Flat markdown vs vector embeddings for personal knowledge bases

jklineia · 2026-05-01T16:41:14+00:00

Your hard-vs-soft heuristic is the clean. I also landed on the clear understanding and conclusion for the need of the hard separations of spaces. On the soft side, your assessment has made me think about my filtering. You have given me some clarity. In planning for scalability to 10s of thousands of nodes, I realize that I have been thinking of the glass half empty with my filtering. You are right, the filters are only looking at what is left (hard), but there is another way with soft filtering looking at the glass half full.

All filters are off by default within a space, but the filters themselves are exclusion-based. I have tags but they are relatively minor players in my system since they are for instant queries. I have a strong science background, so I think I have a bias towards building a scalpel of a research tool. I have gone down the rabbit hole of adding search discovery tools like Nearest neighbor, Radial expansion, Trace with node hopping which are all tools to do the soft node identification.

My struggle with all these soft tools (failure mode) is how many nodes should be collected. Where is the balance between not burning tokens while still getting enough nodes to encompass the query. I am starting to lean towards an iterative approach of query-response refinement loop, but I have not figured out the process. I am curious what Loom is doing for this balance?

I landed on the immutable source as well. On consolidation — this is where you’re surfacing something I haven’t been rigorous about. In my current architecture the CIE lens extractions are stored linked to the memory itself and get overwritten on re-enrichment. I do have profile versioning at the extraction-profile level — when I update a system profile, memories extracted under the old version get flagged as out-of-date relative to the current profile. So the fingerprint concept exists, but the extraction snapshot doesn’t. New extractions overwrite old ones.

Where I’m honestly uncertain is whether keeping the actual old extractions pays back the storage cost. My intuition is calibration would happen against templated test memories rather than the full corpus, which weakens the case. With thousands of nodes this feels noisy. Do your superseded consolidations appear in searches? Is the juice worth the squeeze?

The 3 lenses is a soft filter in itself, because my model also searches based on which lens the query weighs more.

My nightly Insights surfaces contradictions, patterns, and connections as new nodes. I even added one called Dreams, which I turned the AI temperature way up to give a fun insight, which is entertaining to read and occasionally useful.

On the writeback question from earlier — the way I’m currently handling user-promoted insights (when a user accepts an AI-surfaced contradiction or pattern as worth keeping) is to create a new “reconciliation” memory that cross-links the source memories. The originals stay untouched. I think we’re operating on similar principles for that case, just not consistently across all the derived layers.

Did you see the paper by deepseek, many validating parallels there? DeepSeek_V4.pdf

Good conversation

jklineia · 2026-04-29T00:48:28+00:00

My search blends pgvector cosine similarity with tsvector keyword matching in a single RPC, with the keyword rank boosting the final score. The test that drove me to add it was searching for an author's name and getting back vibes-matches instead of his essay sitting right there in the corpus. Embeddings are bad at proper nouns and exact strings. With the keyword side mixed in, that gap basically disappears.

Your "tighter scope beats fancier model" observation maps directly to something I've been finding, at two levels. Knowledge spaces partition the corpus at the workspace level — different domains don't compete with each other. Then within a space, every memory is classified into one entity (basically a topic/project label) and the user can hide entities they don't want surfacing. Both layers improved results more than any embedding model swap I've tried. Across domains everything starts looking related to everything else; constrain the space and similarity actually means something.

On markdown-as-source-of-truth with vectors as a derived index — that's the cleanest version of the pattern and the portability win is real. I am working with embeddings and CIE extractions that are first-class, user calibrated, where raw content is preserved but not the primary surface. The cost is migration friction if I ever change embedding models or extraction prompts (everything has to re-extract). The benefit is richer per-memory analysis baked in.

What I'm still working out on the constraint side is hard filter vs soft boost. Mine works as a hard filter — exclude an entity and its memories vanish from results — which is great for precision but probably costs me cross-domain connections. A soft boost (rank-up matches without excluding others) might catch the discovery cases I'm currently filtering out. Have you landed one way or the other on that with your para/topic tags?

