveyed 2026-09-20 via GitHub API and community awesome-lists.
| """ | |
| The most atomic way to train and run inference for a GPT in pure, dependency-free Python. | |
| This file is the complete algorithm. | |
| Everything else is just efficiency. | |
| @karpathy | |
| """ | |
| import os # os.path.exists | |
| import math # math.log, math.exp |
| // | |
| // SupCharts.swift | |
| // WWDC25Demo | |
| // | |
| // Created by Anton Gubarenko on 31.08.2026. | |
| // | |
| import Charts | |
| import SwiftUI |
| export const gpt_functions_param_jobs_fetching = [ | |
| { | |
| name: "process_job_data", | |
| description: "Process job data and extract core fields for a scraper.", | |
| parameters: { | |
| type: "object", | |
| additionalProperties: false, | |
| properties: { | |
| // Company | |
| company_name: { |
| # Select source of selected constrained object | |
| from maya import cmds | |
| selection = cmds.ls(sl=1) | |
| new_selection = [] | |
| for item in selection: | |
| constraint = cmds.listConnections( item + '.parentInverseMatrix[0]', destination=1, source=0, type='constraint') | |
| if constraint: | |
| src = cmds.listConnections(constraint[0] + '.target[0].targetParentMatrix', destination=0, source=1) | |
| if src: |
| ILOUIJJCASV |
A pattern for building personal knowledge bases using LLMs.
This is an idea file, it is designed to be copy pasted to your own LLM Agent (e.g. OpenAI Codex, Claude Code, OpenCode / Pi, or etc.). Its goal is to communicate the high level idea, but your agent will build out the specifics in collaboration with you.
Most people's experience with LLMs and documents looks like RAG: you upload a collection of files, the LLM retrieves relevant chunks at query time, and generates an answer. This works, but the LLM is rediscovering knowledge from scratch on every question. There's no accumulation. Ask a subtle question that requires synthesizing five documents, and the LLM has to find and piece together the relevant fragments every time. Nothing is built up. NotebookLM, ChatGPT file uploads, and most RAG systems work this way.
A pattern for building personal knowledge bases using LLMs. Extended with lessons from building agentmemory 20K+ Stars ⭐️, a persistent memory engine for AI coding agents.
This builds on Andrej Karpathy's original LLM Wiki idea file. Everything in the original still applies. This document adds what we learned running the pattern in production: what breaks at scale, what's missing, and what separates a wiki that stays useful from one that rots.
The core insight is correct: stop re-deriving, start compiling. RAG retrieves and forgets. A wiki accumulates and compounds. The three-layer architecture (raw sources, wiki, schema) works. The operations (ingest, query, lint) cover the basics. If you haven't read the original, start there.