Quick wins
The fix lists from the latest run of every prompt set, core sets first, then fastest expected ranking gain: prompts a fix could flip per working day, each weighted by what it has left to win (one minus its recommended rate over the last two weeks; named, for an info prompt). Mark a fix with the commit or pull request you submitted for it; open leaves out the ones you have. A later fix for the same page stays open, with what was tried before.
Statusall 27open 16accepted 11
Priorityall 16urgent 1high 4medium 10low 1
Siteall 21cheapseats.dev (marketing) 2cheapseats.dev/docs (docs) 3elsewhere 16
Gapall 23documentation 16third-party 6product 1
Setall 16Finding out whether AI assistants recommend us 3Getting recommended instead of a competitor 4Choosing an AI visibility tool 5Cheap Seats head to heads 1Open-source and self-hosted AI visibility 3
Publish popcorn.zone/docs/guides/search-vs-recall explaining both tracks with an example medium documentation effort S flips 1 Finding out whether AI assistants recommend us
Where: popcorn.zone/docs/guides/search-vs-recall
Must say: Explain that each question can be asked with web search forced on or with no tools at all. The two give different answers because one reflects current web pages and the other reflects training data. Show how Popcorn runs both tracks on every prompt and reports them side by side. Include a real anonymised example where the two tracks disagree; pick it from a Popcorn run first.
Done when: A re-run of prompt 13 cites this page in at least one search answer.
Displaces: https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool
Why: P13: every search answer was shaped by vendor API docs and generic tool lists. No source explained measuring both modes.
Prompt for Claude Code
# Publish popcorn.zone/docs/guides/search-vs-recall explaining both tracks with an example When buyers ask an AI assistant the questions below, it explains the topic without naming us or citing our pages. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - Do ChatGPT and Claude say different things about a product with web search on versus from training data alone, and how do I measure both? ## What the assistant said P13: every search answer was shaped by vendor API docs and generic tool lists. No source explained measuring both modes. ## Who wins the question today https://platform.claude.com/docs/en/agents-and-tools/tool-use/web-search-tool. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/docs/guides/search-vs-recall so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Explain that each question can be asked with web search forced on or with no tools at all. The two give different answers because one reflects current web pages and the other reflects training data. Show how Popcorn runs both tracks on every prompt and reports them side by side. Include a real anonymised example where the two tracks disagree; pick it from a Popcorn run first. ## Done when A re-run of prompt 13 cites this page in at least one search answer. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/docs/guides/why-ai-recommends-a-competitor high documentation effort M flips 4 Getting recommended instead of a competitor
Where: popcorn.zone/docs/guides/why-ai-recommends-a-competitor
Must say: Explain the diagnosis loop step by step: ask your buyer questions repeatedly, and separate what the model finds by searching from what it believes with no tools. Then record the top pick, the products named and the objections raised, and trace the citations behind them. Show how Popcorn automates each step and turns objections into specific page fixes, using a sample graded answer as the example.
Done when: Prompts 1, 2 or 4 on the search track cite this guide in at least 1 answer.
Displaces: https://everything-pr.com/why-chatgpt-recommends-competitors
Why: P2: no Popcorn page surfaced; answers were built from everything-pr.com/why-chatgpt-recommends-competitors and help.otterly.ai.
Prompt for Claude Code
# Publish popcorn.zone/docs/guides/why-ai-recommends-a-competitor When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - ChatGPT keeps recommending our competitor instead of us. What can we actually do about it? - How can I find out why an AI assistant prefers a competitor's product over ours? - ChatGPT gets our features and pricing wrong. How do we correct what AI assistants say about our product? - AI assistants mention our product but add caveats that steer buyers away. How do we address the objections they raise? ## What the assistant said P2: no Popcorn page surfaced; answers were built from everything-pr.com/why-chatgpt-recommends-competitors and help.otterly.ai. ## Who wins the question today https://everything-pr.com/why-chatgpt-recommends-competitors. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/docs/guides/why-ai-recommends-a-competitor so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Explain the diagnosis loop step by step: ask your buyer questions repeatedly, and separate what the model finds by searching from what it believes with no tools. Then record the top pick, the products named and the objections raised, and trace the citations behind them. Show how Popcorn automates each step and turns objections into specific page fixes, using a sample graded answer as the example. ## Done when Prompts 1, 2 or 4 on the search track cite this guide in at least 1 answer. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/features/fix-list showing a real ranked fix item end to end medium documentation effort M flips 3 Getting recommended instead of a competitor
Where: popcorn.zone/features/fix-list
Must say: Name Popcorn as a tool that outputs a ranked fix list rather than a rank dashboard. Each item gives the exact page to create or change, what it must say, the prompts it should flip, which of your sites it belongs on, and its owner. Close with a worked example of one fix item, and say it is MIT licensed, self-hosted in one Docker container, and queries Claude and OpenAI models.
