Vibecode AmICited
track this build5 steps, step by step0%KINDA · weekend project
price $57.73/moyou'd save $692.76/yrbuild time multi-daycategory 📈 seo & marketingreplaced by 0 people
The scoring loop is genuinely weekend-buildable, but a faithful replacement is not, and that gap is the honest reason to keep paying. The naive personal build sends your questions through the model APIs, but API answers are not what a real user sees when they open ChatGPT, Perplexity, or AI Overviews, so that number is only a proxy. AmICited does not use LLM APIs at all: it drives real browsers to ask the actual consumer surfaces the way a person in a given country would, a browser-automation fleet routed through country-level proxies, kept working as every surface changes. The other gaps are the historical archive that compounds from day one and cannot be backfilled, an MCP server that lets your AI agents act on your visibility gaps, and, on higher plans, human AEO consulting from real experience that no script replaces. You can build the weekend proxy; the prompt below is that honest consolation build, with its limits stated plainly.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of AmICited. 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 ===== # AmICited indie build ## Goal Build the smallest trustworthy replacement for the core AmICited workflow for one developer or a tiny team. ## Scope Run your buyer questions through the answer-engine APIs on a schedule, store each answer, and score brand and competitor mentions plus cited sources, as a rough personal proxy for the real consumer-surface answers. ## 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: - the faithful signal: API answers only approximate what the ChatGPT app, AI Overviews, AI Mode, Copilot, and Grok actually serve real users, which is what AmICited measures with real browsers - country-level results: what users see varies by geography, which needs a proxy fleet a solo build will not stand up - scale and reliability: running and retrying thousands of real browser sessions in parallel is the hard engineering, not the scoring - months of stored answers and competitor baselines, without which a single week's visibility number is noise, and which you cannot backfill once you start late - human AEO consultations on higher plans, advice from real experience acting on your data, which no self-hosted script reproduces If those capabilities are essential, use Elmo 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 local AI answer-engine citation tracker for one brand, as a rough personal proxy. Requirements: - Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry. - brand.json holds my brand name, aliases, domain, and competitor names. prompts.json holds up to 50 buyer questions. - `track run` sends every prompt through OpenAI, Anthropic, Gemini, Perplexity, and xAI (Grok) with each provider's web search or grounding tool enabled. Keys live in .env. - Store one immutable row per run, prompt, and provider: the full raw answer, cited URLs, model id, latency, and error text. Never overwrite an existing run, so the archive compounds over time. - Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and keep failed cells visible in the report instead of dropping them. - Detect brand and competitor mentions case-insensitively using the alias list, and record the first-mention character offset as a crude prominence proxy. - Normalize citations to hostname plus canonical path, strip tracking parameters, then compute owned-domain citation share and a top 25 cited-sources table. - `track serve` renders visibility per provider over time, share of voice against each competitor, the sources table, and the prompts where competitors are cited and I am not (the gap list is the point). - `track export` writes runs, mentions, and citations to CSV. - Fixture tests for mention detection and URL normalization. - Out of scope, and be honest in the README that these are exactly what a paid product like this sells: the real consumer surfaces (this uses model APIs, which do NOT match what the ChatGPT app, AI Overviews, AI Mode, Copilot, or Grok actually serve users), country-specific results via a proxied real-browser fleet, running that fleet at scale, and any human AEO consulting. Do not try to automate the consumer web UIs. - README: setup, a per-run cost estimate, a cron line for daily runs, and a plain note that API answers only approximate what users actually see. ## Required capabilities - headless browser automation (Playwright/Puppeteer) to reach the real consumer surfaces, not just model APIs - residential or geo-targeted proxies to query from specific countries - a scalable scheduler/queue and the infra to run and retry many browser sessions in parallel - durable per-run storage that never overwrites an answer, so history compounds - ongoing upkeep as each engine and surface changes its UI, models, and citation format ## 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 AmICited. 