Vibecode Auritrack
track this build5 steps, step by step0%KINDA · weekend project
The core loop, logging expenses by typing a sentence into a chat box and having an LLM extract amount, payee, and category, is very buildable with an Anthropic API key. Budgets, categories, and spending reports are standard CRUD. The real gap is statement import: getting one clean CSV parsed by an LLM is a demo, but reliably parsing messy multi-page bank PDFs across many banks needs batching, fallbacks, and cross-checks that take far longer than a weekend.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Auritrack. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Auritrack indie build ## Goal Build the smallest trustworthy replacement for the core Auritrack workflow for one developer or a tiny team. ## Scope Build a chat input that sends free-text expenses to an LLM with a strict extraction schema, save transactions to SQLite, auto-create categories, track budgets per month, and chart spending. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - battle-tested statement parsing across many banks and layouts - mobile apps and push notifications - Telegram bot logging - predictive spending forecasts - polished AI-written financial reports If those capabilities are essential, use Firefly III instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build me an AI expense tracker to replace Auritrack. Requirements: - Local web app: Node + Express + better-sqlite3, server-rendered, binds to localhost only. - Chat box on the home page: I type "lunch 12.50 at Chipotle yesterday" and the app calls the Claude API (claude-sonnet-5) with a tool schema to extract amount, payee, category, and date, then saves the transaction. Show the parsed result inline with an edit button so I can correct mistakes. - Categories are auto-created the first time the model uses one; also give me plain manual add/edit forms for transactions and categories as a fallback. - Statement import: upload a CSV or a text-layer PDF (use pdf-parse). Send rows or pages to the model in batches with a strict JSON schema, show everything in a review table before committing, and dedupe on date + amount + payee. - Budgets: monthly limit per category with a progress bar that turns red on overspend. - Reports: spending by category per month and a 6-month trend line with Chart.js, plus an "ask about my spending" box where the model writes a SQL query, runs it against a read-only connection, and explains the answer. - Nightly copy of the SQLite file to backups/, keep 30. - ANTHROPIC_API_KEY lives in .env; send the model only the rows a request needs and never log transaction data. - Out of scope: bank sync, scanned-image PDFs, mobile apps, Telegram bots, and multi-user. Note in the README that imports cost real API money and roughly how much per statement. - README: how to map my bank's CSV columns on first import. ## Required capabilities - Anthropic API key - local or hosted database - CSV import - PDF text extraction - charting library ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a lean indie version of Auritrack. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== README.md ===== # Auritrack indie build ## Goal Build the smallest trustworthy replacement for the core Auritrack workflow for one developer or a tiny team. ## Scope Build a chat input that sends free-text expenses to an LLM with a strict extraction schema, save transactions to SQLite, auto-create categories, track budgets per month, and chart spending. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - battle-tested statement parsing across many banks and layouts - mobile apps and push notifications - Telegram bot logging - predictive spending forecasts - polished AI-written financial reports If those capabilities are essential, use Firefly III instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build me an AI expense tracker to replace Auritrack. Requirements: - Local web app: Node + Express + better-sqlite3, server-rendered, binds to localhost only. - Chat box on the home page: I type "lunch 12.50 at Chipotle yesterday" and the app calls the Claude API (claude-sonnet-5) with a tool schema to extract amount, payee, category, and date, then saves the transaction. Show the parsed result inline with an edit button so I can correct mistakes. - Categories are auto-created the first time the model uses one; also give me plain manual add/edit forms for transactions and categories as a fallback. - Statement import: upload a CSV or a text-layer PDF (use pdf-parse). Send rows or pages to the model in batches with a strict JSON schema, show everything in a review table before committing, and dedupe on date + amount + payee. - Budgets: monthly limit per category with a progress bar that turns red on overspend. - Reports: spending by category per month and a 6-month trend line with Chart.js, plus an "ask about my spending" box where the model writes a SQL query, runs it against a read-only connection, and explains the answer. - Nightly copy of the SQLite file to backups/, keep 30. - ANTHROPIC_API_KEY lives in .env; send the model only the rows a request needs and never log transaction data. - Out of scope: bank sync, scanned-image PDFs, mobile apps, Telegram bots, and multi-user. Note in the README that imports cost real API money and roughly how much per statement. - README: how to map my bank's CSV columns on first import. ## Required capabilities - Anthropic API key - local or hosted database - CSV import - PDF text extraction - charting library ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden. ===== .env.example ===== # Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
