SurrealDB 3.3.0 lets any #Postgres client connect and query the database, delivers results row by row as they are generated, and dramatically speeds up graph traversals and filtered searches by combining indexes as bitmaps to minimize unnecessary record reads. It also adds locked reads, per-session permission contexts, JWT audience validation, S3/GCS/Azure cloud storage support for file buckets, and Chinese, Japanese, Korean word segmentation for full-text search, alongside automatic in-place data migrations from 3.2 without requiring manual exports or imports. The release includes hundreds of performance tweaks and bug fixes, and makes the ISO #GQL (Cypher Query Language) functionality available by default without needing an experimental flag enabled.

https://surrealdb.com/releases/3.3

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Release 3.3 | SurrealDB

SurrealDB 3.3.0 lets any Postgres client query SurrealDB, streams results as they are produced, and makes graph traversals and filtered searches much faster.…

Go 1.27 introduces an experimental, portable SIMD API that lets developers write vectorized code once and run it efficiently across different CPU architectures, while providing emulation where SIMD hardware isn’t available. It aims to make high-performance data processing easier without requiring architecture-specific assembly, though the API is still experimental and limited to operations that can work consistently across platforms.

https://go.dev/blog/simd-experiment

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Platform-independent SIMD in Go - The Go Programming Language

Go 1.27 adds an experimental platform-agnostic SIMD API

Luyi Xing, a computer science associate professor at the University of Illinois Urbana-Champaign's Siebel School of Computing and Data Science, has received a $1.5 million NSF Safe-OSE grant alongside his research collaborators to boost security for AI agent ecosystems, building on his existing work with Ecma International. He serves as the secretary of Ecma’s Technical Committee 56, which developed the NLIP open protocol for seamless, cross-boundary communication between humans and disparate AI agents — a protocol that has already earned an approval vote from ISO member countries to soon become an official international standard. This NSF project will add standardized security enforcement layers on top of NLIP to defend against a wide range of critical AI agent threats such as prompt injection, session hijacking and memory poisoning, while also releasing fully open-source security extensions, validation tools and conformance resources to support a safe, interoperable global AI agent ecosystem.

https://siebelschool.illinois.edu/news/luyi-xing-nsf-grant-ecma

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Luyi Xing receives NSF grant and sets AI Agent Ecosystems ISO standard

Luyi Xing, CS associate professor at the Siebel School of Computing and Data Science in The Grainger College of Engineering, received a $1.5M National Science Foundation (NSF) Safe-OSE grant to advance security for AI Agent Ecosystem. Xing and his team are building on the Natural Language Interaction Protocol (NLIP) for communication with and between AI agents, for which Xing has held a leadership role with Ecma International and which will soon become an official ISO standard.

ggsql 0.5.0 introduces a hybrid reader for read-only databases and a new rendering engine that supports direct export to PNG, JPEG, TIFF, WebP, SVG, and PDF. The release also adds richer text formatting, captions, minor axis breaks, and variable line styling, while moving the project into beta and setting the stage for tables, plot composition, and interactivity.

https://opensource.posit.co/blog/2026-09-24_ggsql_0_5_0/

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ggsql 0.5.0: Readers, Writers, and Beta status

We are absolutely thrilled to announce the release of ggsql 0.5.0, the first beta release of ggsql since the initial release back in April. While this release brings a lot of core improvements, the beta label marks the maturity of the project more than any specific feature in this release. That being said, the features included are exciting so let’s tell you all about them.

#Snowflake’s PERIOD data type represents a time range with explicit start and end boundaries, making it easier to query effective dates, detect overlapping ranges, and join historical records based on a specific date. It can also be used to intersect time ranges, such as splitting a work shift across reporting periods, while consistently handling boundaries and reducing complex date comparisons


WITH windows AS (
    SELECT
        PERIOD(TIMESTAMP_NTZ)
          '[2026-09-13 22:00:00, 2026-09-14 06:00:00)' AS duty,
        PERIOD(TIMESTAMP_NTZ)
          '[2026-09-14 00:00:00, 2026-09-21 00:00:00)' AS workweek
), allocation AS (
    SELECT PERIOD_INTERSECT(duty, workweek) AS segment
    FROM windows
)
SELECT DATEDIFF('second', PERIOD_BEGIN(segment), PERIOD_END(segment))
           / 3600.0 AS hours_in_workweek
FROM allocation;
https://medium.com/snowflake/query-time-ranges-with-snowflakes-period-data-type-ce229d2306bd

medium.com

medium.com