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tl;dr is Karan Singh’s project for turning an exported WhatsApp group chat into a short Markdown digest and an importable calendar file. It uses a language model on the user’s laptop to find deadlines, events, and changes of plan—an appealing way to catch details buried in a busy college chat, but one that still needs human checking.

What tl;dr does with a WhatsApp export

The project is aimed at classmates who cannot keep up with a high-volume group chat. Give it an exported WhatsApp conversation and it attempts to identify dated events, deadlines, and changes to plans, then produces a Markdown summary and a calendar file that can be imported into a calendar app. The project description says it can handle Hinglish and relative phrases such as “kal 5 baje,” “parso,” and “ab Friday tak hai.” Those phrases depend on when each message was sent; they cannot safely be turned into calendar dates without that context.

Each event card reportedly includes the numbers of the source messages. That gives a reader a way to check the summary against the original conversation instead of treating the generated calendar as authoritative. The project’s author describes the goal as “A local LLM that reads your college group chat so you don’t have to.”

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How it handles a deadline that changes

A group chat can contain an original deadline, a correction, and later discussion about the correction. Treating each mention as a separate event can leave a calendar full of contradictions. Singh describes tl;dr as trying to keep one event and its history as new messages update or cancel an earlier plan.

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The described pipeline parses the conversation, groups messages into overlapping windows around pauses, extracts possible events, reconciles whether each is new, updated, or cancelled, resolves dates, and renders the outputs. Relative dates are anchored to the date of the message containing the phrase. Overlapping windows help give the model nearby context, while reconciliation attempts to connect later messages to the right event.

That connection is not guaranteed merely because output is structured or event IDs are present. Singh says the model sometimes invented IDs or attached one event’s details to another event with a similar title. The project’s reconciliation logic compares titles and treats the model’s ID as a hint; when a match is uncertain, it favors showing a possible duplicate over silently merging the wrong information.

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What the author’s small evaluation found

Singh reports a hand-labeled test of seven items in a synthetic sample chat, with the prompt held fixed. These are project-specific results from 2026, not an independent benchmark of local language models or chat summarizers.

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Mode Items found Precision Date correctness Time correctness Approximate runtime
Thinking off 5 of 7 100% 57% 57% 58 seconds per run
Thinking on 7 of 7 78% 71% (reported range: 71–100%) 71% (reported range: 29–86%) 330 seconds per run

The author notes that each item changes a seven-item score by about 14 percentage points. The prompt was tuned on the same sample chat, and the thinking-on date results varied between runs. Thinking-off results were identical over nine runs, but missed items. Two attempted prompt changes made results worse and were reverted. Taken together, this experiment illustrates a trade-off in this sample—broader coverage with thinking on, but lower precision and more variable date and time results—rather than establishing a generally superior mode.

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What happened on a larger real chat

In one reported run on a real conversation of 1,178 messages divided into 61 windows, tl;dr found 15 dated events and flagged 18 items as having unclear dates. The author also reported that generic recurring titles were merged across different months, numeric dates such as 01.12.2025 were not parsed, and leaving the year out of the summary obscured a likely wrong-year date.

Singh listed expiry of past items, printing the year, and parsing numeric dates as planned v0.2 work. These observations are from one project run; they do not establish how the tool will perform on another group’s chat or a later version.

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Local processing and privacy limits

The project description names Gemma 4 (gemma4:e4b) through Ollama, with temperature 0, seed 0, and output constrained to a JSON schema. Singh says the model runs on the user’s machine, that chat data is not sent to a cloud API, and that after downloading the model the project can work without an internet connection. The author sums up the project’s advantages as “privacy, cost and control.”

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Those are the builder’s descriptions of the workflow, not the findings of an external security audit. A WhatsApp export contains other people’s messages, names, plans, and in-jokes, so local processing does not remove the need to handle the file carefully. The project’s linked repository was not independently verified for current code, setup instructions, license, or hardware requirements. No specific minimum laptop specification is established.

How to use a generated digest responsibly

  • Check each event against the cited message numbers before relying on its date, time, or details.
  • Pay particular attention when a later message changes a deadline, when a date is expressed relatively, or when an event title is generic and recurring.
  • Review the calendar file before importing it, especially for unclear dates or missing years.
  • Treat the summary as a way to locate important messages faster, not as a replacement for the chat or confirmation from the people organizing the event.

Project details and source

Karan Singh’s project write-up was published on DEV Community on October 3, 2026: tl;dr project article. The report is useful for understanding the intended workflow and the author’s experiments, but its small, tuned evaluation does not support broad accuracy claims.

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