You are building a production product version of Auritrack. Create the following project files first, then implement the application by following them. Keep the files updated as decisions change. Do not collapse this into a single README or prompt. ===== PRODUCT.md ===== # Auritrack product brief ## Problem The core loop, logging expenses by typing a sentence into a chat box and having an LLM extract amount, payee, and category, is very buildable with an Anthropic API key. Budgets, categories, and spending reports are standard CRUD. The real gap is statement import: getting one clean CSV parsed by an LLM is a demo, but reliably parsing messy multi-page bank PDFs across many banks needs batching, fallbacks, and cross-checks that take far longer than a weekend. ## Product outcome Build a chat input that sends free-text expenses to an LLM with a strict extraction schema, save transactions to SQLite, auto-create categories, track budgets per month, and chart spending. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Anthropic API key - local or hosted database - CSV import - PDF text extraction - charting library ## Explicit non-goals for v1 - battle-tested statement parsing across many banks and layouts - mobile apps and push notifications - Telegram bot logging - predictive spending forecasts - polished AI-written financial reports ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build me an AI expense tracker to replace Auritrack. Requirements: - Local web app: Node + Express + better-sqlite3, server-rendered, binds to localhost only. - Chat box on the home page: I type "lunch 12.50 at Chipotle yesterday" and the app calls the Claude API (claude-sonnet-5) with a tool schema to extract amount, payee, category, and date, then saves the transaction. Show the parsed result inline with an edit button so I can correct mistakes. - Categories are auto-created the first time the model uses one; also give me plain manual add/edit forms for transactions and categories as a fallback. - Statement import: upload a CSV or a text-layer PDF (use pdf-parse). Send rows or pages to the model in batches with a strict JSON schema, show everything in a review table before committing, and dedupe on date + amount + payee. - Budgets: monthly limit per category with a progress bar that turns red on overspend. - Reports: spending by category per month and a 6-month trend line with Chart.js, plus an "ask about my spending" box where the model writes a SQL query, runs it against a read-only connection, and explains the answer. - Nightly copy of the SQLite file to backups/, keep 30. - ANTHROPIC_API_KEY lives in .env; send the model only the rows a request needs and never log transaction data. - Out of scope: bank sync, scanned-image PDFs, mobile apps, Telegram bots, and multi-user. Note in the README that imports cost real API money and roughly how much per statement. - README: how to map my bank's CSV columns on first import. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it. ===== AGENTS.md ===== # Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone. ===== MILESTONES.md ===== # Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations. ===== OPERATIONS.md ===== # Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Auritrack capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Auritrack indie build ## Goal Build the smallest trustworthy replacement for the core Auritrack workflow for one developer or a tiny team. ## Scope Build a chat input that sends free-text expenses to an LLM with a strict extraction schema, save transactions to SQLite, auto-create categories, track budgets per month, and chart spending. ## Quick start 1. Install the documented dependencies. 2. Copy `.env.example` to `.env`. 3. Run the development command chosen during implementation. 4. Complete the acceptance checks in `BUILD_PLAN.md`. ## Honest limits This build deliberately does not replace: - battle-tested statement parsing across many banks and layouts - mobile apps and push notifications - Telegram bot logging - predictive spending forecasts - polished AI-written financial reports If those capabilities are essential, use Firefly III instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build me an AI expense tracker to replace Auritrack. Requirements: - Local web app: Node + Express + better-sqlite3, server-rendered, binds to localhost only. - Chat box on the home page: I type "lunch 12.50 at Chipotle yesterday" and the app calls the Claude API (claude-sonnet-5) with a tool schema to extract amount, payee, category, and date, then saves the transaction. Show the parsed result inline with an edit button so I can correct mistakes. - Categories are auto-created the first time the model uses one; also give me plain manual add/edit forms for transactions and categories as a fallback. - Statement import: upload a CSV or a text-layer PDF (use pdf-parse). Send rows or pages to the model in batches with a strict JSON schema, show everything in a review table before committing, and dedupe on date + amount + payee. - Budgets: monthly limit per category with a progress bar that turns red on overspend. - Reports: spending by category per month and a 6-month trend line with Chart.js, plus an "ask about my spending" box where the model writes a SQL query, runs it against a read-only connection, and explains the answer. - Nightly copy of the SQLite file to backups/, keep 30. - ANTHROPIC_API_KEY lives in .env; send the model only the rows a request needs and never log transaction data. - Out of scope: bank sync, scanned-image PDFs, mobile apps, Telegram bots, and multi-user. Note in the README that imports cost real API money and roughly how much per statement. - README: how to map my bank's CSV columns on first import. ## Required capabilities - Anthropic API key - local or hosted database - CSV import - PDF text extraction - charting library ## Delivery order 1. Scaffold the smallest runnable application and document its commands. 2. Implement the primary data model and core workflow. 3. Add validation, safe failure states, and persistence. 4. Cover the critical path with automated tests. 5. Exercise a clean install from the README and fix every missing step. ## Done when - A new user can go from clone to first successful workflow using only the README. - The core workflow works without paid infrastructure unless the brief requires it. - Tests cover the highest-risk behavior. - Known limitations are explicit rather than hidden.
