Vibecode Svix
track this build5 steps, step by step0%KINDA · weekend project
The core loop here is genuinely small: accept an event, look up subscribed endpoints, sign the payload, POST it, retry on failure with backoff, log the attempt. An agent will produce that in a session, and for a single product sending a few thousand events a day it will work fine. What does not fall out of a one-shot is the boring half: a queue that survives restarts, per-endpoint rate limiting and circuit breaking so one dead customer does not poison your worker pool, replay and manual retry tooling, a portal your customers can log into, and signature schemes that third-party libraries already understand. Svix is also open source, which means the honest DIY move is often self-hosting theirs rather than writing your own. Call it a weekend for something you would actually put in front of paying users, and understand that you are now on call for it.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Svix.
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 =====
# Svix indie build
## Goal
Build the smallest trustworthy replacement for the core Svix workflow for one developer or a tiny team.
## Scope
Takes an event via API, fans it out to registered endpoints with HMAC-signed payloads, retries failures on exponential backoff, and records every attempt.
## 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:
- A customer-facing portal where your users manage their own endpoints and see failures
- Battle-tested signature format that existing verification libraries accept out of the box
- Per-endpoint circuit breaking and rate limiting so one slow consumer does not stall everyone
- Operational maturity: dead-letter handling, replay windows, throughput under a spike
- Someone else being paged when delivery breaks at 3am
If those capabilities are essential, use Svix 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 self-hosted outbound webhook delivery service. Single Node.js project, TypeScript, Fastify for HTTP, Postgres via Drizzle ORM, BullMQ on Redis for the delivery queue. Docker Compose for Postgres and Redis. No cloud accounts, no telemetry, no auth provider: a single static API key in .env guards the admin and ingest routes.
Data model:
- applications: id, name, created_at (one per tenant/customer of mine)
- endpoints: id, application_id, url, description, secret, enabled, event_types (text array, empty means all), created_at
- messages: id, application_id, event_type, payload jsonb, created_at
- attempts: id, message_id, endpoint_id, attempt_number, status_code, response_body_excerpt, error, duration_ms, created_at
HTTP API (all JSON, all behind the API key header):
- POST /api/applications, GET /api/applications
- POST /api/applications/:id/endpoints, GET, PATCH, DELETE
- POST /api/applications/:id/messages: body has event_type and payload. Persist the message, resolve matching enabled endpoints, enqueue one delivery job per endpoint, return the message id with 202.
- GET /api/messages/:id/attempts
- POST /api/attempts/:id/replay: re-enqueue that single delivery immediately.
Delivery worker:
- POST the raw JSON payload with headers: webhook-id, webhook-timestamp (unix seconds), webhook-signature as "v1," plus base64 HMAC-SHA256 over "{id}.{timestamp}.{body}" using the endpoint secret.
- 5 second connect timeout, 10 second total timeout.
- Success is any 2xx. Retry on everything else with backoff: 5s, 30s, 5m, 30m, 2h, 5h, then give up and mark the endpoint as failing.
- Record an attempt row for every try, truncating response bodies to 2KB.
- Rate limit per endpoint to 20 concurrent deliveries max using a BullMQ group or per-endpoint limiter.
Also build a minimal server-rendered admin UI at / using Fastify plus plain HTML templates and no frontend framework: list applications, list endpoints, list recent messages, drill into a message to see attempts and hit replay. Ugly is fine, tables and buttons only.
Out of scope: a customer-facing self-serve portal, per-customer login, multi-region, event type schema validation, inbound webhook receiving, transformations.
Include a README with docker compose up, migration command, and one curl example that creates an application, an endpoint pointing at a local sink, and sends a message. Add a small script that runs a throwaway HTTP sink on port 4000 that verifies the signature and prints the payload, so delivery can be tested end to end. Write integration tests for signature generation and for the retry backoff schedule.
