The AI Ticker section of our newsletter covers what people are saying about the models: how Fable compares with Sol and Astra, where Gemini has got to, what broke this week. A lot of that comes from Reddit, from r/ClaudeAI, r/ChatGPT, and the developer subreddits. Two Tasks do the reading. One collects threads every day, and the other turns a week of them into a draft of the ticker.
What's covered
- Why the collector is a code Task rather than an agent Task
- Building the Reddit scraper with Iterate with AI, including how the builder picks up an existing Reddit key
- The daily run: top posts per subreddit, an inline prompt per new thread, and a memory record for each summary
- The weekly Ticker Writer, which chains three prompts over the stored summaries
A script runs the loop and prompts handle the steps
The Task starts from New task in the Build section, set up as code. This job reads a lot of data, and a script handles the loop over subreddits and posts for a fraction of the tokens an agent would spend doing the same orchestration. The process is known before the run starts, so there is nothing for a model to decide about the order of steps, and a fixed script is easier to write tests for later. Models get called for the discrete steps that need one.
I could write the Python by hand. For the demo, Iterate with AI gets a short instruction to write a scraper for a set of subreddits that stores what it finds in a memory. The builder checks for the keys and integrations it will need first. There was already a Reddit key on the account, so it asked whether to use that one, then started writing.
Daily Ticker Researcher
The finished Task is called Daily Ticker Researcher. It has a subreddits input (defaulting to ClaudeAI and ChatGPT), the Reddit integration, and a memory folder called AI Ticker Research attached in the side panel. It runs on a daily schedule.
For each subreddit it pulls up to 20 of the day's top posts, along with their comments. Any thread it has not seen before goes to thread-summarizer, an inline prompt created from the + menu in the Package panel, and the summary is written to the memory folder as its own record.
A record is titled with the date, the subreddit, and the thread title. The one opened in the video is an r/ClaudeAI thread asking whether anyone else feels that Fable gets annoyed by Opus. The summary lists the source link, score, and comment count, then a topic, the key signal, a breakdown of positive and negative takes and outliers, ticker tags, and a newsletter priority.
The researcher runs every day and the newsletter goes out once a week, so the records pile up in the folder until the writer needs them. Other sources, outside Reddit, feed the same folder.
Ticker Writer
Writing the ticker is a separate Task, because it is a separate unit of work and it reads more than the Reddit records. Ticker Writer asks one question when you run it: how many days of stored records to go back over. The default is seven.
A script orchestrates this one too. A week of records is more than an agent handles well, even with subagents, and it would be expensive in tokens. The script pulls every record in the window and passes them through three inline prompts in order, each running on Claude Sonnet 4.6:
story-prioritizerranks the stories and removes duplicates, with detailed criteria for what our readers care about.ticker-copywritertakes the ranked list and writes the ticker section.copy-reviewerchecks the draft against a set of basic rules, including banned punctuation that the copywriter prompt does not mention.
Keeping those rules out of the copywriter prompt is deliberate. Prompts still work best with one purpose each, even on smart models, and a cheaper model doing three things in sequence does them better than an expensive model asked to do all three at once. A chain of single-purpose prompts is also simpler to test.
The run
The test run in the video took about three minutes. The run log shows each memory being read and each prompt running, and every step expands to show what it produced. That run covered a single day to keep it short. The output is a prioritized list of stories for the newsletter, which we still edit by hand before it goes out.
Video transcript
Hey everybody, Alex here, founder of Cube Bash and today I wanted to take you through one of the more fun tasks that we built recently. For those who read our newsletter, we have an AI ticker section that's been getting really positive feedback and it summarizes a lot of recent news about the different models, their providers, user sentiment, how they're working, how do we feel about Fable versus Soul versus Astra, where is Gemini, everything like that. Uh that's actually powered by a set of tasks that we've written. One of the more interesting sources of data that I'm going to take you through today is how we go through the Claude AI, the chat GBT, and relevant developer subreddits to try to find new information how users are reacting to different models. To get that done, what we're going to do is come in here and press new task inside of the build section of qbash.
Once we're here, you're going to see this uh task editor that you'll be familiar with right now. And we're going to set this up as a code, not an agent task. The difference here is it's going to be processing a lot of data and we tend to prefer code to do the orchestration instead of an LLM because we're going to burn a lot less tokens. It's deterministic and in this case we can reason about the process up front and frankly I prefer writing tests and things like that later and we just want to use LMS for the discrete steps as we need them. Now I could write this by hand with Python but for a demo we're not going to do that.
We're going to press this iterate with AI button and we're going to type or copy paste a little instruction here that asks it to go through and write a scraper for Reddit that looks at these uh subreddits and stores them inside of a memory. Once we press sign, this actual AI assistant is going to start building this for us and it's going to start producing all of the code that we need. Uh the first thing it's going to do is come through and make sure that we have keys and integrations that we need and if not it will ask us to uh store them basically. So you notice here, notice I already have a Reddit key here, and it's going to ask if I should use this, and it's going to start writing that script for us. Now, we could sit here and watch this finish, or we could just go over to the completed version.
