arauhala

11 ms·

1.
Predictive database benchmarks vs. RF, AutoML, Elastic etc., up to 10M scale(aito.ai)
1 points by arauhala 2h ago|0 comments
2.
Aito v2: a predictive database over linked data, with vectors, graphs and SQL(aito.ai)
2 points by arauhala 22d ago|0 comments
3.
The User Is the Processor(aito.ai)
2 points by arauhala 2mo ago|0 comments
4.
Value, quality or growth: three investing philosophies based on 12 years of data(aito.ai)
1 points by arauhala 2mo ago|0 comments
5.
A new database category – the predictive database(aito.ai)
1 points by arauhala 3mo ago|0 comments
6.
What if every input, search and list was predictive?(aito.ai)
2 points by arauhala 4mo ago|0 comments
7.
by arauhala 5mo ago
Author here. I wrote this because "AI database" keeps getting used as one category when there are at least four architecturally different approaches, each with a different inference mechanism. Vector DBs do embedding similarity, M
8.
AI Database Landscape in 2026: Vector, ML-in-DB, LLM-Augmented, Predictive(aito.ai)
2 points by arauhala 5mo ago|1 comments
9.
by arauhala 8mo ago
Hmm.. So, I'm correct, you are maintaining QA suites for 10k agents, their prompts, toolboxes and some scenarios. That sounds like an absolutely massive scale to do QA over. Running the test suite must cost fortune and take ages. As su
10.
by arauhala 8mo ago
One of the key techniques is snapshotting the LLM (or any HTTP) request. This means that if the inputs won't change, the LLM will not be called. This will also snapshot /cache LLM verifications steps. This doesn't only saves
11.
by arauhala 8mo ago
Booktest is build based of 2 decade-career in data science. It has been used to support RnD on numerous LLM, ML, NLP, information retrieval and also more traditional software engineering. It was partly inspired by earlier examples (kudos fo
12.
Show HN: Booktest – review-driven regression testing for LLM / ML behavior(github.com)
4 points by arauhala 8mo ago|3 comments
13.
by arauhala 1y ago
I'm bootstrapping my predictive database startup https://aito.ai/ :-) More specifically, I have worked on the demo https://github.com/AitoDotAI/aito-demo to make use cases visual and well describe
14.
by arauhala 2y ago
If I'm reading this correctly, a few themes PG touches here are: 1) loss of control when hiring a professional manager without intrusion to sub organization, because you rely on the manager provided information. If the manager is not s
15.
Review driven development for ML / LMM applications(lumoa.me)
1 points by arauhala 2y ago|1 comments
16.
by arauhala 2y ago
Author here. The article seeks to answer the question "How to make ML / LMM development faster with higher quality?" The long format article seeks address the pain points in current tooling (Jupyter Notebook, unittests) used
17.
Simplifying back end development with NestJS monorepo(aito.ai)
2 points by arauhala 6y ago|0 comments
18.
We use EBS and Auto Scaling groups to move database state during updates(aito.ai)
3 points by arauhala 6y ago|0 comments
19.
by arauhala 6y ago
Regarding this topic, I found the following paper on TNT (three nightmare traits) interesting and bringing some clarity to the issues/confusions you are mentioning: https://www.frontiersin.org/articles/10.3389&#x2F
20.
by arauhala 6y ago
Hi, Author here! :-) The project feels transformative, because the ML part was implemented an RPA developer using predictive database queries. It wasn't that complex data science project, but it demonstrates that simple ML can be done
21.
3 key RPA+ML insights from invoice process automation project(aito.ai)
2 points by arauhala 6y ago|1 comments
22.
by arauhala 6y ago
I wonder if you are familiar with the predictive databases? We at Aito.ai have gotten lot of interest from different RPA/no-node users and providers, and predictive database queries seem like a best intelligent automation. https:&#x2F
23.
by arauhala 6y ago
Isn't this the same as the inverted honesty/humility personality trait from the HEXACO personality model? https://en.m.wikipedia.org/wiki/Honesty-humility_factor_of_t...
24.
by arauhala 6y ago
Hi, One of the aito.ai founders here! :-) We feel that the project was transformative, because the ML project was done end-to-end by RPA developers without data science team help. One of the RPA developer comments about Aito was 'that
25.
Finnish logistics giant uses predictive database for intelligent automation(aito.ai)
3 points by arauhala 6y ago|1 comments
26.
by arauhala 6y ago
I feel this is an interesting and a rather stake on the innovation / startup aspects. I got reminded of this Rand's blog post after PG's blog post and its comments, and also after the why companies stop innovating article. It
27.
Stables and Volatiles (2012)(randsinrepose.com)
2 points by arauhala 6y ago|1 comments
28.
by arauhala 6y ago
True, but it is familiar and quite useful for it's purpose (running scripts) :) For build it mostly works fine. Doing anything else with it would be a total pain.
29.
by arauhala 6y ago
Preach to the choir! :) Make does so many things correctly: - it gives user total freedom to modify the built - yet it has excellent defaults for most situations - it is full blown programming language, - yet it's syntax is extremely s
30.
by arauhala 6y ago
This is a good point, and Aito's inference engine has lot of similarities with search engines. As an interesting, we can provide TF-IDF scored full text search functionality from the same indexes we are also using for inferences. Still

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