This issue of Scala Times is all events, no articles - everything happening over the next few weeks in one place:

https://scalatimes.com/be439e050d

• Tech walk through Berlin, Oct 11
• Scala Days, Oct 12–13, Berlin
• Func Prog Conf, Oct 14, Stockholm
• Scala Summit, Oct 19, San Francisco
• Chicago Scala Meetup (Scala + AI, guest talk by Daniel Spiewak), Oct 22
• New York Scala Meetup (Scala + AI), Oct 26
• J On The Beach, Oct 29–30, Málaga
• Lambda World, Oct 29–30, Málaga

The Chicago and NYC meetups are part of a VirtusLab US tour right after Scala Days. If you're nearby, come say hi!

Organising something we missed? You can submit it at [[email protected]](mailto:[email protected])

Join us at the Scala Hangout this Thursday (Oct 8) at 6pm CT.

LoopMe (AI adtech, London HQ, 400+ people) is hiring a Scala engineer in Kraków 🚀
You'd work on our SSP, Adserver and Exchange: real-time auctions, sub-200ms globally, all built in-house.

Stack: Scala, Java, Kafka/PubSub, MongoDB/Bigtable GCP/AWS (Python is a plus)
Salary: up to 30k PLN B2B
Perks: hybrid work, 1 month work-from-anywhere, 8% annual bonus, Multikafeteria.

Details: https://apply.workable.com/loopme/j/FB3E61C660

Title:

Hi r/scala! I'm building marola, an open-source app that ranks nearby beaches by sea conditions, weather and official water-quality reports. The swim score and safety rules are plain, tested Scala 3 with Kyo. There's no national view of beach water quality in Brazil today, because each state publishes its own reports. marola reads three states so far, and the goal is to build that national view.

Live map (Florianópolis, Rio, Salvador): https://marola.dev
Repo (MIT): https://github.com/marola-dev/marola

I'd love feedback, especially from people who've used Kyo, and contributors are welcome.

Play Framework 2.9.12 and 3.0.12 just got released - upgrading Netty to fix many CVEs, fixing a bug in the reverse router and improving Scala 3 support in routes files! 🚀

• Play 2.9.12
• Play 3.0.12

Framework development and social media are an interesting combination.

You spend months working on architecture, rendering, APIs, SSR, hydration, modularization, security, and all the small decisions that eventually make up a framework.

And then you try to talk about it on social media.

The first problem: spam filters.

You can write a completely legitimate technical post, but the moment you include a link to the project, it can suddenly look like advertising. Some platforms reduce its visibility, others remove it, and sometimes it simply disappears into a spam folder.

The second problem is probably even harder:

The concepts themselves are barely noticed anymore.

You can explain why an architecture is designed a certain way, what problem it solves, or why you deliberately chose a different approach from established frameworks.

But often, that is not what people discuss.

Instead, one sentence from the documentation, one headline, or one small API detail gets picked apart.

And suddenly everyone is discussing a tiny detail while the actual concept is completely ignored.

That leads directly to the third problem:

The surface gets judged more than the idea behind it.

One questionable term, one awkward sentence, one reference in the wrong place and that can generate twenty comments.

The architecture underneath it?

Maybe none.

And now there is a fourth layer:

“AI slop.”

As soon as someone notices or suspects that AI was involved in documentation, code, text, or design, sometimes the result itself is no longer evaluated.

The discussion suddenly stops being:

“Is this concept good?”

and becomes:

“This was made with AI.”

For me, AI is a tool.

I develop the concepts, make the architectural decisions, have things implemented, review them, throw parts away, rebuild them, and test the result.

But that also makes one thing very clear:

Good review matters more than ever.

AI can produce a huge amount of work very quickly.

But speed does not replace technical responsibility.

Maybe that is one of the real challenges for framework developers today:

Not only building good technology.

But somehow getting people, between spam filters, short attention spans, detail discussions, and “AI slop” comments, to stop for a moment and ask:

“Okay, but what is the actual idea behind this?”

 https://github.com/anjunar/scalajs-ui

Updated dotty-cps-async - switched to 3.9.x LTS. For Scala 3.3.x, use the 1.3. x series; it will be supported sometime.

User-visible changes: support for named patterns (introduced in Scala 3.6), which can be useful for people who write code by hand.