.env.example
# Copy to .env and document every variable when it is introduced. # Never put real credentials in this file. APP_ENV=development # Add only values required by the selected implementation.
PRODUCT.md
# Auritrack product brief ## Problem The core loop, logging expenses by typing a sentence into a chat box and having an LLM extract amount, payee, and category, is very buildable with an Anthropic API key. Budgets, categories, and spending reports are standard CRUD. The real gap is statement import: getting one clean CSV parsed by an LLM is a demo, but reliably parsing messy multi-page bank PDFs across many banks needs batching, fallbacks, and cross-checks that take far longer than a weekend. ## Product outcome Build a chat input that sends free-text expenses to an LLM with a strict extraction schema, save transactions to SQLite, auto-create categories, track budgets per month, and chart spending. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Anthropic API key - local or hosted database - CSV import - PDF text extraction - charting library ## Explicit non-goals for v1 - battle-tested statement parsing across many banks and layouts - mobile apps and push notifications - Telegram bot logging - predictive spending forecasts - polished AI-written financial reports ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build me an AI expense tracker to replace Auritrack. Requirements: - Local web app: Node + Express + better-sqlite3, server-rendered, binds to localhost only. - Chat box on the home page: I type "lunch 12.50 at Chipotle yesterday" and the app calls the Claude API (claude-sonnet-5) with a tool schema to extract amount, payee, category, and date, then saves the transaction. Show the parsed result inline with an edit button so I can correct mistakes. - Categories are auto-created the first time the model uses one; also give me plain manual add/edit forms for transactions and categories as a fallback. - Statement import: upload a CSV or a text-layer PDF (use pdf-parse). Send rows or pages to the model in batches with a strict JSON schema, show everything in a review table before committing, and dedupe on date + amount + payee. - Budgets: monthly limit per category with a progress bar that turns red on overspend. - Reports: spending by category per month and a 6-month trend line with Chart.js, plus an "ask about my spending" box where the model writes a SQL query, runs it against a read-only connection, and explains the answer. - Nightly copy of the SQLite file to backups/, keep 30. - ANTHROPIC_API_KEY lives in .env; send the model only the rows a request needs and never log transaction data. - Out of scope: bank sync, scanned-image PDFs, mobile apps, Telegram bots, and multi-user. Note in the README that imports cost real API money and roughly how much per statement. - README: how to map my bank's CSV columns on first import. ## Boundaries Separate the product into replaceable modules for interface, application logic, persistence, external integrations, and operational concerns. Keep domain logic independent from delivery frameworks and vendors. ## Production baseline - Configuration: validated at startup with safe local defaults where possible. - Security: least privilege, input validation, secret redaction, rate limits on abuse-prone paths, and no invented security primitives. - Data: explicit schema and migrations, transactional writes where integrity matters, backup and restore instructions. - Integrations: adapters around third-party providers, idempotent webhook or job processing, bounded retries, and timeouts. - Observability: structured logs with request or operation IDs, an error-tracking hook, and health/readiness checks where a server exists. - Quality: unit tests for domain rules, integration tests at module boundaries, and one end-to-end critical-path test. ## Decision records For each major dependency, document why it was chosen, its failure mode, and how it can be replaced. Do not introduce infrastructure until a requirement justifies it.
