These 5 Github Repos are a goldmine

**Greg Isenberg** (0:00)
I don't know about you, but I've been spending a lot more time on GitHub lately, and there's a real reason behind it. I mean, it's turned into one of the best places to get an unfair advantage in the agentic era, because you're seeing the tools people will be talking about in six months today. A lot of the things that eventually become SaaS companies, agencies, workflows, and startup ideas show up there first. It's really like that famous quote, you know, the future is already here, it just isn't evenly distributed yet. So I'm trying something new with today's episode. I went through a ton of GitHub repos that have been getting attention over the last 30 days, and I picked the six that I think you should know about. If you're trying to build products, if you're trying to make money, if you're trying to save time, or you just want to get more done with AI. Now, some are more technical than others, but I'll explain them all in plain English. You know, what each one does, why it matters, how it actually use it, and how you can try it for yourself today. The last one is from Nvidia. A couple of the other ones are from smaller devs, but every one of them is something I can picture someone using to make money or to save real time. All six are free and open source, meaning the code is free to use. But as a lot of you know, some have dependencies, like one has a dependency on 11 labs, which does cost money, so be aware of that.
Let's get into the episode. I'll see you at the end. If you haven't liked and commented and subscribe, just do it to get your algorithm pumping with a lot of valuable content like this. Let's go.
The first repo is my friend Peter Yang's No AI Slop Skill. I like this one because everyone who uses AI for writing has felt this exact problem. I mean, you ask ChatGBT or Claude to help you write something. It's a tweet. It's a LinkedIn post. Technically, the writing is fine. The grammar is fine. The syntax is fine, but it has this weird smell to it. Maybe it sounds like too polished or too symmetrical or formulaic. Sometimes it has like, it's not X, but it's Y, or uses the word quietly a lot.
I feel like it feels like reading a keynote from a fake SaaS conference. Once you notice it, I feel like you can't unsee it. A lot of people are able to smell it now. What's really cool is Peter's skill is basically an editor that strips those patterns out while trying to preserve your actual voice. The last part is important because a lot of the writing tools make your writing cleaner, but send off the interesting parts and make everyone sound the same. If you're going to sound the same, it's hard to stand out.
You actually want to do the opposite.
What's cool about this repo is it hunts for the patterns that make writing feel AI generated and helps the draft feel like a real human being wrote it. Why does that matter? Like I said, if you're building products today, you're writing constantly and you want to stand out.
I personally am writing tweets, I'm writing landing pages, I'm writing cold emails, launch posts, product updates, onboarding copy. If you have investors, you're writing investor updates and you don't want to sound like generic AI because people are going to feel it.
They might not say this was written by AI, they might not respond and say it, but they'll just trust you less. They're just going to keep scrolling, they're not going to reply. They might even believe what you're saying because they're like, this is AI written. So this repo isn't really about making writing nicer, it's really about making your communication more believable. So if you want to try this one, you can install it as a skill. That's what I would do. You just type in NPX skills, add, and then you put the GitHub link, and I'll include the link to the repo in the show notes, in the description, and actually all of them. So you can just go and do that. Then you can use it inside your agent workflow, wherever you have a draft of something. So the practical way I would run it would be just to write a rough draft first. I wouldn't ask AI to invent the whole thing from scratch. Just write an outline and get the real points down, even if they're messy. And then you would ask the skill to remove the AI sanding patterns while keeping your voice. So that's the workflow to think about. You have a human set of ideas first, and then you have AI cleanup second, which is different than how a lot of people today are creating content. They're just either asking AI to write everything basically, trying to one-shot it, and then saying, hey, this doesn't look good, this doesn't look good. But this sort of like hybrid approach with the skill here, super interesting. So there you go. That's number one. So the second repo is the CRM by TriCompAI. It's an open-source CRM built for AI agents. And that sounds a little technical and maybe boring. I'm going to try to break it down as simple as possible so you can understand how to actually get value out of this if you're building a business. A normal CRM is a place where you store your relationships and your customers and your leads. It's basically a database with names, emails and notes, sometimes deal stages if people are using it for sales. The problem is almost all of them depend on you doing the work. So you have to remember to update the person, remember what happened last time, what they said, who went cold, and just basically keep your CRM clean. Because of that, if you're anything like me, you've had a CRM and it's just turned into these graveyards that you just stop updating. It starts off really exciting and then a few weeks, the data goes stale and the notes get messy, and you just stop updating it, and the people who could actually make you money end up sitting in a tool that nobody wants to open. So what's interesting about this particular repo is that it treats your CRM as a workspace for an agent instead of a filing cabinet for you. So the agent can look at records and research people and enrich company info. It could do scheduled follow-ups and then it could keep the notes current. All the stuff that basically you didn't want to do, it could do it for you and do those tasks. The whole idea of this AI CRM is that it shouldn't just store information, it should maintain the relationship graph for you. It's a big deal if you're a founder because most businesses actually don't fail from a lack of opportunities. A lot of times they fail because the opportunities are just scattered everywhere. You get this warm intro maybe like six months ago or you get feedback from a customer who says check back later and you forget about it. There's real money sitting inside those emails. The use case here isn't to replace Salesforce, I'm not suggesting that. It's like a smaller useful use case. The way I would do it is, say I'm looking for sponsorships for my podcast. I can run my sponsorship pipeline through it, tracking agency leads, I can manage investor updates or if I have investors, I can follow up with everyone who's joined a waitlist to something that I've vibe-coded or I can have it handle customer success just fully for a small SaaS. If I were testing this, I would actually start with one small workflow. I'd make a pipeline called something like, Warm Leads I Can't Afford to Forget. Then I drop in every person who's shown any buying intent and they replied to any email or booked a call or ask about pricing or said they'd circle back in 45 days, or after Christmas or something. Then I'd let the agent do the hard work to keep that alive. In the past, you'd hire someone to do this, and now you have this agent that's doing it for you. Who needs a follow-up?