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.
The readme on Github does say the system produces hallucinated sources https://github.com/anonnerdfrenzy/survival-llm#how-the-model..., but really you're correct, for this kind of highly targeted use case the LLM's summary could probably be ignored entirely and the system could work extremely well even just as a search engine: index all of the content up front, chunked by paragraph and maybe enriched with a HyDE step, have the LLM facilitate running a handful of hybrid keyword/semantic searches based on the user's question and reranking the results, and show the exact pieces of text in the corpus that are most responsive to their question. No summary needed.
A great strength of LLMs is how they provide useful answers even if the prompt has glaring spelling or grammatical mistakes. If your prompt is not great, as you said, they will reply in a way that helps you identify that flaws, and learn how to steer the model better.
They're amazingly useful tools, but they're nowhere the level believers want them to be, not yet.
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.
Why not both? After the collapse of civilization, as long as you can maintain the computer running, you could educate the next generation as soon as they could write. Maybe they will bypass the local agent as they learn their way into the library, to save energy.
Consider they won't have access to the plentiful practice opportunities we have now, most resources would have to be spent with a practical goal.
A field guide to foraging for edible mushrooms will probably be more practical than naïve use of an LLM: “can I eat this mushroom?” is an exercise in trust that I will maybe never be comfortable with.
But “how do I effectively forage for edible mushrooms” is a great stepping stone.
Not sure about how the code in this project works. But the way I see it, an LLM+chat is a great interface for information. If you then connect it to a db with the knowledge/articles you need, then you get a great way to navigate it, without needing the model to memorize all the info
I think that if I were to build this I might skip the LLM and just setup a nice interface to the source files using qmd style search. [0]
That way the tiny model isn't acting as a potentially incorrect interpreter but you still get semantic search to help you find the right documents quickly.
I suppose there is something to be said for a good summarization, I just worry that these tiny models aren't really GOOD summarizers.
Awesome, I didn't know qmd style search, thank you!
I'm all up for using the better/simpler task for the job. That's been a thing lately also with the Jev/decision models hype, most of those things can either already be done by a pretrained classifier[0], or you can start with a bigger model and then train your smaller/adhoc one when you have some data (it doesn't even take much for most applications, you can get about 95% accuracy on email classification with 50-100 examples)
I agree with the OP; this technology does not seem an appropriate fit for survival equipment. By definition survival scenarios can be life and death. Even one "You're absolutely right!" level mistake from an LLM could give a user bad information, leading to John Doe eating a mushroom he really should not have eaten.
I agree with your point, but if John Doe is foraging mushrooms without experience of the local flora (which I doubt this archive contains), then John Doe is kinda asking for it.
The bigger issue in this context would be canning, such as incorrectly explaining the mechanisms to avoid botulism (acid and/or pressure canning), or perhaps incorrectly wiring in repairs. However, again, it’s best to practice with these prior to emergencies anyway.
It should be noted though, that the model does source its claims, so you can fact check it and should review this information regularly.
I think that for situations like this we should invent a device that can hold information for an arbitrary amount of time even without electricity. Maybe we could call it "BOOK" or something like that.
This isn't clever or insightful. You can of course condense down critical information into a survival handbook, but in existential crises you don't necessarily know in advance what you'll need to know. Wikipedia (as a benchmark repository of knowledge) is ~5 million pages and would require a decent-size room to store if printed out. By contrast, a laptop + solar panel can fit in a backpack. SD cards/USB sticks with the capacity to store all of Wikipedia are a few $ each.
If things are so bad that you need to factor in the non-operability of any electronic devices, how many indestructible paper libraries are you planning to build and manage in advance, and at what budget?
Kiwix is great, but I'd also like a language model to be able to deeply interrogate all of that information much faster than I can read.
Just arrange its harness so that its responses are grounded in the source material and require source citations for everything. Then read the citations to confirm it's not a hallucination.
It already is a local archive that can be searched independently, but the LLM does RAG and is, according to them, 95% accurate. It can be useful if you don’t remember where to find a source/what words to search for, but even a durable device with Kiwix would be useless in a a lot of survival situations people imagine.
Undoubtedly useful in the right contexts, particularly for local repair or homesteading, but “survival” does imply that this would be more useful in an SHTF situation.