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 ===== # AmICited indie build ## Goal Build the smallest trustworthy replacement for the core AmICited workflow for one developer or a tiny team. ## Scope Run your buyer questions through the answer-engine APIs on a schedule, store each answer, and score brand and competitor mentions plus cited sources, as a rough personal proxy for the real consumer-surface answers. ## 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: - the faithful signal: API answers only approximate what the ChatGPT app, AI Overviews, AI Mode, Copilot, and Grok actually serve real users, which is what AmICited measures with real browsers - country-level results: what users see varies by geography, which needs a proxy fleet a solo build will not stand up - scale and reliability: running and retrying thousands of real browser sessions in parallel is the hard engineering, not the scoring - months of stored answers and competitor baselines, without which a single week's visibility number is noise, and which you cannot backfill once you start late - human AEO consultations on higher plans, advice from real experience acting on your data, which no self-hosted script reproduces If those capabilities are essential, use Elmo 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 local AI answer-engine citation tracker for one brand, as a rough personal proxy. Requirements: - Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry. - brand.json holds my brand name, aliases, domain, and competitor names. prompts.json holds up to 50 buyer questions. - `track run` sends every prompt through OpenAI, Anthropic, Gemini, Perplexity, and xAI (Grok) with each provider's web search or grounding tool enabled. Keys live in .env. - Store one immutable row per run, prompt, and provider: the full raw answer, cited URLs, model id, latency, and error text. Never overwrite an existing run, so the archive compounds over time. - Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and keep failed cells visible in the report instead of dropping them. - Detect brand and competitor mentions case-insensitively using the alias list, and record the first-mention character offset as a crude prominence proxy. - Normalize citations to hostname plus canonical path, strip tracking parameters, then compute owned-domain citation share and a top 25 cited-sources table. - `track serve` renders visibility per provider over time, share of voice against each competitor, the sources table, and the prompts where competitors are cited and I am not (the gap list is the point). - `track export` writes runs, mentions, and citations to CSV. - Fixture tests for mention detection and URL normalization. - Out of scope, and be honest in the README that these are exactly what a paid product like this sells: the real consumer surfaces (this uses model APIs, which do NOT match what the ChatGPT app, AI Overviews, AI Mode, Copilot, or Grok actually serve users), country-specific results via a proxied real-browser fleet, running that fleet at scale, and any human AEO consulting. Do not try to automate the consumer web UIs. - README: setup, a per-run cost estimate, a cron line for daily runs, and a plain note that API answers only approximate what users actually see. ## Required capabilities - headless browser automation (Playwright/Puppeteer) to reach the real consumer surfaces, not just model APIs - residential or geo-targeted proxies to query from specific countries - a scalable scheduler/queue and the infra to run and retry many browser sessions in parallel - durable per-run storage that never overwrites an answer, so history compounds - ongoing upkeep as each engine and surface changes its UI, models, and citation format ## 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 AmICited. 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 ===== # AmICited product brief ## Problem The scoring loop is genuinely weekend-buildable, but a faithful replacement is not, and that gap is the honest reason to keep paying. The naive personal build sends your questions through the model APIs, but API answers are not what a real user sees when they open ChatGPT, Perplexity, or AI Overviews, so that number is only a proxy. AmICited does not use LLM APIs at all: it drives real browsers to ask the actual consumer surfaces the way a person in a given country would, a browser-automation fleet routed through country-level proxies, kept working as every surface changes. The other gaps are the historical archive that compounds from day one and cannot be backfilled, an MCP server that lets your AI agents act on your visibility gaps, and, on higher plans, human AEO consulting from real experience that no script replaces. You can build the weekend proxy; the prompt below is that honest consolation build, with its limits stated plainly. ## Product outcome Run your buyer questions through the answer-engine APIs on a schedule, store each answer, and score brand and competitor mentions plus cited sources, as a rough personal proxy for the real consumer-surface answers. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - headless browser automation (Playwright/Puppeteer) to reach the real consumer surfaces, not just model APIs - residential or geo-targeted proxies to query from specific countries - a scalable scheduler/queue and the infra to run and retry many browser sessions in parallel - durable per-run storage that never overwrites an answer, so history compounds - ongoing upkeep as each engine and surface changes its UI, models, and citation format ## Explicit non-goals for v1 - the faithful signal: API answers only approximate what the ChatGPT app, AI Overviews, AI Mode, Copilot, and Grok actually serve real users, which is what AmICited measures with real browsers - country-level results: what users see varies by geography, which needs a proxy fleet a solo build will not stand up - scale and reliability: running and retrying thousands of real browser sessions in parallel is the hard engineering, not the scoring - months of stored answers and competitor baselines, without which a single week's visibility number is noise, and which you cannot backfill once you start late - human AEO consultations on higher plans, advice from real experience