## Required capabilities
- A Postgres database
- Redis or Postgres-backed job queue
- A server that stays up, not a serverless function
- Somewhere to store attempt logs that will grow fast
## 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 Svix.
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 =====
# Svix indie build
## Goal
Build the smallest trustworthy replacement for the core Svix workflow for one developer or a tiny team.
## Scope
Takes an event via API, fans it out to registered endpoints with HMAC-signed payloads, retries failures on exponential backoff, and records every attempt.
## 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:
- A customer-facing portal where your users manage their own endpoints and see failures
- Battle-tested signature format that existing verification libraries accept out of the box
- Per-endpoint circuit breaking and rate limiting so one slow consumer does not stall everyone
- Operational maturity: dead-letter handling, replay windows, throughput under a spike
- Someone else being paged when delivery breaks at 3am
If those capabilities are essential, use Svix 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 self-hosted outbound webhook delivery service. Single Node.js project, TypeScript, Fastify for HTTP, Postgres via Drizzle ORM, BullMQ on Redis for the delivery queue. Docker Compose for Postgres and Redis. No cloud accounts, no telemetry, no auth provider: a single static API key in .env guards the admin and ingest routes.
Data model:
- applications: id, name, created_at (one per tenant/customer of mine)
- endpoints: id, application_id, url, description, secret, enabled, event_types (text array, empty means all), created_at
- messages: id, application_id, event_type, payload jsonb, created_at
- attempts: id, message_id, endpoint_id, attempt_number, status_code, response_body_excerpt, error, duration_ms, created_at
HTTP API (all JSON, all behind the API key header):
- POST /api/applications, GET /api/applications
- POST /api/applications/:id/endpoints, GET, PATCH, DELETE
- POST /api/applications/:id/messages: body has event_type and payload. Persist the message, resolve matching enabled endpoints, enqueue one delivery job per endpoint, return the message id with 202.
- GET /api/messages/:id/attempts
- POST /api/attempts/:id/replay: re-enqueue that single delivery immediately.
Delivery worker:
- POST the raw JSON payload with headers: webhook-id, webhook-timestamp (unix seconds), webhook-signature as "v1," plus base64 HMAC-SHA256 over "{id}.{timestamp}.{body}" using the endpoint secret.
- 5 second connect timeout, 10 second total timeout.
- Success is any 2xx. Retry on everything else with backoff: 5s, 30s, 5m, 30m, 2h, 5h, then give up and mark the endpoint as failing.
- Record an attempt row for every try, truncating response bodies to 2KB.
- Rate limit per endpoint to 20 concurrent deliveries max using a BullMQ group or per-endpoint limiter.
Also build a minimal server-rendered admin UI at / using Fastify plus plain HTML templates and no frontend framework: list applications, list endpoints, list recent messages, drill into a message to see attempts and hit replay. Ugly is fine, tables and buttons only.
Out of scope: a customer-facing self-serve portal, per-customer login, multi-region, event type schema validation, inbound webhook receiving, transformations.
Include a README with docker compose up, migration command, and one curl example that creates an application, an endpoint pointing at a local sink, and sends a message. Add a small script that runs a throwaway HTTP sink on port 4000 that verifies the signature and prints the payload, so delivery can be tested end to end. Write integration tests for signature generation and for the retry backoff schedule.
## Required capabilities
- A Postgres database
- Redis or Postgres-backed job queue
- A server that stays up, not a serverless function
- Somewhere to store attempt logs that will grow fast
## 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 Svix.
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 =====
# Svix product brief
## Problem
The core loop here is genuinely small: accept an event, look up subscribed endpoints, sign the payload, POST it, retry on failure with backoff, log the attempt. An agent will produce that in a session, and for a single product sending a few thousand events a day it will work fine. What does not fall out of a one-shot is the boring half: a queue that survives restarts, per-endpoint rate limiting and circuit breaking so one dead customer does not poison your worker pool, replay and manual retry tooling, a portal your customers can log into, and signature schemes that third-party libraries already understand. Svix is also open source, which means the honest DIY move is often self-hosting theirs rather than writing your own. Call it a weekend for something you would actually put in front of paying users, and understand that you are now on call for it.
