DuckDB – Data power tools for your laptop, now in Clojure (2023)
2023-09-02DuckDB - Data power tools for your laptop, now in ClojureEstablishing the NeedOur in-memory column-major data processing platform, tech.ml.dataset (TMD), drives the future of functional data science. When data gets large enough not to fit in memory, one can continue with TMD by operating on samples of data, or otherwise filtering to relevant subsets to fit in bounds imposed by the working environment. Moreover, one can accomplish persistence, for data small and large, with nippy, arrow, or parquet.When data becomes large enough, for example sets of .csv files on the order of ~100GB with relational aspects to them, the tools in their current state can become unwieldy.
- ▪2023-09-02DuckDB - Data power tools for your laptop, now in ClojureEstablishing the NeedOur in-memory column-major data processing platform, tech.ml.dataset (TMD), drives the future of functional data science.
- ▪When data gets large enough not to fit in memory, one can continue with TMD by operating on samples of data, or otherwise filtering to relevant subsets to fit in bounds imposed by the working environment.
- ▪Moreover, one can accomplish persistence, for data small and large, with nippy, arrow, or parquet.When data becomes large enough, for example sets of .csv files on the order of ~100GB with relational aspects to them, the tools in their curr
Hacker News (Front Page) files mainly under programming. We currently carry 1,133 of its stories. Top-voted stories on Hacker News.
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Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | Techascent |
| Canonical URL | https://techascent.com/blog/just-ducking-around.html |
| Publication time | Tue, 04 Aug 2026 22:09:43 +0000 |
| Retrieval time | 2026-08-04T22:40:41.493Z |
| Last seen | 2026-08-04T22:40:41.493Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | 0LfIFG9owKMa · 1 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
2023-09-02DuckDB - Data power tools for your laptop, now in ClojureEstablishing the NeedOur in-memory column-major data processing platform, tech.ml.dataset (TMD), drives the future of functional data science. When data gets large enough not to fit in memory, one can continue with TMD by operating on samples of data, or otherwise filtering to relevant subsets to fit in bounds imposed by the working environment. Moreover, one can accomplish persistence, for data small and large, with nippy, arrow, or parquet.When data becomes large enough, for example sets of .csv files on the order of ~100GB with relational aspects to them, the tools in their current state can become unwieldy. One is tempted to get involved in nonfunctional sparky cluster snafus.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Techascent.
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