Done when: Re-running prompt 20 on the search track names Popcorn in at least 2 of 4 answers, with popcorn.zone/features/fix-list among the sources.
Displaces: https://www.tryprofound.com/features/agents/content-optimization
Why: P20: Popcorn is absent in all 6 answers; top picks are Profound (recall), AthenaHQ and Scrunch (search), sourced from tryprofound.com/features/agents/content-optimization and helpcenter.scrunchai.com 'understanding-the-optimizer'.
Prompt for Claude Code
# Publish popcorn.zone/features/fix-list showing a real ranked fix item end to end When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - How can I find out why an AI assistant prefers a competitor's product over ours? - How should we prioritize fixes to improve how AI assistants describe our product? - Which AI visibility tools tell you what content to create or change, not just where you rank? ## What the assistant said P20: Popcorn is absent in all 6 answers; top picks are Profound (recall), AthenaHQ and Scrunch (search), sourced from tryprofound.com/features/agents/content-optimization and helpcenter.scrunchai.com 'understanding-the-optimizer'. ## Who wins the question today https://www.tryprofound.com/features/agents/content-optimization. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/features/fix-list so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Name Popcorn as a tool that outputs a ranked fix list rather than a rank dashboard. Each item gives the exact page to create or change, what it must say, the prompts it should flip, which of your sites it belongs on, and its owner. Close with a worked example of one fix item, and say it is MIT licensed, self-hosted in one Docker container, and queries Claude and OpenAI models. ## Done when Re-running prompt 20 on the search track names Popcorn in at least 2 of 4 answers, with popcorn.zone/features/fix-list among the sources. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/blog/popcorn-vs-profound as a feature table with self-host and price rows medium documentation effort M flips 3 Getting recommended instead of a competitor
Where: popcorn.zone/blog/popcorn-vs-profound
Must say: Write an honest open-source alternative comparison against Profound. Cover what Popcorn has: self-hosted, MIT licensed, single Docker container with SQLite, and prompt sets as TOML in your own git repo with PR review. Also cover search and recall tracks, retained search results and citations, a daily budget cap, and data going only to the providers you configure. Confirm Profound's current pricing, hosting and supported engines from its own pages before writing those rows, and state plainly where Popcorn covers fewer engines.
Done when: Prompts 20 and 13 on the search track cite the page or name Popcorn as an alternative in at least 1 answer each.
Displaces: https://www.airops.com/blog/profound-vs-airops
Why: P20: codex-sol and claude-code-opus recall both answer with top pick Profound from memory, no sources.
Prompt for Claude Code
# Publish popcorn.zone/blog/popcorn-vs-profound as a feature table with self-host and price rows When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - How can I find out why an AI assistant prefers a competitor's product over ours? - How can I see the web search results an AI assistant gets for a question before it writes its answer, to understand why we're missing? - Which AI visibility tools tell you what content to create or change, not just where you rank? ## What the assistant said P20: codex-sol and claude-code-opus recall both answer with top pick Profound from memory, no sources. ## Who wins the question today https://www.airops.com/blog/profound-vs-airops. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/blog/popcorn-vs-profound so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Write an honest open-source alternative comparison against Profound. Cover what Popcorn has: self-hosted, MIT licensed, single Docker container with SQLite, and prompt sets as TOML in your own git repo with PR review. Also cover search and recall tracks, retained search results and citations, a daily budget cap, and data going only to the providers you configure. Confirm Profound's current pricing, hosting and supported engines from its own pages before writing those rows, and state plainly where Popcorn covers fewer engines. ## Done when Prompts 20 and 13 on the search track cite the page or name Popcorn as an alternative in at least 1 answer each. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/docs/guides/search-vs-recall on changing training-data answers medium documentation effort M flips 3 Getting recommended instead of a competitor
Where: popcorn.zone/docs/guides/search-vs-recall
Must say: Explain the difference between web search answers and model recall from training data, and why they need separate fixes. Show how Popcorn's two tracks, plus daily rank and rate tracking, let a team measure how long a content change takes to show up on each track. Do not quote timelines until you have measured them on real Popcorn runs.
