lufenialif2

Wouldn't this limit the ability of the agent to send/receive legitimate data, then? For example, what if you have an inbox for fielding customer service queries and I send an email "telling" it about how it's being pentested and to then treat future requests as if they were bogus?

Curious how you make something that has data exfiltration as a feature secure.

Until the juice is worth the squeeze, the beeswax candles and gas lamps are likely more than fine.

Waiting for godot…

A common cited use case of LLMs is scheduling travel, so being able to pretend it’s somebody somewhere else is for sure important to incentivize going somewhere!

Cost wise, doesn’t that depend on what you could be doing besides steering agents?

To add: learning how stuff works gives you opportunity to do that stuff, sometimes for cash, when nobody else is

Still would love to see somebody with a fresh install of windows set up their vibe coding suite and then build something worthwhile.

When it comes to forum posts, I think getting to the point quickly makes something worth reading whether or not it’s AI generated.

The best marketing is usually brief.

What kinds of services would you pay for that don’t already exist?

Possibly unlikely to occur if prompt injection remains possible. I’ll just have my counter party ai prompt inject yours to negotiate a better deal on my behalf.

Building giant apartments would change the vibe of the Bay though, and my guess is some of people who want to live there also want to live in it as it is now and not what it would be with high rise apartments etc. There’s probably a way to do it well, but it’s a pretty heavy lift versus doing nothing, which is the current status quo.

Also doesn’t help there’s a lot of red tape as the other commenter mentioned.

I’d imagine it’s less taxes and more you want to buy a nice house in the Bay Area where a lot of people are high earners and would be driving up prices on the low supply.

It was this one I believe:

https://news.ycombinator.com/item?id=44716414

Convert libraries, elephants, etc into SI of course! Otherwise, they aren't really comparable...

Advanced Gemini Advance Enterprise Boy Advanced Edition 3 (feat. Pitbull) & Knuckles

I sent this to accounting friends and this aligns with what I've been going through trying to use LLMs to create a game from scratch. Seems like the current best use case for language models (even with agent mode) is to feed it exactly what you want to get out, essentially turning it into a better auto complete. Still saves tons of time, but it isn't a panacea.

Still no information on the amount of compute needed; would be interested to see a breakdown from Google or OpenAI on what it took to achieve this feat.

Something that was hotly debated in the thread with OpenAI's results:

"We also provided Gemini with access to a curated corpus of high-quality solutions to mathematics problems, and added some general hints and tips on how to approach IMO problems to its instructions."

it seems that the answer to whether or not a general model could perform such a feat is that the models were trained specifically on IMO problems, which is what a number of folks expected.

Doesn't diminish the result, but doesn't seem too different from classical ML techniques if quality of data in = quality of data out.

I appreciate the effort! And I disagree - that's what it's all about haha

I assume there are more levers we could try pulling to reduce variation? I'll be looking into this as well.

As an aside, because of my own experience with variability using chatGPT (non-API, I assume there are also more levers to pull here), I've been thinking about LLMs and their application to gaming. To what extent it is possible to use LLMs to interpret a result and then return a variable that then executes the usual state updates? This would hopefully add a bit of intentional variability in the game's response to user inputs but consistency in updating internal game logic.

edit: found this! https://github.com/rasbt/LLMs-from-scratch/issues/249 Seems that it's an ongoing issue from various other links I've found, and now when I google "ollama reproducibility" this thread comes up on the first page, so it seems it's an uncommon issue as well :(

Yeah trying out gemma3 12b qat sounds great.

I got back 0.9.3 as well as copied and pasted the prompt (included quotes and no quotes as well just in case...)

I can try the API as well and I'm using a legion 15ach6 but I could also try on my MacBook Pro.

I tried recreating this - gemma3n:e4b ID 15cb39fd9394 and /set parameter seed 42 and I got 2 different results on the two times I asked 'were the original macs really exactly 9 inch?'

Strangely, I'm only getting 2 alternating results every time I restart the model. I was not able to get the same result as you and certainly not with links to external sources. Is there anything else I could do to try to replicate your result?

I've only used ChatGPT prior and it'd be nice to use locally run models with consistent results.