AGENTS.md
# Agent instructions - Read `PRODUCT.md` and `ARCHITECTURE.md` before changing code. - Implement milestone by milestone; keep each change reviewable and leave the application runnable. - Treat authentication, payments, encryption, imports, webhooks, and destructive actions as high-risk boundaries when present. - Never invent cryptography or silently weaken a requirement to make a test pass. - Use provider interfaces for external services and deterministic fakes in tests. - Add migrations and rollback or recovery notes for persistent data changes. - Log useful operational context without credentials, tokens, passwords, or personal data. - Update documentation and run all checks before completing a milestone.
MILESTONES.md
# Delivery milestones ## M0 — Decisions and scaffold - Confirm the runtime, persistence model, threat boundaries, and deployment target. - Create a reproducible local environment and continuous checks. ## M1 — Core workflow - Implement the smallest end-to-end product path with validation and tests. - Keep integrations behind interfaces. ## M2 — Trust layer - Add secure failure behavior, recovery paths, audit-relevant events, and data safeguards. - Test abuse cases and destructive operations. ## M3 — Operability - Add structured logs, error reporting hooks, health signals, backup/restore documentation, and deployment configuration. ## M4 — Release gate - Run a clean-install test, critical-path end-to-end test, dependency review, and documented rollback exercise. - Compare the shipped behavior with `PRODUCT.md` and publish remaining limitations.
OPERATIONS.md
# Operations ## Before release - Validate configuration and secrets at startup. - Define backup, restore, and rollback procedures and test them. - Document logs, error tracking, health signals, and alert ownership. - Set dependency update and vulnerability review expectations. ## Incident checklist 1. Contain the issue without destroying evidence or user data. 2. Record the timeline and affected scope. 3. Rotate exposed secrets and revoke compromised sessions or credentials. 4. Restore from a verified source when needed. 5. Document the root cause, remediation, and regression test. ## Launch constraint Do not market omitted Auritrack capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
$ choose a build depth, inspect the files, then open the complete pack in your agent
They pay because a hardened parsing pipeline, mobile apps, and chat-anywhere logging remove all friction, and $3/mo is cheaper than maintaining your own LLM plumbing.
xbattle-tested statement parsing across many banks and layouts
xmobile apps and push notifications
xTelegram bot logging
xpredictive spending forecasts
xpolished AI-written financial reports
share on X ↗"I just replaced Auritrack ($3/mo) with one prompt"
Don't feel like building it? These folks already made it free.
TaxHackerFeed it receipts, invoices or bank statements and let an LLM fill the ledger; it is useful, young and very much your server.open source↗no votes, no pay-to-list · just what's real
Auritrack pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| free | $0/workspace | $0/workspace | Manual app; 1 AI report/month; 1 custom report/month; 1 period report/month; 1 export/month; 0 alerts |
| plus | $3/workspace | $2.50/workspace | 50 coins/month or 600/year; 3 AI reports, 3 custom reports, 3 period reports, and 5 exports/month; 0 alerts |
| pro | $8/workspace | $6.67/workspace | 150 coins/month or 1,800/year; unlimited chat, deep dives, and voice; 15 AI/custom/period reports and 30 exports/month; alerts included |
| premium | $20/workspace | $16.67/workspace | 450 coins/month or 5,400/year; all report types and exports included; transaction imports remain pay per use |
free tier1 AI report, 1 custom report, 1 period report, and 1 export per month; 0 alerts; transaction imports and additional AI actions are pay per use
billingfree + monthly/annual paid plans (annual is 2 months free)
hidden costsSubscription coins refill each billing cycle and do not roll forward; purchased top-up coins do not expire, but public top-up prices were not disclosed. Transaction imports remain pay per use even on Premium.
verified 2026-08-10 · source ↗
Vibecode Auritrack
Kinda. The core of Auritrack is buildable in a weekend with the prompt on this page, but there are real gaps: battle-tested statement parsing across many banks and layouts, mobile apps and push notifications. Read the honest list above before committing.
How much does Auritrack cost?
Auritrack costs about $3/month (Plus, checked 2026-08-01), which is $36 per year.
What do I lose by replacing Auritrack?
Honestly: battle-tested statement parsing across many banks and layouts; mobile apps and push notifications; Telegram bot logging; predictive spending forecasts; polished AI-written financial reports. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Auritrack?
Yes: TaxHacker (Feed it receipts, invoices or bank statements and let an LLM fill the ledger; it is useful, young and very much your server.) The prompt is for when you want it exactly your way.