My productivity app is a never-ending .txt file (2020)

📝 Discussion Summary (Click to expand)

The discussion revolves around personal productivity systems, particularly the merits of plain text solutions versus dedicated applications.

Here are the 3 most prevalent themes:

1. Preference for Simplicity and Stability of Plain Text Systems

Many users express strong loyalty to using simple text files (like .txt or .md) for tracking tasks and notes, citing their longevity, portability, and low overhead as key advantages over complex applications.

Key Quotation: One user notes the advantages of these systems: "Proven effective after 14 years of heavy use... Zero dependencies... Maximally portable... Outage-proof," and concludes that for fluent users, this simplicity "is already 'more productive' than almost any alternative could be" ("swatcoder").
Key Quotation: Another user emphasizes the simplicity of the core value: "Why build an app? It seems the whole benefit here is it doesnt need any app. Its completely agnostic and simple" ("jaffa2").

2. Skepticism Regarding Feature Creep in Productivity Apps

There is widespread frustration that modern productivity software often adds unnecessary complexity ("yak shaving") or relies on proprietary formats, undermining true efficiency. This is often framed as a search for stability that has been lost in newer tools.

Key Quotation: A user criticizes the trend in software development: "Somewhere along the line, product people started to mistake following design trends and adding complexity for productivity, forgetting that delivering the right combination of fluency, stability, simiplicity are often the real road to maximizing it" ("swatcoder").
Key Quotation: Another user expresses exhaustion with complexity: "I've used so many 'productivity' apps, it makes me sick to think of it. This has been the most consistent tool I've ever used" ("l0c0b0x") regarding plain text in Notepad++.

3. The Role and Potential of AI Integration

While many laud the stability of text files, a significant sub-theme discusses how emerging local LLMs (Large Language Models) could interface with these text-based systems to add modern search/analysis capabilities without sacrificing data ownership.

Key Quotation: One suggestion is: "Something like this would be perfect for a local LLM assistant" ("sowbug").
Key Quotation: Another user outlines a specific implementation path involving local AI: "a small GUI that just appends to a local .ndjson file. A user just posts with a text box into a feed. Like a one person chat or tweeting into the void. And a local LLM picks apart metadata, storing just enough to index answers to future questions will be" ("tbeseda").

🚀 Project Ideas

Based on the Hacker News discussion, here are the key pain points, frustrations, and unmet needs identified:

Key Pain Points & Unmet Needs

The "Too Complex" Feedback Loop (Yak-Shaving): Many users express fatigue with feature-bloated productivity apps. They revert to simple text files (.txt, .md) because new tools introduce complexity, require constant maintenance/reorganization, or create friction (e.g., "I bounce off so many notes apps before it."). There's a strong desire for tools that prioritize simplicity, stability, and fluency over trends.
Mobile Access and Latency with Plain Text: While plain text files offer portability and stability on desktop (via Vim, Emacs, VS Code), access and editing on mobile (especially iOS) are frequently cited as major friction points. Large text files become laggy, and integration with mobile workflows (like quick capture) is poor unless custom solutions (like Termux, or specific Git syncing apps) are adopted.
Intelligent Search/Summarization for Massive Text Logs: Users maintaining massive, append-only text files (sometimes tens of megabytes) need better ways to surface information lost in the deluge. Traditional grep is good for keyword matching, but users explicitly ask for AI/LLM integration to handle misspellings, contextual summaries ("/tasks last month"), and holistic retrieval across years of unstructured data.

Here are 3 concrete project ideas addressing these needs:

Fluent Text Archive Searcher (FTAS) — Local LLM Indexer for .txt Files

A tool targeting users who love the stability of massive local text files (like the 14-year-old logs mentioned) but need modern retrieval without locking their data into a proprietary cloud service.

🔍 What it does

Integrates a local, privacy-focused LLM (like Ollama or a self-hosted model) to index and create structural embeddings for any specified directory of .txt, .md, or .org files.
Provides a command-line interface (CLI) or simple web UI for querying the archive using natural language, handling synonyms and context beyond simple keyword matching.
Offers "contextual stitching," allowing users to query across documents separated by date (like yearly or monthly files) as if they were one large corpus.

