Roland Huß
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ro14nd.bsky.social
Roland Huß
@ro14nd.bsky.social
150 followers 58 following 6 posts
Software and Chili Geek • Red Hat • O'Reilly Author • Llama Stack • k8spatterns.com • jolokia.org
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ro14nd.bsky.social
I've felt in love with Claude Code's CLI UI. Super well done, and worth a try, even when you're not in the vibe-coding business.
June 27, 2025 at 9:26 AM
ro14nd.bsky.social
The easiest way to detect LLM-generated output: The usage of "—" (U+2014 : EM DASH) instead of "-" (U+002D : HYPHEN-MINUS). Nobody types an em dash manually, but ChatGPT at least LOVES it.

Corollary: Always check your ChatGPT output for hyphens 😜
March 13, 2025 at 9:52 AM
ro14nd.bsky.social
After one year break, we're back for the chilli pepper season 2025. Let's g(r)o(w) ! #chili
Chili Pepper Seeds soaked 24h in chamilea tea for improved germination rates Putting 190 seeds into mini greenhouses Three mini greenhouses for growing chilli pepper
March 5, 2025 at 11:27 AM
ro14nd.bsky.social
Great and imo quite balanced technical view on BlueSky's decentralization features. It's worth a read, even when it's quite a mouthful --> social.coop/@cwebber/113...
Christine Lemmer-Webber (@cwebber@social.coop)
How Decentralized Is Bluesky Really? https://dustycloud.org/blog/how-decentralized-is-bluesky/ A technical deep-dive, since people have been asking me for my thoughts. I'll expand a bit on some of th...
social.coop
November 23, 2024 at 10:05 AM
Reposted by Roland Huß
christophmolnar.bsky.social
Just realized BlueSky allows sharing valuable stuff cause it doesn't punish links. 🤩

Let's start with "What are embeddings" by @vickiboykis.com

The book is a great summary of embeddings, from history to modern approaches.

The best part: it's free.

Link: vickiboykis.com/what_are_emb...
Book outline Over the past decade, embeddings — numerical representations of machine learning features used as input to deep learning models — have become a foundational data structure in industrial machine learning systems. TF-IDF, PCA, and one-hot encoding have always been key tools in machine learning systems as ways to compress and make sense of large amounts of textual data. However, traditional approaches were limited in the amount of context they could reason about with increasing amounts of data. As the volume, velocity, and variety of data captured by modern applications has exploded, creating approaches specifically tailored to scale has become increasingly important. Google’s Word2Vec paper made an important step in moving from simple statistical representations to semantic meaning of words. The subsequent rise of the Transformer architecture and transfer learning, as well as the latest surge in generative methods has enabled the growth of embeddings as a foundational machine learning data structure. This survey paper aims to provide a deep dive into what embeddings are, their history, and usage patterns in industry. Cover image
November 22, 2024 at 11:13 AM
Reposted by Roland Huß
RedHat on Bluesky go.bsky.app/Du6L1Ec
November 22, 2024 at 6:49 AM