> 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”.
Or just.. download the books, and slap a search engine on top of them. It'll be like 10x smaller than the model. "Survival LLM" is absolutely a non-relevant use case.
When you’re living in an igloo trying to stay warm in the hollowed out belly of a cow having a few books around the place is probably more useful than some files on a laptop that ran out of battery just before it got morbidly damp. Plus you can burn them if you really need to.
On the other hand, the laptop struggling to power a good-enough LLM will probably be a great heat source for you in that igloo... for about 45 minutes.
The advantage of a turn-by-turn flow (honestly the chat interface was genius) is that with a little human intervention you can get much better results out of even silly models. The model of choice is strange, though. Not a particularly good one.
Either it depends a lot on the model, or I’m bad at chat, or both, because in my experience once a chat starts going off the rails it’s pretty hard to correct the initial misconception. I find it works better to backtrack to just before the misconception and edit my previous response to head it off at the pass.
Definitely agreed that it's better to not have some text in the context than to have some that needs to be opposed. I suppose I meant steering prompts are a non-obvious innovation to LLM interaction.
We had a set called something like “How to do anything in an emergency” which had instructions for this and more. Alongside lots of creative uses of potassium permanganate.
I’m pretty sure it also told you how to butcher a sheep.
I used to be obsessed with it as a kid. The spectre of nuclear war and a three volume handbook on how to survive the impending apocalypse with nothing more than a plastic bucket, a packet of potassium permanganate, some charcoal and string. The late 80s and early 90s were a wild time to be a kid.
I have a bunch of military field manuals and survival guides, but they all implicitly presume the existence and contactability of civilization sooner or later. Realistically, some pocket computer(s) and solar charger(s) will store far more information, be far more portable, and give you more carrying capacity for things like medical supplies, rope, tools, and other hard-to-substitute items.
Have you re-read it recently? Some of those books were fun to read but not really something to use as a field manual. Then again some of them were great! I had a book that started with explaining how a battery and a light globe worked, went through how to make a DC motor out of copper wire and a cork, and ended with a crystal radio and I think a radio homing beacon (the latter might have been in another book.)
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.
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...
I just use llama cpp , it's light weight, I think you create your own integrations, with a larger model so llama cpp can interract with scripts that would browse the web, control your computer etc... propably will have to download some pip packages
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
Download a good fast local model e.g. Gemma-4-12B (small machine) or Qwen3.6-35B-A3B (larger memory). Or a Nemotron model, if you prefer that.
Then you'll have both your backup/emergency assistant that runs on your own computer and be gaining skills in a common, frequently-updated, well-supported program.
With all due respect: why does this need an LLM, and this much data? Surely a "classic" knowledge base with a more traditional search bar, running on top of the same data this LLM was trained on would be way smaller, and actually as trustworthy as the source material it surfaces for you?
There are a fair number of people who seem to believe that LLMs contain "world knowledge" and are blissfully unaware of how inaccurate an ungrounded chatbot can be. They ask it questions, it gives them confident and plausible (sometimes even correct!) answers, and they leap to the conclusion that these are useful tools for end-of-civilization knowledge bases. I fought this battle countless times on HN and Reddit, generally unsuccessfully.
This project at least attempts to ground its answers with (presumably) human-vetted sources. That will make it much, MUCH more accurate and truthful than a bare model. But there is still a non-zero chance for it to hallucinate answers, especially when posed a question that it doesn't have any source material in its database.
As for why bother hooking up an LLM instead of a simple search engine, search engines don't synthesize data from multiple sources into a coherent answer for you, but LLMs are (mostly) great at that.
That said, while I like this pattern (LLM + knowledge base) for lots of things, this isn't necessarily something I'd trust with my own (or my family's) survival.
Yeah, without the ability to search the web, a very small model is going to be too dumb to be useful, and maybe even dangerous due to hallucinations. But, there is a point where the knowledge of self-hostable models will surprise you. That point is probably not runnable on the average laptop, though. It's maybe in the ~30B range, but more realistically in the 128B MoE range. I guess in their tests Qwen 3.8 27B got as many right as the much larger Nemotron Super, but I'd bet Qwen 3.8 Flash would be better than either, but it needs 128GB of unified RAM or a big GPU and lots of system RAM to run the 4-bit version.
