You spent two hours yesterday refactoring the payment module. You finally got the Stripe webhook handler working. You fixed that edge case where duplicate events were causing double charges. The tests pass. You close your laptop, satisfied.
This morning, you open Claude Code. Fresh session. You type: "Let's continue working on the payment module."
Claude has no idea what you're talking about.
It doesn't know you were in src/payments.py yesterday. It doesn't know about the webhook handler. It doesn't know you spent an hour debugging the idempotency key issue. As far as the model is concerned, this is the first time you've ever mentioned payments.
This is the amnesia problem, and every AI coding tool has it.
The Hidden Cost of Starting Fresh
The amnesia problem isn't just annoying. It's expensive in three ways that compound over time.
Mental overhead. Every morning, you reconstruct context. You remember which files you were in. You re-explain what you were trying to do. You remind Claude about decisions you made. This takes five minutes on a good day, twenty on a bad one. Multiply that by every developer on your team, every day.
Token cost. Without memory, you paste files manually. You dump context into the prompt. You repeat explanations. Each token costs money. A typical context reconstruction burns 2,000-5,000 tokens before you write a single line of code. That's $0.03-0.10 per session just to get back to where you were yesterday.
Inconsistency. You forget to mention something. Maybe it's the decision to use idempotency keys. Maybe it's the reason you chose Redis over PostgreSQL for the queue. Claude gives advice that contradicts what you built yesterday. You catch it, or you don't. Either way, you lose time.
The irony is that your AI pair programmer should be the one remembering things. Instead, you're the one doing the remembering while the AI starts fresh every session.
How Pyckle's Session Memory Works
Session memory is built on three layers: auto-capture, session resumption, and explicit memory.
Auto-capture is the foundation. Every file you edit is registered via register_edit(). Every search query with high confidence is weighted and stored. You don't have to do anything — Pyckle watches what you do and builds a model of your work session.
When you edit src/payments.py, Pyckle notes it. When you search for "webhook handler" and click on a result, Pyckle notes that too. When you come back to src/payments.py three more times in the same session, Pyckle understands that this file is central to your current work.
Session resumption is how you pick up where you left off. Call session_continue() with a description of what you're doing, and Pyckle returns the files you were working on, sorted by relevance.
# End of day — nothing to do, memory auto-captured
# Next morning
session_continue("resuming payment module work")
# Returns:
# [src/payments.py, src/webhooks.py, tests/test_payments.py]
# Context: "Last worked on Stripe webhook handler, 3 edits to process_payment()"The model doesn't start cold. It starts warm — with the files and context from your previous session already loaded.
Explicit memory via add_memory() is for things that behavior can't capture. Decisions. Rationale. Team agreements.
add_memory("We use idempotency keys because of the February incident where duplicate webhooks caused double charges. See INCIDENT-2024-02-15.")This gets indexed and surfaced when relevant. Next time someone asks about payment reliability, the incident context comes along for the ride.
What "Warm" Actually Means
The action graph tracks three signals for every file: edit frequency, search frequency, and recency.
A file you edited once yesterday is less warm than a file you edited five times. A file you edited five times yesterday is less warm than a file you edited twice today. The decay function balances recency against intensity.
When you call session_continue(), the warmest files surface first. These are the files the model predicts you want to work on, based on your actual behavior.
This isn't magic. It's pattern recognition. If you spent all day in the payment module, the payment module is what you're probably continuing.
The system also tracks co-occurrence. Files that get edited together form implicit dependencies. If you always edit payments.py and test_payments.py in the same session, the action graph knows they're related — even if there's no import statement connecting them.
Auto-Capture vs. Explicit Memory
Auto-capture is implicit. It's based on what you do, not what you say. The system infers importance from behavior.
Explicit memory is intentional. It's for facts that behavior can't reveal: decisions, constraints, context that lives outside the code.
Use auto-capture for: which files you're working on, how they relate to each other, what patterns repeat in your workflow.
Use explicit memory for: why you made a decision, what constraints apply, historical context the codebase doesn't encode.
Both feed into the same retrieval system. When you search or resume a session, both types of memory surface relevant context.
Why This Compounds Over Time
The longer you use Pyckle on a codebase, the smarter the action graph becomes.
Week one, it knows which files you touched. Week four, it knows which files you touch together. Month three, it knows your workflow patterns — that you always run tests after editing the API, that you check the webhook handler whenever you modify payments.
This isn't just convenience. It's a fundamentally different relationship with your tools. The AI starts to understand your work the way a long-term collaborator would.
Context windows give you 200K tokens of short-term memory. Session memory gives you persistent, compounding understanding that grows with every session.
Get Started
Session memory is included in Pyckle Pro. Your sessions are automatically captured, and you can resume with session_continue() any time you start a new Claude session.
No configuration required. No manual context loading. Just pick up where you left off.
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