Day 77 of #100daysofnetworks

Local Scene OSINT; the power of Verdant Intelligence

It’s been a while since I’ve felt like doing back to back posts, but I’ve literally made things so easy that I can do this.

In yesterday’s article, I demoed the Verdant Intelligence API and showed how fantastically simple and easy it is to use.

Day 76 of #100daysofnetworks

And today I’m going to do more, but with GrooveSeeker data. I want to learn about my local music scene.

I now have space and time aware AI interfaces that are extremely simple to use. They are so simple to use that there is no point talking about the AI. The AI was never the point. The information is the point. AI can do a lot with information.

Less Talk, More Action

People keep asking me what is the value of real-time data.

If you have real-time data, you can:

• Detect opportunities and capture them in time to make use of them.

• Detect dangers and frustrations and avoid them in time to not be impacted by them.

Today, I want to detect opportunities and capture them so that I can make use of them. That is the value of the API and my AI interface to me right now.

I want to get data about my local scene, and I don’t want to spend a hundred hours building a new context engine or waste my time crawling five websites that Chappie told me represent the entire local scene.

Instead, I can go to my API and just ask it a question. Today’s code.

I created one simple helper function to convert the GrooveSeeker data into a Python dictionary.

def parse_events(text):

    events = []

    for block in text.strip().split("\n\n"):
        lines = block.split("\n")

        event = {"title": lines[0].strip()}

        for line in lines[1:]:
            if ":" in line:
                key, value = line.split(":", 1)
                key = key.strip().lower().replace(" ", "_")
                value = value.strip()
                event[key] = value

        events.append(event)

    return events

And then I asked GrooveSeeker for information.

question = 'what events happen in hillsboro, beaverton, or portland in 2026?'

answer = ask_api(client, question)
event_df = pd.DataFrame(answer)
event_df.head()

Any skilled engineer on Earth can recognize that it is easier to type four lines of code than it is to spend a hundred hours building out a production-grade context engine.

Boom. Data. Two of the lines of code don’t even count. Every Data Scientist on Earth can type:

event_df = pd.DataFrame(answer)
event_df.head()

That doesn’t count. Only this should count. Haha

question = 'what events happen in hillsboro, beaverton, or portland in 2026?'

answer = ask_api(client, question)

We go from a simple question to Data Science in about three seconds. You can ask the question in your preferred language. You don’t have to use English. AI is multilingual.

I can cluster the data extremely easily. Let’s cluster by genre.

event_df['cluster'] = cluster_text(event_df['genres'], similarity_threshold=0.7)

Since it’s a genre, I don’t want exact match. I expect I want somewhere between 0.5 and 0.8. Play with the similarity score to get what you want.

If you cluster it by genre, you can explore the data by genre. This is Data Science.

Neat. That first one is interesting. What’s in there? Be prepared, this is a noise cluster. That’s normal. Internet data is unpredictable, like AI. There are often noise clusters in data about the internet. If you do not know how to deal with noise in data, you should learn. This will be a useful tool for you to learn Data Science in general and it is extremely powerful for OSINT.

If you look in the first cluster, it is that there is no genre. That makes sense. What genre is a Farmer’s Market? What genre is a Hackathon or a Data Conference? GrooveSeeker is event intelligence. Some events have genres. Some events do not. What’s in the next cluster?

Comedy. Cool. If I need a laugh, I know where to go. But it’s not what I want right now. Let’s keep going. I’ll skip until I see something I like.

Jazz. That’s more my pace. What’s in there? A lot.

I’m Out of Space

Substack is saying that I am nearly out of space, so I have to end here. This should be plenty to show that the API is extremely easy to use and that it empowers OSINT, citizen science, and FUN.

Everything is easy now, because this API exists. I can do this whenever I want, to research whatever I want. You can too.

Have a nice day. GrooveSeeker wants you to enjoy life.

Reminder: if you upgrade to a paid subscription, then API access costs $8/month instead of $100/month, but only if you upgrade by 3/20/2026. This is a limited time offer. It is an attempt to bring more value to my paying subscribers, but others can make use of this opportunity if they want to save money.