X post semantic search agent | Shared Grok Conversation

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)

Negative vector (what you skip even if it ranks)

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:

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:

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:

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):

Looks relevant to keyword, judge should kill:

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.

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.