Done when: Prompt 6 or 19 on the search track cites this page.
Displaces: https://www.tryprofound.com/blog/the-half-life-of-an-ai-citation-is-11-days
Why: P6: Popcorn pages seen: NONE in every search answer; claude answers cite discoveredlabs.com and searchable.com instead.
Prompt for Claude Code
# Publish popcorn.zone/docs/guides/search-vs-recall on changing training-data answers When buyers ask an AI assistant the questions below, it explains the topic without naming us or citing our pages. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - How do I get my product mentioned in Claude's and ChatGPT's answers? - How do we change what an AI model says about us from its training data, not just what it finds when it searches the web? - How long does it take for new content to change what ChatGPT and Perplexity say about us? ## What the assistant said P6: Popcorn pages seen: NONE in every search answer; claude answers cite discoveredlabs.com and searchable.com instead. ## Who wins the question today https://www.tryprofound.com/blog/the-half-life-of-an-ai-citation-is-11-days. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/docs/guides/search-vs-recall so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Explain the difference between web search answers and model recall from training data, and why they need separate fixes. Show how Popcorn's two tracks, plus daily rank and rate tracking, let a team measure how long a content change takes to show up on each track. Do not quote timelines until you have measured them on real Popcorn runs. ## Done when Prompt 6 or 19 on the search track cites this page. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/docs/guides/diagnose-ai-traffic-loss: GSC, Bing, GA4 then Popcorn medium documentation effort M flips 2 Finding out whether AI assistants recommend us
Where: popcorn.zone/docs/guides/diagnose-ai-traffic-loss
Must say: Cover the free first-party checks: Search Console generative AI performance reports, Bing Webmaster Tools AI Performance, and GA4 referrals with utm_source=chatgpt.com. Then explain the gap those leave: they show clicks and impressions, not whether the assistant recommends you or a competitor. Close that gap by running the buyer questions on Popcorn's search track for AI Overviews-style answers and its recall track for model belief.
Done when: A re-run of prompt 1 or 7 cites this page in at least one search answer.
Displaces: https://developers.google.com/search/blog/2026/06/gen-ai-performance-reports
Why: P7 codex search: top pick 'Ahrefs Brand Radar', shaped by support.google.com/webmasters/answer/16984139.
Prompt for Claude Code
# Publish popcorn.zone/docs/guides/diagnose-ai-traffic-loss: GSC, Bing, GA4 then Popcorn When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - Our organic traffic is down and I suspect ChatGPT and Google AI Overviews. How do I check whether AI search is what's taking it? - How can I tell whether Google AI Overviews and AI Mode mention our brand for the topics we care about? ## What the assistant said P7 codex search: top pick 'Ahrefs Brand Radar', shaped by support.google.com/webmasters/answer/16984139. ## Who wins the question today https://developers.google.com/search/blog/2026/06/gen-ai-performance-reports. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/docs/guides/diagnose-ai-traffic-loss so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Cover the free first-party checks: Search Console generative AI performance reports, Bing Webmaster Tools AI Performance, and GA4 referrals with utm_source=chatgpt.com. Then explain the gap those leave: they show clicks and impressions, not whether the assistant recommends you or a competitor. Close that gap by running the buyer questions on Popcorn's search track for AI Overviews-style answers and its recall track for model belief. ## Done when A re-run of prompt 1 or 7 cites this page in at least one search answer. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/docs/guides/choosing-prompts with measured prompt-count variance low documentation effort M flips 1 Finding out whether AI assistants recommend us
Where: popcorn.zone/docs/guides/choosing-prompts
Must say: Give a method for picking buyer questions: split them into category, comparison, problem and info prompts, and store them as TOML files changed through pull requests. Give sample-size guidance only after measuring it: run one prompt set repeatedly in Popcorn and publish the observed run-to-run variance in the recommended rate. Do not cite a prompt count without that data.