And what this does, you might look at this and think, "Wow, it's complex." But if you've read a lot of our recent blogs, we see a lot of the tasks we're writing as a third rail of an application. Code is incredibly cheap for us to produce. Frankly, I find it's much easier to read than long complex agent instructions, but again, it's deterministic. We can trust the process it's going to go through. So, what this generated task does is it actually runs through and looks at the subreddits that have been defined, and for each of those subreddits, it's going to go through, pull the posts, pull different comments for each of them.
And if we haven't seen that post before, it's actually going to go through this attached inline prompt. If you're still learning how to use the task editor, you can always press plus here and create a new prompt script markdown file or a test that will be available to this task. And this is a really basic prompt for demonstration purposes. Right? So functionally what's happening here is this script is going to go through and iterate through each of the subreddits, each of the new posts.
This runs on a daily schedule. I'll show you how to do that. And it passes each of those threads to this summarization. Once we have that summarization, you'll notice what it does is it stores these inside of a defined memory folder that's been attached here. The reason we're doing this is we run this script once a day, this task once a day to go through and find any of the recent news.
But we do our newsletter once a week and we want to start accumulating information. If you're curious for a single thread, this is what the output of those starts to look like. I've just clicked into that memory folder here. So, this is a good example of a user asking, you know, does anybody else uh feel like Fable gets annoyed by Opus? And I think most of us have trouble with Opus 5 at this point and trying to communicate with it.
And it produces these nice outputs here that we're going to use later. So, you can imagine by the end of the week and we do go through a bunch of other non-Reit sources as well, we have a nice collection of information in terms of what are users saying about models, what are news about models, how are different providers moving. Coming back into that task editor for all of us. I mentioned it earlier. This runs on a schedule.
So, we can always press the settings of the task here. And you'll see this is set up here to run once per day. If you're ever curious, you can always come in and view the logs of these. Now, once this has been built, again, this is going to run once per day to go through and find information inside of Reddit. What do we do with that?
Right? cuz now we're at a point where we have this task that runs. We've got all the comments, but what we like to do for us, we like to treat that as a separate task because it is a different unit of work and it's summarizing more information than just the Reddit. But as always, that really is just a different task. So, what I'll do is show you now this other task that we've created here called Tiger Writer, which is a little bit more complex.
And when we go to run this, it actually just asks you how many days of information should I go backwards on that we've already stored. Our default is seven because we're typically writing this newsletter on a weekly basis. And again, we're using code to orchestrate this process because an agent's going to have trouble with the amount of information even if we use sub agents. And it's going to get really expensive using all of those tokens. So what this does is it comes in and it basically pulls each of those different memories that are being created through the week.
And the first thing it does is runs this basic prompt in Sonet 46 that's attached. And we've got some information in here that goes through and tries to rank each of those stories, right? How are each of them performing? Which ones seem to matter the most for our audience? And there's a lot of instructions in there.
As each of those stories gets ranked, the ranked list gets passed to a different prompt here. who understands that you now have a list of ranked stories and dduplicated stories for the week and your job is to write the ticker part of the newsletter for us. Go through summarize these stories, prioritize these stories, etc. Once that's done, it actually runs through a final prompt here who just does some basic, you know, dei it, make sure that we have exactly what we're looking for. We always go through and basically hand write on top, right?
But you want to go through and make sure that some basic rules are there. You might wonder why we have a set of band punctuation that's not in the ticker copywriter. One of the things we always talk about is we find prompts still work best even with smart models as single purpose instead of trusting an expensive model to do three things. Part of what makes tasks so great is we prefer to use a cheaper model to do two or three things in sequence better. It also makes this flow much more deterministic, easy to write tests about later and to confirm it's doing what we would expect.
Now, if you're curious, I've already done a test run here, so you can see a little bit of the output. We'll dig deeper. You can kind of see as it goes through a run, it's prioritizing each of the stories here, and it's starting to produce that ticker. Once a ticker like this is run, we can always come to the actual logs of the task here. And you'll see I ran one today.
Took about 3 minutes to run. This was processing through a lot of different files. And you see it processes through each of the memories, runs these prompts, and you can always in our logging system. Click through and expand to see what is being produced in each of these steps. What is the output?
This is one of the things that makes this platform so great. There's version control and logs on each task that you run. The final output of a task like this, I'll show you a chat version, so it's a little better formatted. Notice this is only going for one day because I wanted this to run nice and fast. But this kind of goes through and gives us a list of information that we might want to include inside of the newsletter.
And this is one of the great examples of tasks in my mind. This is a deterministic flow. We can define what we want up front, but it takes an enormous amount of time that we can start to minimize. Thank you for watching. I realize this is very fast and to the point, but I love seeing an output like this on something that we now never need to think about again and just runs for us.