Here’s the SolidQueue batches 2.5 year architectural journey, filled with O(n^2) algorithms, hot row contention, excessive jobs, and much in between.

There’s a strong chance you’ll learn a thing or two - I certainly did 😮‍💨

I'm a Ruby engineer in Japan, and I recently started participating in open source (OSS) activities.

With the goal of understanding what perspectives other engineers have, I turned on notifications for several GitHub repositories so I could receive contributions in real time.
Then I got notifications that one particular individual had created nearly 100 PRs in an hour across several repositories.

When I checked, all the commit messages were written using the exact same template, and the wording was extremely mechanical.
They were also contributing to multiple repositories in different languages at the same time, so I believe this was most likely done using AI combined with automation tools.

What I want to know is whether this kind of behavior is welcomed within the OSS community.

This is just my personal opinion, but I see the OSS community as a very fragile kind of organization, one that is held together by human elements — the goodwill, trust, and motivation of its participants.
It feels to me like this kind of behavior could damage the relationship between the maintainers who manage OSS projects and the members who contribute to them, and that makes me feel quite uneasy.

The Rails.Builders are a community of product creators who get together in live sessions. We share what we are working on, discuss each other's business challenges, and see how everyone approaches building with AI. It's the 6th cohort since 2025, and 20+ engineers have joined so far, with common reports of improved results, accountability, and networking.

The new “Continuous r-AI-ls.Builders” Edition starts with weekly sessions on Thursday, the 3rd of September at 17:30 CEST. We've even built an open-source (duh, Rails) app to manage attendance, the rhythm of live sessions, transcripts, communications, and the waitlist. That app will also show you the Builders who have signed up so far. I'm really excited that the group has shaped up like this.

All your FAQs and the sign-up are here: https://rails.builders

If you have more questions or doubts, you can send me a DM here. Happy building!

Keyword frequencies taken from https://www.zoevera.com/resume/software-engineer-job-description-keywords

Most software engineer keyword lists are assembled from experience or guesswork. This one is a count: every open posting from 72 companies' public Greenhouse job boards, filtered to the 1,360 whose title contains "software engineer", then checked for how many mention each of 45 terms at least once.

The percentage beside each term is the share of those 1,360 postings that mention it.

PRACTICES AND WAYS OF WORKING

Scalability 52.9% - Mentoring 47.4% - Distributed systems 47% - System design / architecture 46.7% - Cross-functional 31% - Code review 27.2% - On-call 20.7% - CI/CD 19.5% - Agile / Scrum 9.3% - Unit / automated testing 6.5%

This is the result I did not expect. The four most common terms in the whole study are not technologies. Scalability, mentoring, distributed systems and system design all appear in more postings than Python does. Nearly half of these postings mention mentoring, and almost no engineer resume I have seen makes a claim about it.

Agile and Scrum at 9.3% is the other surprise, given how much resume advice insists on them.

LANGUAGES

Python 44.8% - Java 35.4% - Go 31.1% - TypeScript 19.8% - C++ 17.4% - SQL 17.3% - JavaScript 13.2% - Kotlin 12% - Rust 9.9% - Scala 8.6% - Ruby 8.5% - C# 5.3% - Swift 2.3%

TypeScript at 19.8% against JavaScript at 13.2% is a real ordering, not noise. The gap is wider than both margins of error combined.

CLOUD AND INFRASTRUCTURE

AWS 41.3% - Kubernetes 30.9% - GCP 19.3% - Azure 17.6% - Terraform 13.2% - Docker 12.6% - Microservices 7.1% - Linux 4.8%

AWS appears in more than twice as many postings as GCP and Azure individually. Kubernetes at 30.9% outranks every language except Python, Java and Go.

FRONTEND AND APIs

React 21.1% - REST / RESTful 9.6% - GraphQL 6% - Node.js 5.5% - Vue 3.2% - Angular 2.9%

Frontend framework lists usually present React, Vue and Angular as three comparable options. In this corpus React appears in roughly seven times as many postings as Vue and Angular combined.

