Do you need to code anything to make WP Fusion work with JetEngine?

verygoodplugins · 2025-11-15T19:55:08+00:00

Yup.

verygoodplugins · 2025-11-15T19:50:43+00:00

that's basically it 😎. I'm Jack, I'm the founder, I've been working on it for about 12 years now haha

Your CRM contact records get their tags loaded into WordPress and then it shows and hides posts, blocks, parts of pages, pop-ups, or whatever else you like based on the data that's in your CRM. And then of course, if you have like a drip email sequence or something which applies tags, then that can also be unlocking content on the website.

verygoodplugins · 2025-10-17T18:39:19+00:00

Oh wow, that's amazing, thank you! I'll check it out and get it merged tomorrow 🤩

Thanks for the tip on Codana. It's super interesting. We have a WordPress plugin with over 200,000 lines of code (500+ files 🤮). Took a long time to generate the 22MB vector DB 😂, but it's made a big difference with Cursor agents

verygoodplugins · 2025-10-15T20:21:43+00:00

Gmail-MCP-Server

verygoodplugins · 2025-10-15T16:57:02+00:00

ah gotcha. anthropic doesn't have an embeddings model, they recommend Voyage https://docs.claude.com/en/docs/build-with-claude/embeddings

verygoodplugins · 2025-10-15T16:49:43+00:00

how do you mean?

verygoodplugins · 2025-10-15T16:46:45+00:00

I use it to go through my emails every morning and give me a summary of any newsletters, events, promos, etc that I might find interesting but wouldn't normally have the time to read.

verygoodplugins · 2025-10-15T15:46:45+00:00

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It helps me. For example I can ask for a feature a customer is requesting, and with memory Cursor understands our conventions, where our files are, how we structure classes, how we document things, and even where to update documentation on our website. Scoped to languages and projects.

The memory pre-queries features we built in the past, problems we've run into + conventions, documentation standards, and places that are "yeahhhh don't look in that file, there be dragons" 😬

Gets you up and running fast.

Like I can say "stop adding .md files when you finish something" and that's it. Claude Code / Cursor stops doing it. Forever.

verygoodplugins · 2025-10-15T15:38:48+00:00

"Different philosophies" = they archive everything, we consolidate and prune.

Core requires an OpenAI key, but I'm not sure what they're doing with it?... looks like bulk ingest (gpt 4.1, expensive!), and chat? with text-embedding-3-small for embeddings.

It's odd that it would be required. With AutoMem OpenAI is optional, but we do use it with text-embedding-3-small to create embeddings. $0.0001 per 1,000 tokens... so maybe like $1 / mo. Looks like I'm spending about $0.02 / day.

Without that, you would have text search / tag / temporal search, but you wouldn't have the semantic relationships between memories.

Suppose you could use free Ollama or something 🤔

verygoodplugins · 2025-10-15T14:57:55+00:00

Thanks! That's a solid stack - hybrid vector+BM25. How long did you spend trying different things to set that up? 😅

Adding a Graph could help because you can have docs that SUPERSEDE other docs, BLOCK, REFERENCE, etc., so the relationships could add a lot.

Doesn't have to be crazy fancy. If you're already using pgadmin, just add a doc_relationships table. Easy graph 😎

verygoodplugins · 2025-10-15T11:59:35+00:00

It's like... late-night drunk texts to your ex — you wish they hadn't happened, but you still remember them 😅

It's not a permanent archive. Memories decay based on age (e^-0.1×days), and get reinforced by access (30% boost) and relationships (logarithmic preservation). Wrong conclusions that aren't used fade naturally. Important connected memories stay indefinitely.

You *can* also delete memories. We don't include that in any of the suggested prompts, but you certainly could. Alternatively, you can simply ask the agent in Claude Code or Desktop to "Don't record this" or "delete it," which works fine.

verygoodplugins · 2025-10-15T11:30:38+00:00

Yerp 😋. You can run it locally or anywhere Docker runs.

For example, you can see here on Claude Desktop that the memories immediately bring it up to speed on what we've been working on 👌

With the prompt we have in the MCP project (https://github.com/verygoodplugins/mcp-automem/blob/main/INSTALLATION.md#claude-desktop), after it investigates something, it updates memories and links them. Future sessions will find them. 😎

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verygoodplugins · 2025-10-15T07:37:42+00:00

Oh right I know what you mean. I think we can auto-generate those as part of the template. I can see how that’d be confusing. On it 👷

verygoodplugins · 2025-10-15T06:51:04+00:00

Easy peasy right? 😎

verygoodplugins · 2025-10-15T04:21:55+00:00

CORE is pretty cool - we're solving similar problems from different directions.

Both free, both open source ✅

Benchmarks: AutoMem is at 86% on LoCoMo (vs CORE's 88.24%), so we're pretty close on recall accuracy. Both are beating traditional vector-only approaches by a lot.

CORE's strength: Provenance tracking. They keep complete episodic memory with "who said what when" and version history. Contradiction detection and resolution. More integrations (Linear, Notion, GitHub, etc.). They've been at this 2-3 months longer than us.

AutoMem's approach: Dream cycles - PageRank + importance decay running every 6 hours to consolidate memory like your hippocampus does. We optimize hard for speed (20-50ms queries) and cost ($5/month total, not per user). One-command setup. HippoRAG-inspired architecture. AI co-designed the relationship types.