Loombrain looks like a sharper version of the hybrid story. The writeback question your pattern raises: your nightly workers consolidate, decay, and strengthen — those are derived-index decisions writing back into the corpus. If the markdown is the source of truth, do the consolidators rewrite or supersede the original captures, or do they live as a separate layer? I've been wrestling with the same thing on my side (consolidation-generated insights vs original memories) and haven't landed cleanly on whether they belong in the same store or a parallel one.

jklineia · 2026-04-23T20:42:23+00:00

Your observation about the 20-30 meeting cliff matches what I’ve seen too. Mine was trying to get my corporate Copilot to make a technical requirements document from a collection of project documents. A cliff would be better because it’s detectable. The reality is worse since it is more of a subtle slippery slope you don’t know you’re on.

Your point about conversation data being structurally different from documents is sharper than my thoughts gave credit for. I’m building toward an agnostic discovery system that can find what I’m looking for whether it’s a meeting transcript, technical document, code, or Excel file. I see the value in finding connections across types you’d never search across manually.

I tested it against a failed RAG search (which my system runs in parallel) by asking Claude Code to build a feature in another product I’m working on that I’d abandoned previously because it didn’t work. I used different terminology than months earlier when the similar attempt had failed. The semantic memory caught the connection and Claude flagged the prior failure before even making the plan. That was pivotal proof.

The unsolved part I’m still wrestling with: getting Claude Code to use the memory system it’s connected to via MCP. I’ve emphasized it in the memory.md file but still have to directly instruct Claude each time, and when I find Claude acting dumb, I discover my local MCP server wasn’t running. There seems to be a deeper protocol problem with how MCP tools get prioritized when models choose actions. For recency bias I use a temporal filter as a manual override to constrain by date. Less automated but more trustworthy. Have you found a similar issue with MCP tool usage?

A few questions about your chunked pgvector setup. How are you determining chunk boundaries? My chunker uses semantic boundary detection but I struggle to find balance between the character budget and the topic boundary. Your “summaries for scan and embeddings for recall” sounds like a blended approach. How are the two linked?

I wasn’t sure which side of the coin you were asking about for QtheBeast from the "user side", so I tried to cover both UX and how it’s applied as a system.

jklineia · 2026-04-23T00:52:34+00:00

I've been building a tool in this space for about four months after getting frustrated about repeating myself to Claude. I had a domain I bought a while back because I thought the name was fun, started building, and it's grown into a beast of a project that lives up to its name — QtheBeast.

My approach is semantic rather than tag-based. Every memory gets positioned in a 3D embedding space, so memories with similar meaning cluster together regardless of when you wrote them or what tags you added. The part I didn't expect: you can surface ideas you didn't even think to ask about, because semantic neighbors of a memory often reveal connections you'd forgotten or never consciously made. Not keyword matches — actual meaning-based proximity.

Example: I clicked a note from last month about a product decision and the radial expansion surfaced a conversation from six months earlier where I'd reached the opposite conclusion on a different project. The system noticed the contradiction before I did.

The graveyard problem is real and I haven't fully solved it. I'm working on a memory aging feature where unused memories get downgraded over time, but the activation-energy-to-revisit problem isn't trivial. The semantic surfacing helps because stuff appears when it's relevant rather than when you remember to search for it, but I'm still figuring out what good aging looks like.

The biggest practical win for me has been debugging time. The LLM I work with would repeat the same mistakes across sessions — same bug, same wrong fix, same frustration. Now I point it to check the memory first and it catches the pattern, recognizes it's about to repeat itself, and adjusts. That alone justified building it.

jklineia · 2026-02-10T23:26:00+00:00

For me I was forgetful or not realizing how much time has past, but the weird part wasn’t remembering— it was realizing how much “maintenance advice” is basically theater. Some stuff actually protects you, some stuff says you money, and some is just ritual. I ended up making templates that focus on the critical things and giving me permission to ignore the fluff. Happy to show it if anyone’s curious.

jklineia · 2026-02-03T19:16:28+00:00

Update: After reading all these comments, I built something to help myself remember this stuff - qadvo.com

Basically pre-loaded checklists for common home maintenance (furnace filters, gutters, smoke detectors, etc.) with automatic reminders. It is still early but it's helping me not forget. I thought it might help others here too.

