Vibecode Ghostfeed
track this build5 steps, step by step0%KINDA · weekend project
No model here was trained by Ghostfeed · every step is somebody else's hosted inference, spread across four vendors. A Gemini image-edit call swaps your avatar into frame 0 of an existing clip, an approval gate makes you accept that frame before anything expensive runs, and then the video step splits: the default clone family sends the approved frame plus the original clip to Kling motion-control, so the render inherits the source video's motion, while the prompt family animates the still alone on PixVerse, Seedance, Grok or Kling. Rebuild the loop for yourself with a fal.ai key, a Gemini key and ffmpeg over a long weekend and it works. What does not fall out of that weekend is the rest of it · a template library scraped and scene-cut into 9:16 clips, a director that writes slideshow copy and casts every background itself, a timeline editor with its own render workers, 44 agent-API tools, and a TikTok app that took platform review to get. Personal version, yes. The thing you would actually run daily, no.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Ghostfeed. 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 ===== # Ghostfeed indie build ## Goal Build the smallest trustworthy replacement for the core Ghostfeed workflow for one developer or a tiny team. ## Scope Swap an avatar photo into frame 0 of a reaction clip with a hosted image-edit model, approve that frame, then send it plus the source clip to a hosted motion-control model so the render copies the original's motion, and burn the hook text over the result. ## 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 template library scraped from TikTok and Instagram and scene-cut into ready 9:16 clips, instead of clips you source and trim yourself - avatar consistency across renders · one stored reference photo re-fed into every call, plus the draft-and-approve flow that keeps a face you picked - a director loop that writes the slideshow copy, searches for backgrounds, looks at the candidates and casts them, then rotates the deck into variants, plus a timeline editor with its own render workers - 44 agent-API tools · the same workspace drivable from Claude, which is the part with no weekend equivalent - a TikTok app that already cleared review, and a reaper that refunds most stalled renders instead of quietly eating them If those capabilities are essential, use ComfyUI 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 a personal UGC reaction video factory to replace Ghostfeed. Requirements: - Node + Express + better-sqlite3, a localhost dashboard with three panes: my avatar photos, a folder of reaction clips I own, and a render queue. - First frame: pull frame 0 from a chosen clip with ffmpeg, send it plus my avatar photo to Gemini's image-edit API (key in .env) and tell it to replace the whole person while holding the pose, framing, and lighting. Keep that instruction in prompts/first-frame.txt so I can tune it without code changes. - Nothing animates until I approve the frame, so give me approve, regenerate, and discard buttons · the frame costs cents on my invoice, the video dollars. - Motion: on approve, POST the frame AND the original clip to fal.ai's Kling motion-control queue so the render copies the source video's motion, poll to done, write the mp4 to media/, store the real cost on the row. - Second mode: animate the frame alone from a written motion prompt via fal.ai's PixVerse image-to-video, for when I have no clip worth copying. - Burn the hook line over the top third with ffmpeg drawtext, 1080x1920 out, clear of the TikTok UI safe area. - A node-cron tick polls pending jobs every 90s, retries 3 times with backoff, and marks the row before submitting so a retry never double-bills me. - No accounts, no telemetry, everything on my disk except the model calls. - Out of scope: posting to TikTok (that OAuth review takes weeks · export the mp4 and upload by hand), slideshows, and teams. Only ingest clips I have the right to use. - README: getting the two keys, what one 5-second motion-control render costs, and where media lands on disk. ## Required capabilities - fal.ai key for Kling motion-control and image-to-video - Gemini or GPT-image key for the first-frame swap - ffmpeg - reaction clips you have the right to reuse - an always-on box for the polling loop ## 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 Ghostfeed. 