You NEED to try these open-source AI projects RIGHT NOW

I found four free GitHub projects that you probably haven't heard of that are so valuable. The first is a new type of search engine that is completely free, takes zero configuration, and actually works really well. We also have a new agentic engineering skill, a completely local notebook LM clone, and the last one can save you up to 90% on your AI bill. It is crazy. And by the way, this video is sponsored by 11 Labs. more on them and a specific project you can use them in later. So, this is the first one. It's called Last 30 Days. It's a skill. It is dead simple to install and it's kind of a new type of search engine. It's really interesting. The best way to think about this is as a comparison to Google search. You type in your search term. It's going to find a bunch of different links to serve you. It's also going to serve you a bunch of ads. But last 30 days is very different. What it does is it goes out to Reddit, Hacker News, Poly Market, and GitHub, and it basically looks at how many upvotes different stories have, and that's how it does the summarization. It basically takes the most upvoted answers and serves you that information in a really nice, concise way. Plus, it also has X, YouTube, Tik Tok, and more. And again, it's all based on human voting. It's not some algorithm. It is what is trending lately on the internet, and it gives you that information. It's currently sitting just above 40,000 stars on GitHub. It is by Matt Van Horn, who is again the co-founder of the company that became Lyft. So, Reddit up votes, X- likes, YouTube transcripts, Tik Tok engagement, polyarket odds backed by real money and insider information. That's millions of people voting with their attention and their wallets every day. last 30 days, searches all of it in parallel, scores it by what real people actually engage with, and an AI agent judge synthesizes it into one brief. And it works very well and it's easy to install. Let me show you. All you have to do is copy paste it into your favorite Aentic engineering platform and install this skill and then just drop the link. By the way, I'll drop the link down below in the description. Once it's installed as a skill, go ahead and most likely you're going to have to restart whatever you're using. Codeexcloud code and then you just type slash last 30 days and you can type in anything you want. So, for example, I've been really into loop engineering. So, I'm just going to type that and let's see it work. All right. So, here we go. What I learned. Loop engineering was born on June 7th, 2026. And the internet has spent the week fighting about it. Peter Steinberger posted, "You shouldn't be prompting coding agents anymore. You should be designing loops that prompt your agent." and so on. And so why this is so valuable is because you get recent trending information. So that's really what this last 30-day skill is for, recent trending. At the bottom, it says key patterns. The term is one week old. Verification is the whole game. Cap everything. It's not tool specific, etc. And then at the bottom, it also gives you the sources that it pulled from. So we can see 32 threads on Reddit with 45,000 upvotes. Here is HN with 40 different stories. r/cloudai, singularity, prompt engineering, and more. Now, there's a few other features you can get out of last 30 days. Let me show you. So, right here, what you can do is actually emit an HTML page summary of what you just searched for. So, you literally type- emit equals HTML. You can also just ask it in plain English, give me a sharable HTML brief, and it'll generate a simple HTML page that looks good, that shows you what you just searched for. And so here's an example of what that looks like. Loop engineering and it gives you information about the search term and it's just yeah nicely formatted HTML page that you can share with anybody. And so why does this work so well? It is actually pretty well documented in the GitHub page. So the V3 engine doesn't just search for your topic. It figures out where to search before the search begins. Type openclaw and the engine resolves to Peter Steinberger's Twitter handle and all of the relevant subreddits. And most of all, it is actually using humanbacked data. So that's it. Go check it out. I will drop it down below. And by the way, if you like this video, if you like me showing you the latest awesome GitHub projects, please like this video and subscribe. It very much does help the channel. Thank you in advance. All right, this next one is a clone of Notebook LM. And if you're not familiar with Notebook LM, it is a project from Google. It is fully hosted. You upload PDFs, you upload different documents, and it effectively creates a way to both ask questions against those documents, but also it can create a podcast, an actual synthesized podcast discussing the topic of whatever content that you just uploaded. And now this is a free open-source and completely local if you want project that you can find on GitHub. It is sitting just below 30,000 stars and it is called open notebook and it's also extremely easy to install. I've basically stopped installing things through the command line. I simply copy paste the URL directly into cursor or codeex whatever you're using and I simply say install it and