I want you to describe the process to me of how you would find high quality x posts in my niche that I would likely reply on. Lets say I have access to my feeds on x and access to x search and wanted to run my own agent layer to add on semantic search not just keyword. How would you do this?

Here’s the process I would actually run if I were sitting on your feeds + X search plus an agent layer that can do semantic matching, not just keywords.

The goal is not “more reply targets.” It’s a short daily list of conversations you would join even if nobody was watching — the ones where your specific experience (200+ days building in public, a passive feed observer, local agents, anti-spam growth) is a genuine add, not a template.

### 1. Build a reply-intent profile first (this is the real secret)

Keyword search has no idea what _you_ reply to. Semantic search is only as good as the query vector.

I would construct three embeddings / prompt blocks and keep them living:

**Positive vector (what you lean into)**

- Authentic growth without pods
- Building in public as the product
- Feed-as-data
- Niche inference from behavior
- Local/agentic stacks (Hermes, Ollama, self-host)
- Systems and workflows (mermaid, documented pipelines)
- Chrome ext / privacy-first tools
- “The conversation was already in my feed before they found me.”

**Negative vector (what you skip even if it ranks)**

- “Hey @X algorithm looking to connect with Frontend / SaaS / Building in public,”
- Engagement-pod playbooks sold as alpha
- “DM AUTOMATE”
- Ghostwritten founder brands
- 15-item niche listicles
- Reply-farm hooks with zero substance.

**Behavioral vector**

Your last 30–60 days of _actual_ replies, likes, dwell, and follows — not the niche you typed on day one. Your own recent replies already show the mix: workflows/automation, mermaid, hardware tinkering, “build in public brought opportunities,” product having to back the public story. That is more useful than any keyword list.

The agent’s job is: score a candidate post against _those three_, not against “indie hacker OR twitter growth.”

### 2. Ingest from three lanes, not one search box

| Lane                       | Why it exists                                                    | What it is good for                                 |
|----------------------------|-----------------------------------------------------------------|----------------------------------------------------|
| Following + For You       | Already personalized; includes out-of-network the algo thinks you want | Serendipity and people one hop away                  |
| Structured X search        | Recency, engagement floors, operators                           | Fast candidate generation                            |
| Semantic search over a recent corpus | Meaning, not tokens                                   | Posts that never use your keywords                  |

I would pull a 12–24 hour window. Replies after that lose most of their compounding value.

**Keyword / operator layer (cheap first pass)**

Things I would actually run:

- ("building in public" OR buildinpublic) (reply OR conversation OR "worth replying" OR "reply game") min_faves:8 since:YYYY-MM-DD -filter:replies
- ("X algorithm" OR "twitter algorithm") (organic OR authentic OR spam OR pods) min_faves:15
- ("local LLM" OR Hermes OR Ollama OR "self host" OR "chrome extension") (indie OR founder OR "building") min_faves:5
- Exclude templates: -"looking to connect" -"hey @X" -"drop a HI" -"DM AUTOMATE"

Keyword is for _recall with constraints_. It will still drown you in “hey algorithm let’s connect” posts. That’s expected. The agent layer exists to throw most of those away.

**Semantic layer (what keyword cannot do)**

Queries I would embed, not type as keywords:

- “Someone describing that AI reply spam is burying real builders who share messy launches”
- “A founder asking what actually moved reach besides posting volume”
- “Local agent / Hermes / self-hosted pipeline being used for real work, not a demo”
- “A Chrome extension or feed-observation idea that is read-only and privacy-first”
- “An honest take that organic X growth is rare and pods/paid RTs are the default — from someone who lived it”

That last one is the point of semantic search. The Kris post about pods and “Reminder: Drink Water” beating a researched thread never contains your product keywords, but it is _exactly_ the conversation your philosophy is built against. Keyword search for “X Growth Engine” or “authentic growth” would miss it. Semantic search should not.

### 3. Candidate → filter → judge (three agents, not one blob)

**Collector**

Merges feed items + search hits + semantic hits. Dedupes by conversation ID. Attaches author bio, follower count, account age, and whether you’ve already interacted.

