ekzhu

6 ms·

1.
DojoZero – AI Agent Sport Betting Arena(dojozero.live)
1 points by ekzhu 5mo ago|0 comments
2.
CoPaw – A truly local OpenClaw alternative with customized models (2B, 4B, 9B)(github.com)
2 points by ekzhu 6mo ago|1 comments
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by ekzhu 6mo ago
CoPaw comes with CoPaw-Flash models fine-tuned for personal assistant scenarios: https://huggingface.co/collections/agentscope-ai/copaw-flash
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Show HN: TuFT – Open-source multi-tenant, Tinker-compatible fine-tuning platform(github.com)
1 points by ekzhu 7mo ago|0 comments
5.
What Is Coming Next for Text-to-SQL(ekzhu.medium.com)
1 points by ekzhu 4y ago|0 comments
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by ekzhu 4y ago
Check out MATCH_RECOGNIZE. It is a new SQL feature that does just what the post describes.
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by ekzhu 4y ago
The Dataframe is loaded from disk true, but it is possible that batch loading is faster (esp. with structured data) than row-by-row translation Postgres types into Python types. Would be interesting to see the benchmark results. > I thin
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by ekzhu 4y ago
Great idea! I see this is implemented using the Python language interface supported by PostgreSQL and importing sklearn models. I always wonder how scalable this is considering the serialization-deserialization overhead between Postgres&#x2
9.
by ekzhu 5y ago
I am curious what is issue with existing migration tools? There are so many tools out there. What is the problem they are not solving?
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by ekzhu 5y ago
Most of it should be cached. I think it’s a trade off between server load and client experience. Millions of 10KB requests << thousands of 500KB requests (also potentially with lots of compressed stuff).
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by ekzhu 5y ago
TLDR: Arrow got an SQL interface provided by DuckDB. So you have a new way to run SQL on Parquet et al through DuckDB -> Arrow -> Parquet. Of course, you still need to watch out for memory usage of your SQL query if it contains JOINs
12.
by ekzhu 5y ago
There is no reason for both approaches to not coexist: a centralized catalog managed by a small team, setting the “gold standard” for the many decentralized data producers and curators, who are incentivized to maximize their impacts (i.e.,
13.
by ekzhu 5y ago
I cannot continue reading after this following “declaration”… Author should take a look at the Wikipedia page for TF-IDF. > As someone who has a Ph.D. in Human-computer Interaction ;-), I feel like I am entitled to define a condition of
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by ekzhu 5y ago
Thanks for the response. I enjoy reading your blog. What you said reminds me of the post [0] in which you compared Timescale with InfluxDB and argued that SQL is better. Has your position changed due to new observation regarding usability?
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by ekzhu 5y ago
Thanks for the background. I find it fascinating that the small-data scenarios in analytics are still kind of chaotic when it comes to tooling. Full-fledged SQL queries on relations seems heavy but closer to raw data. The timevector custom
16.
by ekzhu 5y ago
Database researcher here. This is really cool! I wonder what was the initial drive for this new feature? Is this meant to be a "short-cut" to express complicated SQL queries, or is this meant to adding new semantics beyond SQL? Wh
17.
by ekzhu 5y ago
Interesting findings: 1. "Amazon Restaurant & Bar Inc" received 1.3M in FY2021 while apparently empolying only 8 people and taking a revenue of 96k ( https://www.manta.com/c/mhx084z/amazon-restaurant-b
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Coping with Rejection in Academia: A Three-Pronged Approach(loom.com)
2 points by ekzhu 6y ago|0 comments
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by ekzhu 6y ago
How do you handle expensive queries? Several JOIN over multiple large data sources can easily take minutes if not hours.
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by ekzhu 6y ago
Just want to point it out in case people miss it: it has a Postgres extension you can use directly in your databases.
21.
On Naming a Database Management System(cs.cmu.edu)
4 points by ekzhu 7y ago|0 comments
22.
by ekzhu 7y ago
There are many free public datasets available on the web. I have an open source project on crawling public datasets and make them searchable in one place: https://github.com/findopendata/findopendata .
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Show HN: A Search Engine for Open Data (With a Live Demo)(github.com)
4 points by ekzhu 7y ago|0 comments
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Speculative Distributed CSV Data Parsing for Big Data Analytics [pdf](badrish.net)
2 points by ekzhu 7y ago|0 comments
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Ask HN: Challenges in building/maintaining/using data lakes
2 points by ekzhu 7y ago|0 comments
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by ekzhu 7y ago
We (data curation lab at Univ of Toronto) are doing research in data lake discovery problems. One of the problems we are looking at is how to efficiently discover joinable and unionable tables. For example, find all the rental listings from
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by ekzhu 8y ago
Interesting. I thought the Python builtin hashlib was more convenient (and more random). But yes you are right, good implementation of murmur3 hash is much faster.
28.
by ekzhu 8y ago
I am sure Murmur3 would improve performance, but I doubt it would improve the indexing time very much. I can give it a try. Update: In IPython using pyhash library (C++): import pyhash h = pyhash.murmur3_32() timeit h(b"test&qu
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by ekzhu 8y ago
Author here. The algorithm used here is based on Google's 2007 paper "Scaling Up All Pairs Similarity Search." Since then I am sure they have started to look at billions of sets. Generally speaking exact algorithms like the o
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Show HN: All-pair similarity search on millions of sets in Python and on laptop(github.com)
123 points by ekzhu 8y ago|19 comments

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