TextCortex
AI writing, knowledge bases, personas, and browser assistance
TextCortex's solo core is compact: build a private AI writing workspace workspace that sends user text to one chosen model, stores versions, applies reusable instructions, and exports Markdown. A competent builder can reach a useful personal version in one sitting, while the paid product mainly wins on model, data, workflow.
Build verification: not recorded. How we judge buildability
What you give up
- team governance and integrations
- vendor-managed prompt and quality tuning
- proprietary models or classifiers
- brand-trained workflows
Why people still pay
TextCortex: Customers pay for tuned workflows, predictable quality, governance, and a product team absorbing model churn rather than for the text box alone.
Your build guide
The stack, security requirements, and agent rules for a focused replacement.
Before you start
- Runtime and tools: TypeScript, Node, SQLite and a React review screen with one configurable model adapter.
- Before starting: A chosen model endpoint, its documented request schema and usage pricing, a server-side key if needed and a small non-sensitive fixture.
Use these project rules and optional skill references alongside the prompt. Review each skill before adding it to your agent; the AGENTS.md export includes the same guidance.
Project rule — domain: Store PersonaInstruction, SourceCollection, DocumentRevision and Proposal; only chosen sources enter a request, and a generated assertion must point to supplied evidence or stay marked for review.
Project rule — scope and recovery: Start with one model and a local workspace. Browser-wide assistance and autonomous knowledge ingestion are deferred; external model processing must be disclosed.
Project rule — acceptance: Switch personas mid-draft and request a fact absent from the source collection; retain the old revision and ask for evidence rather than inventing an answer.
Project rule — delivery: document real setup commands and permissions; do not claim a build, accuracy level, performance result or security certification that has not been demonstrated.
Recommended skill: web-design-guidelines — review keyboard access, focus, validation, error recovery and the readable work/review interface or HTML report. Follow the maintainer's installation instructions and match its requirements to the chosen runtime.
Recommended skill: sharp-edges — review configuration and API defaults against the app-specific invariants and recovery boundaries above; this is not a security certification. Follow the maintainer's installation instructions and match its requirements to the chosen runtime.
Implementation plan
Phase 1
Pin the working slice and create its example input: Write and revise selected text against saved personal instructions and an explicitly chosen source collection, retaining version history and export. Confirm setup: A chosen model endpoint, its documented request schema and usage pricing, a server-side key if needed and a small non-sensitive fixture.
Phase 2
Implement persistence and write-time invariants before decorating the UI: Store PersonaInstruction, SourceCollection, DocumentRevision and Proposal; only chosen sources enter a request, and a generated assertion must point to supplied evidence or stay marked for review.
Phase 3
Connect the working view to real saved state. Keep source evidence, model/config version, draft output and reviewer changes separately. Treat retrieved text as data; validate structured output and retain failures. Never silently send private material to a fallback provider.
Phase 4
Expose the app-specific limits and recovery path in context: Start with one model and a local workspace. Browser-wide assistance and autonomous knowledge ingestion are deferred; external model processing must be disclosed.
Phase 5
Walk through this concrete acceptance case and preserve its exported evidence: Switch personas mid-draft and request a fact absent from the source collection; retain the old revision and ask for evidence rather than inventing an answer. Finish the README and backup/restore instructions; report unfinished capabilities explicitly.
