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These are compact product-style scenarios built from the public release
artifacts.
- The first three come from the bundled interactive session artifact
[../../results/try_it_sessions.json](../../results/try_it_sessions.json).
- The last one comes from the held-out extraction examples in
[../../results/extraction_examples.json](../../results/extraction_examples.json).
The point is not just that the extractor matches GPT-4.1-style labels. The
point is that a later system can ask a concrete question and get back a useful,
inspectable answer from stored memory.
## 1. Keep hard limits and notification preferences
**Conversation turns**
> [2025-03-01] Dana: We have 20 concurrent jobs max on GitHub Actions right now. Keep Snyk Slack notifications aggregated and concise, not one alert per repo.
> [2025-03-07] Dana: No mTLS yet. Put it in phase two after the canary rollout.
**Stored memory**
- GitHub Actions concurrency limit: 20 concurrent jobs
- Snyk Slack notifications aggregated and concise; no separate alerts per repo
- No mutual TLS enabled; mutual TLS will be implemented in phase two after the canary rollout
**Later question**
What is the current concurrency limit, how should Slack alerts behave, and when
is mTLS planned?
**Answer from memory**
The current GitHub Actions concurrency limit is 20 jobs. Slack notifications
should be aggregated and concise. mTLS is planned for phase two after the
canary rollout.
**Why it matters**
This is the kind of operational detail that gets buried in chat but needs to
survive into later workflow drafts and agent actions.
## 2. Replace stale travel plans with the current one
**Conversation turns**
> [2025-06-12] Maya: We booked the Lisbon trip for September 14. I want a quiet hotel near Alfama, and no red-eye flights.
> [2025-07-02] Maya: Update the plan: Lisbon is off. We are going to Porto on September 21 instead, still no red-eye flights.
**Stored memory**
- Lisbon trip booked for September 14
- Prefers a quiet hotel near Alfama
- Prefers no red-eye flights
- Lisbon trip canceled; Porto trip scheduled for September 21
- No red-eye flights for Porto trip
**Later question**
Where is the trip now, on what date, and what flight constraint still applies?
**Answer from memory**
The current trip is to Porto on September 21, 2025, and red-eye flights are
still off limits.
**Why it matters**
Memory systems often keep both the old and new plan, but the downstream system
still has to recover the latest valid state.
## 3. Keep routines and health constraints available later
**Conversation turns**
> [2025-08-03] Sam: My doctor wants me to keep sodium under 2 grams a day. I started painting on weekends because it helps me decompress.
> [2025-08-17] Sam: I bought watercolors and signed up for a Saturday class downtown.
> [2025-09-01] Sam: Skip late-night coffee from now on; it wrecks my sleep.
**Stored memory**
- Doctor recommended Sam limit sodium intake to 2 grams per day
- Sam paints on weekends as a decompression activity
- Sam bought watercolors and signed up for a Saturday painting class downtown
- Sam decided to skip late-night coffee starting now
**Later question**
What hobby did Sam start, what class did he sign up for, and what health
constraints matter now?
**Answer from memory**
Sam started painting, signed up for a Saturday watercolor class downtown,
should keep sodium under 2 grams per day, and wants to avoid late-night coffee.
**Why it matters**
This is the difference between transcript search and usable memory. The durable
facts can drive reminders, planning, or coaching later.
## 4. Keep infrastructure bottlenecks structured
**Conversation turn**
> yeah, no real caching beyond basic Docker layer caching. Jenkins nodes have limited capacity, and we sometimes hit queue delays during peak commits.
**Stored memory**
- No Docker caching beyond basic layer caching
- Jenkins nodes have limited capacity; peak commits cause queue delays
**Later question**
What is our current caching setup, and why do builds sometimes queue up?
**Answer from memory**
There is no special caching beyond basic Docker layer caching. Builds queue up
because Jenkins nodes have limited capacity during peak commits.
**Why it matters**
The stored memory stays compact and directly useful. A later system does not
need to reread the full conversation turn to answer the operational question.
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