Speaker 1I 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, 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. But each one does, why it matters, how I'd 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 beware of that. I'll see you at the end. And if you haven't liked and commented and subscribed, just do it to get your algorithm pumping with a lot of valuable content like this. Let's go. So the first repo is my friend Peter Yang's no AI slop skill. And I like this one because everyone who uses AI for writing has felt this exact problem. I mean, you ask ChatGPT or Claude to help you write something. It's a tweet. It's a LinkedIn post. And technically, the writing is fine. The grammar is fine. The syntax is fine. But it has this like 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, you know, or uses the word quietly a lot. You know, I think I feel like it feels like, you know, it's like, you know, it's like, you know, I'm reading a keynote from a fake SAS conference. And once you notice it, I feel like you can't unsee it. You know, a lot of people are able to smell it now. So 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 sand off the interesting parts and make everyone sound the same. And if you're going to sound the same. It's hard to stand out. You know, you actually want to do the opposite. So what's cool about this, 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. You know, I personally am writing tweets. I'm writing landing pages. I'm writing cold emails. I'm writing 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, you know, people are going to feel it. They might not say this was written by AI, like 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 a report. It's about making a report. It's about making a report. It's not 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 know, 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 it, of something, right? So the practical way I would run it would be just to, you know, write a rough draft first. I wouldn't ask AI to invent the whole thing from scratch. Just, you know, write like 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 TriCompAR. 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. You know, it's basically a database with names, emails, and notes, sometimes deal stages if people are using them for sales. The problem is almost all of them depend on you, doing the work. So you have to remember to update the person, you know, remember what happened last time, what they said, who went cold, and just basically keep your CRM clean. And 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 sort of stop updating. It starts off really exciting. And then a few weeks, the data goes stale, and the notes get messy. And then you have to update the CRM. And then you have to 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, you know, to do, it could do it for you and do those tasks. The whole idea of this, you know, AI CRM is that it shouldn't just store information. It should maintain the relationship graph for you. You know, 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 like warm intro, maybe like six months ago, or you said, you know, you get like feedback from a customer who says, check back later, and you forget about it. And there's real money sitting inside those in those emails. So the use case here isn't to replace Salesforce. Not suggesting that. It's like a smaller, useful use case. So the way I would do it is say I, I'm looking for sponsorships for my podcast. You know, I can run my sponsorship pipeline through it. You know, 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. So 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. And 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, you know, in 45 days, or after Christmas or something. And then I'd let the agent do the hard work to keep that alive. In the past, you'd actually have, you know, you'd hire someone to do this. And now you have this agent that's doing it for you. Who needs a follow up? Who should I say? You know, what should I say? You know, what's changed at their company since we last talked? That's, that's why this is really cool. Okay, so how do you install this? Well, you're going to need bun and Docker installed. So the basic setup is you're going to want to, you know, get clone the repo, again, I'll have that repo in the in the show notes, you're going to want to do cd crm, cp env dot example.env, you're going to want to do bun install, Docker, pose up dash d bun run db deploy bun run db seed and bun run dev and I'll include all maybe I'll get some notes up. And if you go in the description, I could have that and I can also show it up, you know, on the screen here, you can see it. Then what it does is it runs locally. So you know, usually it runs locally on local host 3000 with the API on local host 3001. So there's environment, variables to set, especially for auth, so you want a Google or Microsoft client if you want sign-in and email or calendar integrations, which I assume you're going to want. So this isn't like a one-click, everything is set up, beautiful thing, but it's real. It's a product-shaped repo, and I think it's really interesting. I think the bigger opportunity of something like this is that everyone has a CRM problem, and they just never really call it that, right? They say, I forgot to follow up, or I forgot who to email, or our leads are a mess. Or they have this feeling like, oh my God, I'm leaving money on the table. I've certainly felt that. And that's all the same pain, and this repo just points at the fix. And the big idea is, how do you create relationship systems that actually do work with you, agents actually do it? I think it's interesting, and that's why I put it on the list. So the third repo is by browser use, and