Tuesday, 3rd February 2026
This is the difference between Data and a large language model, at least the ones operating right now. Data created art because he wanted to grow. He wanted to become something. He wanted to understand. Art is the means by which we become what we want to be. [...]
The book, the painting, the film script is not the only art. It's important, but in a way it's a receipt. It's a diploma. The book you write, the painting you create, the music you compose is important and artistic, but it's also a mark of proof that you have done the work to learn, because in the end of it all, you are the art. The most important change made by an artistic endeavor is the change it makes in you. The most important emotions are the ones you feel when writing that story and holding the completed work. I don't care if the AI can create something that is better than what we can create, because it cannot be changed by that creation.
I just sent the January edition of my sponsors-only monthly newsletter. If you are a sponsor (or if you start a sponsorship now) you can access it here. In the newsletter for January:
- LLM predictions for 2026
- Coding agents get even more attention
- Clawdbot/Moltbot/OpenClaw went very viral
- Kakapo breeding season is off to a really strong start
- New options for sandboxes
- Web browsers are the "hello world" of coding agent swarms
- Sam Altman addressed the Jevons paradox for software engineering
- Model releases and miscellaneous extras
Here's a copy of the December newsletter as a preview of what you'll get. Pay $10/month to stay a month ahead of the free copy!
Introducing Deno Sandbox (via) Here's a new hosted sandbox product from the Deno team. It's actually unrelated to Deno itself - this is part of their Deno Deploy SaaS platform. As such, you don't even need to use JavaScript to access it - you can create and execute code in a hosted sandbox using their deno-sandbox Python library like this:
export DENO_DEPLOY_TOKEN="... API token ..."
uv run --with deno-sandbox pythonThen:
from deno_sandbox import DenoDeploy sdk = DenoDeploy() with sdk.sandbox.create() as sb: # Run a shell command process = sb.spawn( "echo", args=["Hello from the sandbox!"] ) process.wait() # Write and read files sb.fs.write_text_file( "/tmp/example.txt", "Hello, World!" ) print(sb.fs.read_text_file( "/tmp/example.txt" ))
There’s a JavaScript client library as well. The underlying API isn’t documented yet but appears to use WebSockets.
There’s a lot to like about this system. Sandboxe instances can have up to 4GB of RAM, get 2 vCPUs, 10GB of ephemeral storage, can mount persistent volumes and can use snapshots to boot pre-configured custom images quickly. Sessions can last up to 30 minutes and are billed by CPU time, GB-h of memory and volume storage usage.
When you create a sandbox you can configure network domains it’s allowed to access.
My favorite feature is the way it handles API secrets.
with sdk.sandboxes.create( allowNet=["api.openai.com"], secrets={ "OPENAI_API_KEY": { "hosts": ["api.openai.com"], "value": os.environ.get("OPENAI_API_KEY"), } }, ) as sandbox: # ... $OPENAI_API_KEY is available
Within the container that $OPENAI_API_KEY value is set to something like this:
DENO_SECRET_PLACEHOLDER_b14043a2f578cba...
Outbound API calls to api.openai.com run through a proxy which is aware of those placeholders and replaces them with the original secret.
In this way the secret itself is not available to code within the sandbox, which limits the ability for malicious code (e.g. from a prompt injection) to exfiltrate those secrets.
From a comment on Hacker News I learned that Fly have a project called tokenizer that implements the same pattern. Adding this to my list of tricks to use with sandoxed environments!