acting on your data, which no self-hosted script reproduces ## 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 local AI answer-engine citation tracker for one brand, as a rough personal proxy. Requirements: - Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry. - brand.json holds my brand name, aliases, domain, and competitor names. prompts.json holds up to 50 buyer questions. - `track run` sends every prompt through OpenAI, Anthropic, Gemini, Perplexity, and xAI (Grok) with each provider's web search or grounding tool enabled. Keys live in .env. - Store one immutable row per run, prompt, and provider: the full raw answer, cited URLs, model id, latency, and error text. Never overwrite an existing run, so the archive compounds over time. - Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and keep failed cells visible in the report instead of dropping them. - Detect brand and competitor mentions case-insensitively using the alias list, and record the first-mention character offset as a crude prominence proxy. - Normalize citations to hostname plus canonical path, strip tracking parameters, then compute owned-domain citation share and a top 25 cited-sources table. - `track serve` renders visibility per provider over time, share of voice against each competitor, the sources table, and the prompts where competitors are cited and I am not (the gap list is the point). - `track export` writes runs, mentions, and citations to CSV. - Fixture tests for mention detection and URL normalization. - Out of scope, and be honest in the README that these are exactly what a paid product like this sells: the real consumer surfaces (this uses model APIs, which do NOT match what the ChatGPT app, AI Overviews, AI Mode, Copilot, or Grok actually serve users), country-specific results via a proxied real-browser fleet, running that fleet at scale, and any human AEO consulting. Do not try to automate the consumer web UIs. - README: setup, a per-run cost estimate, a cron line for daily runs, and a plain note that API answers only approximate what users actually see. ## 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 AmICited capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# AmICited indie build ## Goal Build the smallest trustworthy replacement for the core AmICited workflow for one developer or a tiny team. ## Scope Run your buyer questions through the answer-engine APIs on a schedule, store each answer, and score brand and competitor mentions plus cited sources, as a rough personal proxy for the real consumer-surface answers. ## 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: - the faithful signal: API answers only approximate what the ChatGPT app, AI Overviews, AI Mode, Copilot, and Grok actually serve real users, which is what AmICited measures with real browsers - country-level results: what users see varies by geography, which needs a proxy fleet a solo build will not stand up - scale and reliability: running and retrying thousands of real browser sessions in parallel is the hard engineering, not the scoring - months of stored answers and competitor baselines, without which a single week's visibility number is noise, and which you cannot backfill once you start late - human AEO consultations on higher plans, advice from real experience acting on your data, which no self-hosted script reproduces If those capabilities are essential, use Elmo 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 local AI answer-engine citation tracker for one brand, as a rough personal proxy. Requirements: - Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry. - brand.json holds my brand name, aliases, domain, and competitor names. prompts.json holds up to 50 buyer questions. - `track run` sends every prompt through OpenAI, Anthropic, Gemini, Perplexity, and xAI (Grok) with each provider's web search or grounding tool enabled. Keys live in .env. - Store one immutable row per run, prompt, and provider: the full raw answer, cited URLs, model id, latency, and error text. Never overwrite an existing run, so the archive compounds over time. - Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and keep failed cells visible in the report instead of dropping them. - Detect brand and competitor mentions case-insensitively using the alias list, and record the first-mention character offset as a crude prominence proxy. - Normalize citations to hostname plus canonical path, strip tracking parameters, then compute owned-domain citation share and a top 25 cited-sources table. - `track serve` renders visibility per provider over time, share of voice against each competitor, the sources table, and the prompts where competitors are cited and I am not (the gap list is the point). - `track export` writes runs, mentions, and citations to CSV. - Fixture tests for mention detection and URL normalization. - Out of scope, and be honest in the README that these are exactly what a paid product like this sells: the real consumer surfaces (this uses model APIs, which do NOT match what the ChatGPT app, AI Overviews, AI Mode, Copilot, or Grok actually serve users), country-specific results via a proxied real-browser fleet, running that fleet at scale, and any human AEO consulting. Do not try to automate the consumer web UIs. - README: setup, a per-run cost estimate, a cron line for daily runs, and a plain note that API answers only approximate what users actually see. ## Required capabilities - headless browser automation (Playwright/Puppeteer) to reach the real consumer surfaces, not just model APIs - residential or geo-targeted proxies to query from specific countries - a scalable scheduler/queue and the infra to run and retry many browser sessions in parallel - durable per-run storage that never overwrites an answer, so history compounds - ongoing upkeep as each engine and surface changes its UI, models, and citation format ## 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
