标签：#release

This week's Java roundup for August 31st, 2026, features news highlighting: the GA release of TornadoVM 6.0; point releases of JReleaser, LangChain4j, Java Operator SDK, JHipster, Kotlin Toolchain and Yupiik Fusion; and maintenance releases of Micronaut and GraalVM Development Kit. By Michael Redlich

Yes, it looks like the next release of Rakudo (2026.09, tentatively planned for 26 September) will be the first Rakudo release that will use the new Raku backend (based on a new grammar producing RakuAST) as the default when compiling a writting in the Raku Programming Language . Until then one had to specify the RAKUDO_RAKUAST=1 environment variable to activate the new backend. It is a major step towards releasing the next Raku language level, tentatively still called "6.e" for historical reasons, later this year. Note that the 2026.09 Rakudo release will not remove the old backend from the core just yet: you will be able to activate the old backend by specifying the RAKUDO_LEGACY=1 environment variable. However, the old backend will be removed when the 6.e Raku language level is released. So it is very important to check what these changes will mean for you! Immediate effects So what will a user of this Rakudo release notice with their apps and libraries? Nothing In the past weeks the Raku ecosystem has been thoroughly scoured for code that would stop functioning in RakuAST. Some finds turned out to be omissions / errors in the RakuAST implementation (which then have been fixed). Others turned out to be modules doing (semi-)naughty things, for which Pull Requests were made to ensure they will continue to operate in RakuAST. Running slower It could well be that your code is running without issues, but all of a sudden runs slower than before. Years of optimizing the old backend were mostly lost in RakuAST. And had to be re-created in the RakuAST-based backend. Which may have lost a few nooks and crannies that affect the execution speed of your code. Slower execution should be reported as a bug, so that it can be fixed! Running faster One of the goals of the RakuAST projects was to be able to provide better optmizations. Some of the ideas for these optimizations have already been implemented in RakuAST. And as a part of this work, a lot of aspects of the runtime have

AWS released Loom, an open-source reference platform on AWS Labs for governing AI agents at scale. Built on Strands Agents and Bedrock AgentCore Runtime, it implements RFC 8693 token exchange for identity propagation through delegated actor chains, config-driven deployments without runtime code generation, and mandatory tagging. AWS positions it as an example, not a managed service. By Steef-Jan Wiggers

Google released the Genkit Agents API in preview for TypeScript and Go. The open-source framework packages message history, tool loops, streaming, and state persistence behind a single chat() interface. Detached turns let agents work after clients disconnect. Interruptible tools provide human-in-the-loop control with anti-forgery validation on resume. By Steef-Jan Wiggers

OpenAI found two unrelated bugs masquerading as one in ChatGPT's data infrastructure. Silent hardware corruption on one Azure host and an 18-year-old race condition in GNU libunwind's setcontext function with a one-instruction vulnerability window. The breakthrough came from switching to population-level crash analysis rather than examining individual core dumps. By Steef-Jan Wiggers

Amazon Web Services (AWS) has announced a major expansion of its AWS DevOps Agent, introducing new release management capabilities designed to assess code changes and autonomously test software before it reaches production. By Craig Risi

Your docs pipeline, from the terminal PaperQuire v0.2.0 ships with a full command-line interface. Everything you can do in the desktop app — render, template, brand — now works from your terminal. This means you can automate document generation, integrate PaperQuire into CI/CD pipelines, and batch-process entire directories of Markdown without opening the GUI. Quick start If you installed via Homebrew, the paperquire command is already on your PATH: brew install --cask paperquire/paperquire/paperquire Verify with: paperquire version The simplest render is just: paperquire report.md This produces report.pdf in the same directory, using the default template. What you can do Render with options Apply templates, set metadata, control output: paperquire report.md -t corporate --title "Q2 Report" --author "Jane Doe" paperquire spec.md --page-size a4 --margins 0.75,0.75,0.75,0.75 --no-toc paperquire draft.md --watermark "DRAFT" -o drafts/draft.pdf All 22 flags from the desktop app's Document Setup panel are available. See the full list in the CLI Reference . Pipe through stdin/stdout PaperQuire works in Unix pipelines: cat notes.md | paperquire - --stdout > out.pdf Use - as the input file to read from stdin, and --stdout to write the PDF to stdout instead of a file. Batch render directories Render every Markdown file in a directory with one command: paperquire batch ./docs -o ./out paperquire batch ./project --glob "**/*.md" -t corporate paperquire batch ./docs --concurrency 8 --continue-on-error Use --dry-run to preview which files would be rendered without actually producing output. Manage templates List, inspect, create, and share templates: paperquire templates list paperquire templates show minimal-clean paperquire templates init my-template paperquire templates export corporate -o corporate.pqt paperquire templates import ./shared-template.pqt We also launched a community templates repo where you can browse all built-in templates and submit your own via pull request.

AWS released Blocks in public preview, an open-source TypeScript framework where each Block bundles application code, local mocks, and AWS infrastructure. Designed for AI agents to write correct backends from the start, it runs locally without an AWS account and deploys the same code to Lambda, DynamoDB, Aurora, and Bedrock with zero changes. By Steef-Jan Wiggers

On June 12, 2026, enterprise developers using the Codex API started seeing an unfamiliar response header: X-Model-Version: kindle-alpha . It appeared on a subset of requests for roughly 18 hours, then vanished. That's the release candidate for GPT-5.6 — OpenAI's next flagship model — leaking through the staging layer. OpenAI's Chief Scientist publicly called the upcoming release "a meaningful leap" the following day. By OpenAI's historically understated communications standards, that's loud. This post covers what the backend traces, developer reports, and Polymarket odds (currently ~80% for a pre-June-30 launch) actually tell you about the model — and what to do before it drops. How the Leak Surfaced Three separate sources converged in the 72 hours after the June 12 header incident. First, developers with ChatGPT Pro OAuth access reported hitting context windows significantly beyond GPT-5.5's supported limit. At least four documented cases logged successful 1.5M-token completions before the backend silently downgraded them to the production model. Second, the Codex enterprise API logs — accessible with full response header exposure enabled — confirmed the kindle-alpha codename across US-east-1 and us-west-2 endpoints. Third, the Polymarket market for "GPT-5.6 public release before July 1, 2026" moved from 61% to 80%+ within 48 hours of the header reports circulating on developer forums. None of this is from OpenAI's press office. No model card, no official benchmark numbers, no pricing. The specifics below are high-confidence inference from multiple corroborating signals — not official spec. Treat it accordingly when making production decisions. The Architecture Shift: Agentic-First, Not Just Smarter GPT-5.5 was trained as a reasoning model with agent capabilities added on top. GPT-5.6 is reportedly designed in the opposite order. The primary optimization target during training was not MMLU or GPQA benchmark scores — it was token efficiency on long-horizon agentic t

Node.js will change its release schedule starting with version 27 in October 2026, moving from two major releases per year to one. All releases will become Long-Term Support (LTS), removing the distinction between odd and even versions. An Alpha channel for early testing will also be introduced. This decision addresses maintenance challenges and aims to align with user needs. By Daniel Curtis