Yeah, it’s counterintuitive because it’s a lot more work for a human to draw a picture (much less a photorealistic picture) than to write a few words, but human language grammar actually has a lot of strict rules that makes that stream of letters work as “valid” output, much less “decent” output that kinda matches the prompt/description. Transpose a pair of letters or even substitute a single letter (or token) and you’ve got an output that just doesn’t work, in a way that generated images don’t have to worry about.

Yeah, the smarter way to use LLM-based agents is carefully defined tasks. Mozilla describes their vulnerability assessment processes in this blog post.

Mozilla describes the process they’ve used: building a harness that instructs a model to find a specific category of vulnerability on a specific interface, and then write up its findings. It’s a narrow enough context that the model gets specific instructions, and a simple definition of success, and it sets up many such tasks that can be fed into the existing process for verifying and triaging bugs. Note that the output for this LLM pipeline basically feeds into the same interface for accepting bug reports from the public, or from their human contributors within the project.

There’s a couple of takeaways here, too:

• This pipeline is model agnostic. Mozilla set it up before Mythos was released, and its description of other models (Opus 4.7, Codex) confirms that Mythos is better but not a true game changer. The ability to swap out other models provides some assurance that the work done to develop the pipeline will be useful when cheaper or better models come along, or when a model becomes unavailable (like when a provider decides a particular model is too expensive to run, or a provider goes under).
• The increase in automated output (and presumably automation-assisted contributions from the public) has given the humans more work to do. Automation in this context actually increases the demand for human labor.
• Other projects will need to develop their own custom pipelines, specific to their project, to get good results from LLM based agents.

There are ways to use these tools, but none of it really seems like a truly revolutionary/disruptive change to how large projects are managed.

Yeah if I were starting now I’d be looking at jellyfin. But I paid the lifetime plex pass, and inertia/laziness what it is, so I haven’t found a reason to actually switch yet.

This one is the same author addressing this specific Mozilla release.

but isn’t the memory on the Neo on the same die as the processor?

Not actually on the same die, but in the same package, stacked on top using TSMC’s Integrated Fan-Out Package on Package (InFO-PoP).

So the memory still needs to be sourced from memory manufacturers, sent to TSMC, and then have TSMC package it all together in a single package. It’s unclear whether they had locked up this supply at pre-AI prices, though. The underlying A18 Pro chip/package was annoinced and launched about 18 months ago, so if they had the manufacturing pipeline set up for that they might have kept the contractual rights to continue buying memory at the old prices.

God I wish democracy meant that we could vote on decisions like this

You can! Only problem is that it’s one vote per dollar instead of one vote per person.