# AmICited product brief ## Problem The scoring loop is genuinely weekend-buildable, but a faithful replacement is not, and that gap is the honest reason to keep paying. The naive personal build sends your questions through the model APIs, but API answers are not what a real user sees when they open ChatGPT, Perplexity, or AI Overviews, so that number is only a proxy. AmICited does not use LLM APIs at all: it drives real browsers to ask the actual consumer surfaces the way a person in a given country would, a browser-automation fleet routed through country-level proxies, kept working as every surface changes. The other gaps are the historical archive that compounds from day one and cannot be backfilled, an MCP server that lets your AI agents act on your visibility gaps, and, on higher plans, human AEO consulting from real experience that no script replaces. You can build the weekend proxy; the prompt below is that honest consolation build, with its limits stated plainly. ## Product outcome Run your buyer questions through the answer-engine APIs on a schedule, store each answer, and score brand and competitor mentions plus cited sources, as a rough personal proxy for the real consumer-surface answers. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - headless browser automation (Playwright/Puppeteer) to reach the real consumer surfaces, not just model APIs - residential or geo-targeted proxies to query from specific countries - a scalable scheduler/queue and the infra to run and retry many browser sessions in parallel - durable per-run storage that never overwrites an answer, so history compounds - ongoing upkeep as each engine and surface changes its UI, models, and citation format ## Explicit non-goals for v1 - the faithful signal: API answers only approximate what the ChatGPT app, AI Overviews, AI Mode, Copilot, and Grok actually serve real users, which is what AmICited measures with real browsers - country-level results: what users see varies by geography, which needs a proxy fleet a solo build will not stand up - scale and reliability: running and retrying thousands of real browser sessions in parallel is the hard engineering, not the scoring - months of stored answers and competitor baselines, without which a single week's visibility number is noise, and which you cannot backfill once you start late - human AEO consultations on higher plans, advice from real experience acting on your data, which no self-hosted script reproduces ## 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 local AI answer-engine citation tracker for one brand, as a rough personal proxy. Requirements: - Node 22, TypeScript, SQLite via better-sqlite3, a CLI, and a plain server-rendered dashboard. Local only, no accounts, no telemetry. - brand.json holds my brand name, aliases, domain, and competitor names. prompts.json holds up to 50 buyer questions. - `track run` sends every prompt through OpenAI, Anthropic, Gemini, Perplexity, and xAI (Grok) with each provider's web search or grounding tool enabled. Keys live in .env. - Store one immutable row per run, prompt, and provider: the full raw answer, cited URLs, model id, latency, and error text. Never overwrite an existing run, so the archive compounds over time. - Cap concurrency at 3 per provider, retry twice on 429 and 5xx with backoff, and keep failed cells visible in the report instead of dropping them. - Detect brand and competitor mentions case-insensitively using the alias list, and record the first-mention character offset as a crude prominence proxy. - Normalize citations to hostname plus canonical path, strip tracking parameters, then compute owned-domain citation share and a top 25 cited-sources table. - `track serve` renders visibility per provider over time, share of voice against each competitor, the sources table, and the prompts where competitors are cited and I am not (the gap list is the point). - `track export` writes runs, mentions, and citations to CSV. - Fixture tests for mention detection and URL normalization. - Out of scope, and be honest in the README that these are exactly what a paid product like this sells: the real consumer surfaces (this uses model APIs, which do NOT match what the ChatGPT app, AI Overviews, AI Mode, Copilot, or Grok actually serve users), country-specific results via a proxied real-browser fleet, running that fleet at scale, and any human AEO consulting. Do not try to automate the consumer web UIs. - README: setup, a per-run cost estimate, a cron line for daily runs, and a plain note that API answers only approximate what users actually see. ## 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 AmICited 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
Because the scoring is the cheap half and everything real is the expensive half. AmICited does not call the model APIs, it drives real browsers through country-level proxies to capture what users genuinely see, which is faithful and hard to run at scale and keep working as the surfaces change. It stores every full answer so the value compounds into a history you cannot recreate once you start late, exposes an MCP server so your AI agents can act on the gaps, and on higher plans adds human AEO consulting that acts on your data from real experience. A personal API script gives you a rough proxy; it does not give you any of that.