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 ===== # Ghostfeed indie build ## Goal Build the smallest trustworthy replacement for the core Ghostfeed workflow for one developer or a tiny team. ## Scope Swap an avatar photo into frame 0 of a reaction clip with a hosted image-edit model, approve that frame, then send it plus the source clip to a hosted motion-control model so the render copies the original's motion, and burn the hook text over the result. ## 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 template library scraped from TikTok and Instagram and scene-cut into ready 9:16 clips, instead of clips you source and trim yourself - avatar consistency across renders · one stored reference photo re-fed into every call, plus the draft-and-approve flow that keeps a face you picked - a director loop that writes the slideshow copy, searches for backgrounds, looks at the candidates and casts them, then rotates the deck into variants, plus a timeline editor with its own render workers - 44 agent-API tools · the same workspace drivable from Claude, which is the part with no weekend equivalent - a TikTok app that already cleared review, and a reaper that refunds most stalled renders instead of quietly eating them If those capabilities are essential, use ComfyUI 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 a personal UGC reaction video factory to replace Ghostfeed. Requirements: - Node + Express + better-sqlite3, a localhost dashboard with three panes: my avatar photos, a folder of reaction clips I own, and a render queue. - First frame: pull frame 0 from a chosen clip with ffmpeg, send it plus my avatar photo to Gemini's image-edit API (key in .env) and tell it to replace the whole person while holding the pose, framing, and lighting. Keep that instruction in prompts/first-frame.txt so I can tune it without code changes. - Nothing animates until I approve the frame, so give me approve, regenerate, and discard buttons · the frame costs cents on my invoice, the video dollars. - Motion: on approve, POST the frame AND the original clip to fal.ai's Kling motion-control queue so the render copies the source video's motion, poll to done, write the mp4 to media/, store the real cost on the row. - Second mode: animate the frame alone from a written motion prompt via fal.ai's PixVerse image-to-video, for when I have no clip worth copying. - Burn the hook line over the top third with ffmpeg drawtext, 1080x1920 out, clear of the TikTok UI safe area. - A node-cron tick polls pending jobs every 90s, retries 3 times with backoff, and marks the row before submitting so a retry never double-bills me. - No accounts, no telemetry, everything on my disk except the model calls. - Out of scope: posting to TikTok (that OAuth review takes weeks · export the mp4 and upload by hand), slideshows, and teams. Only ingest clips I have the right to use. - README: getting the two keys, what one 5-second motion-control render costs, and where media lands on disk. ## Required capabilities - fal.ai key for Kling motion-control and image-to-video - Gemini or GPT-image key for the first-frame swap - ffmpeg - reaction clips you have the right to reuse - an always-on box for the polling loop ## 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 Ghostfeed. 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 ===== # Ghostfeed product brief ## Problem No model here was trained by Ghostfeed · every step is somebody else's hosted inference, spread across four vendors. A Gemini image-edit call swaps your avatar into frame 0 of an existing clip, an approval gate makes you accept that frame before anything expensive runs, and then the video step splits: the default clone family sends the approved frame plus the original clip to Kling motion-control, so the render inherits the source video's motion, while the prompt family animates the still alone on PixVerse, Seedance, Grok or Kling. Rebuild the loop for yourself with a fal.ai key, a Gemini key and ffmpeg over a long weekend and it works. What does not fall out of that weekend is the rest of it · a template library scraped and scene-cut into 9:16 clips, a director that writes slideshow copy and casts every background itself, a timeline editor with its own render workers, 44 agent-API tools, and a TikTok app that took platform review to get. Personal version, yes. The thing you would actually run daily, no. ## Product outcome Swap an avatar photo into frame 0 of a reaction clip with a hosted image-edit model, approve that frame, then send it plus the source clip to a hosted motion-control model so the render copies the original's motion, and burn the hook text over the result. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - fal.ai key for Kling motion-control and image-to-video - Gemini or GPT-image key for the first-frame swap - ffmpeg - reaction clips you have the right to reuse - an always-on box for the polling loop ## Explicit non-goals for v1 - a template library scraped from TikTok and Instagram and scene-cut into ready 9:16 clips, instead of clips you source and trim yourself - avatar consistency across renders · one stored reference photo re-fed into every call, plus the draft-and-approve flow that keeps a face you picked - a director loop that writes the slideshow copy, searches for backgrounds, looks at the candidates and casts them, then rotates the deck into variants, plus a timeline editor with its own render workers - 44 agent-API tools · the same workspace drivable from Claude, which is the part with no weekend equivalent - a TikTok app that already cleared review, and a reaper that refunds most stalled renders instead of quietly eating 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 me a personal UGC reaction video factory to replace Ghostfeed. Requirements: - Node + Express + better-sqlite3, a localhost dashboard with three panes: my avatar photos, a folder of reaction clips I own, and a render queue. - First frame: pull frame 0 from a chosen clip with ffmpeg, send it plus my avatar photo to Gemini's image-edit API (key in .env) and tell it to replace the whole person while holding the pose, framing, and lighting. Keep that instruction in prompts/first-frame.txt so I can tune it without code changes. - Nothing animates until I approve the frame, so give me approve, regenerate, and discard buttons · the frame costs cents on my invoice, the video dollars. - Motion: on approve, POST the frame AND the original clip to fal.ai's Kling motion-control queue so the render copies the source video's motion, poll to done, write the mp4 to media/, store the real cost on the row. - Second mode: animate the frame alone from a written motion prompt via fal.ai's PixVerse image-to-video, for when I have no clip worth copying. - Burn the hook line over the top third with ffmpeg drawtext, 1080x1920 out, clear of the TikTok UI safe area. - A node-cron tick polls pending jobs every 90s, retries 3 times with backoff, and marks the row before submitting so a retry never double-bills me. - No accounts, no telemetry, everything on my disk except the model calls. - Out of scope: posting to TikTok (that OAuth review takes weeks · export the mp4 and upload by hand), slideshows, and teams. Only ingest clips I have the right to use. - README: getting the two keys, what one 5-second motion-control render costs, and where media lands on disk. ## 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 Ghostfeed capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Ghostfeed indie build ## Goal Build the smallest trustworthy replacement for the core Ghostfeed workflow for one developer or a tiny team. ## Scope Swap an avatar photo into frame 0 of a reaction clip with a hosted image-edit model, approve that frame, then send it plus the source clip to a hosted motion-control model so the render copies the original's motion, and burn the hook text over the result. ## 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 template library scraped from TikTok and Instagram and scene-cut into ready 9:16 clips, instead of clips you source and trim yourself - avatar consistency across renders · one stored reference photo re-fed into every call, plus the draft-and-approve flow that keeps a face you picked - a director loop that writes the slideshow copy, searches for backgrounds, looks at the candidates and casts them, then rotates the deck into variants, plus a timeline editor with its own render workers - 44 agent-API tools · the same workspace drivable from Claude, which is the part with no weekend equivalent - a TikTok app that already cleared review, and a reaper that refunds most stalled renders instead of quietly eating them If those capabilities are essential, use ComfyUI 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 a personal UGC reaction video factory to replace Ghostfeed. Requirements: - Node + Express + better-sqlite3, a localhost dashboard with three panes: my avatar photos, a folder of reaction clips I own, and a render queue. - First frame: pull frame 0 from a chosen clip with ffmpeg, send it plus my avatar photo to Gemini's image-edit API (key in .env) and tell it to replace the whole person while holding the pose, framing, and lighting. Keep that instruction in prompts/first-frame.txt so I can tune it without code changes. - Nothing animates until I approve the frame, so give me approve, regenerate, and discard buttons · the frame costs cents on my invoice, the video dollars. - Motion: on approve, POST the frame AND the original clip to fal.ai's Kling motion-control queue so the render copies the source video's motion, poll to done, write the mp4 to media/, store the real cost on the row. - Second mode: animate the frame alone from a written motion prompt via fal.ai's PixVerse image-to-video, for when I have no clip worth copying. - Burn the hook line over the top third with ffmpeg drawtext, 1080x1920 out, clear of the TikTok UI safe area. - A node-cron tick polls pending jobs every 90s, retries 3 times with backoff, and marks the row before submitting so a retry never double-bills me. - No accounts, no telemetry, everything on my disk except the model calls. - Out of scope: posting to TikTok (that OAuth review takes weeks · export the mp4 and upload by hand), slideshows, and teams. Only ingest clips I have the right to use. - README: getting the two keys, what one 5-second motion-control render costs, and where media lands on disk. ## Required capabilities - fal.ai key for Kling motion-control and image-to-video - Gemini or GPT-image key for the first-frame swap - ffmpeg - reaction clips you have the right to reuse - an always-on box for the polling loop ## 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