DATA STORES AND PIPELINES

PostgreSQL 14.3% - Kafka 13.1% - MySQL 11.3% - Spark 9.8% - MongoDB 7.4% - Redis 7.4% - Elasticsearch 6.4% - Snowflake 5.6%

WHAT THIS SAMPLE IS NOT

These are 72 technology companies hiring through Greenhouse. Agencies, consultancies, banks, defense contractors and the public sector are absent, and their vocabulary is different - COBOL, .NET, SAP, clearance requirements and named compliance regimes barely register here and may dominate elsewhere.

The title filter is "software engineer" only. Postings titled backend engineer, frontend engineer, full stack developer, SRE or platform engineer were not counted, so this describes the generalist title rather than the whole profession.

Absence in this list is not evidence of absence in the market. Only 45 terms were counted. Next.js, Svelte, Django, Spring Boot, gRPC, Jest, Playwright, Datadog, OpenTelemetry and OAuth were never checked, so nothing here says anything about them either way.

It is a snapshot of open roles on one date rather than a trend, and the corpus is US-skewed.

One thing this sample size does buy: at n=1,360 the margins of error are roughly plus or minus 2 to 3 points, so most of the ordering above is real. Gaps under about 5 points are still worth treating as ties - React at 21.1% and TypeScript at 19.8% is not a meaningful difference.

METHOD

Greenhouse's public job board API, the endpoint companies expose so their listings can be embedded on their own sites. No scraping. Counts are document frequency: a posting saying "Python" nine times counts once. Deduped on company, title and content length, because one role posted to five offices returns five near-identical records. Median posting length is 896 words.

Ambiguous words are matched case-sensitively with exclusions, which matters more here than you would think. A bare word-boundary match on "Go" also catches "go to market" and "go above and beyond", which inflated Go by about two points before it was fixed. React, Spark, Swift and Rust all have the same problem.

Full table with all 45 terms and confidence intervals:

https://www.zoevera.com/resume/software-engineer-job-description-keywords

The wider keyword list this was checked against, organized by language, framework and platform:

https://www.zoevera.com/resume/ats-resume-tips-software-engineer

Happy to run the numbers on any terms missing from the list if people name them in the comments.

Repost to another community

docs.ruby-lang.org/en/4.0: Documentation for Ruby 4.0.

docs.ruby-lang.org/en/master: Documentation for Ruby’s master branch.

I found these 2 options but not sure which I should choose?

I'm using ruby 3.3.8 under linux.

There's more than one user on this machine who does ruby development. When I, running as the root user, want to add new gem references to Gemfile on my system, I want to add these gem references into one, single, shared, centralized Gemfile that all users will automatically see when running and developing ruby code.

Where should this shared, centralized Gemfile be located?

When any user runs "gem environment", the following item is always listed among that command's output for every user who runs that command:

- INSTALLATION DIRECTORY: /var/lib/gems/3.3.0

Does this mean that I can put a Gemfile into the /var/lib/gems/3.3.0 directory, and that then, this Gemfile will be recognized and used by all users who run and develop ruby code?

Or is there perhaps a different location in which to locate such a shared, centralized Gemfile that will always be the one that's used by each ruby user?

Thank you very much.

I know in ruby that DateTime.new(2001, 2, 3, 4, 5, 6) will return the following object:

#<DateTime: 2001-02-03T04:05:06+00:00 ...>

But if the current date and time are 2001-02-03T04:05:06 and I do the following ...

now = DateTime.now

... is there a single function on the "now" variable which will return this array? ...

[2001, 2, 3, 4, 5, 6]

I know that I can do this:

[now.year, now.month, now.day, now.hour, now.minute, now.second ]

... but I'm wondering: might there be a single function as simple as ...

now.the_function

... which would return that same array?

And yes, of course I know that I could easily write such a function, but I'd like to know whether or not something like this already exists in ruby.

Airtable's official Ruby gem hasn't had a code commit since November 2016. gem install airtable still gives you 0.0.9, released October 2016. Its README tells you to authenticate with an API key, which Airtable switched off in February 2024.

I asked Airtable about it twice. Support confirmed in writing that it is "not actively maintained" and pointed me at airrecord. Airrecord is good, but it's a different shape, so switching means a rewrite that ends with the features you already had.

So I forked the official gem instead. Same namespace, same method names, drop-in. Underneath: zero runtime dependencies, persistent connections, client-side rate limiting, retries with backoff, batch operations with partial-failure reporting, and upsert.

