Punny!
brainchild… I see what you did there :D
34 publicly visible posts • joined 16 Mar 2026
To be fair, for most of that time software has been deterministic. If you want to limit an API's blast radius you either ensure the calling code doesn't do bad things (from the caller side) or establish permissions on the credential (from the service side)
Neither of those are really viable when you want a (effectively) non-deterministic software system to take action on your behalf automatically. You can't control exactly what it will do and if you lock down the credential too much it won't be able to do what you want. So what do you do? Human-authorize each call? Write some heuristic to monitor the calls? Allow the bad things but have rollback functionality?
It's still being worked on.
There is no difference between text and instructions, it's all just tokens. LLMs are fed input tokens and each one affects the output. There is no reason to expect anything else because this is their design.
The question is how can we prevent things we don't want becoming input to the LLM? The solution is still being worked on.
Exactly. I don't think it's really possible in a single model and session scenario but I'm sure people are working on something like a separate model trained to detect malicious instructions.
It's tricky because the network being affected by the input is how the whole thing works so you can't just instruct the model to disregard certain parts. The agent/harness will have to perform sanitation steps first.
"AI" isn't just code, is a combination of weights - which is data - and software which uses the weights. While the software and weights determine the peak capability of the system, the prompt determines how close a given session gets to its goal, so in that sense AI can very much be prompted to be "smarter". Dumb prompt, dumb output. Smart prompt, smart output.
It's surprising anyone would expect an LLM not to consider some of the input provided to it. Its whole purpose and method of operation is to take all input and correlate it using the weights. It doing that is not "not smart", it's simply doing what it does. To prevent malicious output one needs to prevent malicious input and this is unfortunately a difficult problem since humans are rather good at encoding malicious intent in language. Even humans are regularly affected by this problem (see: advertising, influencers and scams) so it's hardly surprising AI isn't magically good at it.
Marketing wonks just can’t help shoving the word EXPERIENCE! into every single thing, even when it doesn’t make sense, as here.
My rule of thumb is when you see the word experience you know you’re reading BS. It certainly holds up here.
Hear, hear! Apple regularly states (including in Tim's Community Letter) that they put customers at the center of everything they do. To an extent they do but there is an unfortunate list of things where they clearly do not.
Now they have a great opportunity to undo some of those things. I hope they avail themselves of it.
Beyond the visual quality improvements what I find exciting about this is how it might impact performance.
In the same way upscalers render at a lower resolution, neural rendering might allow the engine to reduce quality settings, using much simpler geometry, textures and shaders and eschewing the tricks engines use to enhance realism. It will be interesting to see just how rudimentary the rendered frames can get with the output remaining near photorealistic.