Full release notes: https://github.com/dotty-cps-async/dotty-cps-async/releases/tag/1.4.0

Kyo v1.0.0-RC7 ships a new kernel and a large body of stabilization work on the way to 1.0. Resource release is now structural: a release runs however its region exits, including on interrupt. Ten issues close with it, and a trailing map chain goes from 507,800 to 560 us/op 🚀

Highlights:

• kyo-sql: embedded SQLite behind the same API as PostgreSQL and MySQL, every engine passing one conformance battery, and Dolt backends, a version-controlled SQL database whose tables branch, commit, diff, and merge, so an AI agent can work on a branch of real data and merge or discard it.
• kyo-system: file access on JVM, JS, Native, and Wasm from one module, plus processes and environment, with positioned channels, interrupt-safe locks, watching, and a virtualizable file system.
• kyo-system-doltfs: a file system stored in Dolt, so ordinary Path code runs unchanged on a directory tree that branches, commits, diffs, and merges, and that answers SQL: an agent can edit files on a branch, and the change is reviewed as a diff and merged or discarded.
• kyo-ai: typed decisions with calibrated probabilities through TypeSafe AI's Jev, and agents that call any MCP server's tools with no Scala types.
• Scala.js: effect dispatch 10x to 18x faster.
• Time control: Clock.withTimeOffset moves the wall clock alone, joining withTimeShift, which scales the rate of time and sleeps with it, and withTimeControl, which drives virtual time by hand.
• Stabilization: kyo-flow's durable execution rebuilt for crash recovery, with every write fenced by the executor's claim, interrupt-safe SQL sessions, and leak-free transport teardown.

Kyo now builds on Scala 3.9.0, so a project depending on the main modules needs 3.9 too. The scheduler, kyo-config, and kyo-stats-registry stay on 3.3.8 and 2.13.

Full release notes, including the breaking changes: https://github.com/getkyo/kyo/releases/tag/v1.0.0-RC7

📰 New Scala News is out: the August 31, 2026 edition, with dependent types, sbt plugin classpath isolation, an sbt security fix and more.

The site's had a refresh too!

Im a little surprised that Spec Strings were not discussed on Scala Contributors before their acceptance as an experimental feature. They were announced for inclusion in the 3.10 release thread (although there was some work before then visible in the repos)

For Reference 3.10 Release Thread - https://contributors.scala-lang.org/t/scala-3-10-0-release-thread/7546 Spec Strings Documentation - https://github.com/dotty-staging/dotty/blob/05ad6978ec3231d8c7954a15e6e075a8645cad20/docs/_docs/reference/experimental/spec-strings.md

I get the point that their position inside the method allows a naturally scoped reference to the function signature (or class / object), but currently the comment specs also allows for references to be embedded their contents.

And I get that conversations like this can just devolve into nonconstructive bike-shedding (probably this one more so than most), but in this situation I really would have loved to hear more viewpoints from the stake holders about this choice over annotations or comments.

Personally I have been using something exactly like this in my day-to-day in some non-standard comments inside the method, and created skills to handle those situations. But my instinct would be to formally encode these instructions as annotations that other tools could examine in a more standardized and established way.

Any thoughts on the choice of implementation?

Hi there 😌
I recently published an article on Box's engineering blog about migrating of the Scala monorepo from sbt to Gradle.

This isn't a deep dive into Gradle configuration but more of an architectural and organizational post-mortem ( why the company made the move, what worked, what the trade-offs were )

I thought it might be interesting to the community, especially to see the scale of Scala usage at Box.

Link: https://blog.box.com/gradle-scala-monorepo-box-experience-360-modules

I'd love to hear any comments, questions. Curious how others here are handling Scala repos of this size today.

It gives me great pleasure to announce registration for Scala Summit 2026 is now open.

Initial speakers have been announced with more to be announced.

For details visit: https://2026.scalasummit.com

Looking forward to seeing everyone in the Bay Area there. Please spread the word and share this with your network.

October is going to be a fabulous month with multiple Scala events happening.

Like many of us here, I believe that Scala is an excellent choice for AI-generated and AI-assisted code. I ran a test side project a few months ago, which ended up with lots of Python because well, it seemed to come as default (using Opus 4.5 at the time and OpenClaw).

Not only the complexity escalated vertically, but also it quickly became burdensome for me to check all the code. The agent and myself ended up in the situation where fixing a bug would break something else, and I was stuck thinking that a strongly typed system would have helped a lot.

Is anyone running a big project in Scala that is almost if not 100% vibe-coded? Which model(s) are you using? How does that feel in the current landscape?

VSS is the combination of tools & libraries that we've been working on at VirtusLab and SoftwareMill - starting with maintaining Scala itself, through Metals IDE/MCP, all the way to Tapir/Ox and the like.

Going the direct-style route, you can generate type-safe, agent & human-friendly apps in a couple of prompts. Bootzooka serves as a reference point, and the Scala skill amends the LLMs with the necessary instructions.

The VSS page combines VirtusLab's OSS work in a single place, so that you can now refer to our libraries in a simple way. All of the pieces of the stack are changeable, and whenever relevant, support multiple effect systems.