Why HN commenters would love it

Addresses Pain Point 3 (Intelligent Search): Directly fulfills the need expressed by users like sublinear ("There should be some means to index/search this kind of semi-structured text") and nottorp asking if an LLM could find references including misspellings.
Preserves Plain Text Foundation: It doesn't force users away from text files; instead, it enhances them, appealing to the "data-first" philosophy mentioned by Brajeshwar.
Technical Utility: Enables powerful retrospective analysis on personal data (like the diet tracking example by jaredsohn) using modern ML techniques applied to classic data formats.

Example output

$ ftas "What were my initial thoughts on restructuring the database connection around July 2023?"

[SOURCE: 2023-07-15_work.md: Line 450]
The initial idea was to switch to Postgres connections via a config file instead of env vars, mainly because of CI/CD simplicity.

[SOURCE: 2023-07-17_logs.txt: Line 19021]
Spoke with Jane. Database connection migration might break legacy reporting. Maybe stick with env vars for V1, transition in Q4. (Notes stress/anxiety attached to this item.)

TextSync Mobile Gateway — Plain Text Editor for iOS/Android with Zero-Friction Sync

A specialized mobile application designed solely to provide a fast, native interface for adding to, and viewing recent context within, existing cloud-synced plain text files, mitigating mobile latency issues.

🔍 What it does

Connects directly to user-defined cloud storage locations (iCloud Drive, Nextcloud, Dropbox, treating them as the source of truth).
Features an aggressively optimized rendering engine for large files, focusing on fast display of the last 50 entries or most recently edited sections.
Provides an "Append-Only Capture Mode" where input automatically prepends a standardized timestamp/delimiter header, ensuring zero input latency and handling the mobile capture problem mentioned by tipsyrobot.

Why HN commenters would love it

Addresses Pain Point 2 (Mobile Access): Directly solves the frustration shared by users like davidzimmerjr and tipsyrobot regarding poor editing experience/lag on mobile for large .txt files.
Fluency and Simplicity: It’s a wrapper, not a new system. It supports the required text formats (Markdown, etc.) but eschews complex features like linking graphs or database views, aligning with the desire for something simpler than feature-heavy apps.
Version Control Agnostic: It works with whatever sync mechanism the user already trusts (GitJournal users, Dropbox users, etc.), acting as a specialized mobile front-end.

Example output

User opens TextSync Mobile Gateway. The app immediately pulls the last 50 lines of ~/Documents/Journal/master.md.

Input Screen (Prepending Mode) User types: Had a great conversation with the team about Project X architecture. The app prepends: ## 2025-11-20T14:30:00Z | Had a great conversation with the team about Project X architecture. The file is instantly synced via the underlying cloud provider (iCloud/Dropbox).

Task Flow Reorganiser — Anti-Overwhelm Task Management Overlay

A lightweight, cross-platform tool that sits on top of a simple text-based To-Do list hierarchy (like ordered markdown bullets or simple text sections) to provide the organizational structure many users crave without abandoning the file structure.

🔍 What it does

Connects to a designated to-do text file (e.g., todos.txt).
Renders the file not as a stream, but based on section headers (# Triage, * Now, etc.) identified by the tool, allowing for drag-and-drop reordering of tasks within those sections.
Offers automated promotion/archiving: Tasks marked complete (- [x]) are auto-removed or moved to a separate log file (archive.txt via a nightly cron/background job).

Why HN commenters would love it

Addresses Pain Point 1 (Complexity vs. Structure): Solves the need identified by SkyPuncher who wanted reorderable sections (Triage, Now, Today) and task simplicity, and users like dbl000 who get overwhelmed by sprawling, unsorted lists.
Guardrails for the Free Spirit: Provides the "guardrails" (tunaofthewall) for organization needed to maintain discipline (jaffa2's point about discipline) but keeps the editing layer entirely plaintext.
Solves Interface Inertia: Users like ZachSaucier want actual checkboxes and reordering without using a proprietary app—this tool provides that graphical overlay while the underlying data remains portable text.

Example output

User has a file big_todos.txt containing:

* Triage
  - [ ] Refactor authentication service (Needs Review by Sam)
  - [ ] Pay electric bill

User opens Task Flow Reorganiser. The tool displays "Refactor authentication service" prioritized under "Triage." The user drags it under a section called "Now" which they manually added. The application updates the backing text file to maintain the new structure and markers, solving the drag-and-drop requirement without complex data models. - Monetization: Hobby