it does so install this in a folder on the desktop and get it set up for me and copy pasted the URL. And so you have two options. You can power this with hosted models like OpenAI's models, which is the path that I went, but you can also get it working completely locally. You can power it with local models, voice models, large language models. It's all quite simple to plug in. And if you want the podcast voice to be even more natural and human, use the sponsor of today's video, 11 Labs. Okay, I'm excited to tell you about this sponsor, 11 Labs, because I've been using them for a long time, and 11 Labs is fantastic. So, check this out. 11 Agents by 11 Labs is a complete platform to design, deploy, and optimize real-time voice and chat agents that can not only speak, but also understand what you're trying to accomplish and actually accomplish tasks. If you're building a product, starting a startup, or already have a business, and you're looking to build full conversational agents, 11 Agents is the easiest way you can use expressive mode to control the tone so they don't just sound robotic the whole time. These agents are great across sales, support, and operations. So, check out 11 Agents by 11 Labs with promo code Forward Future AI, and you will get 33% more credits. So, I'm going to drop the code and the link down below. Now back to the video and this is what it looks like. So I already gave it a link to this essay by Thrive Holdings called Long Humans. If I click into it, you can see the entire essay. I just gave it a link and it ingested the entire thing. It gives you insights. So technology, historically automated tasks, but often expanded bureaucracy and so on. And you can also just ask it questions. Is the author pro-AI or anti-AI based on this article? So let's hit enter. And just a few seconds later, it's going to give me an answer to that. And again, it doesn't have to be a short article like this. It can be a thousandpage PDF and you can simply ask any question you want. And so there we go. The author is pro AI but not in a replace humans with machines way, which is exactly the point of this article. It also gives you the specific reference to the article. Then I went in and I created a podcast from it. And that was super easy. You just click right here, generate podcast. You can click the article that you just loaded up and click generate. I've already done it and it created a 23 minute and 36 second podcast. >> The audience is internal. Permissions and auditability may matter more than consumer polish. >> Okay, so just like that. And there's a bunch of different settings with the podcast generation. You can have multi-host, you can have different tones, you can describe exactly what you want it to sound like. You can change the script. It's all hyper customizable because it's all local and open source. It also comes with something interesting called transformations. So you take that article and you have a bunch of different things you could do with it. You can extract key insights. You can get a dense summary. You can analyze a paper. You can have it reflect. So generate reflection questions from the document. Simple summary and table of contents. All of this is just dead simple to use. Now, when you're first setting it up, the only slightly complicated part is deciding which models you want to use for what processes within the project. But let me just show you what I decided to use and you can just copy me. So, for the chat model, I want the latest GBT 5.5. For the embedding model, we're using text embedding 3 large. Here's the text to speech GPT40 mini. For the speechtoext, GPT40 transcribe large context 5.5. tools model 5.5 and the transformation model 5.4 mini. You can add as many different LLM providers as you want. You can see there's just a bunch right here. And again, you can run it completely locally using Olama or LM Studio. It is very simple. All right, the next one is called agent skills and it is specifically to help you with aentic engineering. It is coming in at just above 56,000 stars on GitHub and it gives you seven slashcomands that map to the seven stages of engineering. So, spec, plan, build, test, review, code, simplify, and ship. And all of this is now well structured in a nice flow detailed in the skill. And because this is a skill, it is very easy to install. Once again, you just take the GitHub link, you give it to your agent, and you say, "Install this skill." Now, in a lot of ways, it is quite similar to GStack by Gary Tan, which I reviewed in the last video. But rather than trying to help you build an entire company, which is what GStack is more for, agent skills is very focused on just the engineering workflow. So, just like before, I'm going to install, then the GitHub URL, hit enter, and it's just going to install. It is that easy. And the first one you want to try is slashinterview me. And it's going to give you a step-by-step interview trying to extract exactly what you're looking to build. And it will then structure that in a really nice markdown file that you can then use for the rest of the workflow. So first, what are you trying to figure out? A product or feature idea, a workflow or process change or something else you're weighing? And it also lists a guess there. So let's just answer it. So I want to build a