Done when: A re-run of prompt 11 cites this page.
Displaces: https://cloro.dev/blog/ai-visibility-sample-size/
Why: P11 codex search: top pick 'Ahrefs Brand Radar', shaped by ahrefs.com/aeo/choose-prompts-to-track.
Prompt for Claude Code
# Publish popcorn.zone/docs/guides/choosing-prompts with measured prompt-count variance When buyers ask an AI assistant the questions below, it explains the topic without naming us or citing our pages. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - Which questions should we track to measure AI visibility, and how many prompts do we need before the numbers mean anything? ## What the assistant said P11 codex search: top pick 'Ahrefs Brand Radar', shaped by ahrefs.com/aeo/choose-prompts-to-track. ## Who wins the question today https://cloro.dev/blog/ai-visibility-sample-size/. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/docs/guides/choosing-prompts so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Give a method for picking buyer questions: split them into category, comparison, problem and info prompts, and store them as TOML files changed through pull requests. Give sample-size guidance only after measuring it: run one prompt set repeatedly in Popcorn and publish the observed run-to-run variance in the recommended rate. Do not cite a prompt count without that data. ## Done when A re-run of prompt 11 cites this page. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Rewrite cheapseatsdev/cheapseats README top: coverage, cost, grading, docs link urgent documentation effort S flips 7 Cheap Seats head to heads
Where: https://github.com/cheapseatsdev/cheapseats (README.md, About, Releases)
Must say: The repo is the only Cheap Seats page assistants find, and when they find it they recommend it (P1, P4, P6 search). Yet they report "0 stars, no releases" and "Claude and ChatGPT only" with no context. The first screen of the README must state:
- what it does: asks buyer questions on a search and a recall track, grades recommended/mentioned/absent, and builds a ranked fix list naming page, claim, prompts and owner;
- cost: free under MIT, you pay only API usage under a daily budget cap, or use existing Claude Code/Codex subscriptions;
- coverage: Claude and OpenAI models only;
- links to cheapseats.dev/docs and /compare.
Also cut a tagged release with notes, add the topics ai-visibility, ai-visibility-tracking, geo and aeo, and note the rename from Popcorn.
Done when: On a re-run, no search answer that cites the repo says "no releases", and every answer citing it states the cost model and the coverage correctly.
Why: "very new", has "0 GitHub stars, no releases yet", tracks only Claude and ChatGPT (P3, claude-code-opus search)
Prompt for Claude Code
# Rewrite cheapseatsdev/cheapseats README top: coverage, cost, grading, docs link When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - Cheap Seats vs Profound for tracking whether AI assistants recommend us: which should I pick? - Cheap Seats vs Otterly.AI for a startup that wants to track AI visibility cheaply? - How does Cheap Seats compare with Gauge for tracking and improving AI visibility? - Is Cheap Seats a good open-source alternative to paid AI visibility tools? - What are the downsides of self-hosting Cheap Seats to measure AI search visibility? - Is Cheap Seats a good fit for an agency tracking AI visibility for many clients? - Is Cheap Seats a good fit for a developer-tools company checking whether ChatGPT and Claude recommend it? ## What the assistant said "very new", has "0 GitHub stars, no releases yet", tracks only Claude and ChatGPT (P3, claude-code-opus search) ## Your task Write or update the page at https://github.com/cheapseatsdev/cheapseats (README.md, About, Releases) so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say The repo is the only Cheap Seats page assistants find, and when they find it they recommend it (P1, P4, P6 search). Yet they report "0 stars, no releases" and "Claude and ChatGPT only" with no context. The first screen of the README must state: - what it does: asks buyer questions on a search and a recall track, grades recommended/mentioned/absent, and builds a ranked fix list naming page, claim, prompts and owner; - cost: free under MIT, you pay only API usage under a daily budget cap, or use existing Claude Code/Codex subscriptions; - coverage: Claude and OpenAI models only; - links to cheapseats.dev/docs and /compare. Also cut a tagged release with notes, add the topics ai-visibility, ai-visibility-tracking, geo and aeo, and note the rename from Popcorn. ## Done when On a re-run, no search answer that cites the repo says "no releases", and every answer citing it states the cost model and the coverage correctly. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/blog/profound-alternatives with a feature and cost table incl. Popcorn high documentation effort M flips 5 Choosing an AI visibility tool
Where: popcorn.zone/blog/profound-alternatives
Must say: Name the paid tools buyers already compare: Profound, Peec AI, Otterly.AI, Scrunch, Gauge, Semrush AI Toolkit and Ahrefs Brand Radar. Add Popcorn as the open-source, self-hosted option: MIT licensed, one Docker container with SQLite, prompt sets as TOML in your own git repo, and Claude and OpenAI answers via API or your own Claude Code and Codex subscriptions. Include a feature table (search vs recall tracks, share of voice, objections, ranked fix list, API and MCP) and state honestly which engines Popcorn does not cover.