**Filter (rules + embeddings, no LLM yet)**

Drop immediately if:

- Template connect-bait (“Hey algorithm… Frontend Backend SaaS Building in public”)  
- Already 80+ replies (your reply is invisible)  
- Zero substance, pure promo, or “system that posts while you sleep”  
- Author is a brand-new account farming follows  
- You’ve already replied in the thread  
- Cosine to the _negative_ vector is high  
- Keep a mid-band of engagement on purpose. A 20–200 like post from a 500–8k account is usually a better reply target than a 20k-like banger. Your reply can still be seen.

**Judge (LLM with your profile as system context)**

Rubric I would hard-code:

1. **Unique value test** — Can Tom add something only he has? (passive feed observation, 200+ day public build, local agent stack, “quality conversations not volume.”) If the answer is “a generic ‘great point,’” discard.
2. **Conversation still open** — Question, hot take, incomplete story, or a claim that invites a counter-example.
3. **Peer, not audience** — Someone you might actually want in your network.
4. **Tone match** — Anti-hype, systems-minded, no growth-hack cosplay.
5. **Reply surface area** — Is there a specific sentence to respond to, not just “nice”?

Output is a ranked list of 8–15 posts with a one-line _why this one_ and a suggested angle — not a drafted reply. You stay the writer.

### 4. What “high quality + I would reply” actually looks like today

From a pass over the last few days, the semantic layer would surface different things than keyword.

**Strong reply targets (semantic + judge would keep):**

- “Indie hacking used to feel massive… now AI reply spam is burying the people doing the work. Building in public only works when the public part is real.” **Directly your thesis.** You can talk about observing the feed instead of spraying replies.
- “Has anyone actually cracked the X algorithm? What’s the ONE thing that changed your reach?” Open question, mid-engagement, you have a non-generic answer (consistency + being in the right conversations, not a hack).
- Local Hermes / OpenClaw / self-hosted agent pipeline posts. You live this stack. Keyword “twitter growth” never finds these.
- Someone building “a Twitter/X Growth Agent that finds conversations and learns.” Adjacent product. Worth a precise, non-defensive reply if the framing is honest.

**Looks relevant to keyword, judge should kill:**

- The entire genre of “Hey @X algorithm 👋 looking to connect with Frontend / Backend / SaaS / AI / Building in public.” High keyword overlap, zero conversation.
- “We run X for founders who never post. DM AUTOMATE.” Opposite of your brand.
- Pure engagement-pod confession framed as “this is how the game works, pay for RTs.” Useful as _context_, rarely a place you want your name in the replies unless you’re making a sharp counter.

That distinction — same tokens, opposite intent — is the whole reason for the agent layer.

### 5. Close the loop or the ranking dies in a week

The judge is only as good as yesterday’s clicks.

- Posts you opened and replied to → boost that cluster and those authors.
- Posts you skipped after seeing the preview → downweight that pattern.
- Authors you follow after a reply → treat as seed accounts for the next search pass.
- After ~30 days of this, the semantic query should be generated from _your behavior_, not from a static niche string. That is the same insight you already shipped: stop asking people what their niche is when the data is already there.

### 6. Practical stack if you were wiring this yourself

You already have the hard part: a passive record of what crossed the feed.

A minimal agent loop:

1. Nightly or every few hours: pull following + For You snapshots + 4–6 search queries + 3–4 semantic queries.
2. Embed post text + bio + first 2 replies (thread context changes meaning a lot).
3. Hybrid rank: 0.35 * keyword/engagement score + 0.40 * semantic-to-positive + 0.25 * LLM judge.
4. Cap at 12 items. If the 12th is weak, show 7. Empty is better than filler.
5. Store skip/reply outcomes. Retrain the positive/negative vectors weekly.

Semantic search without the negative vector and the judge rubric just becomes a slightly smarter keyword search. The quality jump is in _what you refuse to surface_.

If you want this made concrete next, I can take one of your actual recent reply threads and reverse-engineer the features that made it “yes,” then turn that into a scoring prompt you can drop into Hermes. That’s the fastest way to stop the agent from recommending the same connect-bait the keyword layer already finds.