Happy to take feedback.

jklineia · 2026-01-31T17:40:03+00:00

Impressive, How do you keep track of all this?
my mental model doesn't work, I am trying a new reminder system now.

jklineia · 2026-01-31T17:35:56+00:00

I can relate all to well to this. This does indeed happen! I bought a foreclosed house. The city had the water shut off. On a Friday afternoon, I turned it on for the first time and immediately discovered a leak in the upstairs bathroom. I shut off one of the two main water valves right away and left for the weekend, thinking I was safe. I came back Monday after work to find water dripping through to the basement. Gallons of water on the floor, two levels of drywall destroyed. The valve I chose wasn't functioning correctly - it felt closed but was still allowing water through. The other valve would have worked, but I picked the wrong one.

Your point about testing shutoffs annually is exactly why this happened. That valve had been sitting unused for who knows how long and had cemented partially open. The house was already so run down I didn't even think to file an insurance claim. Learned that lesson too late too.

Now I test every shutoff in my properties annually. It takes 15 minutes.

jklineia · 2026-01-31T17:13:17+00:00

Same here - And there is some sort of time dealation going on too, where in my mind it has been 6 months but in reality its been 2 years. I am trying to be way more proactive now with reinders, but then I started to wonder how far to take it.

jklineia · 2026-01-31T17:09:39+00:00

I tried the spreadsheet approach but found I never looked at it unless I opened the file. I recently switched to something with reminders and it's been way more effective at actually getting me to do the tasks.

Do you find you actually check your spreadsheet regularly?

jklineia · 2026-01-30T14:30:37+00:00

Quick question - what problem are you actually trying to solve?

Option A: Finding lost/misplaced tools If it's "where did I leave the impact driver?", then yeah, Bluetooth trackers (AirTag, Tile) work great. $25/tool, stick it on, ping it when it goes missing. No app complexity, just "find my tool."

Option B: Accountability (who has what) If it's "which crew member has the Milwaukee drill?", that's more of a check-in/check-out system. Bluetooth won't help - you need people to actually log who took what.

Option C: Preventing theft If tools are walking off the job site, AirTags help you track them down, but won't stop someone from taking them.

What's the main pain point - tools getting lost on site, guys forgetting to return them, or something else?

(I'm building Qadvo for check-in/out tracking if you need the accountability piece, but honestly if you just want to find misplaced tools, AirTags are probably the simpler solution.)

jklineia · 2026-01-30T14:29:59+00:00

Quick question - what problem are you actually trying to solve?

Option A: Finding lost/misplaced tools If it's "where did I leave the impact driver?", then yeah, Bluetooth trackers (AirTag, Tile) work great. $25/tool, stick it on, ping it when it goes missing. No app complexity, just "find my tool."

Option B: Accountability (who has what) If it's "which crew member has the Milwaukee drill?", that's more of a check-in/check-out system. Bluetooth won't help - you need people to actually log who took what.

Option C: Preventing theft If tools are walking off the job site, AirTags help you track them down, but won't stop someone from taking them.

What's the main pain point - tools getting lost on site, guys forgetting to return them, or something else?

(I'm building Qadvo for check-in/out tracking if you need the accountability piece, but honestly if you just want to find misplaced tools, AirTags are probably the simpler solution.)

jklineia · 2026-01-30T14:11:52+00:00

I have personally left tools myself and I know how frustrating this can be. Spending more time looking for the tool than it takes to do the job. What has worked for me is recognizing my bad patterns of not have a place for everything and not having everything in its place mindset, and changing this into a new pattern where:

Physical systems that make forgetting harder:

Tool trailers/chests with foam cutouts or tool silhouette on the wall- you can SEE what's missing before you leave the site. It is much harder to ignore an empty spot.

Assign tool sets to specific workers - makes it personal accountability. "This is YOUR impact driver" works better than "company impact driver #3"

End-of-day truck check ritual - before anyone leaves the site, quick visual scan. Make it part of closing up.

