SEsecondrow·Aug 20, 2025
In Why Semantic Layers Matter (and how to build one with DuckDB)xorq-labs
xorq
Python2025–2025
xorq-labs / xorq
xorq
The ledger for agentic data work.
3Distinct people
4Mentions
0Explicit recommenders
558GitHub stars
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The ledger for agentic data work.
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This week on Hacker News
Nov 7–Nov 14 UTC · Direct repository links and separately labeled related project stories. Indexed through 11/14/2026, 5:04:57 PM UTC.
No direct repository links were found in the indexed HN discussions over the past seven days. Older discussions are available below.
Mentions over time
HN mentions by week4 dated mentions
Observed mentions: 2025-03-27 17:27:31 UTC to 2025-08-20 15:28:08 UTC. Weeks start Monday, UTC.
Counts come from the repository’s dated source evidence. Index cutoff: 2026-11-02 08:16:59 UTC.
View mention counts and date ranges
| Period | Start (UTC) | End (exclusive, UTC) | Mentions | Submissions | Comments |
|---|---|---|---|---|---|
| Mar 24 | 2025-03-24 00:00:00 UTC | 2025-03-31 00:00:00 UTC | 1 | 1 | 0 |
| Mar 31 | 2025-03-31 00:00:00 UTC | 2025-04-07 00:00:00 UTC | 0 | 0 | 0 |
| Apr 7 | 2025-04-07 00:00:00 UTC | 2025-04-14 00:00:00 UTC | 0 | 0 | 0 |
| Apr 14 | 2025-04-14 00:00:00 UTC | 2025-04-21 00:00:00 UTC | 0 | 0 | 0 |
| Apr 21 | 2025-04-21 00:00:00 UTC | 2025-04-28 00:00:00 UTC | 0 | 0 | 0 |
| Apr 28 | 2025-04-28 00:00:00 UTC | 2025-05-05 00:00:00 UTC | 0 | 0 | 0 |
| May 5 | 2025-05-05 00:00:00 UTC | 2025-05-12 00:00:00 UTC | 0 | 0 | 0 |
| May 12 | 2025-05-12 00:00:00 UTC | 2025-05-19 00:00:00 UTC | 0 | 0 | 0 |
| May 19 | 2025-05-19 00:00:00 UTC | 2025-05-26 00:00:00 UTC | 0 | 0 | 0 |
| May 26 | 2025-05-26 00:00:00 UTC | 2025-06-02 00:00:00 UTC | 0 | 0 | 0 |
| Jun 2 | 2025-06-02 00:00:00 UTC | 2025-06-09 00:00:00 UTC | 0 | 0 | 0 |
| Jun 9 | 2025-06-09 00:00:00 UTC | 2025-06-16 00:00:00 UTC | 0 | 0 | 0 |
| Jun 16 | 2025-06-16 00:00:00 UTC | 2025-06-23 00:00:00 UTC | 0 | 0 | 0 |
| Jun 23 | 2025-06-23 00:00:00 UTC | 2025-06-30 00:00:00 UTC | 0 | 0 | 0 |
| Jun 30 | 2025-06-30 00:00:00 UTC | 2025-07-07 00:00:00 UTC | 0 | 0 | 0 |
| Jul 7 | 2025-07-07 00:00:00 UTC | 2025-07-14 00:00:00 UTC | 0 | 0 | 0 |
| Jul 14 | 2025-07-14 00:00:00 UTC | 2025-07-21 00:00:00 UTC | 1 | 0 | 1 |
| Jul 21 | 2025-07-21 00:00:00 UTC | 2025-07-28 00:00:00 UTC | 0 | 0 | 0 |
| Jul 28 | 2025-07-28 00:00:00 UTC | 2025-08-04 00:00:00 UTC | 1 | 1 | 0 |
| Aug 4 | 2025-08-04 00:00:00 UTC | 2025-08-11 00:00:00 UTC | 0 | 0 | 0 |
| Aug 11 | 2025-08-11 00:00:00 UTC | 2025-08-18 00:00:00 UTC | 0 | 0 | 0 |
| Aug 18 | 2025-08-18 00:00:00 UTC | 2025-08-25 00:00:00 UTC | 1 | 0 | 1 |
What Hacker News says
4 sourcesShow HN: Xorq – open compute catalog for AI
Hi HN, Hussain and Dan from Xorq here.
