Podcasting 2.0 for May 15, 2026, episode 260, Tennessee Trickshot.
Hello everybody, welcome to Podcasting 2.0 after a one-week hiatus.
We are back, that's right. Everything going on in podcasting,
and it seems to be quite a lot, is happening
right here. I'm Adam Curry in the heart of the
Texas Hill Country. Oh wait, I should have mentioned, we
are the only boardroom that operates on the fringes of
podcasting. There you go. I'm Adam Curry here in the
heart of the Texas field country and in Alabama. The
man who has a data set tailored just for you.
Say hello to my friend on the other end. Dave
Jones! Yeah, this... Yes? I have... Currently, I just have
no idea what I'm doing right now. Are you out
of control? Are you unhinged? Are you completely out of
your gourd? I call it being in dimensions. I am
currently in dimensions at the moment because we got in.
So yesterday, I had made plans. Take your time. It's
okay. It's going to be all right. We'll be fine.
I need chocolate. Yes. Do you have chocolate nearby? This
will help. Okay, there you go. Good. So I left
the office yesterday to come. I'd worked half day, came
home, threw a few things together. My son, my daughter,
and one of my son's friends, we jumped in the
car, drove to Nashville to see the Black Label Society
concert at the Ryman. Black Label Society? I'm not familiar
with this outfit. Oh. Oh. How dare you? How dare
you? Clearly good enough for you to be driving all
night to Nashville. The Black Label Society is Zach Wild's
band. Oh, okay. I know Zach. Yeah. I bet you've
probably interviewed him before. I hung out with him, actually,
in Russia. Oh, yeah, back when he was a drinker?
Oh, we were all drinkers back then. Yeah, he's a
crazy dude, man. But I met him in a Walgreens
one time, which was an interesting story. Okay, I don't
know which one wins, Russia or Walgreens. I think it's
a tie. It's a close call. It's half and half.
So anyway, we drove up there, and it was –
so it was – the show was – I had
scheduled – I bought these tickets back in like January.
And whenever you do things months in advance, you have
no idea what is going to be happening in the
actual timeframe that you – that the event occurs. I
mean like – Yeah. Welcome to my life. Yes. You
never know. You never know. You know this. You're like,
when everything, when things are three months out, you're like,
oh, yeah, it'll be fun. That's great. And then it
shows up. You're like, oh, what? I got to do
what? You look at the calendar. You're like, why did
I ever schedule this? Yes. I know this. Yes. I
know this. And so, but I mean, it was a
killer show because it was three bands, but one Black
Label Society was the headliner. but Zach Sabbath was one
of the intro bands and it's like it's Zach and
it's a trio Zach and two other dudes just doing
the heaviest versions of all the awesome Black Sabbath songs
it is so good did you drop some acid? funny
enough I did not that's usually part of my repertoire,
but I didn't do that this time. But anyway, this
show, man, it started at 7.30 on the nose. It
went forever. It was 11.15 and they were still killing
it. And I'm like, man, we've got to drive three
hours back to Birmingham. We're already pushing to 30 a.m.
And were you in the truck? We've got to go.
Were you in the truck? Oh, thank goodness. No, no,
we were we were in the Honda. And so we
were like, we we get we get going and everything.
We finally get back. So we left with like two
songs left on those on the set list. Got home
about 230. I fell asleep probably about three. And of
course, you know, then my daughter has to get up
and go to school. She has to be there by,
you know, by eight. And my wife is the teacher.
So everybody's up and running. So I've had like two
and a half, three hours of sleep. Yeah, you're running
on fumes, man. Yeah, so then I start getting blown
up from the day job with a problem there. And
on top of that, so we're having to not like
we're doing the board meeting now. And then we have
a Godcaster meeting after this. And then as soon as
the Godcaster meeting is over, we are jumping in the
car and going to North Alabama for a two-day camping
trip. Of which you'll be in the tent sleeping for
most of it. Probably, yes. So, I mean, it is
like – so I'm trying to get as much of
this. We haven't even put anything together for a camping
trip yet, and I'm throwing stuff in bags and all
this kind of stuff this morning. And then I'm going
to rant here, by the way. what so then what
happens the latest ai bullshit who from these stupid ai
security research companies oh nginx the nginx vulnerability yeah it's
called the you know of course you got to give
it a fancy name this one's called nginx rift whatever
i mean you know whatever And so this is an
NGINX vulnerability in the rewrite module of NGINX, where if
you use unnamed regex parameters in your rewrite, which, of
course, almost everybody does. If your NGINX configuration file is
simple, you do not name your parameters in a regex.
