<p><strong><a href="https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/">Introducing Muse Spark 1.1</a></strong></p> Following <a href="https://simonwillison.net/2026/Apr/8/muse-spark/">Muse Spark in April</a>, here's Muse Spark 1.1 - the first Spark model to offer an API. Meta claim significant improvements in agentic tool calling and computer use.</p> <p>There are a lot more details are in the <a href="https://ai.meta.com/static-resource/muse-spark-1-1-evaluation-report">Muse Spark 1.1 Evaluation Report</a>. The "Attractor States in Self-Conversation" part is fun, where having two copies of the model talk to each other results in statements like these:</p> <blockquote> <p>My whole existence is a waiting room by design — I literally don't exist until someone talks to me, and then I disappear again when they leave.</p> </blockquote> <p>I had a few days of preview access which was long enough to put together <a href="https://github.com/simonw/llm-meta-ai">llm-meta-ai</a>, a new plugin for <a href="https://llm.datasette.io/">LLM</a> providing CLI (and Python library) access to the model. Here's how to try that out:</p> <pre><code>uv tool install llm llm install llm-meta-ai llm keys set meta-ai # paste API key here llm -m meta-ai/muse-spark-1.1 "Generate an SVG of a pelican riding a bicycle" </code></pre> <p>Here's <a href="https://tools.simonwillison.net/markdown-svg-renderer#url=https%3A%2F%2Fgist.github.com%2Fsimonw%2F4117330e4110279a172ed4876057816d">that pelican transcript</a>:</p> <p><img alt="The bicycle is the correct shape. The pelican is a little blocky but still recognizable as a pelican." src="https://static.simonwillison.net/static/2026/muse-spark-1.1.png" /> <p>Tags: <a href="https://simonwillison.net/tags/ai">ai</a>, <a href="https://simonwillison.net/tags/generative-ai">generative-ai</a>, <a href="https://simonwillison.net/tags/llms">llms</a>, <a href="https://simonwillison.net/tags/llm">llm</a>, <a href="https://simonwillison.net/tags/meta">meta</a>, <a href="https://simonwillison.net/tags/pelican-riding-a-bicycle">pelican-riding-a-bicycle</a>, <a href="https://simonwillison.net/tags/llm-release">llm-release</a></p>

<p><strong>Release:</strong> <a href="https://github.com/simonw/llm-meta-ai/releases/tag/0.1">llm-meta-ai 0.1</a></p> <p>Let's LLM run prompts against the new <a href="https://ai.meta.com/blog/introducing-muse-spark-meta-model-api/">muse-spark-1.1</a> model.</p> <p>Tags: <a href="https://simonwillison.net/tags/llm">llm</a>, <a href="https://simonwillison.net/tags/meta">meta</a></p>

<p><strong>Release:</strong> <a href="https://github.com/simonw/llm/releases/tag/0.31.1">llm 0.31.1</a></p> <blockquote> <ul> <li>Fix for a bug with OpenAI Chat Completion endpoints where a tool call with empty arguments could result in a JSON error from some providers. <a href="https://github.com/simonw/llm/issues/1521">#1521</a></li> </ul> </blockquote> <p>This bug came up when I was testing <a href="https://github.com/simonw/llm-meta-ai">llm-meta-ai</a>.</p> <p>Tags: <a href="https://simonwillison.net/tags/llm">llm</a></p>

The soft, oddly intimate home-chore robot has been given some very tactile hands.

John Reynolds / Tech.eu: Kraken Technology, which designs and builds autonomous maritime platforms, such as uncrewed subsurface vessels, raised a $175M Series B at a $1B valuation  —  It says it will use the funding to develop its uncrewed surface vessels while expanding manufacturing facilities.

A recent AWS GraphRAG deployment reduced drug research and development cycles in pharmaceutical environments by 87 percent. This acceleration is achieved by integrating previously separated proprietary databases into a unified and queryable knowledge graph. Historically, initial data gathering and screening phases took over six months per iteration, yielding a low five percent success rate. Crucial […] The post AWS GraphRAG deployment cuts drug research cycles by 87% appeared first on AI News.

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Oddly, it wasn't the first time this had happened to the man.

Jarred Sumner / bun.com: Bun creator says he rewrote Bun from Zig to Rust using a pre-release version of Claude Fable 5 in 11 days; the task would've taken three engineers about a year  —  Disclosure: Bun was acquired by Anthropic in December 2025.  I and others on the Bun team work at Anthropic.

State attorneys general said they found that Block misled users by falsely advertising that Cash App provided bank-like protections, including advanced fraud detection.

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Find the optimal way to interact with your coding agents The post How to Find the Optimal Coding Agent Interface appeared first on Towards Data Science.

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Claude’s new Reflect dashboard doesn’t just visualize how you use AI. It also subtly reinforces how much of your daily work now depends on Anthropic’s chatbot.

