Tag / Python

Header image for my article on PromoForge, catchy tagline.
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PromoForge

Over the past few years I’ve been running a gaming YouTube channel, Chambers Plays Games, and like most small creators I’ve found that making the content is only half the battle. The other half is the relentless work of promoting it… writing tweets, scheduling posts, engaging with other channels, and keeping track of which accounts in your following list are actually worth keeping around. I was doing all of this manually, which was both time-consuming and inconsistent. So, as I’ve done more than once before with problems that annoy me enough, I decided to write a tool to solve it.

That tool is PromoForge.

This isn’t my first attempt at Twitter automation. Back in early 2018, as part of a #30DayDev challenge, I built and open-sourced the Microcosm retweet bot, a Python and Tweepy script that retweeted content from a configurable set of hashtags on a schedule. It did one thing and did it reasonably well, and I wrote about it on this blog at the time. The repository is now archived. PromoForge is in some ways the natural successor to that project, built on the same instinct to automate the repetitive parts of being online, just considerably more ambitious about what “automated” actually means.

What started as a fairly simple idea (fetching my YouTube videos and generating tweets for them) has grown into something considerably more ambitious over the last ten days. This post covers what’s been built, some of the more interesting challenges I ran into along the way, and where I plan to take it next.

What Is PromoForge?

At its core, PromoForge is a promotion and audience-growth platform built specifically for gaming content creators. It has two entry points: a command-line tool for headless operation, and a full web UI that you run locally and access through your browser. The web interface is where most of the action happens and it covers three broad areas of work: content scheduling, audience growth, and channel maintenance.

The scheduling side of things works by connecting to the YouTube Data API, fetching your public videos, and then generating algorithm-optimised tweets for each one. It detects which game is being played from the video title using a combination of exact matching and fuzzy search, selects appropriate hashtags from a curated set, and composes a tweet from a template library. You can export everything as a CSV or push directly to Buffer or X’s own scheduling API. The whole flow from “fetch my videos” to “tweets are scheduled” can be done in a couple of clicks.

The tweet generation interface showing video source options, days to look back, number of tweets, start date, posting time, and tweet spacing settings
The generation panel. You choose the video source (recent videos, full history, highest viewed, etc.), set your schedule preferences, and hit Fetch & Generate. The AI enhancement feature can then refine each tweet using the Anthropic API.
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Python Logo on a stylized background
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Python Style Guides

In my pursuit to update my open-source Python Twitter bot, the retweet-bot, I decided to implement style recommendations followed by other open-source projects. This led me to use Pylint and follow the official PEP guidelines in the project. However, I was not prepared for the level of difficulty and pain that followed while implementing style guidelines. While my codebase now follows a consistent style found throughout the open-source Python world, it presented numerous pain points. Through this article, I aim to provide an overview of the pros and cons of Python style guidelines, and to enlighten readers to view these guidelines as flexible, especially for older codebases.

As developers, we have a responsibility to ensure that our code is of high quality and meets certain standards. This is the primary focus of Python style guidelines, and we will explore the pros and cons of both Pylint and PEP8 in this article. I will share my perspective as a relatively novice Python developer.

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Microcosm Twitter Bot Header Image
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Microcosm Twitter Bot

I set a goal for #30DayDev January to create a Twitter bot, there weren’t many requirements beyond that it had to retweet people using the hashtag. I can say that after the first week of January I have achieved that goal and released DevMicrocosm into the wild. It followed my rough plan for the project, using Python and Tweepy. This combination of language and library reaffirmed my expectation that it would be quick to develop and deploy.

Due to the Python being able to test commands directly in the terminal, it led to some fantastic opportunities to rapidly prototype some ideas, one of these was to use fully fledged configurations for multiple hashtags. This means that there is no need to redeploy the application every time you want to update what hashtags are being retweeted, you can simply change the config file and the next time the python file is run it’ll use the updated settings.

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