iiinova — where your digital self keeps evolving

Roland Huß

@ro14nd.bsky.social

Software and Chili Geek • Red Hat • O'Reilly Author • Llama Stack • k8spatterns.com • jolokia.org

Roland Huß @ro14nd.bsky.social · 01/12/2025

omg, never could imagine that I was ever scared about something like "auto compaction". It always feels like speed dating (never did it but that how I imagine it :)

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Roland Huß @ro14nd.bsky.social · 27/06/2025

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.

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Roland Huß @ro14nd.bsky.social · 13/03/2025

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 😜

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Roland Huß @ro14nd.bsky.social · 05/03/2025

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

Three mini greenhouses for growing chilli pepper

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Roland Huß @ro14nd.bsky.social · 23/11/2024

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...

social.coop

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...

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Reposted by Roland Huß

Christoph Molnar @christophmolnar.bsky.social · 22/11/2024

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...

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.

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Reposted by Roland Huß

Jeff MAURY @jeffmaury.bsky.social · 22/11/2024

RedHat on Bluesky go.bsky.app/Du6L1Ec

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