IdeaWell

Project ideas from Hacker News discussions.

To those who fired or didn't hire tech writers because of AI

Original Article

Hacker News Discussion

📝 Discussion Summary (Click to expand)

Here are the four most prevalent themes from the discussion, presented with supporting quotations.

1. The Debate on AI’s Impact on Tech Writer Employment

Opinions diverge on whether AI will cause a net loss of jobs or simply redistribute them. Some argue that companies will need fewer writers, while others predict the rise of a "creator economy" where more businesses become viable, leading to the same number of employed writers across a larger number of companies.

"I've always said that we won't need fewer software developers with AI. It's just that each company will require fewer developers but there will be more companies." — aurareturn

"There's another scenario... 100 companies employ 1000 engineers" — SturgeonsLaw

2. The Persistence of the "Human in the Loop"

There is a strong consensus that AI currently lacks the critical judgment and empathy required for high-quality technical writing. Humans are seen as essential for curating content, understanding user pain points, and making strategic decisions about what information is important.

"My whole comment was about the need for a thinking, feeling human being." — nicbou

"It has no sense of truth or value. You need to check what it wrote and you need to tell it what’s important to a human." — richardw

3. The Concern Regarding Quality and Hallucinations

A recurring theme is that while AI can generate text quickly, the output is often unreliable, verbose, or hallucinatory. Without human oversight to catch errors and ensure accuracy, the result is often "slop" that degrades the user experience.

"Yesterday I was writing an obsidian plugin using the latest and most powerful Gemini model and... it still used a non existent method (retrieveSecret) to get the individual secret value." — nstart

"LLMs are good at writing long pages of meaningless words." — LtWorf

4. The Value of Tech Writers Beyond Documentation

Participants argued that technical writers serve a broader role than just writing. They often act as the "first user" of a product, bridging gaps between engineering and end-users, and spotting usability issues that product managers might miss.

"The best tech writers I've known have been more like anthropologists, bridging communication between product management, engineers, and users." — sehugg

"They act as stand-ins for actual users and will flag all sorts of usability problems." — drob518

🚀 Project Ideas

AI-Augmented Tech Writer Co-Pilot

Summary

  • [Solves the frustration expressed by tech writers like nicbou who argue that AI cannot replicate the human curation, empathy, and user pain-point identification inherent to good documentation.]
  • [Core value proposition: A tool that empowers human tech writers to leverage AI for productivity without sacrificing judgment or quality, focusing on the "why" and "context" rather than just generating text.]

Details

Key Value
Target Audience Professional technical writers, developer advocates, and senior engineers responsible for documentation in mid-to-large sized tech companies.
Core Feature An IDE-integrated extension that analyzes code, commits, and user feedback to suggest not just text, but gaps in documentation, potential user confusion points, and structural improvements based on the writer's specific style guide and audience persona.
Tech Stack VS Code Extension (TypeScript), LLM API (e.g., Anthropic/GPT), Vector database for company-specific knowledge/context, CI/CD integration hooks.
Difficulty Medium
Monetization Revenue-ready: B2B SaaS subscription ($20-50/user/month) or enterprise license.

Notes

  • [HN users like sevensor and drob518 emphasized that tech writers act as "anthropologists" bridging gaps between teams and "stand-ins for actual users." This tool supports that human role by automating the tedious parts while surfacing data for human judgment.]
  • [Potential for high discussion on HN as it addresses the nuance of the "AI vs. Human" debate in a constructive way, appealing to both pragmatists and quality purists.]

Context-Aware API Documentation Validator

Summary

  • [Solves the problem where LLMs hallucinate API methods or usage patterns (as described by nstart using Gemini/Obsidian), leading to "polluted" codebases and developer frustration.]
  • [Core value proposition: A testing framework specifically for documentation that treats API docs as code, automatically verifying that every documented example, method signature, and description matches the actual running implementation.]

Details

Key Value
Target Audience API platform teams, SDK maintainers, and library developers.
Core Feature Parses documentation (Markdown, RST, OpenAPI) to extract code snippets and claims, then spins up ephemeral environments to execute and validate them against the real API or codebase.
Tech Stack Python/Rust (for parsing and execution), Docker (for ephemeral envs), CLI tool, GitHub Action/CI integration.
Difficulty Medium
Monetization Hobby (Open Source core) with Revenue-ready: Enterprise self-hosting or managed cloud service for private repos.

Notes

  • [This directly addresses the "bus factor" and reliability concerns raised by ap99. It ensures that even if AI generates the draft, the validation layer ensures truth.]
  • [High practical utility for HN readers building developer tools, as bad API docs are a universal pain point.]

Internal "Institutional Memory" Knowledge Curator

Summary

  • [Solves the issue raised by aurareturn where reducing headcount (e.g., firing tech writers/engineers) increases the "bus factor" risk and loss of institutional context.]
  • [Core value proposition: An agent that passively observes internal communications (Slack, Jira, PR reviews) to build a dynamic knowledge graph, automatically updating documentation with the "unwritten rules" and context that usually only experienced employees know.]

Details

Key Value
Target Audience Startups and scaling companies experiencing rapid growth or turnover.
Core Feature Integrates with communication tools to detect decisions, context, and workarounds, then auto-suggests updates to the central wiki/docs, flagging outdated information.
Tech Stack LLM for NLP/summarization, Knowledge Graph database (e.g., Neo4j), OAuth APIs for Slack/Jira/Linear.
Difficulty High
Monetization Revenue-ready: B2B SaaS ($10-30/seat/month).

Notes

  • [Addresses the "5 engineers could be turned into maybe two" but maintaining bus factor argument. It preserves the knowledge that leaves when an employee leaves.]
  • [Highly relevant to the discussion on scaling and efficiency vs. risk, a constant topic on HN.]

"Human-in-the-Loop" Documentation Review Platform

Summary

  • [Solves the "untraceable errors" and quality degradation mentioned by jerf and the lack of accountability in AI-generated content.]
  • [Core value proposition: A workflow tool designed for the final mile of documentation—structuring the review process to highlight AI hallucinations, tone inconsistencies, and missing context, ensuring a human expert validates the output before publication.]

Details

Key Value
Target Audience QA teams, product managers, and senior developers reviewing technical content.
Core Feature A diff-view interface that highlights generated text against source code/context, flagging potential hallucinations or confidence scores, and enforcing a sign-off workflow similar to code reviews.
Tech Stack React, Node.js, Diffing algorithms, Integration with Markdown/Docs-as-Code repo.
Difficulty Low/Medium
Monetization Revenue-ready: Per-seat licensing or integrated into existing docs hosting platforms (e.g., ReadTheDocs enterprise).

Notes

  • [Directly counters the "AI is good enough" argument by formalizing the review step that HN users like DeborahWrites argue is non-negotiable.]
  • [Provides a tangible solution to the liability and trust issues discussed by users like rsynnott.]

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