Vibecode Littlebird
track this build5 steps, step by step0%KINDA · weekend project
Public detail on this one is thin, so treat this entry as a first pass rather than a teardown. Anything in the "AI assistant that reads and drafts for you" shape is mostly a wrapper: a model API, a place to put your context, and a loop that runs on a schedule. A coding agent can rebuild that shape in an evening and you keep the API bill instead of the subscription. What you will not rebuild in an evening is the polish, the mobile surface, and whatever integrations the paid product ships against your actual accounts. Verdict stays "kinda" until someone confirms whether the real value here is the agent loop or the connectors underneath it.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Littlebird. 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 ===== # Littlebird indie build ## Goal Build the smallest trustworthy replacement for the core Littlebird workflow for one developer or a tiny team. ## Scope A local web app where you chat with a model over your own uploaded notes and links, plus a scheduled job that produces a digest of anything you told it to watch. ## 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: - Mobile app and notifications; your version is a browser tab - Prebuilt connectors to email, calendar, Slack and whatever else the paid product authenticates against - Someone else tuning prompts and swapping models when a better one ships - Reliability: your cron job dies silently and nobody pages you - Any team or sharing features, if the product has them If those capabilities are essential, use Littlebird 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 a local personal AI assistant web app in an empty folder. No accounts, no cloud, no telemetry. Stack, no substitutions: - TypeScript, Node 20, Fastify for the server - SQLite via better-sqlite3, file at ./data/assistant.db - Server-rendered HTML with htmx for interactivity, plain CSS, no React, no Tailwind - OpenAI-compatible chat completions and embeddings, base URL and key read from .env (OPENAI_API_KEY, OPENAI_BASE_URL, CHAT_MODEL, EMBED_MODEL) - node-cron for scheduled jobs, all in one process Features in scope: 1. Context library: paste text, upload .md/.txt, or submit a URL. URLs are fetched server side and reduced to readable text with @mozilla/readability plus jsdom. Chunk to ~800 tokens, embed, store chunks and vectors in SQLite. 2. Chat: a single conversation view. On each message, retrieve the top 8 chunks by cosine similarity, put them in the system prompt with source titles, stream the reply back. Persist all turns. 3. Watchlist: each row is a name, a URL or a search query, and a cadence (daily or weekly). A cron job fetches each item, diffs the extracted text against the last stored snapshot, and asks the model to summarize what changed in under 80 words. Skip items with no meaningful diff. 4. Digest: a /digest page listing the latest summaries newest first, plus a digest.md file written to ./out/ after each run so it is greppable. 5. Every model call is logged to a calls table with model, token counts and latency, and /usage shows totals by day so you can see what you are spending. Out of scope, do not build: user accounts, OAuth to any third party, email or push notifications, mobile app, multi-user sharing, vector database services, Docker. Deliver: npm install, npm run dev on port 3000, a seeded example watchlist item, .env.example with every variable, and a README that states plainly what works and what breaks if the process dies. ## Required capabilities - An LLM API key (OpenAI or Anthropic) in .env - Node 20+ and a machine or small VPS that stays awake for the cron job - Willingness to paste your own context in instead of having connectors do it ## 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 Littlebird. 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 ===== # Littlebird indie build ## Goal Build the smallest trustworthy replacement for the core Littlebird workflow for one developer or a tiny team. ## Scope A local web app where you chat with a model over your own uploaded notes and links, plus a scheduled job that produces a digest of anything you told it to watch. ## 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: - Mobile app and notifications; your version is a browser tab - Prebuilt connectors to email, calendar, Slack and whatever else the paid product authenticates against - Someone else tuning prompts and swapping models when a better one ships - Reliability: your cron job dies silently and nobody pages you - Any team or sharing features, if the product has them If those capabilities are essential, use Littlebird 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 