Anthropic Claude 3.5 can create icalendar files, so I did this

Original Article ↗ Hacker News Discussion ↗

Large language models like Claude 3.5 and ChatGPT are increasingly being used to turn messy inputs—such as school calendars in PDFs or photos—into structured outputs like iCalendar files, CSVs, or scripts, saving users significant manual data-entry work. Commenters report that these tools are often remarkably accurate and versatile for coding, scheduling, and data extraction, but emphasize that small, hard-to-spot errors and confident hallucinations mean outputs must still be carefully verified, especially in high‑stakes contexts. This leads to broader reflections on trust, liability, and the need for validation layers or multiple models to check each other as AI-generated content becomes more pervasive.

Practical uses of LLMs for calendars & data extraction

  • Many people use Claude/ChatGPT/Gemini to:
    • Extract dates from PDFs, screenshots, school calendars, banking screenshots, OPML, etc.
    • Generate .ics files, Google Calendar links, CSVs, Markdown checklists.
    • Create recurring events (e.g., kids’ school schedules, conference dates, movie-anniversary calendars).
  • Several describe workflows: first have the model tabulate or summarize dates, manually review, then generate the calendar file or a small script to do it.

Accuracy, hallucinations, and “trust but verify”

  • Users warn that extraction is often “99–99.9%” correct, but small off‑by‑one or missing‑record errors are easy to miss and can be serious in high‑stakes domains.
  • Strong thread around the erosion of trust when LLMs confidently state plausible but wrong “facts”.
  • Suggested mitigations:
    • Use LLMs to write validators or scripts rather than trusting direct outputs.
    • Have multiple models perform the same task and compare.
    • Treat LLM output like junior‑dev work: let it do the bulk, then review.
  • Debate over the phrase “trust but verify”:
    • Some call it an oxymoron; others argue trust has degrees and verification doesn’t negate trust.
    • Alternatives: “assume good faith but check”, “trust does not exclude control”.

Model comparisons and behavior

  • Several report Claude 3.5 as:
    • Better at coding and following instructions than some previous tools.
    • Less prone to blatant hallucinations in some factual and coding tasks.
  • Others note:
    • ChatGPT and GPT‑4o can also generate ICS via text or code interpreter, but may initially refuse or be more awkward.
    • Gemini can do similar extraction with some prompting tricks.
  • Some keep subscriptions to multiple models because each has strengths and different refusal behaviors on sensitive topics.

File formats, interoperability, and ecosystem

  • Strong appreciation for human‑readable, open formats like ICS, CSV, Markdown and OPML because:
    • They’re easy for LLMs to generate and for humans to inspect.
    • They enable ad‑hoc tooling and automation outside proprietary silos.

Community sentiment

  • Many are enthusiastic about LLMs as “PDA‑like” assistants for tedious data entry.
  • Others worry about verification overhead, liability, and the forum veering into product‑promo territory.