kendallgclark

5 ms·

1.
Knowing, Remembering, Exactly, Vaguely: An Agent-Native Database (PlatypusDB)(pentad.ai)
2 points by kendallgclark 2mo ago|1 comments
2.
by kendallgclark 2mo ago
PlatypusDB is an agent-native, bitemporal event database: Gleam (OTP) control plane over a Zig data plane, in-process with WunderOS. Custom-built, not assembled, for agentic workloads inside WunderOS. The Merkle WAL is the database. Graph,
3.
by kendallgclark 2mo ago
Thanks. Gotta be the most often ignored rule on this site. But still.
4.
The Social Tier: Remembering Who Said What(pentad.ai)
1 points by kendallgclark 2mo ago|2 comments
5.
by kendallgclark 6mo ago
Thinking about data in terms of energy basins, frequencies, and SNR is different than what I’ve done before. But pushing a signal below the noise floor is analogous to tombstoning a tuple in a database.
6.
by kendallgclark 6mo ago
Thanks for the question. Inlike vector databases that append embeddings to an HNSW graph, my working memory substrate natively supports mathematical forgetting. I use a Squelch primitive—a SIMD-parallelized saturating subtraction over an 8-
7.
by kendallgclark 6mo ago
Well probably this recent piece by Kanerva. https://arxiv.org/abs/2503.23608
8.
by kendallgclark 6mo ago
Thanks. Yes all spaces are crowded. IMO the value here will be quasi brain-like operations on data that are fast and efficient. We overuse LLMs which aren’t too fast and very inefficient. So the value here is being able to support a shift o
9.
by kendallgclark 6mo ago
State of the art for HDC/VSA? Or for agentic memory?
10.
Looking for Partner to Build Agent Memory (Zig/Erlang)
7 points by kendallgclark 6mo ago|9 comments
11.
by kendallgclark 1y ago
Not at all. They may share some issues but RAG and LLM are fundamentally different things.
12.
by kendallgclark 1y ago
LOL. No. All me, hater.
13.
by kendallgclark 1y ago
Fixed. Thanks.
14.
by kendallgclark 1y ago
That might be RAG’s benefit if LLMs were more steerable but they can be stubborn.
15.
RAG Is a Fancy, Lying Search Engine(labs.stardog.ai)
43 points by kendallgclark 1y ago|12 comments
16.
by kendallgclark 1y ago
He didn’t write as if he hated liberalism. Maybe he did. But in his work you get deep, principled critique from the basis of epistemology and selfhood. Lenin wrote like someone who hates liberalism. Stephen Miller gives that vibe from the r
17.
by kendallgclark 1y ago
Not really competitive with Stardog given our leading LLM integration with Voicebox. 85% pass@1 to exit POV with new customer.
18.
by kendallgclark 1y ago
The use case at NASA isn’t even new. We built this precise thing in 2008. All standards-based. See https://www.w3.org/2001/sw/sweo/public/UseCases/Nasa/ for a public case study. This work led t
19.
by kendallgclark 2y ago
That’s tough. Not sure what that has to do with Stardog. Biggest companies in the world rely on it daily and you say it’s trash. I couldn’t find an email from you using it since 2013. I guess we figured something out. NNs are cool too; at l
20.
by kendallgclark 2y ago
OWL ontologies making a big comeback as part of Knowledge Graph groundings for LLM outputs. And several SPARQL and RDF knowledge graph startups are VC-baked and thriving. The world is a big place.
21.
by kendallgclark 2y ago
You seem nice.
22.
by kendallgclark 2y ago
https://www.stardog.com/blog/skathe-is-a-private-gpu-cloud/
23.
Startups should host LLMs on their own GPUs(stardog.com)
2 points by kendallgclark 2y ago|1 comments
24.
by kendallgclark 2y ago
We do this and we aren't huge; and we did it for two reasons: world-class UX for our customers and world-class unit economics for ourselves.
25.
Show HN: Stardog Voicebox, hallucination-free Data Assistan t
1 points by kendallgclark 2y ago|1 comments
26.
by kendallgclark 3y ago
100% hallucination free because we don’t use RAG, we use Semantic Parsing. Uses fine-tuned Llama 2. Knowledge Graph backend has federated graph streams for real-time data access, as well as a bunch of neurosymbolic AI, GNN-powered few shot
27.
Stardog Voicebox: Question Answering Powered by LLM and Knowledge Graph(stardog.ai)
9 points by kendallgclark 3y ago|2 comments
28.
by kendallgclark 3y ago
Happy customer here —— maybe the first or second? Distributed systems are hard; #iykyk. Antithesis makes them less hard (not in line an NP hard sense but still!).
29.
by kendallgclark 3y ago
We at Stardog -- https://stardog.com/ -- have been using Antithesis as early adopters to build our distributed knowledge graph platform, which includes a Zk-based HA clustered graph database. Antithesis is great; has saved
30.
by kendallgclark 3y ago
Posted anywhere to avoid an exceptionally $$ subscription?

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