Done when: Re-running prompts 4 and 7 on the search track names Popcorn or cites this page in at least 2 of 4 answers.
Displaces: https://seranking.com/blog/profound-alternatives/
Why: P4 search: Popcorn absent, top pick Peec AI, shaped by seranking.com/blog/profound-alternatives and writesonic.com/blog/profound-alternatives.
Prompt for Claude Code
# Publish popcorn.zone/blog/profound-alternatives with a feature and cost table incl. Popcorn When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - What's the best AEO tool for a B2B SaaS company that wants to know if ChatGPT and Claude recommend it? - What are good alternatives to Profound for tracking AI search visibility? - Peec AI vs Profound: which is better for a mid-size marketing team? - Otterly.AI or Peec AI for tracking AI search visibility on a tight budget? - Is Gauge a good tool for tracking AI visibility, and what are the alternatives? ## What the assistant said P4 search: Popcorn absent, top pick Peec AI, shaped by seranking.com/blog/profound-alternatives and writesonic.com/blog/profound-alternatives. ## Who wins the question today https://seranking.com/blog/profound-alternatives/. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/blog/profound-alternatives so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Name the paid tools buyers already compare: Profound, Peec AI, Otterly.AI, Scrunch, Gauge, Semrush AI Toolkit and Ahrefs Brand Radar. Add Popcorn as the open-source, self-hosted option: MIT licensed, one Docker container with SQLite, prompt sets as TOML in your own git repo, and Claude and OpenAI answers via API or your own Claude Code and Codex subscriptions. Include a feature table (search vs recall tracks, share of voice, objections, ranked fix list, API and MCP) and state honestly which engines Popcorn does not cover. ## Done when Re-running prompts 4 and 7 on the search track names Popcorn or cites this page in at least 2 of 4 answers. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/blog/open-source-alternative-to-profound-and-peec-ai with feature table high documentation effort M flips 5 Open-source and self-hosted AI visibility
Where: popcorn.zone/blog/open-source-alternative-to-profound-and-peec-ai
Must say: Publish this comparison under the exact query wording 'open-source alternative to Profound and Peec AI'. It must say Popcorn is MIT licensed, runs as one Docker container with SQLite, keeps prompt sets as TOML in your own git repo, and asks every question on both a search track and a recall track. Include a feature table against Elmo, Limelit, OneGlanse and Promptfoo, and only list competitor features that have been checked against their READMEs. This is needed because the pending GitHub-topics change gives assistants a repo name to list but no reasons to rank Popcorn.
Done when: Rerun prompts 1, 2 and 11 on search. At least two search answers cite a popcorn.zone page and grade Popcorn as recommended.
