y14

11 ms·

1.
Show HN: The Data MCP – chat with any database, with memory and rules(thedatamcp.com)
2 points by y14 8mo ago|0 comments
2.
Building Reliable AI Analysts: Observability Framework for Text-to-SQL Systems(bagofwords.com)
1 points by y14 11mo ago|0 comments
3.
Semantic Layers Are Bad for AI(bagofwords.com)
1 points by y14 11mo ago|0 comments
4.
by y14 1y ago
Thanks for the question. Avoiding context bloat and overall engineering the context is (still) most of work. What’s been working: - Role scoped calls: data modeling, code gen, are separate calls where each gets its own tailored context - Co
5.
by y14 1y ago
The hardest problems building this weren’t in the LLM logic, but in everything around it —-observability, access control, and managing context across dbt, Tableau, and code. Finding the balance between a strict semantic layer and LLM agency
6.
Show HN: I built an open-source AI data layer that connects any LLM to any data(github.com)
18 points by y14 1y ago|3 comments
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Show HN: Open-Source AI Analyst in Slack (LLM and Python, Self-Hosted)(bagofwords.com)
3 points by y14 1y ago|0 comments
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Show HN: I built an AI-powered children's book generator for my 2-year-old(imaginebooks.com)
2 points by y14 2y ago|0 comments
9.
I built an AI-powered children's book generator for my 2-year-old(imaginebooks.com)
2 points by y14 2y ago|0 comments
10.
Protecting LLM Instructions Leakage(bagofwords.com)
1 points by y14 2y ago|0 comments
11.
AI Software Engineer: Full Coding Prompt(bagofwords.com)
2 points by y14 2y ago|0 comments
12.
AI Software Engineer Planning Prompt(bagofwords.com)
2 points by y14 2y ago|0 comments
13.
Cnvrg.io Metacloud – an ML platform with BYOC/S(cnvrg.io)
2 points by y14 5y ago|0 comments
14.
Why we decided to release a free ML platform to the data science community(cnvrg.io)
6 points by y14 6y ago|0 comments
15.
Show HN: Machine Learning terms on every new tab(dictionary.cnvrg.io)
2 points by y14 7y ago|0 comments
16.
How to apply continual learning to your machine learning models(towardsdatascience.com)
1 points by y14 7y ago|0 comments
17.
How to use continual learning to your machine learning models(blog.cnvrg.io)
1 points by y14 7y ago|0 comments
18.
How to save up to 80% in cloud costs when training deep learning models(cnvrg.io)
3 points by y14 8y ago|0 comments
19.
Things to do while waiting for your models to converge(cnvrg.io)
4 points by y14 8y ago|0 comments
20.
Machine Learning Podcasts(cnvrg.io)
3 points by y14 8y ago|0 comments
21.
Deploy machine learning to production using Kubernetes(cnvrg.io)
9 points by y14 8y ago|1 comments
22.
Research – an important role in reproducible data science(cnvrg.io)
1 points by y14 8y ago|0 comments
23.
100 most promising private artificial intelligence companies(cbinsights.com)
2 points by y14 10y ago|0 comments
24.
by y14 10y ago
As a ruby developer who had to move to python for its data science support, it’s very nice to see that people help Ruby to evolve into this direction. Ruby is a beautiful language that should be expanded beyond web development and these kin
25.
Emotify – Extract emotions from any given text(emotify.accessible.ai)
2 points by y14 10y ago|0 comments
26.
by y14 10y ago
This is a problem we (accessible.ai) have been working on in the past few months. We're still tuning and improving but with a multi-modal approach of signal processing, speech recognition and text analysis we were able to come up with
27.
Yelp Dataset Challenge(yelp.com)
26 points by y14 12y ago|18 comments
28.
by y14 12y ago
Automatic Reference Counting Swift uses Automatic Reference Counting (ARC) to track and manage your app’s memory usage. In most cases, this means that memory management “just works” in Swift, and you do not need to think about memory manage
29.
by y14 12y ago
The mac integration for calls is great.
30.
by y14 12y ago
This is excellent. I always fail to keep track on my TODOS in files. Very useful.

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