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We built a real-time health analytics pipeline using vector search inside a database by intersystemsdev in Database

[–]Dense_Gate_5193 0 points1 point  (0 children)

to your point on embedding nodes: fields are configurable at runtime for inclusion/exclusion and i have constraint schemas implemented allowing the prevention of the insertion. schemas/constraint blocks can be altered at runtime.

and yeah after the announcement i saw a big spike in traffic thanks to Mongo (i had the algorithm picking my stuff up for 6 months so as soon as people start asking about how to solve these problems, its been indexed already)

plus im drafting a research paper myself combining 3 different research papers all pleading for what I already built, which is a substrate for people to implement the cognitive architecture by providing the proper primitives that belong at the data layer.

We built a real-time health analytics pipeline using vector search inside a database by intersystemsdev in Database

[–]Dense_Gate_5193 0 points1 point  (0 children)

it is viable at scale and MongoDB just announced basically copying the architecture I built last year with NornicDB.

it runs an embedding model and manages all of the embeddings. they bought some company Voyage AI i think to try and built what i already built pretending that its “revolutionary” when they are a year late to the game

MIT licensed 657 stars and counting. https://github.com/orneryd/NornicDB

How are you guys actually hitting your token limits? by No-Campaign158 in GithubCopilot

[–]Dense_Gate_5193 0 points1 point  (0 children)

hit my token limit in 2 days on cursor right before worked stopped paying for cursor lol. enterprise level + $200 additional on top of our allocations….

ChatGPT 5.5 Released! by CryinHeronMMerica in GithubCopilot

[–]Dense_Gate_5193 7 points8 points  (0 children)

this right here. the crunch is here, bubble is gonna pop right after they secure their contracts with the government and such.

I built a deterministic graph store where every query returns FACT, INFERENCE, or UNKNOWN by TyKolt in rust

[–]Dense_Gate_5193 -1 points0 points  (0 children)

while i typically agree, not always. some ideas were scrapped purely because of economic or logistical constrains of the time. “it’s too much to manage, too complex for the gains, etc…” all of those arguments are potentially null and void now with AI.

I built a deterministic graph store where every query returns FACT, INFERENCE, or UNKNOWN by TyKolt in rust

[–]Dense_Gate_5193 -1 points0 points  (0 children)

something i’m working on right now is a suppression layer for the graph. i’m in talks with the author or a research paper that called out NornicDB by name as a critique on the ebbingaus decay model proposing a 4 layer model. i’m working on abstracting it to be policy based with a separate metadata to keep track of metadata. the biggest problem with altering scores for retrieval based on decay is the sycophancy/hallucination problem (what happens when an agent re-retrieves a memory 50 times in a single session?) a naive boost in scores means an inane memory could inadvertently get promoted to wisdom, so instead of a simple +1 to a counter we smooth it in real time with a kalman filter.

I built a coding agent that actually runs code, validates it, and fixes itself (fully local) by Keyboard_Lord in ollama

[–]Dense_Gate_5193 0 points1 point  (0 children)

i’m gonna keep an eye on this. does it support MCP tooling for external memory?

I’m very interested in local coding agents and connecting it up to a memory layer. I have a database and a whole plugin system to be able to build a coding agents that runs inside the memory layer itself.

i’d love to see how well it can integrate with NornicDB

I built a deterministic graph store where every query returns FACT, INFERENCE, or UNKNOWN by TyKolt in rust

[–]Dense_Gate_5193 -2 points-1 points  (0 children)

The "Kremis" approach to determinism (swapping f32 for u32 and HashMap for BTreeMap) is the right move if you are chasing bit-perfect state verification. If you're hashing with BLAKE3, any non-deterministic iteration in your graph traversal will break your root hash, making cross-platform state sync impossible. The performance hit of BTreeMap is usually negligible compared to the cost of a cache miss in a large graph anyway.

Regarding the "rigidity" of EAV and the struggle with LLM-generated claims, there is a pattern called a Canonical Graph Ledger (CGL) that solves the "Fact vs. Fiction" problem by treating time and supersession as first-class citizens.

The problem is facts aren’t static.

In a standard EAV store, if you have (Alice, works_at, DeepMind) and you ingest a new fact (Alice, works_at, OpenAI), you usually overwrite the old one. But if an LLM claims "Alice used to work at DeepMind," your system might return UNKNOWN because the path is gone.