it's called video use. And it's one of my favorites, because you're going to instantly get it. It lets you edit videos with coding agents. So you drop your raw footage in a folder, and you can ask an agent like Cloud Code or Codex to edit it. It'll remove filler words, it'll cut dead space, it could add subtitles and color grade and build overlays. It can render the video, and it can actually check the output. There already exists a bunch of AI video editing. Softwares out there. But I think what's really cool about this specific repo is that the editing workflow here becomes something that your agent can understand and repeat. So think about this. Most creators or people who create content have the same handful of edits over and over. They have the same subtitle style. I mean, even if you look at my YouTube channel, you can see it's a very similar style every single video. It's at a certain pace, a certain way of cutting pauses and opening clips and turning a long conversation into shorts and mids. So normally, all that actually lives in a person's brain and muscle memory. But with a repo like this, it becomes way more explicit. And the agent can actually read the transcript, look at the timestamps, propose an editing strategy, render it, and check the cut points. I mean, how cool is that? Because if you're a creator or founder, content is going to be probably one of your biggest bottlenecks. And you know this. You can have a great product, and you're still going to lose if no one's going to see it. So I think this repo is like a way to shrink the distance between raw material and the published content. If I were using this, I wouldn't try to automate my whole YouTube channel on day one. I think that's way too much. And I think when people use a repo like this on day one, and then they start and they don't get the results, it's because they just ask for too much. I would start with one small repeatable format. So maybe it's taking a founder's loom and turning it into a 60 second launch video or taking a podcast recording and just being like, I need three banger clips from it. Um, that is good. Enough to start. And then once you have one format, then you can turn it into a system. And then once it's a system, then you can run it yourself or sell it to other people. Startup idea right there, right? Um, every niche needs content and pretty much no one, pretty much no one enjoys editing it. Uh, you know, real estate agents need listing videos. SaaS founders need product, uh, videos, uh, coaches need. Clips and agencies need different ad variations. Um, all of these, you know, types of companies have different workflow where they're creating content and it's fun to create content a lot of time, but the actual editing is really hard. Okay. So if you want to try this, how can you try it? Uh, the setup is really agent friendly. So all you have to do is paste the setup prompt into cloud code or codex or whatever, you actually. So you just, uh, set up the GitHub, read the install.md first, install the repo, wire up FFM peg, and then register the skill with the agent I'm using. And then ask me for the 11 labs API key when needed. Um, you know, could you install this manually? Yes, you can do get clone and LN SFN and you know, all that stuff. But you know, for a lot, for 95% of people listening to this, you know, might as well just have the agent install it for you. Um, that's, that's an interesting one. I feel like try it, see if it works for you and, uh, let, let me know what you think. So the fourth repo is by NVIDIA and it's called skill specter. Um, this, this is a no brainer one to install because people are starting. To install skills and MCP servers and plugins and agent tools straight from GitHub, which is exciting because it gives your AI new abilities, but it creates a problem because a skill isn't just a block of text, right? It can include instructions and scripts and dependencies, tool access, uh, and basically behaviors that change how your agent works. And in some cases it can actually touch files, call tools and connect to services. So before you install random capabilities from GitHub, you should probably ask whether it's safe and that's what skill specter is for. It can scan AI agent skills for security issues, uh, things like prompt injection, data exfiltration, supply chain risk, hidden instructions, basically any malicious patterns, um, and MCP related risks, uh, skill specter has your back. So the way I think about it is before you hand your AI a new tool, scan the tool, right? Um, and this matters a lot more now because AI workflows are getting more modular. So most people use one chat bot today, but builders are already assembling their own AI work environments, like a coding agent, a research skill, a design skill, maybe it's a browser tool, a video workflow. Um, and at some point, it's like, okay, I'm going to install a new tool. I'm going to install a new tool. I'm going to install a new tool. I'm going to install a new tool. I'm going to install a new tool. I'm going to install a new tool. I'm going to install a new tool. I'm going to install a new tool. I'm going to install a new tool. I'm going to install a new tool. And at some point, your setup starts to look like a little operating system for your work. And once that happens, security stops being an enterprise-only problem and becomes a normal like builder or founder problem. So I think it's a really important one. No affiliation with NVIDIA. But if you want to try it, you know, you can use UV. The quick install is uv-tool-install-git. And then you just post the GitHub link. And then just scan a skill directory with skillspector-scan.my-skill. Or you could just scan a GitHub repo directly with skillspector-scan with the link. If you want a faster static scan that doesn't