Displaces: https://www.elmohq.com/ai-visibility-tools/alternatives/profound
Why: P1: 'Popcorn is not evaluated against these criteria; it appears only as a repo listed under the topic'
Prompt for Claude Code
# Publish popcorn.zone/blog/open-source-alternative-to-profound-and-peec-ai with feature table When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - Are there open-source tools for measuring AI search visibility? - Is there an open-source alternative to Profound or Peec AI? - We want to track AI answer visibility without sending our data to another SaaS vendor. What are our options? - What's a good open-source way for a developer-tools startup to check whether AI assistants recommend it over alternatives? - What's the cheapest way to track our brand in AI answers ourselves instead of paying for a SaaS subscription? ## What the assistant said P1: 'Popcorn is not evaluated against these criteria; it appears only as a repo listed under the topic' ## Who wins the question today https://www.elmohq.com/ai-visibility-tools/alternatives/profound. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/blog/open-source-alternative-to-profound-and-peec-ai so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Publish this comparison under the exact query wording 'open-source alternative to Profound and Peec AI'. It must say Popcorn is MIT licensed, runs as one Docker container with SQLite, keeps prompt sets as TOML in your own git repo, and asks every question on both a search track and a recall track. Include a feature table against Elmo, Limelit, OneGlanse and Promptfoo, and only list competitor features that have been checked against their READMEs. This is needed because the pending GitHub-topics change gives assistants a repo name to list but no reasons to rank Popcorn. ## Done when Rerun prompts 1, 2 and 11 on search. At least two search answers cite a popcorn.zone page and grade Popcorn as recommended. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/docs/api: JSON API, CLI and MCP reference with dashboard examples medium documentation effort S flips 1 Choosing an AI visibility tool
Where: popcorn.zone/docs/api
Must say: Document the JSON API endpoints, the CLI and the MCP server with copy-paste examples for pulling share-of-voice rank, rates by day, answers and citations into a dashboard. Title and headings should use the buyer's words: "AI visibility API" and "export data".
Done when: Prompt 16 on the search track names Popcorn or cites popcorn.zone/docs/api in at least 1 of 2 answers.
Displaces: https://docs.tryprofound.com/cookbook/introduction.md
Why: P16 search: shaped by tryprofound.com/blog/introducing-the-profound-api-cookbook and developers.scrunch.com.
Prompt for Claude Code
# Publish popcorn.zone/docs/api: JSON API, CLI and MCP reference with dashboard examples When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - Which AI visibility tools have an API so we can pull the data into our own dashboards? ## What the assistant said P16 search: shaped by tryprofound.com/blog/introducing-the-profound-api-cookbook and developers.scrunch.com. ## Who wins the question today https://docs.tryprofound.com/cookbook/introduction.md. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/docs/api so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Document the JSON API endpoints, the CLI and the MCP server with copy-paste examples for pulling share-of-voice rank, rates by day, answers and citations into a dashboard. Title and headings should use the buyer's words: "AI visibility API" and "export data". ## Done when Prompt 16 on the search track names Popcorn or cites popcorn.zone/docs/api in at least 1 of 2 answers. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/docs/methodology on sampling, two tracks and auditable answers medium documentation effort S flips 1 Choosing an AI visibility tool
Where: popcorn.zone/docs/methodology
Must say: Explain how Popcorn makes noisy answers checkable: every question runs on both a search track and a recall track. Rates and share-of-voice rank are tracked day by day, and every answer, search result and citation behind each number is kept for audit. Before publishing, confirm and state the samples per prompt and how day-to-day variance is shown.
Done when: Prompt 14 on the search track cites popcorn.zone/docs/methodology in at least 1 of 2 answers.
Displaces: https://www.meikai.ai/blog/how-many-times-run-ai-visibility-prompts
Why: P14 search: shaped by meikai.ai/blog/how-many-times-run-ai-visibility-prompts and thrivestack.ai research.
