Vibecode Pangram
track this build5 steps, step by step0%NOT REALLY · don't bother
The interface is a text box and a percentage, which is exactly the kind of thing that tricks you into thinking it is a weekend project. The product is not the text box: it is a classifier trained on a very large, continuously refreshed corpus of human writing paired with output from every model release, tuned hard against false positives because accusing a real person of cheating is the failure mode that ends the company. You can absolutely build a local detector from perplexity and burstiness features in an afternoon, and it will be confidently wrong often enough to be useless for any decision that matters. Nobody outside your own head will accept your homemade score, and the calibration drifts every time a new frontier model ships. Build it to understand the problem, not to rely on it.
the project pack4 files · fastest path to a working personal build
You are building a lean indie version of Pangram. 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 ===== # Pangram indie build ## Goal Build the smallest trustworthy replacement for the core Pangram workflow for one developer or a tiny team. ## Scope Paste text, score it locally with a small language model's token log-probabilities plus a few style statistics, and get a hand-wavy human-or-machine guess with a loud accuracy disclaimer. ## 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: - Calibration: a real false positive rate you can quote, instead of a vibe - Coverage of new models, which changes every few weeks whether you update or not - Sentence-level and mixed-authorship detection rather than one blunt document score - Any external credibility, since a self-built score persuades exactly zero teachers, editors or clients - Throughput, batch uploads, API access and document parsing If those capabilities are essential, use Pangram instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a local AI-text-detection playground so I can see how weak naive detection actually is. No cloud calls, no accounts, no telemetry. Stack, no substitutions: Python 3.11, FastAPI + uvicorn, transformers + torch, a single server-rendered HTML page with vanilla JS. No build step, no database, no Docker. Core loop: 1. One page with a large textarea and an Analyze button. 2. POST /analyze takes the text and returns a JSON report. 3. Scoring runs entirely locally with gpt2 (small) loaded once at startup via transformers. Download on first run and cache it. Features to compute per submission: - Mean token log-probability and perplexity under gpt2. - Burstiness: standard deviation of per-sentence mean log-probability. - Rank-based signal: fraction of tokens that were in the model's top-10 predictions. - Style stats: sentence length mean and variance, type-token ratio, punctuation counts, count of common LLM filler phrases from a hardcoded list. Output: a 0-100 score from a simple weighted formula over those features with the weights in a single WEIGHTS dict at the top of the file, per-sentence heat colouring in the page, and a full feature table so I can see what drove the score. Calibration, and be honest about it: - Add a CLI command: python calibrate.py --human dir --ai dir that runs the features over two folders of .txt files, prints mean and spread per class, and prints the ROC AUC of the current weights. - The web page must show a fixed banner: this is an uncalibrated heuristic, not evidence, false positives are common. Explicitly out of scope: user accounts, saved history, PDF or DOCX parsing, any hosted API, model fine-tuning, mixed-authorship segmentation, batch uploads. Deliver: main.py, calibrate.py, features.py, templates/index.html, requirements.txt, and a README with run instructions and one paragraph stating plainly why this cannot match a trained commercial detector. No .env needed since there are no secrets; say so in the README. ## Required capabilities - Local Python with torch and transformers - A couple of GB of disk for small model weights, CPU works but is slow - Your own labelled samples of human and AI text if you want any idea of accuracy ## 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 Pangram. 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 ===== # Pangram indie build ## Goal Build the smallest trustworthy replacement for the core Pangram workflow for one developer or a tiny team. ## Scope Paste text, score it locally with a small language model's token log-probabilities plus a few style statistics, and get a hand-wavy human-or-machine guess with a loud accuracy disclaimer. ## 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: - Calibration: a real false positive rate you can quote, instead of a vibe - Coverage of new models, which changes every few weeks whether you update or not - Sentence-level and mixed-authorship detection rather than one blunt document score - Any external credibility, since a self-built score persuades exactly zero teachers, editors or clients - Throughput, batch uploads, API access and document parsing If those capabilities are essential, use Pangram instead of pretending the gap is solved. ===== AGENTS.md ===== # Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs". ===== BUILD_PLAN.md ===== # Build plan ## Original build brief Build a local AI-text-detection playground so I can see how weak naive detection actually is. No cloud calls, no accounts, no telemetry. Stack, no substitutions: Python 3.11, FastAPI + uvicorn, transformers + torch, a single server-rendered HTML page with vanilla JS. No build step, no database, no Docker. Core loop: 1. One page with a large textarea and an Analyze button. 2. POST /analyze takes the text and returns a JSON report. 