Vibecode ChatGPT
track this build5 steps, step by step0%NOT REALLY · don't bother
price $20/moyou'd save $240/yrbuild time not realistically solocategory ✨ ai assistantsreplaced by 0 people
You can build a chat UI over APIs, but you cannot solo-recreate the model, multimodal stack, tools, memory/product layer, safety systems, and scale.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of ChatGPT. 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 ===== # ChatGPT indie build ## Goal Build the smallest trustworthy replacement for the core ChatGPT workflow for one developer or a tiny team. ## Scope Build a chat interface that calls OpenAI/Anthropic/local models, stores conversations, and adds file upload/tools. ## 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: - frontier models - multimodal features - tool ecosystem - reliability - safety - product updates - mobile apps If those capabilities are essential, use Open WebUI 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 chat client on the OpenAI and Anthropic APIs to replace the text-chat part of ChatGPT Plus. Requirements: - A local web app: Node + Express + better-sqlite3, one page with a streaming reply pane (SSE), a model picker, and a conversation sidebar. - OpenAI and Anthropic keys in .env; system prompt editable per conversation. - Conversations stored in SQLite with FTS5 search; export any thread to Markdown. - File upload for text and PDF (pdf-parse); contents go straight into the context window, no vector database. - A usage footer: tokens and estimated cost per conversation and per month, computed from the usage field in API responses, so I can compare real spend against the $20 flat fee. - Localhost only. No accounts, no telemetry, everything stays on my machine except the API calls. - Out of scope: image generation, voice mode, browsing agents, memory, and mobile apps. Do not attempt any of them; that gap is the subscription. - README: where to get each key, links to current per-token pricing, and state plainly that this replaces text chat only, the frontier model itself cannot be rebuilt. ## Required capabilities - LLM API key or local model - hosted app - vector store if retrieval - auth/storage - tool integrations ## 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 ChatGPT. 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 ===== # ChatGPT indie build ## Goal Build the smallest trustworthy replacement for the core ChatGPT workflow for one developer or a tiny team. ## Scope Build a chat interface that calls OpenAI/Anthropic/local models, stores conversations, and adds file upload/tools. ## 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: - frontier models - multimodal features - tool ecosystem - reliability - safety - product updates - mobile apps If those capabilities are essential, use Open WebUI 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 chat client on the OpenAI and Anthropic APIs to replace the text-chat part of ChatGPT Plus. Requirements: - A local web app: Node + Express + better-sqlite3, one page with a streaming reply pane (SSE), a model picker, and a conversation sidebar. - OpenAI and Anthropic keys in .env; system prompt editable per conversation. - Conversations stored in SQLite with FTS5 search; export any thread to Markdown. - File upload for text and PDF (pdf-parse); contents go straight into the context window, no vector database. - A usage footer: tokens and estimated cost per conversation and per month, computed from the usage field in API responses, so I can compare real spend against the $20 flat fee. - Localhost only. No accounts, no telemetry, everything stays on my machine except the API calls. - Out of scope: image generation, voice mode, browsing agents, memory, and mobile apps. Do not attempt any of them; that gap is the subscription. - README: where to get each key, links to current per-token pricing, and state plainly that this replaces text chat only, the frontier model itself cannot be rebuilt. ## Required capabilities - LLM API key or local model - hosted app - vector store if retrieval - auth/storage - tool integrations ## 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 ChatGPT. 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 ===== # ChatGPT product brief ## Problem You can build a chat UI over APIs, but you cannot solo-recreate the model, multimodal stack, tools, memory/product layer, safety systems, and scale. ## Product outcome Build a chat interface that calls OpenAI/Anthropic/local models, stores conversations, and adds file upload/tools. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - LLM API key or local model - hosted app - vector store if retrieval - auth/storage - tool integrations ## Explicit non-goals for v1 - frontier models - multimodal features - tool ecosystem - reliability - safety - product updates - mobile apps ## 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 chat client on the OpenAI and Anthropic APIs to replace the text-chat part of ChatGPT Plus. Requirements: - A local web app: Node + Express + better-sqlite3, one page with a streaming reply pane (SSE), a model picker, and a conversation sidebar. - OpenAI and Anthropic keys in .env; system prompt editable per conversation. - Conversations stored in SQLite with FTS5 search; export any thread to Markdown. - File upload for text and PDF (pdf-parse); contents go straight into the context window, no vector database. - A usage footer: tokens and estimated cost per conversation and per month, computed from the usage field in API responses, so I can compare real spend against the $20 flat fee. - Localhost only. No accounts, no telemetry, everything stays on my machine except the API calls. - Out of scope: image generation, voice mode, browsing agents, memory, and mobile apps. Do not attempt any of them; that gap is the subscription. - README: where to get each key, links to current per-token pricing, and state plainly that this replaces text chat only, the frontier model itself cannot be rebuilt. ## 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 ChatGPT capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# ChatGPT indie build ## Goal Build the smallest trustworthy replacement for the core ChatGPT workflow for one developer or a tiny team. ## Scope Build a chat interface that calls OpenAI/Anthropic/local models, stores conversations, and adds file upload/tools. ## 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: - frontier models - multimodal