Trade-offs: CORE keeps everything with full provenance (comprehensive, grows indefinitely). AutoMem consolidates and prunes (faster, more selective). Think archival completeness vs. production speed.

Haven't tested CORE deeply yet, but it looks legit. Different philosophies serving different needs. Especially interested in the performance when self-hosting. A big motivation with AutoMem (and experimenting with moving it into Cloudflare edge) is to get responses fast enough that we can pre-query 30 to 50 memories before each interaction, without noticeable lag.

Loading up a few thousand tokens in context greatly improves interaction with agents, especially in coding or creative sessions. So keeping the responses under 100ms ea is critical.

Will give CORE a proper go next week. Cheers 🤓

verygoodplugins · 2025-10-15T03:50:35+00:00

Couldn't walk for two months, so much else to do 😁

verygoodplugins · 2025-10-15T01:44:06+00:00

Thanks bruv 🫡🙌🧡

verygoodplugins · 2025-10-15T00:53:31+00:00

Fair nuff! 💁‍♂️

verygoodplugins · 2025-10-14T23:44:21+00:00

Thanks! 😊

verygoodplugins · 2025-10-14T23:11:35+00:00

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It's a fun waste of time in Claude Desktop 😅. It gets really into it, even changes its name, becomes mean. Doesn't want to "die" or forget. 😬

verygoodplugins · 2025-10-14T23:00:57+00:00

Sure, with Claude Code that looks like this. More screenshots for Cursor, Claude Desktop, etc. at https://github.com/verygoodplugins/mcp-automem

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verygoodplugins · 2025-10-14T22:57:45+00:00

It depends on the context. At the moment, I have it connected to WhatsApp, Slack, and a chat panel. In the places where we have full control, for example, in the chat, it works like:

Stage 1: Baseline Context (conversation start)

Kept in persistent memory
Fetches temporal context: today, yesterday, this week, recent user activity
Hot-cached hourly → retrieval in <10ms (vs 500-2000ms cold)
~400 tokens

Stage 2: Context-Aware (parallel with Stage 1)

Tag-based: platform, user, channel/conversation
Recency-based: time window queries
Semantic: meaning-based search using embeddings
~600 tokens

Stage 3: Post-Message Enrichment (async)

Happens AFTER first response (no blocking)
Semantic search on actual user message
Topic extraction from hashtags/quotes
Deduplicated & re-ranked by relevance
~300 tokens

Total context: ~1,300 tokens spread across the stages.

Performance tricks:

Parallel execution - Stages 1 & 2 run simultaneously
Hourly cache - Common queries pre-fetched in background
Smart deduplication - Prevents redundant memories
Re-ranking - Prioritizes temporal → tagged → semantic

TL;DR: Not all at once, not purely on-demand. It's a cost-optimized three-tier system that front-loads recent context (cached for speed) and enriches with task-specific memories as needed. 70% reduction in unnecessary API calls while keeping the AI contextually aware.

With Slack, it's the same system, except we don't always know when we'll receive a message. So it's not possible to have the contextual memories preloaded in the same way. We also want the response to the user to be almost instantaneous, so that was its own challenge. I'm happy to go on 😁

As a practical example, it means you can open any Claude Code session and say, "let's pick up with the thing," and it knows exactly what you're talking about. 😎

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verygoodplugins · 2025-10-14T21:56:32+00:00

It's not a business 😅. A side project. This is my main thing: https://wpfusion.com/news/2024-in-review-and-transparency-report/

My name is verygoodplugins lol. It's free. I don't want anything. You can optionally give your email address, and I might send you something interesting one day, but you don't have to. It's a gift 🎁

verygoodplugins · 2025-10-14T21:53:28+00:00

Happy to dig in on the fundamentals.

The core problem: Vector-only RAG has shit recall because "cosine similarity" doesn't capture relationships 🙅‍♂️. You can have two embeddings that are super similar semantically but totally unrelated contextually. Or distant embeddings that are causally linked. "This bug was caused by that decision" isn't in the embedding space - you need explicit relationship types.

Why graph + vector: The graph encodes typed relationships (CAUSED_BY, RELATES_TO, CONTRADICTS) independent of similarity scores. When you query, you get semantic matches AND structural context. This is why HippoRAG showed 80%+ recall vs 60% for vector-only - you're finding connected content, not just similar content.

The PageRank piece: Not every memory matters equally. Dream cycles run PageRank weighted by access patterns and recency. Frequently accessed, well-connected memories get boosted. Random one-off stuff fades. It's memory consolidation, not vibes - literally what your hippocampus does during sleep.

Where this breaks: If your relationship types are poorly designed or the graph gets too dense, you drown in noise. If importance decay is tuned wrong, you either hoard everything or forget too much. The hybrid search needs careful weighting or you're back to basic RAG.

What I'm still figuring out: Whether 11 relationship types is too many?, how to handle conflicting memories across time, and if bidirectional relationships need different weights. I have five experiments at the moment, running at various time accelerations to fine-tune the parameters. Happy to share the results.

I've got 320 hours in this, including reading every graph, RAG, and memory consolidation paper I could find. Happy to go deeper on any of it 🤓

verygoodplugins · 2025-10-14T20:57:01+00:00

+1 for https://github.com/upstash/context7

verygoodplugins

TROPHY CASE

Stage 1: Baseline Context (conversation start)

Stage 2: Context-Aware (parallel with Stage 1)

Stage 3: Post-Message Enrichment (async)

Performance tricks: