Simon Willison’s Weblog

Why are my live regions not working? (via) Useful article to help understand ARIA live regions. Short version: you can add a live region to your page like this:

<div id="notification" aria-live="assertive"></div>

Then any time you use JavaScript to modify the text content in that element it will be announced straight away by any screen readers - that's the "assertive" part. Using "polite" instead will cause the notification to be queued up for when the user is idle instead.

There are quite a few catches. Most notably, the contents of an aria-live region will usually NOT be spoken out loud when the page first loads, or when that element is added to the DOM. You need to ensure the element is available and not hidden before updating it for the effect to work reliably across different screen readers.

I got Claude Artifacts to help me build a demo for this, which is now available at tools.simonwillison.net/aria-live-regions. The demo includes instructions for turning VoiceOver on and off on both iOS and macOS to help try that out.

# 3:54 am / claude-artifacts, aria, ai-assisted-programming, javascript, accessibility, screen-readers

microsoft/phi-4. Here's the official release of Microsoft's Phi-4 LLM, now officially under an MIT license.

A few weeks ago I covered the earlier unofficial versions, where I talked about how the model used synthetic training data in some really interesting ways.

It benchmarks favorably compared to GPT-4o, suggesting this is yet another example of a GPT-4 class model that can run on a good laptop.

The model already has several available community quantizations. I ran the mlx-community/phi-4-4bit one (a 7.7GB download) using mlx-llm like this:

uv run --with 'numpy<2' --with mlx-lm python -c '
from mlx_lm import load, generate

model, tokenizer = load("mlx-community/phi-4-4bit")

prompt = "Generate an SVG of a pelican riding a bicycle"

if tokenizer.chat_template is not None:
    messages = [{"role": "user", "content": prompt}]
    prompt = tokenizer.apply_chat_template(
        messages, add_generation_prompt=True
    )

response = generate(model, tokenizer, prompt=prompt, verbose=True, max_tokens=2048)
print(response)'

Here's what I got back.

Update: The model is now available via Ollama, so you can fetch a 9.1GB model file using ollama run phi4, after which it becomes available via the llm-ollama plugin.

# 5:57 pm / phi, generative-ai, ai, microsoft, llms, uv, pelican-riding-a-bicycle, llm, ollama