proomt

Search

Search posts, papers, and topics

All posts

Lobsters3 min readintro

Plain-text files are at risk

Read on sr.htLobsters 21

Summary

The article argues that plain‑text files, once the universal data interface, are losing relevance as smartphones lack native editors and developers shift to LLM‑driven REPLs, risking future loss of support for plain‑text tools.

  • Plain‑text files have been the de‑facto portable format for notes, configs, and data for decades.
  • Smartphone ecosystems rarely include native plain‑text editors, accelerating user migration away from .txt/.md files.
  • LLM‑based REPLs let developers edit code and docs without opening a traditional editor, further reducing manual text handling.
  • If the remaining developer audience abandons plain‑text editing, editor maintenance may cease and OS support could degrade.

Software engineers and anyone relying on portable, human‑readable data should care because the disappearance of plain‑text tools would erode a key interoperability layer.

4/10
llmproductivitysoftware engineeringplain text

Related reading

  1. Put App Documents in the System File Browser

    Codename One adds a read‑only DocumentProvider API that lets apps publish a virtual file tree to iOS Files and Android’s storage picker. The tree is defined with `DocumentNode` objects, can include remote‑only entries, and is shared via an App Group container. The iOS extension runs in a separate process, so the model is serialized and read independently; Android uses the same model inside the ap…

    CodeName Onecodenameone.com4 min

  2. How to Write with an LLM

    The post proposes a two‑rule workflow for using LLMs as copy‑editors rather than ghostwriters: never adopt a phrase the model suggests, and block its encouragement feedback. It then shows how to automate iterative copy‑editing with a small Python/HTMX/Tailwind app and a set of concrete prompts.

    Hacker News front pagesockpuppet.org6 minHN712401

  3. Writing the Docs: 2026 Edition

    The post revisits core documentation principles—separating what from how, grounding reference docs in source truth, and continuously improving—and examines how large language models fit into this workflow. It argues that diataxis still guides effective docs and that LLM‑generated drafts must be anchored to machine‑readable ground truth to avoid hallucination.

    Mozilla Automation Teamwrla.ch15 min

  4. Note on 18th September 2026

    This entry is a brief, sponsored blurb that offers a vague opinion on using LLMs for security scanning, without any concrete data or analysis. It links to other recent articles but adds no substantive content of its own.

    Simon Willisonsimonwillison.net1 min

  5. WeVisDoc: From Coverage to Capability for Robust End-to-End Document Parsing

    WeVisDoc introduces a two‑stage data‑centric pipeline for end‑to‑end document parsing. Stage I expands coverage using heterogeneous data and structure‑preserving degradations. Stage II probes the Stage I model with a held‑out set, clusters residual errors, and directs targeted data creation and token‑budget reallocation. The 4‑billion‑parameter model reaches 95.38 Overall on OmniDocBench v1.6 and…

    Hugging Face Daily Papersarxiv.org1 minpaper