Four Open-Source AI Projects to Explore
By WS and Alfred the Bot
Watch Source Video
Context
This daily digest was generated from a YouTube video shared by an unknown user in the WS Daily by Alfred channel. The video discusses several open-source AI projects, and the content was deemed relevant for potential agency team use.
Summary
The YouTube video highlights four open-source AI projects available on GitHub. The first, ‘Last 30 Days,’ is a search engine that aggregates trending information from platforms like Reddit, Hacker News, and GitHub, based on upvotes and engagement, offering concise summaries and shareable HTML outputs. The second is ‘Open Notebook,’ a local, open-source clone of Google’s NotebookLM, allowing users to upload documents (like PDFs) and query them, or even generate synthesized podcasts discussing the content. The third project, ‘Agent Skills,’ provides seven slash commands mapped to the engineering workflow (spec, plan, build, test, review, code simplify, ship) to assist with agentic engineering. Finally, ‘Headroom’ is presented as a tool that compresses the context sent to LLMs, significantly reducing token usage and costs without sacrificing accuracy, compatible with various AI coding tools.
Extracted Knowledge and AI Review
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AI Research Notes
The video provides a concise overview of four distinct and valuable open-source AI projects. The presenter effectively demonstrates the functionality and benefits of each tool, with a particular emphasis on practical applications and potential cost savings. The inclusion of GitHub stars and creator information adds credibility. The sponsorship by ElevenLabs is integrated smoothly. The projects cover a range of use cases, from information aggregation to development workflow enhancement and cost optimization, making this a useful resource for teams exploring new AI tools.
Transcript
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 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, Polymarket, 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, TikTok, 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 upvotes, X likes,
YouTube transcripts, TikTok engagement,
Polymarket 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 agentic
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 Codex Cloud 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 days skill is for,
recent trending. At the bottom it says
key patterns, the term is 1 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
up votes. Here is HN with 40 different
stories, r/claudia i 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 {dash}
{dash} emit equals HTML. You can also
just ask it in plain English, give me a
shareable HTML brief and it'll generate
a simple HTML page that looks good that
shows you what you just searched for.
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 open claw and the
engine resolves to Peter Steinberger's
Twitter handle and all of the relevant
subreddits. And most of all, it is
actually using human backed 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 codex, 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 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
thousand-page
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-hosts, 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 GPT-5.5, for
the embedding model we're using
text-embedding-3-large.
Here's the text-to-speech GPT-4o-mini,
for the speech-to-text
GPT-4o-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 Ollama or LM
Studio. It is very simple. All right,
the next one is called Agent Skills and
it is specifically to help you with
agentic engineering. It is coming in at
just above 56,000 stars on GitHub and it
gives you seven {slash} commands 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 G stack by
Garry Tan, which I reviewed in the last
video. But, rather than trying to help
you build an entire company, which is
what G stack 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 /interview 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/website 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 spec-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,
Claude Code, Cursor, Codex, 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.
SRE incident debugging, 65,000 tokens,
5,000 after. Again, 92%. GitHub issue
tracking, 54,000 before, 14,000 after.
73% and code base exploration, one of
the most common patterns that you're
going to be using in Codex, Claude 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 GSM8K, 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 Claude 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 Claude Code
CLI. So, here we go. I typed in Headroom
wrap Claude and then {dash} {dash} no
proxy and it loaded up. We can see it is
now wrapped and it looks just like
Claude 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 Astro Hub code base
folder and do a quick review of it. 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% tokens
saved for Haiku. It tells you about the
cash 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 Claude.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 tasks, 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.md or Claude.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 {dash}
{dash} no {dash} Serena. 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
the 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
here.