An AI release trackernew models and features gathered on a schedule, checked against the source

Method

1. 1

   Write down what you are watching for

   Before any automation, decide what counts. Make a Watch list document with a finding for each thing you track: the vendors whose models you use or might use; the capabilities that change your plans (tool calling, context length, structured output, vision); the numbers that matter (price per million tokens, rate limits); licences for open-weight models; and deprecation notices for anything in production.

   This document is what keeps the weekly run from filling the library with noise. Your assistant reads it every time, and you can change it whenever your needs change.

2. 2

   Let your assistant build the shelf

   Connect the assistant you already use to know.sh, then give it your notes and let it do the setup: “Using know.sh, create a shelf called AI releases, a Watch list document from the notes below with one finding per item, and a document called AI releases — October 2026.”

   Open the result and fix it in the editor. The watch list is short, so read every finding: the wording you settle on here is what the weekly run will match against.

3. 3

   Schedule the weekly run

   Create a scheduled task in your assistant. In Claude, scheduled tasks are on the Pro, Max, Team and Enterprise plans and run in the cloud even while your computer is asleep; in ChatGPT, scheduled tasks are on every plan and deliver their results by notification or email. Describe it in plain words: “Every Monday at 8 a.m., search the official release notes, changelogs and blogs of the vendors on my watch list for anything released in the past seven days.”

   When the result arrives, reply file this in know.sh, and your assistant adds each release to the month’s document. Some setups can file straight into know.sh during the scheduled run itself; that is newer and not every plan or connector supports it yet, so check Revisions after each run either way.

4. 4

   File each release against its official source

   Each release becomes one finding, titled in the same pattern so the month reads in order: Released by Northwind, 29 September: Lumen 2 Pro. The body says what shipped, what it costs, how it answers the watch list, and anything the announcement leaves vague. The source is the vendor’s own page.

   Coverage from newsletters and social media is a lead, not a source. If your assistant finds a release only in second-hand reports, it files it as a Question (“Reported: Northwind to retire Lumen 1 in January”) until an official page confirms it.

5. 5

   Read the week and fix it up

   Ten minutes each Monday: open the month’s document and read the new findings. Merge the two findings that describe the same launch, correct a price your assistant misread, delete what turned out to be irrelevant, and mark the one or two releases your team should try as Key. Write the verdict yourself, as a Decision: “Try Lumen 2 Pro for the summarisation service; it is cheaper at our volume and supports tool calling.”

   Highlight the lines that affect production, such as a deprecation date. Highlighted findings show their numbers in bold wherever they appear in the index.

6. 6

   Let the digest write itself, then share it

   At the end of the month, ask your assistant to draft the document’s overview as a digest: three sections, Worth trying, Worth knowing and Deprecations, each line citing its finding number. Edit it in your own words, then press Share and post the read-only link in the team channel.

   Each month is its own document, so each month has its own link; the team channel becomes the archive of links, and the shelf keeps the whole history in one place.

7. 7

   Ask the library before you plan

   Before a planning meeting, ask your assistant a question the library can answer: “Using know.sh, which releases on the AI releases shelf this quarter support tool calling with a context of at least 200,000 tokens, and what do they cost? Cite finding numbers.” Because every finding links to its official source, the answer is something you can check in minutes rather than take on trust.

   Look up (⌘K) finds a model name or a word such as deprecation across every month at once.