On a schedule
Add a daily research digest to a Doc
Each morning a dated section with a short digest of your source is added to a running Doc.
- Runs
- On a schedule
- Apps
- Google Docs
- AI
- Yes, uses the built-in AI allowance
- Access
- read+write (doc shared with the service account as Editor)
Send this to your assistant
Create a ClickRuntime automation: every morning at 7am, take the top 10 Hacker News stories, summarise each in two lines and add today's section to the end of my Research Digest doc.
First time? Adding ClickRuntime to your assistant takes about two minutes.
Why use it
Why teams use this automation
An investor wants a daily read on what Hacker News is talking about, how a particular stock moved and whether new filings landed. He used to open several tabs each morning, skim, and forget what he saw by lunchtime. There is no memory of the earlier digests, so patterns across weeks, like a rising topic or a company filing repeatedly, never become visible.
Each day, ClickRuntime fetches the source data over HTTP, hands it to its built-in AI for analysis and appends a dated section to the end of your Google Doc. Over weeks the Doc becomes a research journal with one entry per day, written in the same format each time.
Founders, analysts and students use it to follow a topic or a company without checking the sources by hand.
What you get out of it
A daily briefing you do not assemble
The analysis is written before you start your day. You spend the time reading and thinking instead of opening tabs and copying out the interesting items. That leaves your morning free for thinking about what it means.
A searchable history of the topic
Every entry stays in the Doc, so you can look for a company or a theme across months. That is impossible when you only skim live pages that change by the hour. That long memory is the real value of a daily log.
Trends emerge from repetition
When the same topic shows up in ten daily entries, you notice it. A rising theme or a company filing repeatedly is a signal you would miss reading one page at a time.
Consistent format makes skimming fast
Each section follows the same layout, so you find the key point in seconds. That consistency matters when you read many entries in a row to catch up after a trip.
Source data is fetched for you
Live prices, filings or ranked lists are retrieved at the scheduled time. The entry reflects the same moment each day, which makes day-to-day comparisons fair. Because the data is pulled at the same hour each day, the entries line up and trends are real.
How it works
What happens, step by step
Every day ClickRuntime fetches the current top 10 stories from Hacker News.
AI summarises them in a short numbered list.
The summary is added to the end of your research Google Doc under today's date, so the Doc becomes a running archive.
Before it runs live
- Connect Google Docs
What you get
Build your own
Need it to work differently?
Your own rules
Conditions, thresholds, filters and exceptions, written the way you'd explain them to a colleague.
Any number of apps
One automation can read from one app and update several others. Over 248 apps are ready, and anything with an API works too.
AI where it helps
Summaries, classification, extraction and drafts use ClickRuntime's built-in AI, with no extra key.
On a schedule or on each event
Runs as often as every 5 minutes, or the moment an app sends an event, and keeps track of what it already handled.
Changes you can ask for
- Run it at a different time, more or less often, or only on weekdays
- Change what the AI looks for, or how it writes the result
- Add another app, for example also send it to Slack
- Combine it with other Google Docs steps into one automation
Under the hood
The code that runs this automation
from google.oauth2.credentials import Credentials
from googleapiclient.discovery import build
from datetime import date
from clickruntime import on_trigger, credentials, http, ai
DOC_ID = "research-digest-doc-id"
@on_trigger
def run(event):
ids = http.get("https://hacker-news.firebaseio.com/v0/topstories.json").json()[:10]
stories = [http.get(f"https://hacker-news.firebaseio.com/v0/item/{i}.json").json() for i in ids]
listing = "\n".join(f"{s['title']} ({s.get('url', 'https://news.ycombinator.com/item?id=' + str(s['id']))})" for s in stories)
summary = ai.generate("Summarise each story in two lines, keeping its link:\n" + listing, max_output_tokens=900).text
docs = build("docs", "v1", credentials=Credentials(credentials.connection("google", "work")), cache_discovery=False)
end = docs.documents().get(documentId=DOC_ID).execute()["body"]["content"][-1]["endIndex"] - 1
docs.documents().batchUpdate(documentId=DOC_ID, body={"requests": [
{"insertText": {"location": {"index": end}, "text": f"\n{date.today():%A %d %B %Y}\n{summary}\n"}}]}).execute()
return {"stories": len(stories)}FAQ
Common questions
Which sources can it follow?
Is the analysis investment advice?
Does the Doc get messy after months of entries?
What permissions does it need?
Does it need AI?
How many runs does it use?
Can I change how it works?
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