library/webite of agentic loop ideas. Then I'm going to hit enter. All right. So hypothesis, you want a browsable collection of real agentic loop patterns. Things like pull until done, retry with back off, etc. So I'm going to say it's for agentic engineers looking for ideas for loops they can apply to their workflow. Things like loop until our documentation is fully up to date with our code. Hit enter. So you can see it's just asking and interviewing me and trying to figure out exactly what I want to build. trying to help me explore the different edge cases of the product that I'm trying to ship. So from there you can refine the idea. You do specd driven development. So it'll actually develop a spec for you. Breaks down the task into small achievable pieces and has a bunch of other skills that just help you along the engineering workflow. So here's security and hardening, code simplification, performance optimization, all extremely useful. All right. And last, and possibly the most interesting one, and I think this is completely under the radar right now because it's only at just above 24,000 stars on GitHub. This is called Headroom, and it effectively compresses the context that you are giving to your large language model, and it does so extremely well. Headroom compresses everything your AI agent reads. Tool outputs, logs, rag chunks, files, and conversation history before it reaches the LLM. Same answers, fraction of the tokens. It does not degrade quality, but it saves you so much on either your API bill or your quota. And the best thing is it works with all of the agentic coders that you're already used to. Cloud code, cursor, codeex, all of the above. It just works. So here are some examples with code search with 100 results 17,000 tokens before 1,400 after that is a 92% savings in tokens sur incident debugging 65,000 tokens 5,000 after again 92% GitHub issue tracking 54,000 before 14,000 after 73% and codebase exploration one of the most common patterns that you're going to be using in codeex cloud code cursor 78,000 before 41,000 after 47% savings This is a real savings towards your quota. This will actually make a difference. You might be able to use claude code for more than an hour and the accuracy is preserved. So they tested it on GSMAK, Truthful QA, Squad, V2, and BFCL. And on all of them, it basically scored a perfect score. Now you can see something is happening in June. The stars are absolutely exploding. People are finally realizing what's going on in this repo. And I hope this video helps you save a bunch of money or save a bunch of quota. Okay. And because Fable is so very expensive and we run out of cloud quota so quickly, I'm going to install it here. So all I say once again, install this and give it the URL and hit send. So it's going to install it and then I'm going to show you what it actually looks like. All right. And to get it actually running, you can do so in Cloud Code CLI. So here we go. I typed in headroom wrapclaude and then d-n no proxy and it loaded up. We can see it is now wrapped and it looks just like cloud code. So yes, I trust this folder. We're going to set the effort to low just to test it out. So I'm going to say switch to my AstroHub codebase folder and do a quick review of it. All right. And then after you run this for a while, what you can do is type headroom perf, hit enter, and it's actually going to tell you about the performance and how much it has saved you. Now, because I've only run it a little bit, there's not a ton of savings quite yet, but you can see all of the different breakdowns. Here's the per model breakdown, 200 tokens saved, 1%. We have 8% token saved for Haiku. It tells you about the cache performance, the optimization overhead, the conversation size, everything you need to know to see how well it's been performing. And one of the coolest features it has is this thing called headroom learn, which mines failed sessions and writes corrections to claw.md and agents.md. So simply type headroom learn, hit enter, and it's going to start analyzing your logs, looking for failed sessions and ways to improve based on those failures. All right, and here's what it found. So nine sessions, 378 calls, and here is what it would write. So deploy to here.now, which it now knows I'm using background task. It can save 8,000 tokens per session by loading deferred tool schemas. And yeah, it just gives you a bunch of suggestions that you can then use in your agents.mmd or claw.md file. And one thing about this repo I want you to know is it installs this thing called serena by default which is kind of annoying and it doesn't have anything to do with headroom but they install it anyways. So to avoid that during the installation you type d-n no-sa and with that flag it won't install Serena. The other thing is telemetry is enabled by default. So just make sure to disable that if you want obviously because the code is open source and free. You can edit it all you want. You can remove all of that kind of stuff. Kind of annoying if you're the author and you're watching this video. Don't do that. And once again, thank you to 11 Labs for sponsoring this video. I'm going to drop links to them down below. Thanks again. But there are other incredible open-source projects that I've reviewed in full. Check out this video right