# Ghostfeed product brief ## Problem No model here was trained by Ghostfeed · every step is somebody else's hosted inference, spread across four vendors. A Gemini image-edit call swaps your avatar into frame 0 of an existing clip, an approval gate makes you accept that frame before anything expensive runs, and then the video step splits: the default clone family sends the approved frame plus the original clip to Kling motion-control, so the render inherits the source video's motion, while the prompt family animates the still alone on PixVerse, Seedance, Grok or Kling. Rebuild the loop for yourself with a fal.ai key, a Gemini key and ffmpeg over a long weekend and it works. What does not fall out of that weekend is the rest of it · a template library scraped and scene-cut into 9:16 clips, a director that writes slideshow copy and casts every background itself, a timeline editor with its own render workers, 44 agent-API tools, and a TikTok app that took platform review to get. Personal version, yes. The thing you would actually run daily, no. ## Product outcome Swap an avatar photo into frame 0 of a reaction clip with a hosted image-edit model, approve that frame, then send it plus the source clip to a hosted motion-control model so the render copies the original's motion, and burn the hook text over the result. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - fal.ai key for Kling motion-control and image-to-video - Gemini or GPT-image key for the first-frame swap - ffmpeg - reaction clips you have the right to reuse - an always-on box for the polling loop ## Explicit non-goals for v1 - a template library scraped from TikTok and Instagram and scene-cut into ready 9:16 clips, instead of clips you source and trim yourself - avatar consistency across renders · one stored reference photo re-fed into every call, plus the draft-and-approve flow that keeps a face you picked - a director loop that writes the slideshow copy, searches for backgrounds, looks at the candidates and casts them, then rotates the deck into variants, plus a timeline editor with its own render workers - 44 agent-API tools · the same workspace drivable from Claude, which is the part with no weekend equivalent - a TikTok app that already cleared review, and a reaper that refunds most stalled renders instead of quietly eating 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 me a personal UGC reaction video factory to replace Ghostfeed. Requirements: - Node + Express + better-sqlite3, a localhost dashboard with three panes: my avatar photos, a folder of reaction clips I own, and a render queue. - First frame: pull frame 0 from a chosen clip with ffmpeg, send it plus my avatar photo to Gemini's image-edit API (key in .env) and tell it to replace the whole person while holding the pose, framing, and lighting. Keep that instruction in prompts/first-frame.txt so I can tune it without code changes. - Nothing animates until I approve the frame, so give me approve, regenerate, and discard buttons · the frame costs cents on my invoice, the video dollars. - Motion: on approve, POST the frame AND the original clip to fal.ai's Kling motion-control queue so the render copies the source video's motion, poll to done, write the mp4 to media/, store the real cost on the row. - Second mode: animate the frame alone from a written motion prompt via fal.ai's PixVerse image-to-video, for when I have no clip worth copying. - Burn the hook line over the top third with ffmpeg drawtext, 1080x1920 out, clear of the TikTok UI safe area. - A node-cron tick polls pending jobs every 90s, retries 3 times with backoff, and marks the row before submitting so a retry never double-bills me. - No accounts, no telemetry, everything on my disk except the model calls. - Out of scope: posting to TikTok (that OAuth review takes weeks · export the mp4 and upload by hand), slideshows, and teams. Only ingest clips I have the right to use. - README: getting the two keys, what one 5-second motion-control render costs, and where media lands on disk. ## 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 Ghostfeed 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
The generation stack spans Google, fal.ai, Replicate and BytePlus, and any one of them can change price, quality or content filters in a week · the subscription outsources that churn and turns per-second render invoices into one number. Stalled renders get reaped and refunded rather than billed, which a DIY build does not do for you. And the TikTok side arrives already through platform review, which is the weeks a solo builder spends before posting anything.