a local personal AI assistant web app in an empty folder. No accounts, no cloud, no telemetry. Stack, no substitutions: - TypeScript, Node 20, Fastify for the server - SQLite via better-sqlite3, file at ./data/assistant.db - Server-rendered HTML with htmx for interactivity, plain CSS, no React, no Tailwind - OpenAI-compatible chat completions and embeddings, base URL and key read from .env (OPENAI_API_KEY, OPENAI_BASE_URL, CHAT_MODEL, EMBED_MODEL) - node-cron for scheduled jobs, all in one process Features in scope: 1. Context library: paste text, upload .md/.txt, or submit a URL. URLs are fetched server side and reduced to readable text with @mozilla/readability plus jsdom. Chunk to ~800 tokens, embed, store chunks and vectors in SQLite. 2. Chat: a single conversation view. On each message, retrieve the top 8 chunks by cosine similarity, put them in the system prompt with source titles, stream the reply back. Persist all turns. 3. Watchlist: each row is a name, a URL or a search query, and a cadence (daily or weekly). A cron job fetches each item, diffs the extracted text against the last stored snapshot, and asks the model to summarize what changed in under 80 words. Skip items with no meaningful diff. 4. Digest: a /digest page listing the latest summaries newest first, plus a digest.md file written to ./out/ after each run so it is greppable. 5. Every model call is logged to a calls table with model, token counts and latency, and /usage shows totals by day so you can see what you are spending. Out of scope, do not build: user accounts, OAuth to any third party, email or push notifications, mobile app, multi-user sharing, vector database services, Docker. Deliver: npm install, npm run dev on port 3000, a seeded example watchlist item, .env.example with every variable, and a README that states plainly what works and what breaks if the process dies. ## Required capabilities - An LLM API key (OpenAI or Anthropic) in .env - Node 20+ and a machine or small VPS that stays awake for the cron job - Willingness to paste your own context in instead of having connectors do it ## 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 Littlebird. 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 ===== # Littlebird product brief ## Problem Public detail on this one is thin, so treat this entry as a first pass rather than a teardown. Anything in the "AI assistant that reads and drafts for you" shape is mostly a wrapper: a model API, a place to put your context, and a loop that runs on a schedule. A coding agent can rebuild that shape in an evening and you keep the API bill instead of the subscription. What you will not rebuild in an evening is the polish, the mobile surface, and whatever integrations the paid product ships against your actual accounts. Verdict stays "kinda" until someone confirms whether the real value here is the agent loop or the connectors underneath it. ## Product outcome A local web app where you chat with a model over your own uploaded notes and links, plus a scheduled job that produces a digest of anything you told it to watch. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - An LLM API key (OpenAI or Anthropic) in .env - Node 20+ and a machine or small VPS that stays awake for the cron job - Willingness to paste your own context in instead of having connectors do it ## Explicit non-goals for v1 - Mobile app and notifications; your version is a browser tab - Prebuilt connectors to email, calendar, Slack and whatever else the paid product authenticates against - Someone else tuning prompts and swapping models when a better one ships - Reliability: your cron job dies silently and nobody pages you - Any team or sharing features, if the product has them ## 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 a local personal AI assistant web app in an empty folder. No accounts, no cloud, no telemetry. Stack, no substitutions: - TypeScript, Node 20, Fastify for the server - SQLite via better-sqlite3, file at ./data/assistant.db - Server-rendered HTML with htmx for interactivity, plain CSS, no React, no Tailwind - OpenAI-compatible chat completions and embeddings, base URL and key read from .env (OPENAI_API_KEY, OPENAI_BASE_URL, CHAT_MODEL, EMBED_MODEL) - node-cron for scheduled jobs, all in one process Features in scope: 1. Context library: paste text, upload .md/.txt, or submit a URL. URLs are fetched server side and reduced to readable text with @mozilla/readability plus jsdom. Chunk to ~800 tokens, embed, store chunks and vectors in SQLite. 2. Chat: a single conversation view. On each message, retrieve the top 8 chunks by cosine similarity, put them in the system prompt with source titles, stream the reply back. Persist all turns. 