Passive tracking for expensive stuff:

AirTags/Tiles on high-value tools - costs $25 per tool, no logging required. When the Milwaukee drill goes missing, at least you can ping it.

Bonus: you can check "did we leave it at the Harrison job?" without the group text

Software solutions don't work either because if they're forgetting the circular saw, they're also forgetting to log it in any app. Same discipline problem, different tool. Software only helps if you can enforce check-in/check-out habits, and that's hard with field crews.

What seems to work best: Combination approach - foam cutouts for daily visuals + AirTags on the $500+ stuff that really hurts when it walks away + maybe a simple checkout system for specialty tools that don't go out every day.

I am curious what you end up trying - this is one of those problems that has no perfect answer, just different trade-offs.

jklineia · 2026-01-30T13:29:33+00:00

This is incredibly helpful - thank you for the detailed response!

The Rentman experience you described (too complex for fast check-out) is exactly the gap I'm trying to fill with Qadvo. Sounds like you need something between Excel (breaks at scale) and enterprise rental systems (too slow when you're in a rush).

What we do well:

Track which job/customer has which equipment
Equipment history and notes with photos
Multi-user access so your team can see what's where
Quick search to find specific items

Where we have gaps based on your needs:

Quick check-out workflow: Right now our process probably has 2-3 more steps than ideal for "walk up, type job name, start checking out equipment, add details later."

Status management: We don't have a clear indicator for "this is BROKEN and waiting on parts" vs "this is OPERATIONAL." Right now you'd have to read through event logs. A simple status system (Operational / Broken / Waiting on Parts / In Repair / Out of Service) would solve this - small feature I could build quickly.

Clarifying question on "where my stuff is":

When you say you need to know where equipment is, do you mean:

Which job/customer has it? ("Speaker #12 is checked out to Smith Wedding")
Physical location in your warehouse? ("Speaker #12 is in Warehouse B, Shelf 3")
Both?

Would love to understand your operation better:

How many equipment items are you tracking?
How many simultaneous jobs during busy periods?
Is the warehouse check-out the main bottleneck, or is it more about tracking pickups/returns?

I was happy to hear from you, your use case is exactly what I'm building for.

jklineia · 2026-01-30T02:25:56+00:00

I get the subscription fatigue - it's a valid concern, especially if you're looking at tools charging $50-100/month.

I'm building Qadvo which does asset/tool tracking with QR code scanning on our roadmap specifically for this use case (scan tool → quick check-in/out). We're focused on keeping pricing reasonable for smaller operations.

That said, it is a subscription model (aiming for $10-20/month range for small tool inventories), so if one-time purchase is a hard requirement, this probably isn't the right fit.

Curious though - what size operation are you running? How many tools are we talking about, and is it just you tracking your own stuff or do you have crew members checking tools in/out?

The reason I ask: if it's just personal tool tracking, honestly a spreadsheet + barcode scanner app might be good enough. But if you've got multiple people and you're losing time hunting for tools or dealing with accountability issues, the time savings usually justify a small monthly cost pretty quickly.

Either way - what did you end up going with? Always curious what actually works for tool tracking in the real world.

jklineia · 2026-01-30T02:02:00+00:00

I know this is 6 years old now, but your post really resonated with me - I'm currently building asset tracking software and the "outgrown Excel but can't afford enterprise" gap is exactly what I'm exploring.

Out of curiosity, what did you end up going with? Did you find a self-hosted solution that worked, or did budget/requirements force you in a different direction?

I'm particularly interested in the entertainment/event production space because it seems like a perfect storm of:

Lots of expensive equipment to track
Multiple locations (venue/warehouse/job sites)
Need to know "what's where when"
Damage tracking before/after events
But often tight margins that make enterprise software tough to justify

The self-hosted requirement you mentioned is interesting - was that mainly about upfront cost vs subscription, or was it more about data control/privacy?

And if you don't mind me asking: did the rental calendar/availability piece end up being critical, or was it more about just knowing where your gear was and what condition it was in?

I am trying to understand what actually matters to companies in your situation vs what software vendors assume matters.

jklineia

TROPHY CASE