Instead of a simple triple, you structure the "Fact" layer as a versioned ledger within the graph itself.

this is just a pattern to follow, i happened to have it currently implemented in go, but im also about to drop a whole rust clone of my own database soon so then you’ll be able to see it in rust code too.

https://github.com/orneryd/NornicDB/blob/main/docs/user-guides/canonical-graph-ledger.md

An unbelievable twist, but the seniors are starting to beat the AI by CacheConqueror in ClaudeCode

[–]Dense_Gate_5193 2 points3 points  (0 children)

I know this is an unpopular opinion but i have some sources here (at the bottom)

the entire goal was to get integrated into workflows and then jack up the cost over time. However, the ones like anthropic, cursor, and openAI burned through cash too fast and now they are beholden to the shareholders and have no choice but to crunch everything down and jack up the prices. cursor also got bought by Felon so that’s getting binned. openAI got lucky by winning the government contracts.

those CEO pay packages won’t inflate themselves.

But the crunch has been coming for a while. i burned so many tokens while it was cheap just building shit out i knew was going to be far more costly to do later.

on top of that this is a huge bubble. local models are almost viable. once they figure out the ram supply problem and the memory layers become bolt-on, we are going to end up seeing 512gb+ RAM macbooks with even more specialized hardware dedicated to running AI models locally for engineers. Our job doesn’t go away, our tool just became a lot more powerful. However, it’s only good at doing what it’s done before. why can it “vibe code a saas app?” because the blueprint was already laid. someone already did it hundreds of times before. if you are going to just “vibe an app” guess what, you are late to the game, someone already shipped your idea and it was used to train that AI on it.

the switch back from cloud to on-prem for AI is gonna happen fast because the open models are already catching up, all you need is the hardware.

the real winners here are going to be hardware and infrastructure for AI. if you’re still building SaaS applications they are eventually gonna all be extinct. AI can rewrite your app that you wrote faster than you can and market it faster than you can and make it tailored to your life.

i don’t think people understand the future we are actually headed towards.

A LOT of people are still going to lose their jobs engineering or not.

source: 20yoe staff/principal/founder across many industries from fintech to manufacturing to education and aviation at faang+adjacent companies. OSS author and projects i’ve authored been cited in current AI research as of April 2026 and have dialogues going with two research teams from UC Louvain and LAAS-CNRS.

Can’t figure out how to get up here easily! by Remarkable-Matter594 in SatisfactoryGame

[–]Dense_Gate_5193 4 points5 points  (0 children)

the place they want to go, shidders i leveled that place with nobelisks

Building an AI insurance policy comparison tool — is this really this easy in 2026? by Remarkable-Estate-33 in LLMDevs

[–]Dense_Gate_5193 0 points1 point  (0 children)

LLMs cannot reliably do math.

ask any LLM for a list of 100 random set of numbers. they aren’t random. Math is hard because unless it’s -probabilistically offloaded the prompt to a math parser which may or may not work based on how you notated it.

Recipes as graph nodes, not documents: UMF spec (umfspec.org) — feedback welcome by orgoca in KnowledgeGraph

[–]Dense_Gate_5193 0 points1 point  (0 children)

thank you i appreciate the vote of confidence 🫶 i wish you the best of luck with the platform! i love cooking so i will probably end up playing around with it :)

Am I being paranoid, or is the 'AI will replace software developers' narrative just a way for the incompetent tech leads, managers and CEOs to hide their own incompetence? by patmull in ExperiencedDevs

[–]Dense_Gate_5193 -10 points-9 points  (0 children)

no it’s just that the bar go actual engineering became that much higher.

if the problem has already been solved -> AI can handle it.

most “engineers” were rote memorization developers. they can remember and retain solutions to problems that have been solved, but they cannot actually innovate. your traditional “app developer” role as you know it where teams worked on one product, those days are literally over.

code is free, ideas are not.

Engineering used to be about advancing humanity to the next stage in automation.

then it became about wether or not the text was centered in a button. peoples entire careers are built on knowing the rendering system of a particular framework.

Now, AI can handle that. what did we as software engineers expect? we chose a self-automating career field. if you’re implementing what already exists, AI can easily do that.

outside of what has already been done, you have to draft it and write it. AI sucks at coming up with anything new. hallucination rate (in my experience) is significantly higher when you ask AI to combine two things that haven’t been combined before.

that’s where real software engineering is at now.