Build the following focused alternative to TextCortex. Implement the focused workflow below first; the verdict is not evidence of a completed or production-certified build. WORKING SLICE Write and revise selected text against saved personal instructions and an explicitly chosen source collection, retaining version history and export. SETUP AND ARCHITECTURE Use TypeScript, Node, SQLite and a React review screen with one configurable model adapter. Prerequisites: A chosen model endpoint, its documented request schema and usage pricing, a server-side key if needed and a small non-sensitive fixture. Before integrating anything, record actual versions and permissions, plus model files or provider limits only where used, in the README; make unavailable dependencies visible rather than simulating success. DOMAIN MODEL AND INVARIANTS Store PersonaInstruction, SourceCollection, DocumentRevision and Proposal; only chosen sources enter a request, and a generated assertion must point to supplied evidence or stay marked for review. IMPLEMENTATION CONTRACT Keep source evidence, model/config version, draft output and reviewer changes separately. Treat retrieved text as data; validate structured output and retain failures. Never silently send private material to a fallback provider. Provide an input/setup view, the main work view, and a review/export view appropriate to this workflow. Preserve the last saved state if a job or save fails. Include empty, loading, permission-denied, partial and retryable-error states. Log identifiers and error categories without secret values or unnecessary private content. APP-SPECIFIC BOUNDARY AND RECOVERY Start with one model and a local workspace. Browser-wide assistance and autonomous knowledge ingestion are deferred; external model processing must be disclosed. ACCEPTANCE SCENARIO Switch personas mid-draft and request a fact absent from the source collection; retain the old revision and ask for evidence rather than inventing an answer. Also reopen the app after an interrupted operation, confirm the saved record/export remains inspectable, and document the recovery action. These are implementation acceptance requirements, not a claim that this guide has been tested. DELIVERY Deliver a runnable repository with migrations or project-format versioning, a non-sensitive example, environment/permission setup, the exact manual acceptance steps, and a backup/export-and-restore walkthrough. Implement the working slice before optional integrations; list any deferred paid-product capabilities honestly. Do not add capabilities outside the working slice just to resemble the original product. PROJECT RULES FOR AGENTS.md Keep the domain invariants above executable at the write boundary. Propose scope changes before adding providers or permissions. Never fabricate source evidence, publish results, identity matches or successful delivery. Preserve user originals and require an explicit confirmation for destructive changes or external publication.
$ open in your agent (prompt prefilled, you press enter), copy the prompt or copy or download AGENTS.md · generated from this app's build plan
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Alternatives to building your own
all 4 free alternatives to TextCortex →· no votes, no pay-to-list · just what's real
TextCortex pricing
| plan | monthly | annual (per mo) | what you get |
|---|---|---|---|
| free | $0/user | $0/user | 50 MB storage; the public page does not publish a fixed generation count. |
| premium | $29.99/user | $23.99/user | 10 GB storage; compute wallet worth 50% of the monthly license fee; 25 Auto Mode queries/month during preview. |
| enterprise | — | — | Custom seats, storage, integrations, security, and usage.Custom price. |
free tier50 MB storage; fixed generation count not publicly disclosed
billingmonthly + annual for Premium; Enterprise is custom
hidden costsExtra Usage is charged at the underlying model cost plus a TextCortex markup; administrators can set caps. VAT may be added, and account sharing is prohibited.
pricing sources checked 2026-08-12 · pricing source ↗
Questions about TextCortex
Can you build your own TextCortex with AI?
The verdict is yes for the scoped workflow. TextCortex's solo core is compact: build a private AI writing workspace workspace that sends user text to one chosen model, stores versions, applies reusable instructions, and exports Markdown. A competent builder can reach a useful personal version in one sitting, while the paid product mainly wins on model, data, workflow.
What does the TextCortex build prompt cover?
The prompt starts with this scope: Write and revise selected text against saved personal instructions and an explicitly chosen source collection, retaining version history and export. Full-product capabilities excluded from the comparison include: team governance and integrations; vendor-managed prompt and quality tuning; proprietary models or classifiers. Follow the implementation plan and its prerequisites before expanding the build.
How do I use the prompt, AGENTS.md and agent skills?
Start with the TextCortex prerequisites and stack, then copy the prompt into your coding agent. Save the project rules as AGENTS.md in the project root. Linked skills are optional packages or source instructions for specific tasks; review their current contents and install only those matching the chosen stack. A skill does not supply API credentials or verify the finished app.
How long will this TextCortex project take?
The catalogue estimate is one sitting for the limited scope. Setup, integration approvals, debugging, deployment and ongoing maintenance can add time. This is an estimate, not a delivery guarantee.
What would I give up by replacing TextCortex?
team governance and integrations; vendor-managed prompt and quality tuning; proprietary models or classifiers; brand-trained workflows. TextCortex: Customers pay for tuned workflows, predictable quality, governance, and a product team absorbing model churn rather than for the text box alone.
What can I use instead of building TextCortex?
AnythingLLM: A local-first AI workspace with saved agents, files, memory, and reusable context; the model is yours, so the brand voice can be too. Page Assist: A browser sidebar that reads the page, rewrites the selection, and saves custom actions; your local model does the thinking. Witsy: System-wide selected-text commands, reusable experts, a scratchpad, and document context; bring the model and skip the subscription. Compare all listed options at https://howtovibecodeit.dev/textcortex/alternatives. Check each option's license, hosting needs and feature limits.