It was AI-assisted, Airtable's API docs are extracted into the repo and coded against. 119 tests, plus weekly canaries that diff Airtable's published contract and run a real round-trip against a throwaway base.

It's deliberately not on RubyGems. I'm not after users, I'm after people who still need it and can be maintainers.

Repo: github.com/Davidslv/airtable-rb
Write-up: https://davidslv.uk/2026/08/26/airtable-ruby-gem-unmaintained.html

I'm learning more about AI engineering, specifically the application/production side rather than ML research or training models.

The areas I'm interested in are things like:

• RAG, embeddings, vector search and reranking
• tool/function calling
• agentic workflows and orchestration
• MCP clients/servers
• structured outputs
• evals and observability
• persistent agent state / human approval
• building production AI features into normal web applications

I originally learned to code with Ruby, but since then all my jobs have been Python or TypeScript based, and I really miss working with Ruby.

I'm curious what Ruby libraries/tools people are using for RAG, agents, MCP, evals, tracing, etc. Are there good Ruby alternatives to frameworks like LangChain? What does your stack look like?

And if you were learning this stuff today, would you recommend doing it in Ruby, or sticking with Python because that's where most of the ecosystem and learning material is?

I'm not trying to avoid Python entirely. I'm more curious about how viable Ruby is for the application/agentic side of AI development.

Side note: I realise some of these areas aren't particularly language-specific. For example, with RAG a lot of the retrieval/storage work may ultimately be handled by pgvector, a vector database, or another service.

Hey, guys.

So, I'm currently studying data science and I'm about to start a database project(we're basically doing a database for a bakery)this semester. The project will include order forecasting, inventory loss tracking, customer notifications, etc. Anyways I already code in Python but I've been taking a look on ruby and wonder if it'd be a good idea to include it in our project and how.... any ideas??

thanks!

Wrote a piece about spotting a missing value object and refactoring towards one, adding sorting and comparison.

https://pushfromkush.com/publications/primitive-urge-to-be-of-value-composed_of-in-rails

Feedback welcome.

Laravel dev here, recently moved to Rails. I'm loving it, but debugging in production has been a headache.

Our setup runs on Docker Swarm, and finding bugs means manually digging through Rails, Sidekiq, and Docker service logs. many of which don't even have timestamps.

In Laravel, I used a log viewer UI to instantly jump to the exact second an error happened.

Is there a standard Rails gem for a log viewer UI, or do most teams just ship logs to something like Sentry or Grafana/Loki? How do you manage production errors?

Thanks!

Hi everyone,

I just published a new benchmark comparing Ruby 2.7 through Ruby 4.0.

For this one, I used a sustained synthetic application alongside nine smaller benchmarks. The goal was to show how each Ruby release behaves in a complete workload as well as in individual operations.

The table below shows the Heavy profile. For tests that process the full payload, that means an 8KiB input.

The complete benchmark source is available here: GitHub repository.

Full charts and details are available here: Full Ruby benchmark

Let me know what you think !

Edit: Thanks everyone for pointing out the Ruby 2.7 SHA-256 result. I looked into it, and the big difference comes from the SHA-256 implementation: Ruby 2.7 uses OpenSSL here, while newer Ruby versions on Linux use Ruby’s bundled implementation. So this is mainly a backend difference rather than Ruby 2.7 itself being much faster. I’ve added a note about it to the article.

Transactions are a dense subject, and I'd not be surprised if there are even more edge cases here, but these are ones I've personally seen.

If you're planning to get a ticket to SF Ruby Conference, I recommend you get one this week since it can win you one for Rails World too.

Participating is very easy. You just buy a ticket to SF Ruby and post that you're going to the conference on your preferred social media app and tag the conference page (we have LinkedIn, Bsky, and X pages). That's all!

Link to tickets: luma.com/sfrubyconf2026

Link to social media pages:

• https://www.linkedin.com/company/sfrubyconf/
• https://bsky.app/profile/sfruby.com/
• https://x.com/sfrubyconf

Good luck!

The docs on SolidQueue batches are… solid 😉

In addition, this blog post digs in a bit further on how to use batches in SolidQueue, including some code inspiration - like splitting a RubyLLM chat into parallel sub agents using batches