features - tool ecosystem - reliability - safety - product updates - mobile apps If those capabilities are essential, use Open WebUI 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 chat client on the OpenAI and Anthropic APIs to replace the text-chat part of ChatGPT Plus. Requirements: - A local web app: Node + Express + better-sqlite3, one page with a streaming reply pane (SSE), a model picker, and a conversation sidebar. - OpenAI and Anthropic keys in .env; system prompt editable per conversation. - Conversations stored in SQLite with FTS5 search; export any thread to Markdown. - File upload for text and PDF (pdf-parse); contents go straight into the context window, no vector database. - A usage footer: tokens and estimated cost per conversation and per month, computed from the usage field in API responses, so I can compare real spend against the $20 flat fee. - Localhost only. No accounts, no telemetry, everything stays on my machine except the API calls. - Out of scope: image generation, voice mode, browsing agents, memory, and mobile apps. Do not attempt any of them; that gap is the subscription. - README: where to get each key, links to current per-token pricing, and state plainly that this replaces text chat only, the frontier model itself cannot be rebuilt. ## Required capabilities - LLM API key or local model - hosted app - vector store if retrieval - auth/storage - tool integrations ## 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
# ChatGPT product brief ## Problem You can build a chat UI over APIs, but you cannot solo-recreate the model, multimodal stack, tools, memory/product layer, safety systems, and scale. ## Product outcome Build a chat interface that calls OpenAI/Anthropic/local models, stores conversations, and adds file upload/tools. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - LLM API key or local model - hosted app - vector store if retrieval - auth/storage - tool integrations ## Explicit non-goals for v1 - frontier models - multimodal features - tool ecosystem - reliability - safety - product updates - mobile apps ## 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 chat client on the OpenAI and Anthropic APIs to replace the text-chat part of ChatGPT Plus. Requirements: - A local web app: Node + Express + better-sqlite3, one page with a streaming reply pane (SSE), a model picker, and a conversation sidebar. - OpenAI and Anthropic keys in .env; system prompt editable per conversation. - Conversations stored in SQLite with FTS5 search; export any thread to Markdown. - File upload for text and PDF (pdf-parse); contents go straight into the context window, no vector database. - A usage footer: tokens and estimated cost per conversation and per month, computed from the usage field in API responses, so I can compare real spend against the $20 flat fee. - Localhost only. No accounts, no telemetry, everything stays on my machine except the API calls. - Out of scope: image generation, voice mode, browsing agents, memory, and mobile apps. Do not attempt any of them; that gap is the subscription. - README: where to get each key, links to current per-token pricing, and state plainly that this replaces text chat only, the frontier model itself cannot be rebuilt. ## 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 ChatGPT 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
They pay for access to a continuously improving platform, not a textarea and send button.
xfrontier models
xmultimodal features
xtool ecosystem
xreliability
xsafety
xproduct updates
xmobile apps
share on X ↗"I just replaced ChatGPT ($20/mo) with one prompt"
Don't feel like building it? These folks already made it free.
AnythingLLMA private AI desk for documents and agents; install it, then choose local models or somebody else's meter.open source↗
Cherry StudioA desktop model switchboard with assistants, files, agents and MCP; bring keys or make your laptop sweat.open source↗
JanA local-first desktop assistant with cloud connectors and tools; your RAM gets the invoice.open source↗all 8 free alternatives to ChatGPT →· no votes, no pay-to-list · just what's real
ChatGPT pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| free | $0 | $0 | 27K Instant context (about 12 pages); exact message, upload, image, voice, and research counts are variable and not published as fixed numbers. |
| go | $8 | — | About 10× Free usage; 54K Instant context and up to 256K reasoning context. |
| plus | $20 | — | 54K Instant context; up to 256K reasoning context; 40 ChatGPT agent messages/month. |
| pro 5x | $100 | — | 5× Plus usage; 128K Instant context and up to 400K reasoning context. |
| pro 20x | $200 | — | 20× Plus usage; 128K Instant context and up to 400K reasoning context; 400 ChatGPT agent messages/month. |
| business | $25/user | $20/user | Minimum 2 seats; 1 hour of Voice in Chat included; flexible credits available for advanced usage. |
| enterprise | custom | — | Custom seats, controls, context, and flexible-credit allowance. |
| edu | custom | — | Institutional deployment; custom seats and usage. |
free tier27K Instant context (about 12 pages); fixed message/upload/image limits are not publicly guaranteed
billingindividual plans are monthly only; Business is monthly or annual; Enterprise and Edu are custom contracts
hidden costsBusiness/Enterprise/Edu advanced usage can be metered through flexible credits. Published examples include extra Voice in Chat at 5 credits/minute and Voice in Work/Codex at about 6 credits/minute; Business seat additions can trigger proration/true-ups.
verified 2026-08-12 · source ↗
Vibecode ChatGPT
Not really. ChatGPT's value is not the code: . See the honest breakdown above.
How much does ChatGPT cost?
ChatGPT costs about $20/month (Plus, checked 2026-07-30), which is $240 per year.
What do I lose by replacing ChatGPT?
Honestly: frontier models; multimodal features; tool ecosystem; reliability; safety; product updates; mobile apps. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to ChatGPT?
Yes: AnythingLLM (A private AI desk for documents and agents; install it, then choose local models or somebody else's meter.) Cherry Studio (A desktop model switchboard with assistants, files, agents and MCP; bring keys or make your laptop sweat.) Jan (A local-first desktop assistant with cloud connectors and tools; your RAM gets the invoice.) All 8 curated free alternatives are at vibecodeit.com/chatgpt/alternatives. The prompt is for when you want it exactly your way.