3. Watchlist: each row is a name, a URL or a search query, and a cadence (daily or weekly). A cron job fetches each item, diffs the extracted text against the last stored snapshot, and asks the model to summarize what changed in under 80 words. Skip items with no meaningful diff. 4. Digest: a /digest page listing the latest summaries newest first, plus a digest.md file written to ./out/ after each run so it is greppable. 5. Every model call is logged to a calls table with model, token counts and latency, and /usage shows totals by day so you can see what you are spending. Out of scope, do not build: user accounts, OAuth to any third party, email or push notifications, mobile app, multi-user sharing, vector database services, Docker. Deliver: npm install, npm run dev on port 3000, a seeded example watchlist item, .env.example with every variable, and a README that states plainly what works and what breaks if the process dies. ## 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 Littlebird capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Littlebird indie build ## Goal Build the smallest trustworthy replacement for the core Littlebird workflow for one developer or a tiny team. ## Scope A local web app where you chat with a model over your own uploaded notes and links, plus a scheduled job that produces a digest of anything you told it to watch. ## 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: - Mobile app and notifications; your version is a browser tab - Prebuilt connectors to email, calendar, Slack and whatever else the paid product authenticates against - Someone else tuning prompts and swapping models when a better one ships - Reliability: your cron job dies silently and nobody pages you - Any team or sharing features, if the product has them If those capabilities are essential, use Littlebird 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 a local personal AI assistant web app in an empty folder. No accounts, no cloud, no telemetry. Stack, no substitutions: - TypeScript, Node 20, Fastify for the server - SQLite via better-sqlite3, file at ./data/assistant.db - Server-rendered HTML with htmx for interactivity, plain CSS, no React, no Tailwind - OpenAI-compatible chat completions and embeddings, base URL and key read from .env (OPENAI_API_KEY, OPENAI_BASE_URL, CHAT_MODEL, EMBED_MODEL) - node-cron for scheduled jobs, all in one process Features in scope: 1. Context library: paste text, upload .md/.txt, or submit a URL. URLs are fetched server side and reduced to readable text with @mozilla/readability plus jsdom. Chunk to ~800 tokens, embed, store chunks and vectors in SQLite. 2. Chat: a single conversation view. On each message, retrieve the top 8 chunks by cosine similarity, put them in the system prompt with source titles, stream the reply back. Persist all turns. 3. Watchlist: each row is a name, a URL or a search query, and a cadence (daily or weekly). A cron job fetches each item, diffs the extracted text against the last stored snapshot, and asks the model to summarize what changed in under 80 words. Skip items with no meaningful diff. 4. Digest: a /digest page listing the latest summaries newest first, plus a digest.md file written to ./out/ after each run so it is greppable. 5. Every model call is logged to a calls table with model, token counts and latency, and /usage shows totals by day so you can see what you are spending. Out of scope, do not build: user accounts, OAuth to any third party, email or push notifications, mobile app, multi-user sharing, vector database services, Docker. Deliver: npm install, npm run dev on port 3000, a seeded example watchlist item, .env.example with every variable, and a README that states plainly what works and what breaks if the process dies. ## Required capabilities - An LLM API key (OpenAI or Anthropic) in .env - Node 20+ and a machine or small VPS that stays awake for the cron job - Willingness to paste your own context in instead of having connectors do it ## 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