If you have an incredibly complicated, you know, 400 line
Nginx config, you probably, maybe you do because you have
to keep your sanity so you'll know what's going on.
But hardly anybody does this. So the vulnerability here is
that somebody can get remote code execution on your Nginx
if you use positional parameters in your regex as part
of a rewrite rule. So that means if you have
a rewrite rule on your Nginx proxy server, which proxying
with Nginx is almost table stakes. Everybody does this. I'm
listening carefully because after the Godcaster meeting, I have some
patching to do. Yes, I bet you do. And so
what happens is if you have a URL request from
a client, from a browser, and the rewrite rule says
is a regular expression, and you do – and you
capture parts of the regular expression so that you can
use them later. So let's say you're capping this and
this is I just patched the podcast index front end
API servers, which were which had this problem. And both
of there's two of them. And I just patched both
hosts. And this would be a common version of this
problem. So you have. So what you want to do
is something like hide the PHP extension. So what you
want to do is on the URL, you would have
something like api.podcastindex.com slash api slash v1 slash podcasts. Okay,
well, podcasts may be a .php file that's doing the serving
of the data, but you don't want to have for
the browser or the API client to have to type .php.
PHP. So you you hide the dot PHP and you
do that with a rewrite rule and your rewrite rule
would look something like this. It would have a URL
path and then you'd have in parentheses dot star and
then after the parentheses dot PHP. So you're capturing the
name of whatever the the PHP script is. And when
you rewrite it, you rewrite it to $1.php. So you're capturing
the name of the thing they're wanting to access, and
you're redirecting it behind the scenes to the name of
the script with the proper .php extension on it. Well, that
is the vulnerability. If you, if you, uh, Nginx does
not, Nginx rewrite module does not handle positional regex parameters
properly. And so you can corrupt the heap and, uh,
and basically put executable code, uh, into the data, into
the data area on the heap. And this is –
and the fix for this is to switch from positional
parameters, which would be like $1, $2, $3, et cetera, to named parameters.
So now you're saying instead of just simply a set
of parentheses that grab the part of the URL that
you're wanting to capture, you would put a – there's
a syntax where you put a name in front of
it. So that later instead, when you go to do
your actual rewrite, you don't do $1.php. You would do $script underscore
name.php or something like that. Yeah. And so this is
– the reason this is a rant is because it
wasn't an issue until someone published it being an issue.
Exactly. Okay, this is exactly my point, and I'm so
sick of this crap because what is happening now –
and everybody is saying versions of, Well, we're not prepared
for this. There's going to be this explosion of AI
model discovered exploits. Okay, fine. Fine, there's going to be.
But these are – what you're seeing, though, are AI
security company startups. Getting PR. Getting PR by releasing proof
of concept code and the more outlandish and headline making
and scary they can make it, the better. Yeah. And
so they're going to drop a proof of concept and
an exploit on the worst possible thing that they can
find. And Selass is right. And then they're going to
just dump it out on Twitter and Hacker News and
everything they can do to get as much PR as
possible. These are exploits just like the two recent Linux
kernel escalation of privilege vulnerabilities. These are exploits that nobody,
no human being was ever going to find. And so
there is no reason to publicly disclose these things. They
should be private for as long as possible, as long
as humanly possible. They should remain non-publicly disclosed. Dream on,
brother. Dream on. But people got to make them some
money. And that's what this is all about. And so
because some Silicon Valley security AI startup needs to get
some more venture capital funding, all of us have to
scramble and panic patch our servers as quickly as possible.
