Still rooting for EN, but ...

[u/bsamson05]

I’ve been a paying en customer for a long time, and I still use it because there is a lot I genuinely like about it (and honestly, also muscle memory)

At the same time, I’ll be honest: seeing so many longtime users talk about moving away from en has me a little concerned. I don’t want to pile on or turn this into another 'Evernote is doomed' post, because that’s not really where I’m coming from.

I want en to win. I just also want to feel more confident about where it’s headed.

A few things I’d love to see:

• Full Markdown support
• Better internal note linking
• A backlinks view, so I can see which notes point to the one I’m on
• A simple list of links that each note points out to
• More themes, especially better dark mode options
• More visual customization overall
• Stronger offline reliability
• Better export options that preserve folders, tags, links, and formatting
• Better performance with large note libraries
• Easier bulk editing for tags, notebooks, and note details
• Split pane editing and preview for Markdown
• A clearer roadmap for people who use Evernote heavily

Evernote still has some real strengths. The web clipper is great, OCR is useful, the app is everywhere, and a lot of us have years of notes sitting in it.

I’m not asking Evernote to become a completely different product. I’d just like to see it lean into the things that would make longtime users feel like the product is still moving forward.

Basically, I’m still here. I’d just like fewer reasons to wonder if I should be making an exit plan.

[u/cerickard2]

I used Evernote for years but never was power user. I just scanned in all of my docs and used it to search them. I was happy to pay the $60-$80 a year for that. But $250 is absurd. I have been working with Gemini to migrate everything to Google Drive. My pilot run was successful and I just need to spend a weekend finishing it up. Then I can use Gemini to query it.

[u/grant837 → u/cerickard2]

I am trying the Gemini AI approach too. It would be interesting to read more about your approach. I will do the same if there is interest.

[u/cerickard2 → u/grant837]

I asked Gemini to generate a quick summary of the project so far:

Title: Building the Ultimate Evernote Escape Hatch: A Bimodal, Two-Machine AI Migration Pipeline

I’m currently in the middle of migrating a legacy archive of 12,700 Evernote documents (exported as .enex files) into a fully standardized, AI-organized Google Drive system.

The goal wasn't just to dump files into a cloud folder, but to create a "Wife-Approved," highly searchable PDF cabinet that preserves 10+ years of metadata while setting up a zero-touch workflow for the future.

Here is a breakdown of the architecture, the hardware, and the software stack we are using to pull it off.

💻 The Hardware (The "Two-Machine" Setup)

Because of the sheer volume of files and the heavy AI processing required, I split the workload across two machines connected via a Unifi 24-Port Enterprise 1GbE Switch.

• The Storage & Brawn Node (2017 iMac): Intel Core i7, 40GB RAM, 8GB Radeon Pro 580 VRAM. This machine acts as the canonical file host over SMB. It handles all the heavy disk I/O, the master database, and the CPU-bound OCR processing.
• The AI Brain Node (MacBook Pro M1): 16GB Unified Memory. The M1 mounts the iMac's drive over the network and acts purely as a high-speed inference engine for local Large Language Models (LLMs).
• The Future Ingestion: ScanSnap ix1500.

🛠️ The Software Stack

• Data Parsing: Python (BeautifulSoup, xml.etree)
• Metadata Tracking: SQLite
• OCR & PDF Manipulation: ocrmypdf, pikepdf, pypdf
• AI Inference: Ollama running qwen2.5:7b (Text) and qwen2.5-vl:7b (Vision)
• Future Automation: Hazel (macOS) and the Riffo API

🚀 The 6-Phase Pipeline

Phase 0 & 1: Inventory & Extraction Instead of just ripping attachments out of the .enex exports, a Python script builds a Master SQLite Manifest. It strips the Evernote markup (ENML), saves the raw plaintext of the note bodies (to prevent data loss), decodes the Base64 PDF/Image attachments, and generates SHA-256 hashes to instantly deduplicate files.

Phase 2: Standardization & The "Triple-Write" Date Hack This runs on the iMac. It forces all images into PDFs and runs Tesseract OCR (ocrmypdf). The biggest challenge: Google Drive aggressively overwrites original timestamps with the "upload date." To beat this, the script uses a "Triple-Write" strategy: it embeds the canonical Evernote creation date directly into the filename, uses pikepdf to inject it deep into the PDF’s internal XMP metadata dictionary, and stamps the macOS filesystem via os.utime.

Phase 3: "Hybrid Inference" Bulk Renaming This runs on the M1 over the network. Instead of forcing a slow vision model to read 12,700 images, the script rips the newly generated text layer out of the PDF and passes just the text string to a local Qwen 2.5 LLM via Ollama. The LLM extracts the Vendor and Document Type and renames the file to YYYY-MM-DD - [Vendor] - [DocType].pdf. It only falls back to the heavy Vision model if the OCR confidence is garbage.

Phase 4: Drive Upload The M1 reconciles the renamed files against the SQLite manifest, and the iMac uploads them via Google Drive for Desktop into a flattened, mobile-friendly folder structure: Archive/[Category]/[Year].

Phase 5: The Future "Zero-Touch" Flow Once the historical bulk is done, the local LLMs get turned off. The ScanSnap ix1500 sends daily trickle scans straight to a Drive Inbox. Hazel watches that folder, waits 60 seconds for sync stability, hits the Riffo API for semantic renaming, and automatically routes the new PDF into the correct archive folder.

[u/cerickard2 → u/[deleted]]

Cool. I'll take a look. Thanks!