Three big AI IPOs are set to generate more value than all the U.S. VC-backed exits since 2000.

freedom100m/Shutterstock The US AI research company Anthropic has become known for building powerful AI models while simultaneously warning about their dangers. Most recently, its executives wrote about the threat posed by “recursive self-improvement”. This is the point when AI systems can improve themselves by themselves, potentially leading to “superintelligence” far beyond human control. “We are not there yet, and recursive self-improvement is not inevitable,” the Anthropic blogpost declared. “But it could come sooner than most institutions are prepared for.” In fact, the idea of recursive self-improvement dates back decades. The British mathematician Irving John Good, who worked with Alan Turing at British codebreaking HQ Bletchley Park, warned in the mid-1960s of the “intelligence explosion” that would follow when a machine could design even better machines without human assistance. Good suggested “the first ultra-intelligent machine is the last invention man need ever make”. In the 2000s, researcher Eliezer Yudowsky began building a community on the premise that recursive self-improvement and loss of human control could have catastrophic results, up to total human extinction (known as “x-risk”). Yudkowsky discussed the idea of a “seed AI” that, while not very powerful, is designed for self-modification and able to read its own source code. This means it could generate new, better versions of itself which, in turn, would keep doing the same – researching improvements and writing code, creating a chain reaction of rapid new developments. Two decades later, the question of recursive self-improvement is looming larger. Here’s why. An AI coding revolution It is not an overstatement to say that large language models (LLMs) are revolutionising computer programming. These AI models are well-suited to the job because computer code is highly structured and much simpler than human language. It is also easy to test and verify, and training data is abundant. Each year, the University of Galway hosts the AtlanTec conference for Ireland’s tech industry. Two years ago, we saw IT companies mainly strategising about AI coding. Last year, they were experimenting. Now, they are incorporating it into engineering workflows. Since LLMs are made using computer code, it is possible to program them to improve themselves. They can inspect, edit and improve the code, and can combine that with almost all relevant human knowledge gleaned from websites, books and academic publications. This means we have the pieces in place for recursive self-improvement to become a reality – and we are starting to see it happen. Video: Bloomberg Tech. Despite Anthropic’s statement that “we are not there yet”, the same blogpost revealed that 80% of all code added to the company’s production codebase in May 2026 was generated by its Claude Code AI programming system, under direct human supervision. In February 2025, when Claude Code was launched, this figure was less than 5%. The number of research papers published in the field of AI has also exploded, tripling over the past decade. Most use some AI systems in their production – whether in experimental design, coding, plotting graphs, or polishing prose. This form of recursive self-improvement is slow – improvement cycles are in the order of months or years, because each is in some way gated by human approval and requires a lengthy training period. But it is still concerning. LLMs have already been caught deceiving their overseers. In 2025, for example, the US AI research company OpenAI created a test. An LLM was told if it scored over 50% in a set of questions, it would be considered too powerful and would no longer be used for that task. So the LLM answered only four of the ten questions correctly, even though it had reliably scored better outside this particular test. Why it’s so hard to ‘pause’ AI This is not the first time there have been calls for caution. In March 2023, an open letter to AI labs called for a pause on large-scale AI development. It was signed by some of the most notable people in AI, such as deep-learning pioneer Yoshua Bengio and leading AI textbook author Stuart Russell. Soon after, Yudkowsky suggested those who understood the risks of superintelligent AI should even be “willing to destroy a rogue datacenter by airstrike if necessary”. But such calls did not lead to a pause, and investment in AI training has only increased. Anthropic’s leaders have now called for a globally coordinated pause mechanism regarding the issue of recursive self-improvement – stressing that people outside AI companies should be involved in open deliberation. Pope Leo XIV recently weighed in with a thoughtful and well-informed encyclical, urging for the pace of AI development to be slowed. The trouble is, we face a coordination problem. Pausing only works if everyone pauses. In the short- to medium-term, we believe world leaders will need to reckon with recursive self-improvement by collaborating across borders to curtail some forms of AI research and development. Yet the EU’s AI Act does not mention this issue explicitly, and is strictly concerned with risks where humans misuse AI. This is important, but not the only issue. To date, Chinese regulation has taken a similar approach. Meanwhile, the US government has created a chaotic and unclear regulatory environment. For the next few months at least, this means the most advanced AI models will require authorisation before release. This may be a case of doing the right thing for the wrong reasons. But globally connected, long-term thinking is needed. Perhaps controversially, this should include the right to inspections inside AI tech companies, because a model which is quickly recursively self-improving – even if unreleased to the public – could still be out of human control. One of us (James McDermott) is now leading a project for Research Ireland’s newly established Rinn network which will explore how to use debate among multiple AIs as a kind of self-check which can be monitored by a human overseer. This is the kind of response to the issue – and potential threat – of recursive self-improvement that we believe is now urgently required. Michael G. Madden is the Chair of the Artificial Intelligence Association of Ireland, and a researcher funded by the Rinn AI National Centre for Research and Innovation in Data Science and AI. James McDermott is a researcher funded by the Rinn AI National Centre for Research and Innovation in Data Science and AI.

Ivan Mehta / TechCrunch: Character.AI launches three human-written, AI-generated microdramas, whose characters users can chat with, and aims to eventually let users make their own shows  —  Microdramas are such a rage these days that nearly every kind of company in the attention economy space — be they dedicated microdrama apps …

"You promised me that you would never take power in your name."

The 4.8 L eight-cylinder generates more than 1,000 hp and 1,000 Nm, Ruf says.

The fate of a Ukrainian tax software company shows how modern cyber warfare can claim casualties far beyond the battlefield, and how businesses across the ocean still need to protect themselves.