alt Hacker NewsSurvival LLM: AI assistant and emergency library that runs on your own computer
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Shoutout to project nomad.
its got a ton of useful options, preconfigured, integrates pretty easily, and can use any local LLM or data.
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.
Even in their table it shows the LLM never getting all the questions right.
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 see a lot of comments questioning the usefulness of this, arguing that in a doomsday scenario you will have more important problems (like power) rather than access to the internet.
Which is kind of funny to me given that this year, I (along with 90 million other Iranians), experienced this exact scenario when the Islamic Regime decided to shut down the internet for three months (https://en.wikipedia.org/wiki/2026_Internet_blackout_in_Iran). Certainly something like this would have been very useful.
> 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”.
Honestly, just buy some books.
A detail from the benchmarks in that page is that Nemotron Lightning has good results for a MoE model close to Qwen 27B which is dense. Very interesting considering that Qwen 35B MoE stopped being in Alibaba's plans, and curious if Nemotron is equally capable in other bemchmarks.
All this is interesting, but I always had a question: in a state of disaster or some mishap even if you have an light LLM running - most important is a power source/electricity. Without it nothing will work.
Cool project.
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).
We need this, but running on smartphones.
Also, I am surprised that the difference between 32GB and 128GB in right answers is so small:
* Mac with 128 GB or more memory NVIDIA Nemotron 3.5 Super 21 of 22 right 80 GB
* Windows PC with 32 GB or more NVIDIA Nemotron 3.5 Lightning 19 of 22 right 25 GB
212MB for MacOS and 25MB for Windows? Why are MacOS binaries so large? Is it typical for them to be 10x Windows binary size?
That said, I'm really looking forward to small and quick (latency wise) models are a reality. I can easily see a world where every keystroke, terminal command, etc. goes through a "autocorrect" type model. You could even blend natural language plus terminal program segments and the AI just infers what you intend. Either built-in or a second classifier model, determines if the autocorrect change needs human review first.
I'm not a doomsday prepper but man if you need to run an LLM locally during the apocalypse to figure out how long your food is going to last, I feel like you are in for a tough time.
This kind of stuff really feels like knowledge that probably shouldn't be offload to AI but rather learned and practiced. Again, I don't have a bunker in my backyard but seems like a weird use case.
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.
Better to prepare a bit and never need it.
The required specs seemed a bit high to me, and I was just looking for alternatives.
I found Project Nomad (https://projectnomad.us) which looks more polished. Any other suggestions?
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.?
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.
Great idea, I do a lot of work testing small LLM's in this space and agree if you add some guardrails and train a small embedder, you won't need these large models, as your info is static, you can ship it all as one.
Or try these with your trained embedder: https://ollama.com/library/minicpm-v4.6 https://ollama.com/library/bonsai-2
Emergencies = No Internet
What about no power? You gonna run your generator for your local model? What are we doing here? Have we lost our way completely?
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 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.
From the site:
>Survival LLM >It Can be wrong
thank you!
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
I like to use llamafile for cases like this: https://github.com/mozilla-ai/llamafile
"Survival LLM, the power is out and there's no food, how do I turn your computer on to ask your questions?"
this is more like .. survival harness, no?
>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.
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?!
Really cool idea! Will give it a spin.
I’m currently using https://kiwix.org for the offline access to Wikipedia, but your Survival LLM looks even better.
Wonder why they didn’t run Qwen 3.5 9b on 16th Macs. That’s a super impressive model for size. Is granite better?
Sorry if this is a dump question but I was wondering why not fine tuning each model to get 100% accuracy on those topics?
I made it a while ago now, but similar: https://github.com/x3haloed/eowkit
Helps you download a local LLM and offline Wikipedia. Probably easier to just ask your favorite agent to glue something together yourself these days.
I saw emergency and I immediately thought in case I couldn't access online docs or get my coding/platform questions answered day-to-day.
Apocalypse emergency I don't need help on. My crisis brain has collected more than enough info and capabilities over the decades.
What this needs is a recipe or script that you can point your local agent at and it builds a bespoke implementation that’s, by definition, highly resilient.
Edit: see project nomad.
This could be made a lot more practical (and safer) if made searchable via a simple vector search / embedding that can run on any ordinary computer.
The source (data collection) seems the most important feature here worth reusing.
Odd that it doesn’t run on phones. It isn’t that large, 212MB on a Mac. Phones are basically everyone’s EDC and most are quite powerful.
Alternate idea: Download Unsloth Studio https://unsloth.ai/docs/new/studio#quickstart
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.
The only way Big AI labs will sell us appliances is when the internet was somehow destroyed.
Not sure where this would fit in.
pretty cool I like it. could you support more (small) models in the future, e.g. from the qwen or gemma family?
When I quit the app, why is it still running in the background?
Nice idea! I wonder how the installed IBM Granite 4.2 8B LLM does on machines with "modest" RAM
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.
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?
Have you considered just having a library?
I'd want an Android version...
I recently evaluated LLMs for use for offline navigation, with this very kind of survival scenario in mind: https://fig.blue/posts/directions-from-memory/
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 would be useful if it would shrink a little bit
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.
Just few days ago I thought digital preppers are soon gonna make something like that. It's a generally good idea.
I'd put in this also a ton of software documentation, minimally for Rust.
And a bunch of scientific literature.
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.