This topic is bringing out the hallucination-panic crowd.
It's perfectly safe to use LLMs to surface underlying data and knowledge without having to trust that it's giving you correct advice.
Use the LLM as search technology: string match -> semantic search -> search intelligence.
This is especially helpful when you don't know the right question to ask. Semantics only help you find text that is semantically near the input query. Semantics won't help you when what you are looking for is connected to what you have by something other than semantics, like causation, mechanism, or interpretation.
Trying to figure out why your crops aren't growing well will likely require you to read several articles across a range of sciences, probably scoped to the specific conditions you are dealing with. A semantic search is not going to surface those first.
Allowing an LLM to chain together several sources for you to read first does not require you to ask it for a conclusion.
I set this up on my machine and tested it with the following question:
How can I build a bow drill so I can start a fire?
It got it partially right: spindle, bow with string tied the correct way, and fire board. But then it flubbed by saying you put the spindle between both hands and move your hands back and forth to rotate the spindle and create the necessary friction… this totally ignores using the bow to rotate the spindle! It also mentioned “fanning the sparks” but there is absolutely no way your bow drill is going to create sparks.
I asked a frontier model to check the survival LLM’s conversation trace and it pointed out the errors.
I didn’t see obvious errors in the other 3 prompts I used, but this one seemed pretty egregious for a survival LLM.
I thank the creator for making this, but take it with a grain of salt.
Considering the tendency even now of LLMs to hallucinate, and the hardware requirements for any kind of LLM -why not just gather those resources into some sort of local archive that can be searched without an LLM?
This seems redundant and requiring more overhead while providing less reliability than something like Kiwix to me.
I have some property that doesn't really get cell reception in the summer, and even in the winter the reception is at-best spotty. I use granite and qwen together with a manually curated set of books, articles, etc. to get work done while disconnected. I tend to update the set of resources before leaving town because I'm generally intentionally disconnecting to focus on deep work. Building a workflow that makes that possible has helped me build out a setup that replaces a ton of my day-to-day anthropic/openai use even when I'm near fiber.
It is interesting how, even pre-llms, offline computers change the nature of a lot of activities. E.g., about once a year I'll walk do a random walk in the woods for a few days without paying much attention to maintaining a bearing. With maps and compasses it's a whole skill set to find your way back. With a phone and downloaded maps you just kinda press the power button and check your heading every hour or two.
The "survival" use-case is interesting. I fear a lot of the practical applications of that knowledge are either time-sensitive (e.g., RAG on a "Stop The Bleed" course won't help because by the time you learn the patient is dead) or require tools+skills that aren't easily improvised completely from scratch in high-stress situations (e.g., building shelter or even really simple stuff like building a fire when it's very cold out). And for other stuff common sense prevails (e.g., power out -> keep fridge closed).
If every single example posted has hallucinations and the response is "it's your fault for not checking the sources" then it's obvious that an LLM isn't the right tool for the job. A search engine works perfectly fine for these kinds of datasets.
> Mac or PC with 16–31 GB | IBM Granite 4.2 8B | 14 of 22 right | 6 GB
So on a regular consumer machine this is going to be wrong nearly half the time. Great! Let’s hope it’s only on the simple stuff and not the “how do I perform an emergency appendectomy”.
I give it about 50/50 the internet goes down or becomes effectively unusable for weeks, maybe months, at some point in the next 5 years given abysmal commitment to cybersecurity from almost every company that exists.
Some kid with the right prompt will supply chain attack a dependency of a dependency of firmware for border routers just to watch the world burn, or runaway AI swarms will saturate all bandwidth. Or a Linux kernel zero-click vuln. So many ways this can happen.
>The app is the same everywhere. When it opens, it checks your computer's memory and downloads the AI model that fits. More memory means a smarter model.
first thing an emergency tool should require, internet access.
at first glance I figured it'd be an ultra small specialty trained survival model that'd be small and runnable anywhere, sorta like an emergency flavored version of those tiny tts models that have been all over recently.
It's pretty cool. But if it's such a small model and small corpus, isn't it almost just as good just to have a static wikipedia-style copy of those key sources (WikiMed, MedlinePlus, Ready.gov, CDC etc.)? Maybe if they (and that could be static too), gave you a "what's going on?" question and then told you what you might not know - i.e. "power is out" -> "make sure you open your fridge as little as possible", "if you have backup battery ensure you turn power hungry devices off" etc.?
At what point would it be better to download a few real sources and do plain text search? Why risk a hallucination for content that would probably be smaller than the size of the model?!
I went through a category 5 hurricane that took down everything. Prepared by downloading as much as I could ahead of time but it wasn’t enough. This, or something similar, would have been a blessing.