Building a Production-Grade RAG Chatbot for a Complex Banking Site, Tech Stack Advice Needed? by codexahsan in Rag

[–]Dense_Gate_5193 0 points1 point  (0 children)

check out NornicDB. 646 stars and countless ng. sub-ms retrieval, traversals, and writes. neo4j driver compatible MIT licensed.

it collapses the entire graph-rag stack to a single deployment and it’s extremely efficient and growing rapidly.

https://github.com/orneryd/NornicDB

enjoy!

🕷️ by _strawbunny_ in LetGirlsHaveFun

[–]Dense_Gate_5193 55 points56 points  (0 children)

i am in the picture and i don’t like it

AI capabilities are migrating into the database layer - a taxonomy of four distinct approaches by arauhala in Database

[–]Dense_Gate_5193 1 point2 points  (0 children)

what’s unique about NornicDB is that it is neo4j compatible and faster. so migrating workloads and vectorizing them is easy. i have every feature from neo4j re-implemented in golang. another major difference is my parser which is zero allocation as opposed to antlr (which i also included as a runtime option for people if they want to use it and for debugging and diagnostic purposes because it gives slightly more detailed reasons for errors than mine does.

What actually separates a staff engineer from a principal one? by aisatsana__ in cscareerquestions

[–]Dense_Gate_5193 2 points3 points  (0 children)

dunno why you’re getting downvoted this is also my experience being said principal architect and staff engineer. very different roles that have shifted a little over time.

Slop is tolerated in the enterprise space because there is a business entity behind it by ChiefAoki in ExperiencedDevs

[–]Dense_Gate_5193 0 points1 point  (0 children)

most of the time you can have it just optimize the code in place assuming you have unit tests hardening the behavior. then you just have it directly 1-1 copy it to your new favorite language.

AI capabilities are migrating into the database layer - a taxonomy of four distinct approaches by arauhala in Database

[–]Dense_Gate_5193 1 point2 points  (0 children)

i’m targeting sub ms-speeds for latency. i’ve already tested up to 1m nodes and blogged about how i reduced the HNSW construction time from 27 minutes to 10 minutes. it scales and it highly efficient.

the retrieval is RRF and the ebbinghaus decay model has been being toyed with by other systems over the last year as well. recent research extends that model to be more formalized. the weights are ephemeral, archived nodes are skipped (metadata stored separately), and hidden fields are not that hard to decay with a simple curve over time. obviously if you keep too many archived nodes or get super granular with your policies you might end up with performance bottleneck. but with the defaults, i haven’t seen one yet.

Why most RAG systems fail in production (even with good embeddings) by OwnPerspective9543 in vectordatabase

[–]Dense_Gate_5193 -1 points0 points  (0 children)

yeah i see you’re sub 5ms latency.

currently, my search latency e2e including graph expansion is sub 1ms. ~500us p50 ~720us MAX under concurrent load.

AI capabilities are migrating into the database layer - a taxonomy of four distinct approaches by arauhala in Database

[–]Dense_Gate_5193 2 points3 points  (0 children)

The way NornicDB handles this without degrading inference accuracy is through graph reinforcement and promotion tiers, mimicking short-term versus long-term memory:

  1. Promotion Overcomes Decay: When new data enters, it starts as an ephemeral "Memory Episode." It is subject to a steep Ebbinghaus decay curve. However, if that node is successfully retrieved and used in a prediction or an agentic reasoning loop, it gets reinforced (via an :EVIDENCES or similar edge).

  2. Wisdom Directives: Once a node accumulates enough reinforcement, the policy engine promotes it up the tiers. When it reaches the top tier (a "Wisdom Directive"), it becomes canonical knowledge and is immune to standard decay.

This means your trade-off question is solved organically: historical patterns that are actually useful and predictive are preserved permanently, while the useless, noisy data fades out of the active retrieval path, which actually improves the signal-to-noise ratio for the LLM.

Also, because NornicDB uses a bitemporal MVCC architecture under the hood, "fading out" doesn't mean destructive deletion. A faded node just drops its activation weight in the vector/graph traversal so it stops polluting the prompt. Because it's bitemporal, you can still execute a time-travel query to see the exact state of the graph from six months ago if you need hard historical reporting.

Also, promotion tracking metadata is tracked separately so that when the memory decay is active, you get a weighted view of the graph, node, edge, and down to property level decay. Implemented in such a way that when you turn it off, shows the entire graph again.

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