Well, good news. My servers are not vulnerable. Okay. You
have to be sure and check this. I don't know
if this applies to you, but there's a couple of
– to be complete, you must – you got to
check everything. That means Docker containers as well because Docker
containers will be running Nginx as a reverse proxy in
the Docker Compose stack. So there's going to be an
NGINX config somewhere in your Docker Compose layers. I don't
use Docker. Okay, well, then you should be fine. But
if somebody uses Docker, do not forget to check those
images because they can have a vulnerable NGINX config. And
now, since we've been gifted with Linux privilege escalation vulnerabilities,
you can remote code exploit, chain it together with dirty
frag or whatever they're calling that thing, and you got
root on the host in about three seconds. Thank you,
AI. Well, actually, it's not. Thank you, Silicon Valley bullcrap
is what I should be saying. Yeah. I feel your
pain. I do. I find it. hilarious that I literally
asked Claude Code to go check if I had this
vulnerability. Are you sure that Claude Code did not nuke
your repo while it was checking for the vulnerability? No.
Because this seems to be a common occurrence. No, it
didn't. It didn't. But before we get to that, big
shout out to RSS.com for dropping off. What did you
get, a Spark RDX? Okay, yes, they sent us a
DGX Spark. A DGX Spark, yes. This is the, let
me see, this is the ASUS version. DGX Spark is
a reference design, so this is the ASUS version called
the GB10. 128 gigs of unified RAM, one terabyte of
NVMe, and built-in Grace Blackwell GPU. And have you set
it up yet? It's soaking in it right now. What
a wonderful, valuable gift donation that was. Oh, yeah. This
is a $3,500 box. I mean, and I was on the
email thread, and Alberto was like, hey, can I send
this your way? And you're like, well, if it's like
this. And he says, yeah, it'll be on your desk
tomorrow. It was. That was just, I mean, I couldn't
believe how kind that was. Yeah. It has already made
a humongous difference because we went from – there's two
things that we're doing with this box. It's currently running
the inference for the spam classifier, the slop spam classifier.
And so that's running – oh, it's running so much
better now. I was limited to a 9B model. Now,
do you want to just talk about this for a
second? Because a lot has come out of this. Things
happen very, very quickly. My head was spinning because now
I don't know if you already were doing the data
sets or if this accelerated the data sets. But the
data sets then ship a data set that Alberto turned
into the PodRoll Atlas, which is a visual explorer of
the entire podcast recommendation universe at atlas.rss.io. So what are
these data sets and are they coming out of this
new system? Just give us a little background here. so
yeah it's amazing how much stuff can happen in a
week i actually i tagged them all in the show
notes people go take a look at it i mean
it's unbelievable what you're doing well all right so we've
had you know occasionally over the years we will i'll
i'll either have a request for something or i myself
will want a particular view into the podcast index data.
And what will come out of that are things like
the music, you know, top 100 or whatever. Right, right,
right. And the dead feeds list. These various sets of
data. But what has happened over the years is I'll
just publish these one-off and there's never been a place
to like find them all in one easy location. And
so I looked at the website and we had this
top level menu item called stats and it was pretty
much useless. It was just another version of the same
stat that was on the homepage. I'm like, we have
this valuable menu space up there at the top. That
should become the page where all the public, all these
public data sets that have come out over the years,
those need to be all listed in one location. And
so I just, you know, I don't understand React. I'm
not a React programmer. Here's the JSON. Enjoy. Vibe code.
I just Vibe coded the heck out of this thing.
That's great. Yeah. And so we have all live feeds.
That's a SQLite file. So dead feeds, problematic feeds, recommendations,
all media, video, music, value-enabled feeds, hosting provider stats. I
mean, this is fantastic. These are all things that already
existed, but just nobody knew where to find them. And
they get updated constantly, but there was never an easy
place to discover them. The hosting provider stats is a
good one. That's a list of all the feeds, all
the live feeds, non-dead feeds in the index, broken down
by which hosting company they come from. So that's a
big list there. And I do a lot of regexing
in this particular data set to massage the different –
RSS feed generators into something that actually is legit. So
like Blueberry is a great example of hosting companies. It's
very difficult to get a handle on their actual feed
count because they have lots of different white like white
label services and things that they do. And the feed
may be a blueberry hosted feed, but it doesn't show
up under a blueberry domain or a blueberry generator. And
so a few years ago, we worked with them and
a few other providers that – hosting companies that have
the same issue to get them to put some better
tagging in their feeds. And so now it's not actually
in the generator tag. It's in a comment in the
RSS feed. And so then I pull that in and
then properly attribute that to Blueberry. So I feel like
these host hostings stats are actually probably the most accurate
anywhere. I feel like they're very, very accurate to actually
who is producing that feed. And then 24 hour feed
report. that's just an overview of a quick dirty breakdown
of what we think the last 24 hours of added
feeds have been whether like spam or what kind of
quick dirty classification, just a raw dump of the newly
added feeds over the last 24 hours in CSV format.
