# Reply snippets — F1 / F2 / F3 framings ready to paste into comments

**When to use:** after posting any of the above, when someone replies
with "this matches my pain" or "we've been running into X", use the
matching framing snippet to probe deeper. This is where the 3 product
framings ACTUALLY live — as comments in a back-and-forth, not as the
opening post.

**The goal is not to pitch.** It's to find out which pain they actually
have and whether they'd use a tool that fixed it. If they wouldn't,
that's a stop-signal for that framing.

---

## F1 — noise-robust retrieval at long context

**Use when:** someone mentions LLM confusion in long conversations,
cross-session memory dropping things, "our agent forgets what it
decided last week", retrieval hit-but-miss-answer, long-context models
hallucinating under history weight.

**Snippet:**
```
Yeah that matches exactly what I was seeing in the scaling test — the retrieval brings back the right memories, but they're diluted with related-but-wrong stuff, and the LLM picks the wrong answer out of the pile.

The lever I'm most interested in is multi-signal reranking: instead of pure vector similarity, combining (1) semantic similarity, (2) recency / temporal decay, (3) importance learned from past usage, (4) graph proximity to entities in the query, and (5) feedback from past retrievals — so stale keyword-adjacent items get pushed below evidence turns.

Would something like that — a memory substrate that reranks by multiple signals, not just embedding distance — be useful for what you're building? Or are you already doing something similar inline?
```

**Follow-up if they say yes:**
```
What scale are you at (approximate memory count)? And are you using a specific tool or a custom reranker in-the-agent-loop?
```

**Follow-up if they say no/not really:**
```
Got it — what's the mitigation that's been working for you? (I'm trying to figure out whether the structural reranker approach is actually the right fix, or whether it's a false lead.)
```

---

## F2 — auditable memory / traceable provenance

**Use when:** someone mentions compliance, audit trails, "why did the
agent say X", debugging long-running agents, regulatory concerns,
enterprise deployment of agent memory, or needing to know the source
of a fact.

**Snippet:**
```
This is the one area where my structured-memory setup actually beat the markdown baseline — it hit 94% provenance accuracy (correct session cited per answer) vs 22% for the LLM-written notebook approach and 82% for the rolling markdown file.

For regulated / audit-heavy use cases, "which session did this fact come from, and when" being a first-class property of the memory system (rather than something the LLM has to reconstruct from the dump) seems valuable. Debuggable agent behavior after the fact, audit trail for compliance, etc.

Is provenance / traceability something that's in your buyer requirements, or is it a "nice to have"? Curious because this is where the trade-off between simple markdown and structured memory is cleanest.
```

**Follow-up if they say it's a hard requirement:**
```
In which vertical? Healthcare, finance, legal, government? And what does the audit actually require — session-level, field-level, evidence-chain?
```

**Follow-up if they say it's a nice-to-have:**
```
Would your team pay extra for it (separate SKU), or is the expectation that audit trails are built into any production-grade memory system?
```

---

## F3 — truth-over-time / strict contradiction resolution

**Use when:** someone mentions state updates, supersession,
revisions, agents using stale values, "we had to manually purge old
memories", conflict resolution, temporal reasoning, or "my agent still
thinks X when actually Y now".

**Snippet:**
```
This is the one where my benchmark surprise hit hardest — I built a temporal-succession substrate (RFC 006 in the repo) specifically for the "agent picks the stale value when current value is also in memory" failure mode. When I tested it on LongMemEval's knowledge-update subset, I found the benchmark actually ACCEPTS hedged "old-value-in-one-instance, new-value-in-another" answers as correct, so the feature has no room to win numerically.

But for any application where hedging is NOT acceptable — like a financial or healthcare agent that can't say "your dosage was X, and also Y" — strict current-value resolution is a different product entirely.

Are you in a vertical where hedged answers are unacceptable? If yes, how are you handling it today — prompting, separate update-tracker, something else?
```

**Follow-up if they're in a strict-current-value vertical:**
```
What's the cost of a hedged or stale answer to your users or business — is it reputational, regulatory, or directly financial? Trying to understand whether "strict current value enforcement" is a feature or a product category.
```

**Follow-up if they're fine with hedging:**
```
Useful — that confirms my finding that for most agent applications hedging is an acceptable behavior and the supersession-as-a-product angle is narrow.
```

---

## Catchall — if they ask "so is yantrikdb better than X?"

```
Honestly: on the empirical tests I've run, not clearly better than simpler alternatives. It has architectural features (temporal validity, multi-signal scoring, contest state) but I haven't been able to show they translate into measurable wins vs mem0-style or rolling-markdown-style approaches yet.

That's why I'm asking rather than pitching. The question I'm trying to answer is whether there's a specific pain severe enough that the architecture earns its complexity — and if so, for whom.
```

---

## When to NOT reply

- Single-word comments ("cool", "interesting", "nice"): thank them with a ❤️, no snippet
- Joke comments / memes: respond in kind, no snippet
- Bad-faith or troll ("is this just RAG", asked in bad-faith): don't engage, the GitHub Discussion body already addresses it
- Requests to "just try yantrikdb in my project": redirect to README + GitHub Issues, flag as "genuine request to try" in the tracker

---

## Tracker integration

Every time you use one of these snippets in a real reply, log it in
[`7_response_tracker.md`](7_response_tracker.md) under:
- which channel / which post
- which framing (F1/F2/F3) the responder resonated with
- what they said before AND after your follow-up
- whether they cleared the "concrete pain" / "behavioral request" /
  "design partner" bucket of the pre-registered threshold
