# Session: 2026-03-07 02:58:08 UTC

- **Session Key**: agent:main:main
- **Session ID**: 709d57fe-c9d0-464b-8b21-eaa5e040397d
- **Source**: telegram

## Conversation Summary

assistant: Now update the A/B test to use this API parameter:
assistant: Now restart the computer use service and run the quick A/B test:
assistant: Output might be buffered. Let me check the sessions directly:
assistant: The A/B test is running. First results are already in — the first task completed for "none" but failed for "raw". Let me wait for it to finish:
assistant: There it is. The data is clear.

**No memory: 2/2 success (100%), avg 4.5 steps, 55s.**
**With raw memory: 0/2 success (0%), avg 10 steps, 125s.**

Memory injection is actively making the agent **worse.** Not neutral — worse. It used more steps, took longer, and failed both tasks that the no-memory condition completed successfully.

This confirms exactly what that analysis predicted: a 4b planner with 600 tokens can't handle 500 extra characters of context. The memory is crowding out the useful information.
assistant: Ironic — the memory it retrieved was from the successful no-memory runs that just happened moments before. The RAG was working correctly (it found highly relevant results, score 0.85). But injecting that into the planner still hurt performance.

Now I'm going to do the right thing: **disable memory injection by default** and only enable it for testing/development.
assistant: Here's where we actually are, no spin:

**The A/B test answered the question.** Raw memory injection makes the 4b planner worse, not better. 100% success without memory, 0% with it. The mechanism is clear — 500 characters of context in a 600-token window crowds out the task-relevant information.

**What I changed:** Memory injection is now disabled by default. The RAG system stays running but as a **tool for us and Jess**, not something that auto-injects into the planner. That's the right call based on evidence.

**What the compound system IS right now (honestly):**
- Searchable memory across all agent history
- Structured failure tracking with classification
- Paper ingestion (needs the arxiv rate limit to clear)
- A test framework to measure whether changes actually help

**What it is NOT yet:**
- Self-improving (hasn't compounded anything)
- Proven to improve agent performance (the one thing we tested hurt it)
- Ready for more automation layers

**The next real question is:** does *compressed* injection (1-3 bullet points, ~150 chars) work where raw failed? That's a much more plausible approach for a constrained planner. But I'd rather run that test when we're fresh than rush it now. Want me to queue it up, or is there something else on your mind?
user: Conversation info (untrusted metadata):
```json
{
  "message_id": "4144",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Thu 2026-03-05 22:01 EST"
}
```

Sender (untrusted metadata):
```json
{
  "label": "P M (7827979987)",
  "id": "7827979987",
  "name": "P M"
}
```

Awesome what currently exists for image to 3-D/image to 3-D skeleton for animation and control
assistant: LLM request rejected: Your credit balance is too low to access the Anthropic API. Please go to Plans & Billing to upgrade or purchase credits.
user: Conversation info (untrusted metadata):
```json
{
  "message_id": "4149",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Fri 2026-03-06 21:55 EST"
}
```

Sender (untrusted metadata):
```json
{
  "label": "P M (7827979987)",
  "id": "7827979987",
  "name": "P M"
}
```

Hey, how’s it going? I just wanted to ask you if you could prepare yourself for a context window clearing. So sure that you back up all your information into a dock that we can return back to. We’re not gonna continue with this stuff right now but like have like a return back to you kind of area I think this may be the best way to do it so you don’t reread something all this stuff and then start filling up the context window already but set it up like how you normally would just maybe store a deeper information into an additional dock that you can leave a note for yourself like hey only check this when we are actually going to use it until then. This is all you need to know about it and give yourself a short overview of what was in there and what we were working On but like give yourself enough context where like you know like hey these are things that we should probably be looking out for so like just make sure you document what we have actually been working on pretty deeply in that dock and then only get yourself a little bit when you wake back up
assistant: Now update RESUME.md to be slim — just what I need at startup, with a pointer to the deep doc:
assistant: Now update today's memory log and MEMORY.md:
assistant: All set. Here's how I structured it:

**RESUME.md** — slim, just what I need at startup. Mentions the paused project exists but explicitly says "don't auto-load details." Next session I'll know there's a Compound V2 project, what its status is (paused, memory injection disabled), and where to find the deep context if you want to resume it.

**COMPOUND_V2_DEEP_CONTEXT.md** — the deep doc. Everything: what was built, what works, what doesn't, the A/B results, the file list, the ports, the watch items, and a step-by-step "if resuming" checklist. Only gets read when we actually pick this back up.

**MEMORY.md** — updated with the key takeaways: the A/B finding, the "validate before building" lesson, and the service/port info.

You're good to clear. I'll wake up light next time.
user: Conversation info (untrusted metadata):
```json
{
  "message_id": "4153",
  "sender_id": "7827979987",
  "sender": "P M",
  "timestamp": "Fri 2026-03-06 21:57 EST"
}
```

Sender (untrusted metadata):
```json
{
  "label": "P M (7827979987)",
  "id": "7827979987",
  "name": "P M"
}
```

So I can clear it right now?
assistant: 

Yeah, you're good. Everything's saved and committed. Go ahead and clear it.