Top all-time · #11502
Davis Blalock
Personal blog · dblalock.substack.comaidata
Davis Blalock writes Davis Summarizes Papers, a personal blog at dblalock.substack.com about ai and data. Since 2022, 7 different Hacker News readers have linked it 7 times, most often “Davis Summarizes Papers”. The last post was in 2024, but the archive is still widely read.
Too few sampled comments (4) to say how often HN recommends it.?
Why HN links it
HN readers link Davis Blalock's Substack for summaries of ML research papers and arXiv roundups, covering research rather than AI news and hype.
Summarised by Claude from a sample of 4 HN comments that link here. Each comment is marked recommending, critical or neutral (citing without judging); how this works.
I go through all the machine learning arXiv submissions each week and summarize 10 to 20 of my favorites. Free forever and read by thousands of ML researchers and practitioners.
Most-linked posts?
- Davis Summarizes Papers3 people · 3 mentions · first linked 2022-05-06
What Hacker News says?
High level AI developments: https://importai.substack.com/ https://www.semianalysis.com/ Hardware focused. Paywalled, but long article teasers contain plenty of information. Research Focused: https://codingwithintelligence.com/ https://dblalock.substack.com/ https://twitter.com/arankomatsuzaki
Comment linking /
on “Ask HN: Which sources cover AI developments without falling for the hype?”Submitted Twitter's surprisingly easy path to profitability as a story · 2 points
HN discussionFor fields where ArXiv preprints are the norm, Arxiv-Sanity [1] allows filtering by keywords and basic topic filtering. In ML, I personally like skimming Davis Blalock's Davis Summarizes Papers [2]. [1] https://arxiv-sanity-lite.com/ [2] https://dblalock.substack.com/
Comment linking /
on “Ask HN: How to discover new and interesting papers?”Submitted Models generating training data: win or fake win? as a story · 1 points
HN discussionI found the original paper through this roundup: https://dblalock.substack.com/p/2022-8-21-arxiv-roundup-recs...
Comment linking /p/2022-8-21-arxiv-roundup-recsys-scaling
on “Understanding Scaling Laws for Recommendation Models”Submitted Davis Summarizes Papers as a story · 1 points
HN discussionAbstract: > Instruction tuning enables pretrained language models to perform new tasks from inference-time natural language descriptions. These approaches rely on vast amounts of human supervision in the form of crowdsourced datasets or user interactions. In this work, we introduce Unnatural Instructions: a large dataset of creative and diverse instructions, collected with virtually no human labor. We collect 64,000…
Comment linking /i/93113359/unnatural-instructions-tuning-language-models-with-almost-no-human-labor
on “Unnatural Instructions: Tuning Language Models with (Almost) No Human Labor”