Reverse-engineered from source: letta-ai/claude-subconscious v2.0.2
Generated: 2026-03-28
Claude Code is an AI coding assistant that operates in ephemeral sessions. Every session starts from zero — no memory of past conversations, learned user preferences, project context, or unfinished work. Users must re-explain their codebase, repeat preferences, and re-establish context every time.
F1ReplayTiming pulls its data from four distinct external sources and uses two storage backends to persist processed data. The primary data source is the FastF1 Python library, which itself wraps the official F1 timing API (livetiming.formula1.com) to supply historical session data — laps, telemetry, weather, race control messages, driver/team metadata, circuit geometry, and event schedules. For live sessions, the app connects directly to the F1 SignalR real-time stream (wss://livetiming.formula1.com/signalrcore) via WebSocket. A photo-based broadcast sync feature uses the OpenRouter AI API (specifically Gemini Flash vision model) to extract leaderboard data from screenshots. Pre-computed session data is stored either on the local filesystem or in Cloudflare R2 (S3-compatible object storage).
A nightly cron job that reconciles an agent's constitutional memory (memory.md) against
its evolving knowledge graph (Myelin).
This is "Approach A" in the Procedural Memory Graduation design — external reconciliation. It works, but it curates rather than discovers. The emergent graduation mechanism (Approach B) would complement this by auto-promoting stable graph knowledge.
Write a spec for your cron scheduler. Model it in Lean as a pure state machine — simple, obviously correct. Prove the key invariants: one-shot jobs fire at most once, running jobs can't be scheduled concurrently. AI generates the production C# from the spec. Then FsCheck generates thousands of random event sequences — AddJob, TimerTick, Restart, ClockSkip — and runs each one through both the Lean model and the C# code, step by step. After every event, compare the state: which jobs fired, how many times, what's their status. If the C# scheduler says a one-shot job fired twice and the Lean model says that's impossible, the test fails and tells you exactly which event sequence caused the disagreement. The Lean proofs guarantee the model is correct. The property tests guarantee the C# matches.
The full solution — proving C# correct directly in Lean — requires formally modeling C#'s async runtime, null semantics, exception propa
| $baseUrl = "http://localhost:5000" | |
| $headers = @{ "Content-Type" = "application/json" } | |
| $sessionId = $null | |
| Clear-Host | |
| Write-Host "=== Chat Client (localhost:5000/chat) ===" -ForegroundColor Cyan | |
| Write-Host "Type your message and press Enter. Type 'quit' to exit." -ForegroundColor DarkGray | |
| Write-Host "" | |
| while ($true) { |
My agent's name is Chiti. It runs on Telegram, handles customer support for two SaaS products, drafts tweets, manages invoices, and coordinates with my co-founder across timezones. It's the closest thing I have to a junior employee. And for weeks, it kept forgetting things. Not in a subtle way. I'd spend an hour configuring a daily cron job, switch models, and the next session Chiti would act like we'd never spoken. I'd reference a decision from two days ago and get a blank stare. I'd ask it to continue a task and it would start from scratch. So I stopped building features and spent 5 days whenever I get time, just fixing memory. This is everything I found, everything I broke, and everything that actually worked.
The first problem was simple to describe and painful to diagnose.
ive spent the last year building an operating system for thinking with ai. claude code runs my obsidian vaults it extracts the key concepts, connects them to what you already figured out, and builds a living representation of your thinking i find myself only working in the vault now the markdown files know everything ive discovered, nicely structured and with automatic situational context injection for in-context learning i use a vault index that helps the agent decide what notes to pull in, same pattern as how claude code decides which skills to load (if you think about it, every note is basically a skill in some sense... highly curated knowledge that gets injected when relevant) the deeper thing is that a vault encodes how you think, not just what you thought about. the methodology becomes part of the system its all just markdown files, you own it completely. this is ai as thinking partner, not as a writing assistant
ive spent the last year building an operating system for thinking with ai. claude code runs my obsidian vaults it extracts the key concepts, connects them to what you already figured out, and builds a living representation of your thinking i find myself only working in the vault now the markdown files know everything ive discovered, nicely structured and with automatic situational context injection for in-context learning i use a vault index that helps the agent decide what notes to pull in, same pattern as how claude code decides which skills to load (if you think about it, every note is basically a skill in some sense... highly curated knowledge that gets injected when relevant)
| { | |
| "generated": "2026-03-05T18:08:47.873Z", | |
| "days": 30, | |
| "totals": { | |
| "views": 275 | |
| }, | |
| "daily": [ | |
| { | |
| "day": "2026-03-05", | |
| "views": 52 |
| #!/usr/bin/env bash | |
| POSITIONAL=() | |
| while (( $# > 0 )); do | |
| case "${1}" in | |
| -d|--directory) | |
| SOLUTION_NAME="${2^}" | |
| shift | |
| ;; | |
| *) |