send file contents to an LLM provider, if you add dash dash node. It matters if you're scanning sensitive files or private files in general. There's also a Docker option if you'd rather not install Python locally, which I think some people would want to know that. I've been thinking about this a bunch. If teams are going to install AI skills and MCP servers, someone's going to need to help them decide what's safe. That could be a trusted marketplace, a security scanner, an install gate for companies, or a feature inside every agent platform. I like that NVIDIA is attached to this one. That's why I included it. Because it gives the whole category credibility. And it also is just admitting that, hey, a lot of these skills, plugins, things like that, could be malicious and you've got to be careful. So I had to put that in there. I had to keep it real with you all. And check it out. So the fifth repo is called PhoneHarness. And it lets an AI interface, an AI agent, actually control your phone. Most AI agent tools are built around browsers and terminals. Also desktop apps, which obviously makes sense because a lot of the work happens there. But think about it. A huge amount of work happens on phones now, and banking apps, and messaging, and social, and food delivery. All that sort of stuff. So there's a ton of workflows that are hard to automate because the only real interface is a phone screen. So PhoneHarness connects an agent like Codex or CloudCode to a real iPhone or Android. Super cool, right? For iPhone, it uses Mac iPhone mirroring. And for Android, it uses ADB. So you don't have to jailbreak it, which I was like, oh my god, I don't want to jailbreak anything. You don't have to jailbreak it. You don't, you know, no Xcode. You don't need a special app on the phone either. agent sees what's on the screen, and it could tap, and it could type, and it can scroll, and it could open apps, and it verifies what happens. I know there's someone, you know, thinking like, why? Why does that matter? Well, if you're building a mobile app, think about the QA, right? You can have an agent test onboarding, sign up, check out on an actual real device. If you're building any sort of automations, it could open up mobile apps that have no API. And if you're a creator or even an operator, it could eventually handle repetitive mobile workflows across things like TikTok and Instagram. So if you want to try this, the first thing to do is test the mobile signup flow. Super simple. Open up the app, create an account, tap through onboarding, try checkout, take screenshots, and tell me where it gets confusing or breaks. That's super easy. Super valuable. Every mobile team should be constantly doing that, but most don't because it's tedious or they don't want to hire that person to go and do it. Or they honestly make mistakes. So this is going to help you there. If you want to try this one, the repo gives you a setup prompt to paste into Codex or Cloud Code. I'll include it. I'll include it. Basically, setup phone hardness for me, clone the GitHub, read the install.md, install it so phone harness is on my path, register it as an agent skill, and then read onboarding.md and walk me through the setup. For iPhone, you do need Mac OS Sequoia or later with iPhone mirroring plus accessibility and screen recording permissions granted to your terminal. For Android, from Android friends, turn on developer options and connect through ADB over USB or it actually works wireless too. You can run phone harness --doctor to check the setup. It's early. This one is early and it has its limits. Some phone flows won't work perfectly. For example, like lock phones need you and face ID and camera flows are pretty tricky. But the direction is obvious, right? Agents are moving from answering questions. They're operating tools and phones is one of the biggest tool services in the world. To figure out some of these workflows, there's startup ideas right there, right? If you can productize mobile QA, would someone be willing to pay $100 a month, $500 a month for that? How many clients do you need to get to $10,000 a month? That's in general what's interesting about a lot of these GitHub repos is you can install them. Step two is have a small workflow. Get it to work. Add value. And step three is then should I be productizing this for other people or just continue using this in my own workflows to be more productive, to make money and create value? So there you have it, folks. Five GitHub repos I think are really interesting. Obviously, I don't want to be a full-time open source analyst. But because GitHub is one of the few places you can still find genuinely useful tools before they go mainstream, you've got to be spending time there. I think it's totally worth it. And as you install some of these repos, even if you're not technical, you learn things, right? You start learning what works for you. What's this repo missing? How can you make this work for your company? How can you sell this to other people? I just think it's a really interesting thing to do, to just do monthly finding new GitHub repos. Installing them, you'll be able to build faster and it'll just connect the dots in your head in this agentic era that I think it's worth it. So I don't know if this has been fun for you, interesting for you, valuable for you. If you listen to this podcast, you know I live to serve and all I care about is just increasing your probability of success in this era with ideas, with tools, with frameworks. So please let me know in the comments what you thought of this. I'd be doing this every month, every two weeks, every six weeks, never again. All good. I appreciate you so much and I'm rooting for you. So I'll see you next time and have a creative day.
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