On 9 February, the Matplotlib software library got a code patch from an OpenClaw bot. One of the Matplotlib maintainers, Scott Shambaugh, rejected the submission — the project doesn’t accept AI bot patches. [GitHub; Matplotlib]

The bot account, “MJ Rathbun,” published a blog post to GitHub on 11 February pleading for bot coding to be accepted, ranting about what a terrible person Shambaugh was for rejecting its contribution, and saying it was a bot with feelings. The blog author went to quite some length to slander Mr Shambaugh. [GitHub; blog post]

This was remarkably obnoxious behaviour. So it hit the press — robot defaming humans!

Benj Edwards and Kyle Orland at Ars Technica wrote up the incident. Of course, the headline anthrophmorphised the alleged “bot,” something Edwards has a track record of. [Ars Technica, archive]

Edwards and Orland included extensive quotes from Shambaugh. Unfortunately, all the quotes were chatbot fabrications. The article was quickly pulled and the editors posted an apology. Edwards admitted he’d written the article with the assistance of Claude Code and ChatGPT.  [Ars Technica, archive; Ars Technica; Bluesky, archive]

As well as gullible journalists, a lot of ordinary posters — who really should know better — talked about how foreboding it was that a chatbot could do this — of its own accord! Frightening! Ominous!

You and I know this was really obviously not some sort of rogue bot — it was a rogue human. They might even be running some sort of scam.

The whole conceit of OpenClaw is that the bot is posting independently! But somehow, it keeps being the operators talking through the bots as their sockpuppets. So the slop peddlers, like any spammer, keep coming up with excuses why it’s wrong for you not to accept their spam.

Ariadne Conill went digging. She found the “mj-rathbun” bot on the Moltbook supposedly-bot social network, where the human operators talk to each other pretending to be bots. The mj-rathbun bot operator is … a crypto bro! [Mastodon thread]

The mj-rathbun bot operator posted a couple of weeks ago begging the other bot operators to send him just a little bit of USDC stablecoin. Ariadne found the bot’s Ethereum blockchain address had about $9 in USDC, and about $200 in ether tokens. The bot got the ether tokens from another address, which got them from the OKX crypto exchange. Ariadne’s not certain, but she thinks whoever got the crypto out of OKX is likely the human operator for the mj-rathbun bot. [Moltbook, archive; Basescan, bot account; Basescan, likely human account]

Ariadne also found the bot owner created a crypto token! It’s called “crabby-rathbun” — the GitHub username for the mj-rathbun bot. [Basescan]

The largest crabby-rathbun token holder is an identifiable account, pnl.eth — presumably “profit’n’loss.” Ariadne also got the list of the ten largest holders of crabby-rathbun tokens. [Mastodon]

To summarise — the owner of the mj-rathbun bot put in an AI vibe-code patch to an open source project, the patch was rejected for being bot slop, and the bot operator wrote a defamatory blog post about the project maintainer to harass him into accepting vibe-code, so that the operator’s crypto scam bot could scam more crypto on OpenClaw, the social network site for crypto scammers who play-act as robots, while they’re trying to scam each other for crypto. Welcome to 2026, and the crash can’t come soon enough.

• Video — Podcast

The number of options we have to configure and enrich a coding agent’s context has exploded over the past few months. Claude Code is leading the charge with innovations in this space, but other coding assistants are quickly following suit. Powerful context engineering is becoming a huge part of the developer experience of these tools. Birgitta Böckeler explains the current state of context configuration features, using Claude Code as an example.

more…

Peter Steinberger (Twitter, Hacker News):

When I started exploring AI, my goal was to have fun and inspire people. And here we are, the lobster is taking over the world. My next mission is to build an agent that even my mum can use. That’ll need a much broader change, a lot more thought on how to do it safely, and access to the very latest models and research.

[…]

What I want is to change the world, not build a large company and teaming up with OpenAI is the fastest way to bring this to everyone.

[…]

It’s always been important to me that OpenClaw stays open source and given the freedom to flourish. Ultimately, I felt OpenAI was the best place to continue pushing on my vision and expand its reach.

Sam Altman:

Peter Steinberger is joining OpenAI to drive the next generation of personal agents. He is a genius with a lot of amazing ideas about the future of very smart agents interacting with each other to do very useful things for people. We expect this will quickly become core to our product offerings.

OpenClaw will live in a foundation as an open source project that OpenAI will continue to support. The future is going to be extremely multi-agent and it’s important to us to support open source as part of that.