I think if I had to build an emergency LLM today, that had to run on modest hardware, I'd bet on Gemma 4 12B. It's got excellent reasoning and good vision/audio and speaks 140 languages.
But, I don't know about the "classic" knowledge base theory, though. Is there a high quality curated pile of survival information that could be bundled into a web UI and made searchable in a knowledge base? The LLMs aren't focused on that topic, so there's a lot of wasted space, but they know a little about a lot of things, including survival topics. Combined with Wikipedia, and maybe a few other sources, you're probably better off with one than without.
I guess one could use some huge models plus web search to compile/distill a focused survival knowledge base or LLM.
Edit: Project NOMAD seems to mostly be exactly that. It has authoritative sources in a searchable knowledge base and the tiny LLMs are an afterthought, which is probably what they should be. https://www.projectnomad.us/
On the setup process: "You only click for about 5 minutes; the rest is downloading." On the energy consumption: "A small generator or power station runs it easily"
Five minutes of clicking and a (small) power station is a small price to pay when survival could be at stake.
I can almost guarantee you that by the time you need a survival assistant, you won't be willing to spend gasoline on generating electricity for your GPUs.
(It's helpful for shorter outages. But "survival assistant", I think not, unless you run a compound)
It's disappointing that Gemma E4B seems to perform poorly on this (42% accuracy according to the GitHub). It would be nice if a $25 phone could be loaded up with this and stuffed in a EMP proof metal box with a small solar charger. Maybe a finetune or Gemma 5 will get us there.
This will come in handy after AI takes over the world.
But kidding aside, this is an interesting reveal on the ease with which a custom LLM can be spun up to fit a particular niche application, especially for systems not meant to host an LLM.
If you're actually offline and need information in a 'survival' situation, I'm not so sure the hallucinatory nature of an LLM is the based way to distribute a repository of information. Additionally, there's no need to fit an LLM entirely in memory.
In a theoretical scenario where the internet is gone and isn't coming back: If you can deal with a very low token/second rate there's no reason not to store 'offline' a much larger model like qwen 3.8-flash-next or deepseek v4 flash 1017 and use an inference engine which can stream-read it off disk to extract the information you want, albeit slowly. Of course also download and use things that'll fit in RAM, sure, like the qwen 3.8 27B in the linked page. But no need to restrict yourself to only smaller models.
No thanks, I'll use a generic model along with trusted sources.
Also: "Apple could not verify “Survival LLM” is free of malware that may harm your Mac or compromise your privacy." This is obviously some vibe coded trash from an inexperienced dev.
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.
They're amazingly useful tools, but they're nowhere the level believers want them to be, not yet.
I tried using dictation but I really disliked speaking to it.
You can write a really messed up sentence and it almost always worked out what I meant.
Quite amazing actually
This seems redundant and requiring more overhead while providing less reliability than something like Kiwix to me.
Consider they won't have access to the plentiful practice opportunities we have now, most resources would have to be spent with a practical goal.
A field guide to foraging for edible mushrooms will probably be more practical than naïve use of an LLM: “can I eat this mushroom?” is an exercise in trust that I will maybe never be comfortable with.
But “how do I effectively forage for edible mushrooms” is a great stepping stone.
That way the tiny model isn't acting as a potentially incorrect interpreter but you still get semantic search to help you find the right documents quickly.
I suppose there is something to be said for a good summarization, I just worry that these tiny models aren't really GOOD summarizers.
[0] https://github.com/tobi/qmd
I'm all up for using the better/simpler task for the job. That's been a thing lately also with the Jev/decision models hype, most of those things can either already be done by a pretrained classifier[0], or you can start with a bigger model and then train your smaller/adhoc one when you have some data (it doesn't even take much for most applications, you can get about 95% accuracy on email classification with 50-100 examples)
0: http://jeffyclassify.com/blog/when-llms-fail-at-classificati...
The bigger issue in this context would be canning, such as incorrectly explaining the mechanisms to avoid botulism (acid and/or pressure canning), or perhaps incorrectly wiring in repairs. However, again, it’s best to practice with these prior to emergencies anyway.
It should be noted though, that the model does source its claims, so you can fact check it and should review this information regularly.