After years of struggling with scaling compute that worked in notebooks but failed in production, we decided to do something about it. Data has standards like Iceberg and Delta. But compute is still a mess—trapped in notebooks, duplicated effort across teams, or baked into custom Airflow DAGs. We think of Xorq as the missing analog to Apache Iceberg, but for compute.
We’ve spent the last year building Xorq, an *compute catalog* that helps teams *reuse, ship, and observe* transformations, features, models, and pipelines across engines.
Xorq is built on:
- *Arrow Flight* (`do_exchange`) for high-speed data transport
- *Ibis* for cross-engine expression trees, serialized to YAML
- A portable UDF engine that compiles pipelines to SQL or Python
- `uv` to make Python environments fully reproducible
Xorq fe
Read the rest
atures:
- pandas-style declarative transformations, backed by Ibis
- Multi-engine execution (e.g., DuckDB, Snowflake)
- UDFs as portable Flight endpoints
- Serveable transforms by way of flight_udxf operator
- Built-in caching and lineage tracking
- Diff-able YAML artifacts, great for CI/CD
Xorq use cases:
Since our last major release, it’s been exciting to see the first Xorq use-cases show up in the wild. All with *Python simplicity and SQL-scale performance*.
- Feature Stores (https://www.xorq.dev/blog/featurestore-to-featurehouse)
- Semantic Layers (e.g. https://github.com/boringdata/boring-semantic-layer)
- MCP + ML Integration (https://docs.xorq.dev/vignettes/mcp_flight_server)
We’re open source and learning fast. Would love feedback on what’s useful or missing. Thanks in advance for trying it out!
Check out the demo of the Xorq CLI tool in action: https://asciinema.org/a/730484
---
Get Started
- Github: https://github.com/xorq-labs/xorq
- Xorq docs: https://docs.xorq.dev/
---
Sneak peak - Xorq Compute Catalog UI Console:
Check out this interactive Claude demo showing how the Xorq compute catalog can be visualized to accelerate composition, reuse, and troubleshooting of AI compute: https://claude.ai/public/artifacts/d2f00d2a-a3f9-4032-884e-d...
Xorq is a Python lib https://github.com/xorq-labs/xorq that provides a declarative syntax for defining portable, composite compute stacks for different AI/ML use cases.
In this example, Xorq is used to compose an open source FeatureHouse that runs on DuckLake and interfaces via Apache Arrow Flight.
The post explains how:
- The FeatureHouse is composed with Xorq
- Feature leakage is avoided
- The FeatureHouse can be ported to any underlying query engine (e.g., Iceberg)
- Observability and lineage are handled
- Feast can be integrated with it
In Build an open source FeatureHouse in 300 lines of codeShow HN: Xorq – open-source Python-first Pandas-style pipelines
Hi HN, Dan, Hussain and Daniel here… After years of struggling with data pipelines that worked in notebooks but failed in production, we decided to do something about it. We created xorq to eliminate the constant headaches of SQL/pandas impedance mismatch, runtime debugging, wasteful recomputations and unreliable research-to-production deployments that plague traditional pandas-style pipeline workflows. xorq is built on Ibis and DataFusion.
We’d love your feedback and contributions. xorq is [Apache 2.0 licensed](https://github.com/letsql/xorq/blob/main/LICENSE) to encourage open collaboration.
Repo: https://github.com/letsql/xorq
Docs: https://docs.xorq.dev
Roadmap Issues: https://github.com/letsql/xorq
You can get started `pip install xorq`.
Or, if you use nix, you can simply run `nix run github:xorq-labs/xorq` and drop into an IPython s
Read the rest
hell.
Demo video: https://youtu.be/jUk8vrR6bCw
Here are some vignettes to look into next:
1. MCP Server + Flight + XGBoost: https://docs.xorq.dev/vignettes/mcp_flight_server
2. 1 DuckDB + 2 Writers + 1 Reader: https://docs.xorq.dev/vignettes/duckdb_concurrent
3. OpenAI UDF: https://docs.xorq.dev/tutorials/hn_data_prep
Some features to note:
- Ibis-based multi-engine expression system: effortless engine-to-engine streaming
- Cache expressions with `.cache` operator
- Portable DataFusion-backed UDF engine with first class support for pandas dataframes
- Serialize Expressions to and from YAML
- Easily build Flight end-points by composing UDFs
thanks for checking this out, and we’re here to answer any questions!