3. Scoring runs entirely locally with gpt2 (small) loaded once at startup via transformers. Download on first run and cache it. Features to compute per submission: - Mean token log-probability and perplexity under gpt2. - Burstiness: standard deviation of per-sentence mean log-probability. - Rank-based signal: fraction of tokens that were in the model's top-10 predictions. - Style stats: sentence length mean and variance, type-token ratio, punctuation counts, count of common LLM filler phrases from a hardcoded list. Output: a 0-100 score from a simple weighted formula over those features with the weights in a single WEIGHTS dict at the top of the file, per-sentence heat colouring in the page, and a full feature table so I can see what drove the score. Calibration, and be honest about it: - Add a CLI command: python calibrate.py --human dir --ai dir that runs the features over two folders of .txt files, prints mean and spread per class, and prints the ROC AUC of the current weights. - The web page must show a fixed banner: this is an uncalibrated heuristic, not evidence, false positives are common. Explicitly out of scope: user accounts, saved history, PDF or DOCX parsing, any hosted API, model fine-tuning, mixed-authorship segmentation, batch uploads. Deliver: main.py, calibrate.py, features.py, templates/index.html, requirements.txt, and a README with run instructions and one paragraph stating plainly why this cannot match a trained commercial detector. No .env needed since there are no secrets; say so in the README. ## Required capabilities - Local Python with torch and transformers - A couple of GB of disk for small model weights, CPU works but is slow - Your own labelled samples of human and AI text if you want any idea of accuracy ## 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 Pangram. 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 ===== # Pangram product brief ## Problem The interface is a text box and a percentage, which is exactly the kind of thing that tricks you into thinking it is a weekend project. The product is not the text box: it is a classifier trained on a very large, continuously refreshed corpus of human writing paired with output from every model release, tuned hard against false positives because accusing a real person of cheating is the failure mode that ends the company. You can absolutely build a local detector from perplexity and burstiness features in an afternoon, and it will be confidently wrong often enough to be useless for any decision that matters. Nobody outside your own head will accept your homemade score, and the calibration drifts every time a new frontier model ships. Build it to understand the problem, not to rely on it. ## Product outcome Paste text, score it locally with a small language model's token log-probabilities plus a few style statistics, and get a hand-wavy human-or-machine guess with a loud accuracy disclaimer. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Local Python with torch and transformers - A couple of GB of disk for small model weights, CPU works but is slow - Your own labelled samples of human and AI text if you want any idea of accuracy ## Explicit non-goals for v1 - Calibration: a real false positive rate you can quote, instead of a vibe - Coverage of new models, which changes every few weeks whether you update or not - Sentence-level and mixed-authorship detection rather than one blunt document score - Any external credibility, since a self-built score persuades exactly zero teachers, editors or clients - Throughput, batch uploads, API access and document parsing ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees. ===== ARCHITECTURE.md ===== # Architecture ## Starting brief Build a local AI-text-detection playground so I can see how weak naive detection actually is. No cloud calls, no accounts, no telemetry. Stack, no substitutions: Python 3.11, FastAPI + uvicorn, transformers + torch, a single server-rendered HTML page with vanilla JS. No build step, no database, no Docker. Core loop: 1. One page with a large textarea and an Analyze button. 2. POST /analyze takes the text and returns a JSON report. 3. Scoring runs entirely locally with gpt2 (small) loaded once at startup via transformers. Download on first run and cache it. Features to compute per submission: - Mean token log-probability and perplexity under gpt2. - Burstiness: standard deviation of per-sentence mean log-probability. - Rank-based signal: fraction of tokens that were in the model's top-10 predictions. - Style stats: sentence length mean and variance, type-token ratio, punctuation counts, count of common LLM filler phrases from a hardcoded list. Output: a 0-100 score from a simple weighted formula over those features with the weights in a single WEIGHTS dict at the top of the file, per-sentence heat colouring in the page, and a full feature table so I can see what drove the score. Calibration, and be honest about it: - Add a CLI command: python calibrate.py --human dir --ai dir that runs the features over two folders of .txt files, prints mean and spread per class, and prints the ROC AUC of the current weights. - The web page must show a fixed banner: this is an uncalibrated heuristic, not evidence, false positives are common. Explicitly out of scope: user accounts, saved history, PDF or DOCX parsing, any hosted API, model fine-tuning, mixed-authorship segmentation, batch uploads. Deliver: main.py, calibrate.py, features.py, templates/index.html, requirements.txt, and a README with run instructions and one paragraph stating plainly why this cannot match a trained commercial detector. No .env needed since there are no secrets; say so in the README. ## 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 Pangram capabilities as implemented. The v1 non-goals in `PRODUCT.md` remain user-visible limitations until they are deliberately delivered.