## Product outcome
Takes an event via API, fans it out to registered endpoints with HMAC-signed payloads, retries failures on exponential backoff, and records every attempt.
## Target user
A serious builder who needs a maintainable product foundation rather than a one-off demo.
## Required capabilities
- A Postgres database
- Redis or Postgres-backed job queue
- A server that stays up, not a serverless function
- Somewhere to store attempt logs that will grow fast
## Explicit non-goals for v1
- A customer-facing portal where your users manage their own endpoints and see failures
- Battle-tested signature format that existing verification libraries accept out of the box
- Per-endpoint circuit breaking and rate limiting so one slow consumer does not stall everyone
- Operational maturity: dead-letter handling, replay windows, throughput under a spike
- Someone else being paged when delivery breaks at 3am
## 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 self-hosted outbound webhook delivery service. Single Node.js project, TypeScript, Fastify for HTTP, Postgres via Drizzle ORM, BullMQ on Redis for the delivery queue. Docker Compose for Postgres and Redis. No cloud accounts, no telemetry, no auth provider: a single static API key in .env guards the admin and ingest routes.
Data model:
- applications: id, name, created_at (one per tenant/customer of mine)
- endpoints: id, application_id, url, description, secret, enabled, event_types (text array, empty means all), created_at
- messages: id, application_id, event_type, payload jsonb, created_at
- attempts: id, message_id, endpoint_id, attempt_number, status_code, response_body_excerpt, error, duration_ms, created_at
HTTP API (all JSON, all behind the API key header):
- POST /api/applications, GET /api/applications
- POST /api/applications/:id/endpoints, GET, PATCH, DELETE
- POST /api/applications/:id/messages: body has event_type and payload. Persist the message, resolve matching enabled endpoints, enqueue one delivery job per endpoint, return the message id with 202.
- GET /api/messages/:id/attempts
- POST /api/attempts/:id/replay: re-enqueue that single delivery immediately.
Delivery worker:
- POST the raw JSON payload with headers: webhook-id, webhook-timestamp (unix seconds), webhook-signature as "v1," plus base64 HMAC-SHA256 over "{id}.{timestamp}.{body}" using the endpoint secret.
- 5 second connect timeout, 10 second total timeout.
- Success is any 2xx. Retry on everything else with backoff: 5s, 30s, 5m, 30m, 2h, 5h, then give up and mark the endpoint as failing.
- Record an attempt row for every try, truncating response bodies to 2KB.
- Rate limit per endpoint to 20 concurrent deliveries max using a BullMQ group or per-endpoint limiter.
Also build a minimal server-rendered admin UI at / using Fastify plus plain HTML templates and no frontend framework: list applications, list endpoints, list recent messages, drill into a message to see attempts and hit replay. Ugly is fine, tables and buttons only.
Out of scope: a customer-facing self-serve portal, per-customer login, multi-region, event type schema validation, inbound webhook receiving, transformations.
Include a README with docker compose up, migration command, and one curl example that creates an application, an endpoint pointing at a local sink, and sends a message. Add a small script that runs a throwaway HTTP sink on port 4000 that verifies the signature and prints the payload, so delivery can be tested end to end. Write integration tests for signature generation and for the retry backoff schedule.
## 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 Svix capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.README.md
# Svix indie build ## Goal Build the smallest trustworthy replacement for the core Svix workflow for one developer or a tiny team. ## Scope Takes an event via API, fans it out to registered endpoints with HMAC-signed payloads, retries failures on exponential backoff, and records every attempt. ## 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: - A customer-facing portal where your users manage their own endpoints and see failures - Battle-tested signature format that existing verification libraries accept out of the box - Per-endpoint circuit breaking and rate limiting so one slow consumer does not stall everyone - Operational maturity: dead-letter handling, replay windows, throughput under a spike - Someone else being paged when delivery breaks at 3am If those capabilities are essential, use Svix 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 self-hosted outbound webhook delivery service. Single Node.js project, TypeScript, Fastify for HTTP, Postgres via Drizzle ORM, BullMQ on Redis for the delivery queue. Docker Compose for Postgres and Redis. No cloud accounts, no telemetry, no auth provider: a single static API key in .env guards the admin and ingest routes.