xa template library scraped from TikTok and Instagram and scene-cut into ready 9:16 clips, instead of clips you source and trim yourself
xavatar consistency across renders · one stored reference photo re-fed into every call, plus the draft-and-approve flow that keeps a face you picked
xa director loop that writes the slideshow copy, searches for backgrounds, looks at the candidates and casts them, then rotates the deck into variants, plus a timeline editor with its own render workers
x44 agent-API tools · the same workspace drivable from Claude, which is the part with no weekend equivalent
xa TikTok app that already cleared review, and a reaper that refunds most stalled renders instead of quietly eating them
share on X ↗"I just replaced Ghostfeed ($49/mo) with one prompt"
Ghostfeed pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| free | $0 | $0 | 1 workspace; 1 slideshow project; 3 variants; 0 included video credits. |
| starter | $29 | — | 150 credits/month, advertised as about 25 videos; 1 workspace; extra credits $0.18 each. |
| growth | $49 | — | 300 credits/month, advertised as about 50 videos; 3 workspaces; extra credits $0.16 each. |
| agency | $99 | — | 700 credits/month, advertised as about 115 videos; 10 workspaces; extra credits $0.14 each. |
free tier1 workspace, 1 slideshow project and 3 variants; 0 included video credits, so the free plan renders no paid-credit video.
billingmonthly only; no annual plan is displayed
hidden costsIncluded monthly credits reset and do not roll over; bought extra credits do not expire. Video generally costs 1 credit per rendered second and images cost 1 credit each.
verified 2026-08-14 · source ↗
Is Ghostfeed free?
The free plan renders no video at all: one slideshow project with three variants, one workspace, library and posting access, and the agent API, which is ungated on every plan including this one. Paid is Growth at $49/mo (checked 2026-08-07).
Vibecode Ghostfeed
Kinda. The core of Ghostfeed is buildable in a weekend with the prompt on this page, but there are real gaps: a template library scraped from TikTok and Instagram and scene-cut into ready 9:16 clips, instead of clips you source and trim yourself, avatar consistency across renders · one stored reference photo re-fed into every call, plus the draft-and-approve flow that keeps a face you picked. Read the honest list above before committing.
How much does Ghostfeed cost?
Ghostfeed costs about $49/month (Growth, checked 2026-08-07), which is $588 per year.
What do I lose by replacing Ghostfeed?
Honestly: a template library scraped from TikTok and Instagram and scene-cut into ready 9:16 clips, instead of clips you source and trim yourself; avatar consistency across renders · one stored reference photo re-fed into every call, plus the draft-and-approve flow that keeps a face you picked; a director loop that writes the slideshow copy, searches for backgrounds, looks at the candidates and casts them, then rotates the deck into variants, plus a timeline editor with its own render workers; 44 agent-API tools · the same workspace drivable from Claude, which is the part with no weekend equivalent; a TikTok app that already cleared review, and a reaper that refunds most stalled renders instead of quietly eating them. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Ghostfeed?
Yes: ComfyUI (Node graph for running open image and video models locally · the render pipeline without the hosted invoice.), Wan2.2 (Apache-2.0 open video model with image-to-video, the free stand-in for the hosted animation step if you own a GPU.), Postiz (Open-source social publishing · the scheduling and OAuth half, self-hostable, so you never touch the TikTok API yourself.). Using prior art is also vibecoding; the prompt is for when you want it exactly your way.