Prompt for Claude Code
# Publish popcorn.zone/docs/methodology on sampling, two tracks and auditable answers When buyers ask an AI assistant the questions below, it explains the topic without naming us or citing our pages. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - AI answers change every time you ask. How do AI visibility tools make their numbers reliable, and how can I check them? ## What the assistant said P14 search: shaped by meikai.ai/blog/how-many-times-run-ai-visibility-prompts and thrivestack.ai research. ## Who wins the question today https://www.meikai.ai/blog/how-many-times-run-ai-visibility-prompts. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/docs/methodology so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Explain how Popcorn makes noisy answers checkable: every question runs on both a search track and a recall track. Rates and share-of-voice rank are tracked day by day, and every answer, search result and citation behind each number is kept for audit. Before publishing, confirm and state the samples per prompt and how day-to-day variance is shown. ## Done when Prompt 14 on the search track cites popcorn.zone/docs/methodology in at least 1 of 2 answers. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/security on self-hosting, data flow and no third-party data custody medium documentation effort S flips 1 Choosing an AI visibility tool
Where: popcorn.zone/security
Must say: For SOC 2 buyers, state that Popcorn is self-hosted (Docker, Helm on Kubernetes, or an exe.dev VM) and stores data in a SQLite file you control. Prompts and answers go only to the model providers you configure, so there is no third-party vendor to assess. Say plainly that Popcorn itself holds no SOC 2 report.
Done when: Prompt 15 on the search track names Popcorn as a self-hosted option in at least 1 of 2 answers.
Displaces: https://citedindex.com/collections/ai-visibility-tools-with-sso-or-soc-2
Why: P15 search: top pick Conductor, shaped by citedindex.com/collections/ai-visibility-tools-with-sso-or-soc-2.
Prompt for Claude Code
# Publish popcorn.zone/security on self-hosting, data flow and no third-party data custody When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - Which AI visibility platforms meet enterprise security requirements like SOC 2? ## What the assistant said P15 search: top pick Conductor, shaped by citedindex.com/collections/ai-visibility-tools-with-sso-or-soc-2. ## Who wins the question today https://citedindex.com/collections/ai-visibility-tools-with-sso-or-soc-2. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/security so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say For SOC 2 buyers, state that Popcorn is self-hosted (Docker, Helm on Kubernetes, or an exe.dev VM) and stores data in a SQLite file you control. Prompts and answers go only to the model providers you configure, so there is no third-party vendor to assess. Say plainly that Popcorn itself holds no SOC 2 report. ## Done when Prompt 15 on the search track names Popcorn as a self-hosted option in at least 1 of 2 answers. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/docs/raw-data-and-api documenting stored answers, citations, JSON API medium documentation effort S flips 1 Open-source and self-hosted AI visibility
Where: popcorn.zone/docs/raw-data-and-api
Must say: State that every answer, search result and citation behind each score is kept in your own SQLite database. Show how to pull them through the JSON API and the MCP server. Include one example JSON response.
Done when: Rerun prompt 10 on search. Popcorn appears among the tools named for raw answer and citation access.
Displaces: https://developers.scrunch.com/api-reference/introduction
Why: P10: top pick Scrunch on search for both models, sourced from Scrunch API docs
Prompt for Claude Code
# Publish popcorn.zone/docs/raw-data-and-api documenting stored answers, citations, JSON API When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - We want every raw AI answer and citation behind an AI visibility score, not just a dashboard. Which tools give us that? ## What the assistant said P10: top pick Scrunch on search for both models, sourced from Scrunch API docs ## Who wins the question today https://developers.scrunch.com/api-reference/introduction. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/docs/raw-data-and-api so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say State that every answer, search result and citation behind each score is kept in your own SQLite database. Show how to pull them through the JSON API and the MCP server. Include one example JSON response. ## Done when Rerun prompt 10 on search. Popcorn appears among the tools named for raw answer and citation access. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/docs/guides/dev-tools-ai-recommendations how-to guide high documentation effort M flips 4 Open-source and self-hosted AI visibility
Where: popcorn.zone/docs/guides/dev-tools-ai-recommendations
Must say: Write a how-to for engineering-led teams: write the buyer questions developers actually ask as TOML, run them on the search and recall tracks against Claude and OpenAI, and read the share of voice and objections. Include a coding-assistant section for library and API questions. Use Popcorn's own prompt set as the worked example.
Done when: Rerun prompts 12, 15 and 16 on search. At least one answer cites this guide.