xthe faithful signal: API answers only approximate what the ChatGPT app, AI Overviews, AI Mode, Copilot, and Grok actually serve real users, which is what AmICited measures with real browsers
xcountry-level results: what users see varies by geography, which needs a proxy fleet a solo build will not stand up
xscale and reliability: running and retrying thousands of real browser sessions in parallel is the hard engineering, not the scoring
xmonths of stored answers and competitor baselines, without which a single week's visibility number is noise, and which you cannot backfill once you start late
xhuman AEO consultations on higher plans, advice from real experience acting on your data, which no self-hosted script reproduces
share on X ↗"I just replaced AmICited ($57.73/mo) with one prompt"
Don't feel like building it? These folks already made it free.
ElmoSelf-hosted AEO tracker covering the API-proxy scoring loop; it queries model APIs, which is exactly the faithfulness gap the paid product exists to cross.open source↗no votes, no pay-to-list · just what's real
AmICited pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| starter | $57.73/workspace | $52.91/workspace | 50 credits; 3,000 responses; 1 domain; 200 prompts/domain; 10 competitors; 10 articles; 1 workspace; 1 member. |
| pro | $138.54/workspace | $127/workspace | 120 credits; 8,000 responses; 5 domains; 800 prompts; 30 competitors; 50 articles; 15 workspaces; 10 members/workspace. |
| premium | $577.25/workspace | $529.15/workspace | 500 credits; 33,000 responses; 50 domains; 3,200 prompts; unlimited competitors; 500 articles; 50 workspaces; 100 members/workspace. |
| enterprise | — | $1731.75/workspace | Custom or unlimited limits; annual agreement. |
free tierno permanent free tier; 14-day no-card trial with 1 domain, 5 prompts, 3 competitors and 3 articles
billingmonthly + annual; annual gives 1 month free; Enterprise uses an annual agreement; source prices are in EUR
hidden costs1 response consumes 0.015 credit, while agents/articles consume more; unused credits and article allowances do not roll over; extra credits are sold as top-ups; upgrades can be prorated
verified 2026-08-13 · source ↗
Vibecode AmICited
Kinda. The core of AmICited is buildable in a weekend with the prompt on this page, but there are real gaps: the faithful signal: API answers only approximate what the ChatGPT app, AI Overviews, AI Mode, Copilot, and Grok actually serve real users, which is what AmICited measures with real browsers, country-level results: what users see varies by geography, which needs a proxy fleet a solo build will not stand up. Read the honest list above before committing.
How much does AmICited cost?
AmICited costs about $57.73/month (Starter, checked 2026-08-13), which is $692.76 per year.
What do I lose by replacing AmICited?
Honestly: the faithful signal: API answers only approximate what the ChatGPT app, AI Overviews, AI Mode, Copilot, and Grok actually serve real users, which is what AmICited measures with real browsers; country-level results: what users see varies by geography, which needs a proxy fleet a solo build will not stand up; scale and reliability: running and retrying thousands of real browser sessions in parallel is the hard engineering, not the scoring; months of stored answers and competitor baselines, without which a single week's visibility number is noise, and which you cannot backfill once you start late; human AEO consultations on higher plans, advice from real experience acting on your data, which no self-hosted script reproduces. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to AmICited?
Yes: Elmo (Self-hosted AEO tracker covering the API-proxy scoring loop; it queries model APIs, which is exactly the faithfulness gap the paid product exists to cross.) The prompt is for when you want it exactly your way.