# Littlebird product brief ## Problem Public detail on this one is thin, so treat this entry as a first pass rather than a teardown. Anything in the "AI assistant that reads and drafts for you" shape is mostly a wrapper: a model API, a place to put your context, and a loop that runs on a schedule. A coding agent can rebuild that shape in an evening and you keep the API bill instead of the subscription. What you will not rebuild in an evening is the polish, the mobile surface, and whatever integrations the paid product ships against your actual accounts. Verdict stays "kinda" until someone confirms whether the real value here is the agent loop or the connectors underneath it. ## Product outcome A local web app where you chat with a model over your own uploaded notes and links, plus a scheduled job that produces a digest of anything you told it to watch. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - An LLM API key (OpenAI or Anthropic) in .env - Node 20+ and a machine or small VPS that stays awake for the cron job - Willingness to paste your own context in instead of having connectors do it ## Explicit non-goals for v1 - Mobile app and notifications; your version is a browser tab - Prebuilt connectors to email, calendar, Slack and whatever else the paid product authenticates against - Someone else tuning prompts and swapping models when a better one ships - Reliability: your cron job dies silently and nobody pages you - Any team or sharing features, if the product has them ## 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 a local personal AI assistant web app in an empty folder. No accounts, no cloud, no telemetry. Stack, no substitutions: - TypeScript, Node 20, Fastify for the server - SQLite via better-sqlite3, file at ./data/assistant.db - Server-rendered HTML with htmx for interactivity, plain CSS, no React, no Tailwind - OpenAI-compatible chat completions and embeddings, base URL and key read from .env (OPENAI_API_KEY, OPENAI_BASE_URL, CHAT_MODEL, EMBED_MODEL) - node-cron for scheduled jobs, all in one process Features in scope: 1. Context library: paste text, upload .md/.txt, or submit a URL. URLs are fetched server side and reduced to readable text with @mozilla/readability plus jsdom. Chunk to ~800 tokens, embed, store chunks and vectors in SQLite. 2. Chat: a single conversation view. On each message, retrieve the top 8 chunks by cosine similarity, put them in the system prompt with source titles, stream the reply back. Persist all turns. 3. Watchlist: each row is a name, a URL or a search query, and a cadence (daily or weekly). A cron job fetches each item, diffs the extracted text against the last stored snapshot, and asks the model to summarize what changed in under 80 words. Skip items with no meaningful diff. 4. Digest: a /digest page listing the latest summaries newest first, plus a digest.md file written to ./out/ after each run so it is greppable. 5. Every model call is logged to a calls table with model, token counts and latency, and /usage shows totals by day so you can see what you are spending. Out of scope, do not build: user accounts, OAuth to any third party, email or push notifications, mobile app, multi-user sharing, vector database services, Docker. Deliver: npm install, npm run dev on port 3000, a seeded example watchlist item, .env.example with every variable, and a README that states plainly what works and what breaks if the process dies. ## 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 Littlebird 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 · this prompt is generated from the build plan · improve it via PR
Because assembling the loop is the easy 20 percent and living with it is the other 80. A paid assistant already has the auth flows, the retry logic, the mobile push, and a prompt someone iterated on for months against real complaints. A self-hosted clone works great for two weeks and then you stop opening the tab, which is the actual failure mode of every personal AI build.
xMobile app and notifications; your version is a browser tab
xPrebuilt connectors to email, calendar, Slack and whatever else the paid product authenticates against
xSomeone else tuning prompts and swapping models when a better one ships
xReliability: your cron job dies silently and nobody pages you
xAny team or sharing features, if the product has them
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Littlebird ($20/mo) with one prompt"
Vibecode Littlebird
Kinda. The core of Littlebird is buildable in a weekend with the prompt on this page, but there are real gaps: Mobile app and notifications; your version is a browser tab, Prebuilt connectors to email, calendar, Slack and whatever else the paid product authenticates against. Read the honest list above before committing.
How much does Littlebird cost?
Littlebird costs about $20/month (Plus, checked 2026-08-18), which is $240 per year.
What do I lose by replacing Littlebird?
Honestly: Mobile app and notifications; your version is a browser tab; Prebuilt connectors to email, calendar, Slack and whatever else the paid product authenticates against; Someone else tuning prompts and swapping models when a better one ships; Reliability: your cron job dies silently and nobody pages you; Any team or sharing features, if the product has them. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Littlebird?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.