--- title: Survival LLM: AI assistant and emergency library that... description: Survival LLM is a free, open source AI assistant and emergency library that runs entirely offline on your own computer. It answers questions like treating a... canonical: https://daily.dev/posts/survival-llm-ai-assistant-and-emergency-library-that-runs-on-your-own-computer-lrvo7oius twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Survival LLM: AI assistant and emergency library that runs on your own computer | daily.dev og:description: Survival LLM is a free, open source AI assistant and emergency library that runs entirely offline on your own computer. It answers questions like treating a... og:url: https://daily.dev/posts/survival-llm-ai-assistant-and-emergency-library-that-runs-on-your-own-computer-lrvo7oius og:image: https://api.daily.dev/og/posts/lrVO7OiuS.png og:image:alt: Survival LLM: AI assistant and emergency library that runs on your own computer og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Survival LLM: AI assistant and emergency library that runs on your own computer **[Hacker News](https://daily.dev/sources/hn)** · 4 min read · 0 upvotes · 0 comments ## Summary Survival LLM is a free, open source AI assistant and emergency library that runs entirely offline on your own computer. It answers questions like treating a burn or purifying water using sources such as FEMA, CDC, WikiMed, and about 930,000 Wikipedia articles, and cites the exact passage for every answer. On first launch it detects your computer's memory and downloads a model that fits it, from IBM Granite 4.2 8B on 16-31 GB machines up to NVIDIA Nemotron 3.5 Super on Macs with 128 GB or more. In tests on an earlier build of the library, 22 blackout and first-aid questions were graded blind, and the Nemotron 3.5 Super model got 21 of 22 right while the IBM Granite 4.2 8B model got 14 of 22 right. Setup takes 20 minutes to 2.5 hours and it also lets you search your own PDFs and documents. ## Full article daily.dev links to this article rather than hosting it. Read it at the original source: ## Questions this post answers ### How accurate is a local LLM like IBM Granite 4.2 8B compared to larger models for emergency first-aid questions? In blind grading of 22 blackout and first-aid questions, IBM Granite 4.2 8B (used on machines with 16-31 GB memory) got 14 of 22 right, compared to 21 of 22 for NVIDIA Nemotron 3.5 Super (80 GB download, 128+ GB Mac) and Qwen3.8 27B (18 GB download, 32-96 GB Mac). Smaller models make more mistakes, which is why every answer shows its source articles to verify against. _Comparing local model sizes for accuracy is easier with model benchmarks tracked on daily.dev._ ### How can I run a retrieval-augmented AI assistant fully offline with no internet connection? Survival LLM runs both the AI model and a retrieval library entirely on your own computer, so once setup finishes while online, it works with no internet at all. It searches a local library of plain-text references (medical guides, FEMA/CDC guidance, Wikipedia articles) and shows the exact source passage behind every answer, with no account or tracking involved. _Developers building offline-capable RAG apps follow local-LLM tooling and releases on daily.dev._ ### How much disk space and memory do I need to run a local AI model with a large offline reference library? Free disk space needed equals the model size plus 5 GB, plus about 27 GB during installation of the full library (10 GB for the smaller library variant). The full library is a 9 GB download (19 GB installed) including Wikipedia and iFixit, while a smaller 1.3 GB download (3 GB installed) excludes those. Models range from 6 GB (8B parameters, 16 GB RAM) to 80 GB (128+ GB RAM). _Sizing hardware for local AI deployments gets easier when comparable setups surface on daily.dev._ ## Community take How the wider developer community reacted, aggregated from 2 discussions and 124 comments across hackernews (as of 2026-10-28). **TL;DR:** Reaction is largely skeptical: many argue a plain searchable archive (e.g. Kiwix) or vector/semantic search over the source documents would be safer and more useful than an LLM prone to hallucination, especially in life-or-death scenarios, though some see value in the chat-style interface for navigating unfamiliar information. **Sentiment:** 15% positive · 25% mixed · 60% skeptical **The case for** - The chat interface can help users find relevant information even when they don't know the right search terms. - Grounding answers in cited source passages makes it easier to fact-check than a bare chatbot. - Some found the idea of a portable, offline knowledge assistant genuinely useful for real disconnected scenarios like hurricanes or remote property. **The pushback** - Small local models are prone to hallucination and scored only 14/22 on modest hardware, which is risky for life-or-death advice. - Several argue a non-LLM searchable archive (like Kiwix or a classic search index) would be smaller, more reliable, and just as useful. - A tested bow-drill fire-starting answer was partially wrong, raising doubts about real-world reliability. - Running an LLM requires substantial power and hardware that may not be available during an actual blackout or grid-down emergency. - No Linux or mobile/Android support, which some see as a major gap for an offline/emergency tool. - The macOS 'unverified developer' warning and overall execution read as low-effort or possibly AI-generated to some commenters. **By community** - hackernews (skeptical): Extensive debate largely converges on doubting that an LLM is the right tool here, favoring plain searchable archives, though a vocal minority defends the project's RAG approach and real-world utility. **Hottest debate:** Whether an LLM-based interface adds real value over a simple searchable offline archive (like Kiwix) for life-or-death survival information. **Open questions** - Would a non-LLM semantic/vector search over the same sources perform just as well with far less overhead and hallucination risk? - How reliable is the system when questions fall outside its curated source material? - Can the tool realistically run during an actual power/internet outage given hardware and energy demands? - Why wasn't the app fine-tuned on the test question set to push accuracy higher, and why were certain models (e.g. Qwen variants) not used instead of Granite? **Highlights** > 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. > — [wanderingbit on hackernews · 1 comments](https://news.ycombinator.com/item?id=50268301) > 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. > — [macNchz on hackernews](https://news.ycombinator.com/item?id=50266551) > 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. > — [dvt on hackernews · 3 comments](https://news.ycombinator.com/item?id=50265640) > Please, please do not trust a chatbot in emergencies. There’s a reason why actual survival and emergency preparedness guides include manuals rather than recommend you just use a chatbot: practicality. Just look at those specs needed for accurate-ish results. You really think your 128GB Mac Studio or Framework desktop is going to be able to work in a real emergency situation? Even if it is, how long will it last? What about those precious seconds waiting for inference? The danger here is less hallucinations and more, well, everything else about it. Instead of setting this up, take the time to make sure you have a restocked first aid kit and enough water for a week of use by your household. Stock up on meds and non-perishable food. Plan exit strategies and contingencies. Acquire and store little first aid booklets or manuals of common survival tasks in physical form (or hell, throw onto an eReader - infinitely more battery life than an LLM host in an emergency). This is one of those “sounds good in my head” projects that folks with emergency experience would bristle at. This is a hindrance, not a helper. > — [stego-tech on hackernews](https://news.ycombinator.com/item?id=50268388) > 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. > — [saaaaaam on hackernews · 2 comments](https://news.ycombinator.com/item?id=50265844) **Source threads** - [hackernews](https://news.ycombinator.com/item?id=50264853) · 219 points · 122 comments - [hackernews](https://news.ycombinator.com/item?id=50252314) · 3 points · 2 comments ## Similar posts on daily.dev - [I built a private AI study assistant that runs entirely on my laptop](https://daily.dev/posts/i-built-a-private-ai-study-assistant-that-runs-entirely-on-my-laptop-rkruwx0ax) · XDA Developers · 0 upvotes · 0 comments - [Privacy First, Zero Internet. Download an LLM and use it on your mobile device. No data ever leaves your phone. 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