If things are so bad that you need to factor in the non-operability of any electronic devices, how many indestructible paper libraries are you planning to build and manage in advance, and at what budget?
You can run bonsai2 27b on basically any gaming GPU from the last 15 years including on game consoles.
Everyone has access to this stuff now if they bothered to look beyond the marketing from big labs trying to collect subscription revenue.
I work on compilers, kernels, and operating systems using exclusively consumer GPUs.
Kiwix is great, but I'd also like a language model to be able to deeply interrogate all of that information much faster than I can read.
Just arrange its harness so that its responses are grounded in the source material and require source citations for everything. Then read the citations to confirm it's not a hallucination.
Undoubtedly useful in the right contexts, particularly for local repair or homesteading, but “survival” does imply that this would be more useful in an SHTF situation.
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.
I’m pretty sure it also told you how to butcher a sheep.
I used to be obsessed with it as a kid. The spectre of nuclear war and a three volume handbook on how to survive the impending apocalypse with nothing more than a plastic bucket, a packet of potassium permanganate, some charcoal and string. The late 80s and early 90s were a wild time to be a kid.
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.
I found Project Nomad (https://projectnomad.us) which looks more polished. Any other suggestions?
Apocalypse emergency I don't need help on. My crisis brain has collected more than enough info and capabilities over the decades.
Helps you download a local LLM and offline Wikipedia. Probably easier to just ask your favorite agent to glue something together yourself these days.
The source (data collection) seems the most important feature here worth reusing.
From the command line: unsloth studio
Browse to the URL it gives you (eg http://127.0.0.1:8888/)
Download a good fast local model e.g. Gemma-4-12B (small machine) or Qwen3.6-35B-A3B (larger memory). Or a Nemotron model, if you prefer that.
Then you'll have both your backup/emergency assistant that runs on your own computer and be gaining skills in a common, frequently-updated, well-supported program.
This project at least attempts to ground its answers with (presumably) human-vetted sources. That will make it much, MUCH more accurate and truthful than a bare model. But there is still a non-zero chance for it to hallucinate answers, especially when posed a question that it doesn't have any source material in its database.
As for why bother hooking up an LLM instead of a simple search engine, search engines don't synthesize data from multiple sources into a coherent answer for you, but LLMs are (mostly) great at that.
That said, while I like this pattern (LLM + knowledge base) for lots of things, this isn't necessarily something I'd trust with my own (or my family's) survival.
I think if I had to build an emergency LLM today, that had to run on modest hardware, I'd bet on Gemma 4 12B. It's got excellent reasoning and good vision/audio and speaks 140 languages.
But, I don't know about the "classic" knowledge base theory, though. Is there a high quality curated pile of survival information that could be bundled into a web UI and made searchable in a knowledge base? The LLMs aren't focused on that topic, so there's a lot of wasted space, but they know a little about a lot of things, including survival topics. Combined with Wikipedia, and maybe a few other sources, you're probably better off with one than without.
I guess one could use some huge models plus web search to compile/distill a focused survival knowledge base or LLM.
Edit: Project NOMAD seems to mostly be exactly that. It has authoritative sources in a searchable knowledge base and the tiny LLMs are an afterthought, which is probably what they should be. https://www.projectnomad.us/
Five minutes of clicking and a (small) power station is a small price to pay when survival could be at stake.
tl;dr: Local LLMs are really really bad at offline navigation.
That said, pairing the survival guide with a library is totally the right move.
(It's helpful for shorter outages. But "survival assistant", I think not, unless you run a compound)
But kidding aside, this is an interesting reveal on the ease with which a custom LLM can be spun up to fit a particular niche application, especially for systems not meant to host an LLM.
In a theoretical scenario where the internet is gone and isn't coming back: If you can deal with a very low token/second rate there's no reason not to store 'offline' a much larger model like qwen 3.8-flash-next or deepseek v4 flash 1017 and use an inference engine which can stream-read it off disk to extract the information you want, albeit slowly. Of course also download and use things that'll fit in RAM, sure, like the qwen 3.8 27B in the linked page. But no need to restrict yourself to only smaller models.
Also: "Apple could not verify “Survival LLM” is free of malware that may harm your Mac or compromise your privacy." This is obviously some vibe coded trash from an inexperienced dev.