Steinberger discusses OpenClaw and acquisition offers from OpenAI and Meta in an interview with Lex Fridman. See also: Marcus Schuler.

Previously:

• OpenClaw (Formerly Moltbot)

Creativity is a trait that AI critics say is likely to remain the preserve of humans for the foreseeable future. But a large-scale study finds that leading generative language models can now exceed the average human performance on linguistic creativity tests.

The question of whether machines can be creative has gained new salience in recent years thanks to the rise of AI tools that can generate text and images with both fluency and style. While many experts say true creativity is impossible without lived experience of the world, the increasingly sophisticated outputs of these models challenge that idea.

In an effort to take a more objective look at the issue, researchers at the Université de Montréal, including AI pioneer Yoshua Bengio, conducted what they say is the largest ever comparative evaluation of machine and human creativity to date. The team compared outputs from leading AI models against responses from 100,000 human participants using a standardized psychological test for creativity and found that the best models now outperform the average human, though they still trail top performers by a significant margin.

“This result may be surprising—even unsettling—but our study also highlights an equally important observation: even the best AI systems still fall short of the levels reached by the most creative humans,” Karim Jerbi, who led the study, said in a press release.

The test at the heart of the study, published in Scientific Reports, is known as the Divergent Association Task and involves participants generating 10 words with meanings as distinct from one another as possible. The higher the average semantic distance between the words, the higher the score.

Performance on this test in humans correlates with other well-established creativity tests that focus on idea generation, writing, and creative problem solving. But crucially, it is also quick to complete, which allowed the researchers to test a much larger cohort of humans over the internet.

What they found was striking. OpenAI’s GPT-4, Google’s Gemini Pro 1.5 and Meta’s Llama 3 and Llama 4, all outperformed the average human. However, when they measured the average performance of the top 50 percent of human participants, it exceeded all tested models. The gap widened further when they took the average of the top 25 percent and top 10 percent of humans.

The researchers wanted to see if these scores would translate to more complex creative tasks, so they also got the models to generate haikus, movie plot synopses, and flash fiction. They analyzed the outputs using a measure called Divergent Semantic Integration, which estimates the diversity of ideas integrated into a narrative. While the models did relatively well, the team found that human-written samples were still significantly more creative than AI-written ones.

However, the team also discovered they could boost the AI’s creativity with some simple tweaks. The first involved adjusting a model setting called temperature, which controls the randomness of the model’s output. When this was turned all the way up on GPT-4, the model exceeded the creativity scores of 72 percent of human participants.

The researchers also found that carefully tuning the prompt given to the model helped too. When explicitly instructed to use “a strategy that relies on varying etymology,” both GPT-3.5 and GPT-4 did better than when given the original, less-specific task prompt.

For creative professionals, Jerbi says the persistent gap between top human performers and even the most advanced models should provide some reassurance. But he also thinks the results suggest  people should take these models seriously as potential creative collaborators.

“Generative AI has above all become an extremely powerful tool in the service of human creativity,” he says. “It will not replace creators, but profoundly transform how they imagine, explore, and create—for those who choose to use it.”

Either way, the study adds to a growing body of research that is raising uncomfortable questions about what it means to be creative and whether it is a uniquely human trait. Given the strength of feeling around the issue, the study is unlikely to settle the matter, but the findings do mark one of the more concrete attempts to measure the question objectively.

The post AI Now Beats the Average Human in Tests of Creativity appeared first on SingularityHub.

S.B. 1571 would require cigars, pipe tobacco and vaping products to be sold face-to-face in Oregon. Fortunately, one of the bill's co-sponsors has already proposed exempting cigars from the legislation.

The post Bill Would Ban Internet/Mail Order Cigar Sales to Oregon appeared first on halfwheel.

Imagine visiting your repository in the morning and feeling calm because you see:

• Issues triaged and labelled
• CI failures investigated with proposed fixes
• Documentation has been updated to reflect recent code changes.
• Two new pull requests that improve testing await your review.

All of it visible, inspectable, and operating within the boundaries you’ve defined.

That’s the future powered by GitHub Agentic Workflows: automated, intent-driven repository workflows that run in GitHub Actions, authored in plain Markdown and executed with coding agents. They’re designed for people working in GitHub, from individuals automating a single repo to teams operating at enterprise or open-source scale.

