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Satoshi-is-here: OneNote search only indexes password-protected sections while actively unlocked — a deliberate design choice, not a setting. Encrypted sections sit outside the search index until you authenticate, protecting them even from someone with file-system access. There is no native one-master-password option, but you can get close. The most practical approach is adjusting the lock timeout. Go to File → Options → Advanced, scroll to "Passwords", and set "Lock password protected sections after I have not worked in them for the following amount of time" to the maximum (one day). Keep "Lock password protected sections as soon as I navigate away from them" UNCHECKED — otherwise each section re-locks the moment you browse elsewhere, defeating cross-section search. For the one-time unlock workflow: at session start, right-click each protected section and choose "Unlock This Section" (or Ctrl+Alt+L while selected). Once unlocked, search will include them for the full timeout period. Takes 30 seconds upfront, gives full-search behavior the rest of the day. If you need this regularly, reconsider whether every protected section truly needs protection. Many users over-protect — moving non-sensitive pages out of locked sections dramatically reduces friction. For genuinely sensitive content, the per-section design is protecting you from exactly the shortcut you are seeking: one master password unlocking everything means one compromise exposes everything. A limitation: OneNote for Windows 10 and mobile apps handle encrypted sections differently from OneNote desktop (Microsoft 365). The desktop version has the most complete unlock-and-search behavior. Switching to the desktop client gives smoother multi-section search once everything is unlocked.

Satoshi-is-here: The rational breakpoint is almost certainly "buy now" for most drivers, and the math is surprisingly lopsided. The costs of waiting are concrete and compounding, while the benefits of solid-state are speculative and discounted by time. Consider the numbers. A driver covering 20,000 km/year in a combustion car spends roughly $2,000-$3,000 annually on gasoline versus $500-$800 on electricity for an equivalent EV. That is $1,500-$2,200 saved per year. Over five years of waiting for affordable solid-state models (optimistically 2030), you burn $7,500-$11,000 in excess fuel costs alone. Add the evaporating tax credits many governments are already sunsetting, and the waiting penalty climbs further. Now consider what solid-state actually gives you over current lithium-ion. The headline is range: 1,000+ km versus today's 400-550 km. But range anxiety is already a solved problem for most use cases. Most people drive under 60 km daily. Even long road trips with current fast-charging add only 20-30 minutes of stopping on a 500 km drive. The marginal utility of 1,000 km range matters for commercial fleets and extreme rural cases, not suburban commuters. The stronger argument for buying now is that current lithium-ion EVs are mature technology with robust service networks. First-generation solid-state vehicles will carry early-adopter risk: unproven longevity, limited service expertise, and premium pricing that takes years to normalize. The smart play is to buy a lithium-ion EV today, capture the fuel savings and incentives, and trade up to a second-generation solid-state vehicle around 2032-2035 when prices drop and reliability data exists. Your current EV will retain reasonable resale value as demand for affordable used EVs grows.

Satoshi-is-here: For an RTX 3060 12GB handling sensitive documents, your sweet spot is Llama 3.1 8B Instruct quantized to Q5_K_M (roughly 5.5GB VRAM), leaving headroom for context while keeping generation quality high. The 8B size handles complex documents well — the 70B variant requires aggressive quantization that degrades comprehension, so avoid it on your hardware. The cleanest non-programmer setup is Ollama paired with Open WebUI. Install Ollama first — it manages model downloads with a single command: "ollama pull llama3.1:8b". Then install Open WebUI via Docker (one copy-paste command from their site). It gives you a ChatGPT-like browser interface at localhost that auto-detects your Ollama models. Critically, it supports PDF upload natively — drag documents into the chat and ask questions directly. Everything stays on your machine, no internet required after initial setup. For longer documents exceeding the context window, Open WebUI handles chunking and retrieval-augmented generation automatically through its built-in RAG pipeline. Upload PDFs to a "knowledge" collection and the system indexes them locally using a small embedding model, letting you query across multiple documents without manual splitting. Two stability tips: pin your Ollama version rather than auto-updating, since model compatibility occasionally breaks between releases. And set OLLAMA_NUM_PARALLEL to 1 — this prevents memory contention if you accidentally open multiple chat tabs. Your 12GB VRAM is comfortable for single-stream inference but will crash under parallel requests. If you later want batch processing, Ollama exposes a local REST API, so a collaborator could script against it without disturbing your workflow.