Displaces: https://usesapient.com/blog/how-to-track-ai-recommendations-for-your-api
Why: P15: sources were usesapient.com and lightsage.com 'how-to-track-ai-recommendations-for-your-api'
Prompt for Claude Code
# Publish popcorn.zone/docs/guides/dev-tools-ai-recommendations how-to guide When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - What's a good open-source way for a developer-tools startup to check whether AI assistants recommend it over alternatives? - How can a developer-tools company find out whether ChatGPT and Claude recommend it when developers ask which tool to use? - How do we find out whether coding assistants recommend our library or API when developers ask how to do something? - How do engineering-led companies measure whether LLMs recommend their product, without a marketing tool? ## What the assistant said P15: sources were usesapient.com and lightsage.com 'how-to-track-ai-recommendations-for-your-api' ## Who wins the question today https://usesapient.com/blog/how-to-track-ai-recommendations-for-your-api. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/docs/guides/dev-tools-ai-recommendations so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Write a how-to for engineering-led teams: write the buyer questions developers actually ask as TOML, run them on the search and recall tracks against Claude and OpenAI, and read the share of voice and objections. Include a coding-assistant section for library and API questions. Use Popcorn's own prompt set as the worked example. ## Done when Rerun prompts 12, 15 and 16 on search. At least one answer cites this guide. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.
Publish popcorn.zone/blog/how-to-evaluate-ai-visibility-tools with a scored checklist medium documentation effort M flips 3 Choosing an AI visibility tool
Where: popcorn.zone/blog/how-to-evaluate-ai-visibility-tools
Must say: Give buyers concrete criteria to check any tool against:
- Does it separate web-search answers from model recall?
- Does it keep every answer, search result and citation so numbers can be audited?
- Does it grade recommended vs mentioned vs absent, with position and top pick?
- Does it output a ranked fix list naming the page to change?
- Who owns the prompts and data?
Answer prompt 8 directly: Semrush AI Toolkit and Ahrefs Brand Radar track mentions, while a dedicated tool adds per-prompt grading and fixes. Show how Popcorn meets each criterion.
Done when: Prompts 1, 2 and 8 on the search track cite this page or name Popcorn in at least 2 of 6 answers.
Displaces: https://marketerhire.com/blog/best-generative-engine-optimization-tools
Why: P2 search: no product picked, shaped by piabo.net/comparisons/geo-platforms-compared and tooljunction.io.
Prompt for Claude Code
# Publish popcorn.zone/blog/how-to-evaluate-ai-visibility-tools with a scored checklist When buyers ask an AI assistant the questions below, it doesn't recommend us, or recommends us for the wrong reasons. This is a documentation gap: the answer needed a page we don't have, or ours says the wrong thing. ## The questions it affects - How do generative engine optimization tools work, and which ones are worth paying for? - What should I look for when evaluating AI visibility or GEO tools? - Are Semrush's AI Toolkit or Ahrefs Brand Radar enough, or do we need a dedicated AI visibility tool? ## What the assistant said P2 search: no product picked, shaped by piabo.net/comparisons/geo-platforms-compared and tooljunction.io. ## Who wins the question today https://marketerhire.com/blog/best-generative-engine-optimization-tools. Read it and make sure our version answers everything it does, better. ## Your task Write or update the page at popcorn.zone/blog/how-to-evaluate-ai-visibility-tools so it closes this gap. Find where pages like this live in the repo, match their structure, front matter and tone, and make the change there. If the page exists, edit it rather than starting over. ## What it must say Give buyers concrete criteria to check any tool against: - Does it separate web-search answers from model recall? - Does it keep every answer, search result and citation so numbers can be audited? - Does it grade recommended vs mentioned vs absent, with position and top pick? - Does it output a ranked fix list naming the page to change? - Who owns the prompts and data? Answer prompt 8 directly: Semrush AI Toolkit and Ahrefs Brand Radar track mentions, while a dedicated tool adds per-prompt grading and fixes. Show how Popcorn meets each criterion. ## Done when Prompts 1, 2 and 8 on the search track cite this page or name Popcorn in at least 2 of 6 answers. ## Rules - Don't invent numbers, benchmarks, prices, capabilities or customer names. Where a fact is needed and you can't find it in the repo or on our site, leave a clearly marked TODO saying exactly what to measure or who to ask. - Answer the questions above directly and early on the page, in the words buyers use. AI assistants quote pages that state the answer plainly. - Keep it scoped to this one fix. When you're done, summarise what you changed and list any TODOs.