A few months ago, I resigned from my role. As I sat down to update my resume and prep for interviews, a wave of anxiety hit me: I couldn't remember half of what I actually built over the last two years.
Sure, I remembered the massive architectures and the major outages. But the elegant race-condition fix in the authentication module? The week I spent unblocking the junior devs on a legacy database migration? The subtle performance tweaks that shaved 200ms off our core API response time? Gone. I call this Career Amnesia.
We spend 40+ hours a week shipping value, but because everything is scattered across internal Jira boards, GitHub Enterprise PRs, and Slack channels, the moment we lose corporate VPN access, our professional history vanishes. When appraisal season or job hunts come around, we are left completely weaponless, unable to quantify our value to managers or interviewers.
I looked for solutions. Notion and Obsidian require too much manual formatting and rely on you remembering to structure your data correctly. Existing career tools wanted me to sync my company's proprietary Jira data to a random startup's cloud server—a massive compliance violation waiting to happen.
So, I built Tenurr: a lifelong, private career ledger and performance defense hub designed specifically for individual tech professionals. It's live now at tenurr.com (app is at app.tenurr.com).
Here is the deep breakdown of the modules I built into Version 1.0.0, how it works under the hood, and how I approached the architecture to ensure engineering privacy.
🎯 Key Modules & Product Features
1. Self-Sovereign Work Ledger
Unlike a blank Notion page, Tenurr enforces a strict schema for your logs, eliminating friction and ensuring the AI can easily extract data later.
- Daily Contribution Logs: A clean, chronological timeline to record code changes, system designs, launches, and critical bug fixes.
- Encrypted at Rest: All sensitive logs are encrypted using industry-standard AES-256-GCM to ensure complete privacy.
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Categorization Tags: Custom tag associations (e.g.,
Code,Design,Review) for rapid filtering and structural analysis.
2. AI Appraisal Compiler & PIP Defense
This is the execution engine that bridges the gap between raw engineering tasks and business metrics.
- STAR Method Formatting: Translates messy, technical logs into highly professional STAR (Situation, Action, Result) highlights optimized for managers and review committees.
- AI Evaluation Engine: Generates confidence scores and gap analyses by comparing your logged work history against active workplace objectives.
3. Google Drive Documents Vault
- Sovereign Sync: Connects to an isolated, sandboxed application folder inside your own personal Google Drive account. Raw financial and legal PDFs never sit on our central storage servers.
- Dual-Layer Security: Encrypts file metadata in our database while maintaining raw document assets on your Google Drive. If our servers are ever breached, your financial PDFs simply aren't there to be stolen.
4. Job Application & Kanban Tracker
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Unified Kanban Board: Visual pipelines (
bookmarked,applied,interviewing,offer,rejected) to manage your external search funnel. -
Performance-Optimized Mobile Gestures: Touch gestures featuring horizontal auto-scroll loops and programmatic viewport positioning via
requestAnimationFrameto handle tracking columns smoothly on small screens.
5. ReferMe Link Hub
- Referral Dispatch Card: Fill out your contact details, resume URL, and target company once to generate a custom tracking token.
- View Receipts: Monitor real-time page views and timestamps on your dispatched links so you know exactly when referrers or HR open them.
- Auto-Expiry Security: Configure link active durations to automatically protect your sensitive resume assets after referrals are completed.
⚙️ Under the Hood: Architecture & Schema Choices
To make this secure and performant, I had to be deliberate about the tech stack.
- The Stack: Tenurr is built on Next.js 16.2 (App Router, Turbopack), utilizing PostgreSQL via Prisma on Supabase for the backend.
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Database Schema: Because career data is highly relational, I opted for a normalized SQL schema. The core entities (
Workspaces,Logs,Goals,JobApplications) are strictly typed. For example, aLogisn't just text; it contains aJSONBcolumn for metric impacts (e.g.,{"latency_reduction_ms": 200}), making it easily queryable by the AI compiler. -
The AI Pipeline: The AI features are powered by the powerfull LLM. Instead of sending raw user data in a massive prompt, Tenurr uses a retrieval-augmented generation (RAG) approach, pulling only the strictly relevant
Logsbased on tag metadata to construct the STAR format outputs. -
Google Drive API: By requesting restricted scope access (
auth/drive.appdata), Tenurr can read/write to a hidden application data folder that the user controls, completely sandboxing it from the rest of their Drive files.
💳 Keeping the Servers Running
I built this primarily to solve my own problem, but hosting AI infrastructure and secure databases isn't free. Tenurr integrates via Dodo Payments (supporting regional currency conversion) to keep things sustainable.
- Starter (Free): 6 weeks of history, 1 workplace, max 3 active ReferMe links, and 20 free AI credits/month.
- Pro ($9/mo): Unlimited history, unlimited workspaces, unlimited ReferMe links, and 100 AI credits/month.
- Elite ($19/mo): 300 AI credits/month and dedicated support channels for heavy users.
Built for the Lifelong Job Holder
A software engineer's most valuable asset isn't the company they work for—it's the cumulative knowledge, experience, and historical impact they generate along the way. Your company has an HR system to track you; you deserve a personal system to track your career assets.
Tenurr v1.0.0 is officially live today. Take absolute custody of your career data.
👉 Check it out at tenurr.com and log in to the web app at app.tenurr.com.
I am open to any brutal feedback on the architecture, the schema choices, the UX, or features you'd want to see next!
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