Data model:
- applications: id, name, created_at (one per tenant/customer of mine)
- endpoints: id, application_id, url, description, secret, enabled, event_types (text array, empty means all), created_at
- messages: id, application_id, event_type, payload jsonb, created_at
- attempts: id, message_id, endpoint_id, attempt_number, status_code, response_body_excerpt, error, duration_ms, created_at
HTTP API (all JSON, all behind the API key header):
- POST /api/applications, GET /api/applications
- POST /api/applications/:id/endpoints, GET, PATCH, DELETE
- POST /api/applications/:id/messages: body has event_type and payload. Persist the message, resolve matching enabled endpoints, enqueue one delivery job per endpoint, return the message id with 202.
- GET /api/messages/:id/attempts
- POST /api/attempts/:id/replay: re-enqueue that single delivery immediately.
Delivery worker:
- POST the raw JSON payload with headers: webhook-id, webhook-timestamp (unix seconds), webhook-signature as "v1," plus base64 HMAC-SHA256 over "{id}.{timestamp}.{body}" using the endpoint secret.
- 5 second connect timeout, 10 second total timeout.
- Success is any 2xx. Retry on everything else with backoff: 5s, 30s, 5m, 30m, 2h, 5h, then give up and mark the endpoint as failing.
- Record an attempt row for every try, truncating response bodies to 2KB.
- Rate limit per endpoint to 20 concurrent deliveries max using a BullMQ group or per-endpoint limiter.
Also build a minimal server-rendered admin UI at / using Fastify plus plain HTML templates and no frontend framework: list applications, list endpoints, list recent messages, drill into a message to see attempts and hit replay. Ugly is fine, tables and buttons only.
Out of scope: a customer-facing self-serve portal, per-customer login, multi-region, event type schema validation, inbound webhook receiving, transformations.
Include a README with docker compose up, migration command, and one curl example that creates an application, an endpoint pointing at a local sink, and sends a message. Add a small script that runs a throwaway HTTP sink on port 4000 that verifies the signature and prints the payload, so delivery can be tested end to end. Write integration tests for signature generation and for the retry backoff schedule.
## Required capabilities
- A Postgres database
- Redis or Postgres-backed job queue
- A server that stays up, not a serverless function
- Somewhere to store attempt logs that will grow fast
## 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
# Svix product brief ## Problem The core loop here is genuinely small: accept an event, look up subscribed endpoints, sign the payload, POST it, retry on failure with backoff, log the attempt. An agent will produce that in a session, and for a single product sending a few thousand events a day it will work fine. What does not fall out of a one-shot is the boring half: a queue that survives restarts, per-endpoint rate limiting and circuit breaking so one dead customer does not poison your worker pool, replay and manual retry tooling, a portal your customers can log into, and signature schemes that third-party libraries already understand. Svix is also open source, which means the honest DIY move is often self-hosting theirs rather than writing your own. Call it a weekend for something you would actually put in front of paying users, and understand that you are now on call for it. ## Product outcome Takes an event via API, fans it out to registered endpoints with HMAC-signed payloads, retries failures on exponential backoff, and records every attempt. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - A Postgres database - Redis or Postgres-backed job queue - A server that stays up, not a serverless function - Somewhere to store attempt logs that will grow fast ## Explicit non-goals for v1 - A customer-facing portal where your users manage their own endpoints and see failures - Battle-tested signature format that existing verification libraries accept out of the box - Per-endpoint circuit breaking and rate limiting so one slow consumer does not stall everyone - Operational maturity: dead-letter handling, replay windows, throughput under a spike - Someone else being paged when delivery breaks at 3am ## 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 self-hosted outbound webhook delivery service. Single Node.js project, TypeScript, Fastify for HTTP, Postgres via Drizzle ORM, BullMQ on Redis for the delivery queue. Docker Compose for Postgres and Redis. No cloud accounts, no telemetry, no auth provider: a single static API key in .env guards the admin and ingest routes.