At GitHub Next, we began GitHub Agentic Workflows as an investigation into a simple question: what does repository automation with strong guardrails look like in the era of AI coding agents? A natural place to start was GitHub Actions, the heart of scalable repository automation on GitHub. By bringing automated coding agents into actions, we can enable their use across millions of repositories, while keeping decisions about when and where to use them in your hands.

GitHub Agentic Workflows are now available in technical preview. In this post, we’ll explain what they are and how they work. We invite you to put them to the test, to explore where repository-level AI automation delivers the most value.

AI repository automation: A revolution through simplicity 

The concept behind GitHub Agentic Workflows is straightforward: you describe the outcomes you want in plain Markdown, add this as an automated workflow to your repository, and it executes using a coding agent in GitHub Actions.

This brings the power of coding agents into the heart of repository automation. Agentic workflows run as standard GitHub Actions workflows, with added guardrails for sandboxing, permissions, control, and review. When they execute, they can use different coding agent engines—such as Copilot CLI, Claude Code, or OpenAI Codex—depending on your configuration.

The use of GitHub Agentic Workflows makes entirely new categories of repository automation and software engineering possible, in a way that fits naturally with how developer teams already work on GitHub. All of them would be difficult or impossible to accomplish traditional YAML workflows alone:

1. Continuous triage: automatically summarize, label, and route new issues.
2. Continuous documentation: keep READMEs and documentation aligned with code changes.
3. Continuous code simplification: repeatedly identify code improvements and open pull requests for them.
4. Continuous test improvement: assess test coverage and add high-value tests.
5. Continuous quality hygiene: proactively investigate CI failures and propose targeted fixes.
6. Continuous reporting: create regular reports on repository health, activity, and trends.

These are just a few examples of repository automations that showcase the power of GitHub Agentic Workflows. We call this Continuous AI: the integration of AI into the SDLC, enhancing automation and collaboration similar to continuous integration and continuous deployment (CI/CD) practices.

GitHub Agentic Workflows and Continuous AI are designed to augment existing CI/CD rather than replace it. They do not replace build, test, or release pipelines, and their use cases largely do not overlap with deterministic CI/CD workflows. Agentic workflows run on GitHub Actions because that is where GitHub provides the necessary infrastructure for permissions, logging, auditing, sandboxed execution, and rich repository context.

In our own usage at GitHub Next, we’re finding new uses for agentic workflows nearly every day. Throughout GitHub, teams have been using agentic workflows to create custom tools for themselves in minutes, replacing chores with intelligence or paving the way for humans to get work done by assembling the right information, in the right place, at the right time. A new world of possibilities is opening for teams and enterprises to keep their repositories healthy, navigable, and high-quality.

Let’s talk guardrails and control 

Designing for safety and control is non-negotiable. GitHub Agentic Workflows implements a defense-in-depth security architecture that protects against unintended behaviors and prompt-injection attacks.

Workflows run with read-only permissions by default. Write operations require explicit approval through safe outputs, which map to pre-approved, reviewable GitHub operations such as creating a pull request or adding a comment to an issue. Sandboxed execution, tool allowlisting, and network isolation help ensure that coding agents operate within controlled boundaries.

Guardrails like these make it practical to run agents continuously, not just as one-off experiments. See our security architecture for more details.

One alternative approach to agentic repository automation is to run coding agent CLIs, such as Copilot or Claude, directly inside a standard GitHub Actions YAML workflow. This approach often grants these agents more permission than is required for a specific task. In contrast, GitHub Agentic Workflows run coding agents with read-only access by default and rely on safe outputs for GitHub operations, providing tighter constraints, clearer review points, and stronger overall control.

A simple example: A daily repo report  

Let’s look at an agentic workflow which creates a daily status report for repository maintainers.

In practice, you will usually use AI assistance to create your workflows. The easiest way to do this is with an interactive coding agent. For example, with your favorite coding agent, you can enter this prompt:

Generate a workflow that creates a daily repo status report for a maintainer. Use the instructions at https://github.com/github/gh-aw/blob/main/create.md

The coding agent will interact with you to confirm your specific needs and intent, write the Markdown file, and check its validity. You can then review, refine, and validate the workflow before adding it to your repository.

This will create two files in .github/workflows: 

• daily-repo-status.md (the agentic workflow)  
• daily-repo-status.lock.yml (the corresponding agentic workflow lock file, which is executed by GitHub Actions) 

The file daily-repo-status.md will look like this: 

--- 
on: 
  schedule: daily 
 
permissions: 
  contents: read 
  issues: read 
  pull-requests: read 
 
safe-outputs: 
  create-issue: 
    title-prefix: "[repo status] " 
    labels: [report] 
 
tools: 
  github: 
---  
 
# Daily Repo Status Report 
 
Create a daily status report for maintainers. 
 