README.md
# Pangram indie build ## Goal Build the smallest trustworthy replacement for the core Pangram workflow for one developer or a tiny team. ## Scope Paste text, score it locally with a small language model's token log-probabilities plus a few style statistics, and get a hand-wavy human-or-machine guess with a loud accuracy disclaimer. ## 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: - Calibration: a real false positive rate you can quote, instead of a vibe - Coverage of new models, which changes every few weeks whether you update or not - Sentence-level and mixed-authorship detection rather than one blunt document score - Any external credibility, since a self-built score persuades exactly zero teachers, editors or clients - Throughput, batch uploads, API access and document parsing If those capabilities are essential, use Pangram instead of pretending the gap is solved.
AGENTS.md
# Agent instructions - Optimize for a working, understandable weekend build. - Prefer the fewest moving parts that satisfy the brief. - Do not invent cryptography, security guarantees, APIs, or compliance claims. - Keep secrets out of source control and logs. - Add focused tests for destructive, security-sensitive, and data-loss paths. - Run the project checks before declaring the build complete. - Record any deliberate shortcut in the README under "Tradeoffs".
BUILD_PLAN.md
# Build plan ## Original build brief Build a local AI-text-detection playground so I can see how weak naive detection actually is. No cloud calls, no accounts, no telemetry. Stack, no substitutions: Python 3.11, FastAPI + uvicorn, transformers + torch, a single server-rendered HTML page with vanilla JS. No build step, no database, no Docker. Core loop: 1. One page with a large textarea and an Analyze button. 2. POST /analyze takes the text and returns a JSON report. 3. Scoring runs entirely locally with gpt2 (small) loaded once at startup via transformers. Download on first run and cache it. Features to compute per submission: - Mean token log-probability and perplexity under gpt2. - Burstiness: standard deviation of per-sentence mean log-probability. - Rank-based signal: fraction of tokens that were in the model's top-10 predictions. - Style stats: sentence length mean and variance, type-token ratio, punctuation counts, count of common LLM filler phrases from a hardcoded list. Output: a 0-100 score from a simple weighted formula over those features with the weights in a single WEIGHTS dict at the top of the file, per-sentence heat colouring in the page, and a full feature table so I can see what drove the score. Calibration, and be honest about it: - Add a CLI command: python calibrate.py --human dir --ai dir that runs the features over two folders of .txt files, prints mean and spread per class, and prints the ROC AUC of the current weights. - The web page must show a fixed banner: this is an uncalibrated heuristic, not evidence, false positives are common. Explicitly out of scope: user accounts, saved history, PDF or DOCX parsing, any hosted API, model fine-tuning, mixed-authorship segmentation, batch uploads. Deliver: main.py, calibrate.py, features.py, templates/index.html, requirements.txt, and a README with run instructions and one paragraph stating plainly why this cannot match a trained commercial detector. No .env needed since there are no secrets; say so in the README. ## Required capabilities - Local Python with torch and transformers - A couple of GB of disk for small model weights, CPU works but is slow - Your own labelled samples of human and AI text if you want any idea of accuracy ## 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
# Pangram product brief ## Problem The interface is a text box and a percentage, which is exactly the kind of thing that tricks you into thinking it is a weekend project. The product is not the text box: it is a classifier trained on a very large, continuously refreshed corpus of human writing paired with output from every model release, tuned hard against false positives because accusing a real person of cheating is the failure mode that ends the company. You can absolutely build a local detector from perplexity and burstiness features in an afternoon, and it will be confidently wrong often enough to be useless for any decision that matters. Nobody outside your own head will accept your homemade score, and the calibration drifts every time a new frontier model ships. Build it to understand the problem, not to rely on it. ## Product outcome Paste text, score it locally with a small language model's token log-probabilities plus a few style statistics, and get a hand-wavy human-or-machine guess with a loud accuracy disclaimer. ## Target user A serious builder who needs a maintainable product foundation rather than a one-off demo. ## Required capabilities - Local Python with torch and transformers - A couple of GB of disk for small model weights, CPU works but is slow - Your own labelled samples of human and AI text if you want any idea of accuracy ## Explicit non-goals for v1 - Calibration: a real false positive rate you can quote, instead of a vibe - Coverage of new models, which changes every few weeks whether you update or not - Sentence-level and mixed-authorship detection rather than one blunt document score - Any external credibility, since a self-built score persuades exactly zero teachers, editors or clients - Throughput, batch uploads, API access and document parsing ## Success criteria - The primary workflow is measurable end to end. - Setup is reproducible in a clean environment. - Failure, recovery, and support paths are documented. - Product claims match what the implementation actually guarantees.