Back in 2018 I was working on a project for educating schools in certain parts of Africa and Middle East, on AI. The issue was it had to be air-gapped (no internet guarantees) and small/portable. I ended up downloading Wikipedia (not all of it), and then created a lightweight inverted index (for search) and TextRank (for summaries). The whole thing was on a Raspberry Pi, and installed in a small plastic enclosure. We had many of these and could easily ship them. There was an "AI Learning Module" as well we built, that started with Scratch, and then moved into Python. But if I was to do it all over today, I would probably add a tiny LLM and maybe a small ASR and a TTS model too, and you basically have a completely self-contained AI knowledge assistant in a cigarette box. The point being, you basically also have a "survival AI" you can take with you. Of course you still need electricity...
Love this. And as a trend, I'd love to see more local/small task-specific models/agents. Experts at helping you with that one thing, leaving the everything-and-the-kitchen-sink to the frontier models/agents
It's perfectly safe to use LLMs to surface underlying data and knowledge without having to trust that it's giving you correct advice.
Use the LLM as search technology: string match -> semantic search -> search intelligence.
This is especially helpful when you don't know the right question to ask. Semantics only help you find text that is semantically near the input query. Semantics won't help you when what you are looking for is connected to what you have by something other than semantics, like causation, mechanism, or interpretation.
Trying to figure out why your crops aren't growing well will likely require you to read several articles across a range of sciences, probably scoped to the specific conditions you are dealing with. A semantic search is not going to surface those first.
Allowing an LLM to chain together several sources for you to read first does not require you to ask it for a conclusion.
How can I build a bow drill so I can start a fire?
It got it partially right: spindle, bow with string tied the correct way, and fire board. But then it flubbed by saying you put the spindle between both hands and move your hands back and forth to rotate the spindle and create the necessary friction… this totally ignores using the bow to rotate the spindle! It also mentioned “fanning the sparks” but there is absolutely no way your bow drill is going to create sparks.
I asked a frontier model to check the survival LLM’s conversation trace and it pointed out the errors.
I didn’t see obvious errors in the other 3 prompts I used, but this one seemed pretty egregious for a survival LLM.
I thank the creator for making this, but take it with a grain of salt.
https://www.projectnomad.us/
its got a ton of useful options, preconfigured, integrates pretty easily, and can use any local LLM or data.
This seems redundant and requiring more overhead while providing less reliability than something like Kiwix to me.
I have some property that doesn't really get cell reception in the summer, and even in the winter the reception is at-best spotty. I use granite and qwen together with a manually curated set of books, articles, etc. to get work done while disconnected. I tend to update the set of resources before leaving town because I'm generally intentionally disconnecting to focus on deep work. Building a workflow that makes that possible has helped me build out a setup that replaces a ton of my day-to-day anthropic/openai use even when I'm near fiber.
It is interesting how, even pre-llms, offline computers change the nature of a lot of activities. E.g., about once a year I'll walk do a random walk in the woods for a few days without paying much attention to maintaining a bearing. With maps and compasses it's a whole skill set to find your way back. With a phone and downloaded maps you just kinda press the power button and check your heading every hour or two.
The "survival" use-case is interesting. I fear a lot of the practical applications of that knowledge are either time-sensitive (e.g., RAG on a "Stop The Bleed" course won't help because by the time you learn the patient is dead) or require tools+skills that aren't easily improvised completely from scratch in high-stress situations (e.g., building shelter or even really simple stuff like building a fire when it's very cold out). And for other stuff common sense prevails (e.g., power out -> keep fridge closed).
So on a regular consumer machine this is going to be wrong nearly half the time. Great! Let’s hope it’s only on the simple stuff and not the “how do I perform an emergency appendectomy”.
Honestly, just buy some books.
Some kid with the right prompt will supply chain attack a dependency of a dependency of firmware for border routers just to watch the world burn, or runaway AI swarms will saturate all bandwidth. Or a Linux kernel zero-click vuln. So many ways this can happen.
Better to prepare a bit and never need it.
>The app is the same everywhere. When it opens, it checks your computer's memory and downloads the AI model that fits. More memory means a smarter model.
first thing an emergency tool should require, internet access.
at first glance I figured it'd be an ultra small specialty trained survival model that'd be small and runnable anywhere, sorta like an emergency flavored version of those tiny tts models that have been all over recently.
Maybe that's an idea for someone.
I found Project Nomad (https://projectnomad.us) which looks more polished. Any other suggestions?
I’m currently using https://kiwix.org for the offline access to Wikipedia, but your Survival LLM looks even better.
What about no power? You gonna run your generator for your local model? What are we doing here? Have we lost our way completely?