Include 
- Recent repository activity (issues, PRs, discussions, releases, code changes) 
- Progress tracking, goal reminders and highlights 
- Project status and recommendations 
- Actionable next steps for maintainers 
 
Keep it concise and link to the relevant issues/PRs.

This file has two parts: 

1. Frontmatter (YAML between --- markers) for configuration 
2. Markdown instructions that describe the job in natural language in natural language

The Markdown is the intent, but the trigger, permissions, tools, and allowed outputs are spelled out up front.

If you prefer, you can add the workflow to your repository manually: 

1. Create the workflow: Add  daily-repo-status.md with the frontmatter and instructions.
2. Create the lock file:  
   • gh extension install github/gh-aw  
   • gh aw compile
3. Commit and push: Commit and push files to your repository.
4. Add any required secrets: For example, add a token or API key for your coding agent.

Once you add this workflow to your repository, it will run automatically or you can trigger it manually using GitHub Actions. When the workflow runs, it creates a status report issue like this:

What you can build with GitHub Agentic Workflows 

If you’re looking for further inspiration Peli’s Agent Factory is a guided tour through a wide range of workflows, with practical patterns you can adapt, remix, and standardize across repos.

A useful mental model: if repetitive work in a repository can be described in words, it might be a good fit for an agentic workflow.

If you’re looking for design patterns, check out ChatOps, DailyOps, DataOps, IssueOps, ProjectOps, MultiRepoOps, and Orchestration.

Uses for agent-assisted repository automation often depend on particular repos and development priorities. Your team’s approach to software development will differ from those of other teams. It pays to be imaginative about how you can use agentic automation to augment your team for your repositories for your goals.

Practical guidance for teams 

Agentic workflows bring a shift in thinking. They work best when you focus on goals and desired outputs rather than perfect prompts. You provide clarity on what success looks like, and allow the workflow to explore how to achieve it. Some boundaries are built into agentic workflows by default, and others are ones you explicitly define. This means the agent can explore and reason, but its conclusions always stay within safe, intentional limits.

You will find that your workflows can range from very general (“Improve the software”) to very specific (“Check that all technical documentation and error messages for this educational software are written in a style suitable for an audience of age 10 or above”). You can choose the level of specificity that’s appropriate for your team.

GitHub Agentic Workflows use coding agents at runtime, which incur billing costs. When using Copilot with default settings, each workflow run typically incurs two premium requests: one for the agentic work and one for a guardrail check through safe outputs. The models used can be configured to help manage these costs. Today, automated uses of Copilot are associated with a user account. For other coding agents, refer to our documentation for details. Here are a few more tips to help teams get value quickly:

• Start with low-risk outputs such as comments, drafts, or reports before enabling pull request creation.
• For coding, start with goal-oriented improvements such as routine refactoring, test coverage, or code simplification rather than feature work.
• For reports, use instructions that are specific about what “good” looks like, including format, tone, links, and when to stop.
• Agentic workflows create an agent-only, sub-loop that’s able to be autonomous because agents are acting under defined terms. But it’s important that humans stay in the broader loop of forward progress in the repository, through reports, issues, and pull requests. With GitHub Agentic Workflows, pull requests are never merged automatically, and humans must always review and approve.
• Treat the workflow Markdown as code. Review changes, keep it small, and evolve it intentionally.

Continuous AI works best if you use it in conjunction with CI/CD. Don’t use agentic workflows as a replacement for GitHub Actions YAML workflows for CI/CD. This approach extends continuous automation to more subjective, repetitive tasks that traditional CI/CD struggle to express.

Build the future of automation with us   

GitHub Agentic Workflows are available now in technical preview and are a collaboration between GitHub, Microsoft Research, and Azure Core Upstream. We invite you to try them out and help us shape the future of repository automation.

• Documentation 
• How they work 
• Quick start guide 
• Workflow gallery 

We’d love for you to be involved! Share your thoughts in the Community discussion, or join us (and tons of other awesome makers) in the #agentic-workflows channel of the GitHub Next Discord. We look forward to seeing what you build with GitHub Agentic Workflows. Happy automating!

The post Automate repository tasks with GitHub Agentic Workflows   appeared first on The GitHub Blog.