ARCHITECTURE.md
# Architecture ## Starting brief Build a local AI-text-detection playground so I can see how weak naive detection actually is. No cloud calls, no accounts, no telemetry. Stack, no substitutions: Python 3.11, FastAPI + uvicorn, transformers + torch, a single server-rendered HTML page with vanilla JS. No build step, no database, no Docker. Core loop: 1. One page with a large textarea and an Analyze button. 2. POST /analyze takes the text and returns a JSON report. 3. Scoring runs entirely locally with gpt2 (small) loaded once at startup via transformers. Download on first run and cache it. Features to compute per submission: - Mean token log-probability and perplexity under gpt2. - Burstiness: standard deviation of per-sentence mean log-probability. - Rank-based signal: fraction of tokens that were in the model's top-10 predictions. - Style stats: sentence length mean and variance, type-token ratio, punctuation counts, count of common LLM filler phrases from a hardcoded list. Output: a 0-100 score from a simple weighted formula over those features with the weights in a single WEIGHTS dict at the top of the file, per-sentence heat colouring in the page, and a full feature table so I can see what drove the score. Calibration, and be honest about it: - Add a CLI command: python calibrate.py --human dir --ai dir that runs the features over two folders of .txt files, prints mean and spread per class, and prints the ROC AUC of the current weights. - The web page must show a fixed banner: this is an uncalibrated heuristic, not evidence, false positives are common. Explicitly out of scope: user accounts, saved history, PDF or DOCX parsing, any hosted API, model fine-tuning, mixed-authorship segmentation, batch uploads. Deliver: main.py, calibrate.py, features.py, templates/index.html, requirements.txt, and a README with run instructions and one paragraph stating plainly why this cannot match a trained commercial detector. No .env needed since there are no secrets; say so in the README. ## 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 Pangram 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
Because the number has to be defensible to someone else. Institutions and publishers are not buying a classifier, they are buying a third party willing to stand behind a false positive rate, plus continuous retraining against whatever model came out last month. A local heuristic detector gives you a plausible-sounding percentage with no error bars, which is worse than nothing when the outcome is an accusation.
xCalibration: a real false positive rate you can quote, instead of a vibe
xCoverage of new models, which changes every few weeks whether you update or not
xSentence-level and mixed-authorship detection rather than one blunt document score
xAny external credibility, since a self-built score persuades exactly zero teachers, editors or clients
xThroughput, batch uploads, API access and document parsing
Nothing worth pointing at. That's why the prompt exists.
share on X ↗"I just replaced Pangram ($20/mo) with one prompt"
Vibecode Pangram
Not really. Pangram's value is not the code: The moat is a constantly retrained corpus of human and model text plus a reputation strong enough that other people accept the verdict. See the honest breakdown above.
How much does Pangram cost?
Pangram costs about $20/month (Individual, checked 2026-08-18), which is $240 per year.
What do I lose by replacing Pangram?
Honestly: Calibration: a real false positive rate you can quote, instead of a vibe; Coverage of new models, which changes every few weeks whether you update or not; Sentence-level and mixed-authorship detection rather than one blunt document score; Any external credibility, since a self-built score persuades exactly zero teachers, editors or clients; Throughput, batch uploads, API access and document parsing. If any of those are load-bearing for you, keep paying.
Is there an open-source alternative to Pangram?
No mature open-source alternative worth pointing at, which is exactly why the one-shot prompt on this page exists.