Data model:
- applications: id, name, created_at (one per tenant/customer of mine)
- endpoints: id, application_id, url, description, secret, enabled, event_types (text array, empty means all), created_at
- messages: id, application_id, event_type, payload jsonb, created_at
- attempts: id, message_id, endpoint_id, attempt_number, status_code, response_body_excerpt, error, duration_ms, created_at
HTTP API (all JSON, all behind the API key header):
- POST /api/applications, GET /api/applications
- POST /api/applications/:id/endpoints, GET, PATCH, DELETE
- POST /api/applications/:id/messages: body has event_type and payload. Persist the message, resolve matching enabled endpoints, enqueue one delivery job per endpoint, return the message id with 202.
- GET /api/messages/:id/attempts
- POST /api/attempts/:id/replay: re-enqueue that single delivery immediately.
Delivery worker:
- POST the raw JSON payload with headers: webhook-id, webhook-timestamp (unix seconds), webhook-signature as "v1," plus base64 HMAC-SHA256 over "{id}.{timestamp}.{body}" using the endpoint secret.
- 5 second connect timeout, 10 second total timeout.
- Success is any 2xx. Retry on everything else with backoff: 5s, 30s, 5m, 30m, 2h, 5h, then give up and mark the endpoint as failing.
- Record an attempt row for every try, truncating response bodies to 2KB.
- Rate limit per endpoint to 20 concurrent deliveries max using a BullMQ group or per-endpoint limiter.
Also build a minimal server-rendered admin UI at / using Fastify plus plain HTML templates and no frontend framework: list applications, list endpoints, list recent messages, drill into a message to see attempts and hit replay. Ugly is fine, tables and buttons only.
Out of scope: a customer-facing self-serve portal, per-customer login, multi-region, event type schema validation, inbound webhook receiving, transformations.
Include a README with docker compose up, migration command, and one curl example that creates an application, an endpoint pointing at a local sink, and sends a message. Add a small script that runs a throwaway HTTP sink on port 4000 that verifies the signature and prints the payload, so delivery can be tested end to end. Write integration tests for signature generation and for the retry backoff schedule.
## 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 Svix 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
Webhook delivery is a system where the failure modes are all in the tail: the customer whose endpoint returns 200 but drops the body, the one that goes down for six hours, the traffic spike that queues fifty thousand deliveries behind one timeout. Writing the happy path takes an afternoon. Discovering and handling those tails takes months of production traffic you have not had yet. Teams pay so that outbound webhooks stop being a thing they think about, and so that when a customer complains about a missed event there is a searchable log and a replay button instead of a grep through application logs.
xA customer-facing portal where your users manage their own endpoints and see failures
xBattle-tested signature format that existing verification libraries accept out of the box
xPer-endpoint circuit breaking and rate limiting so one slow consumer does not stall everyone
xOperational maturity: dead-letter handling, replay windows, throughput under a spike
xSomeone else being paged when delivery breaks at 3am
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Svix ($490/mo) with one prompt"
Vibecode Svix
Kinda. The core of Svix is buildable in a weekend with the prompt on this page, but there are real gaps: A customer-facing portal where your users manage their own endpoints and see failures, Battle-tested signature format that existing verification libraries accept out of the box. Read the honest list above before committing.
How much does Svix cost?
Svix costs about $490/month (Professional, checked 2026-08-18), which is $5880 per year.
What do I lose by replacing Svix?
Honestly: A customer-facing portal where your users manage their own endpoints and see failures; Battle-tested signature format that existing verification libraries accept out of the box; Per-endpoint circuit breaking and rate limiting so one slow consumer does not stall everyone; Operational maturity: dead-letter handling, replay windows, throughput under a spike; Someone else being paged when delivery breaks at 3am. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Svix?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.