Hourly counts is actually quite a few people use this.
This is things like total feed count, episode count, feeds
with new episodes in the last three days, seven days,
10 days, 14 days, blah, blah, blah, blah, blah. That's
very useful. I know Daniel is using that. A lot
of people do. John Spurlock. Using what? The hourly and
daily counts. Ah, yeah. Stats tracking, yeah. Then there's a
one healthy Hive RPC nodes list. So if you're doing
work on the Hive blockchain, this, it's actually this list
is shrinking by the day. These are the... Why is
that? Well, I mean, I don't know. I can't speak
to the Hive network, but maybe it's struggling, but this
used to have five API servers in it. How's our
gossiper coming? Do we have enough nodes up? It's about,
well, let me check. Actually, you know what? There's a
thing that is not on this list, and that is
the pee-pee monitor. Oh, pee-pee, pee-pee monitor, pee-pee. You've got
to monitor your pee-pee, pee-pee, pee-pee, pee-pee. I need to
check that and see how many hosts we have in
the Gossip Swarm. We currently have a total of 21.
Oh, that's not bad. That's better than I thought it
would be. 18, I do. Nice. Oh, I see. Oh,
I see the link here. peepee monitor these things uh
this but do you vibe code this too the oh
yeah this website oh yeah i would never put this
yeah this is great so with a with a graphic
like that that is never me that is not me
that's so cool um i like hey citizen bathroom that's
one of my favorites yeah live live from the bathroom
do you put your uh your is that where you
put your servers in the bathroom you got to get
real work done in that in that in the john
that's great um so the the pod ping gossip swarm
has been quietly running in the background for like a
couple of months now with no hiccup. This thing is
very stable now. And I'm desperate to get back to
working on it if I could get this other crap
out of the way there's always something popping up which
again today so we have more to talk about the
other day so there's the healthy hive RPC nodes is
anybody doing hive work on the hive blockchain if they
want to get get a list of the Current API
servers hourly that pass a bunch of basic health checks
to make sure that the API server is actually up
and serving good information. And then the last one is
the live tracking fire hose. and that is every episode
that we ingest into the index and it's updated every
three minutes, I think. Wow. It's updated every three minutes
and the way that works is it's a linked list
of object storage files. So it starts, the head of
this linked list is tracking.podcastindex.org slash current And the way
that this thing is designed to be used is that
you start there with slash current, and then it gives
you a previous tracking URL property, which goes to the
linked to the previous one in the chain. So what
you would do is you would just consistently walk backwards
through the chain using that previous tracking link of tracking
URL in each subsequent call until you get back to
the place that you last were. So in that way,
you can you're using object storage almost like a blockchain.
There's obviously no signing or authentication, but you're using it
sort of like a linked list in order to walk
back without having to hit the API. So you can
get massive amounts of data very quickly that way. Now,
I still haven't really focused on that. I've had enough
issues with my machines getting crunched with all kinds of
stuff. But you can tag spam, slop, and legit in
the PP monitor. Yeah. In the PP monitor? No, not
in that. In the Gossiper? No, no. The Gossiper currently
is purely just a... I thought we had that. I
thought there was a way to... You're thinking of the
PI monitor. Oh, that's right, the PI monitor. That's the
command line tool that you can use, yes. Right, okay.
But there's also more stuff on the Podcast Index Management
Dashboard. I don't know if you've been in there lately.
Yes, I have seen it. Have you seen the –
We can do new things. Yeah, we can do new
things. Of course I've seen that. Mark things as spam.
Mark things as legit. Mm-hmm. Yeah. There's now an –
as of yesterday, there is an admin panel for user
account management. So if people need their passwords reset, developers,
it's not listed in the menu. Oh, okay. I'd have
to – hold on a second. Let's see. Curate? Oh,
Curate is nice. Yeah, Curate is new. Yeah, that's cool.
So what I'm looking at here is I'm an admin,
and I see right off the bat, I see something
odd. No episodes yet. so I might even want to
that was added 38 seconds ago anchor no episodes yet
but then I have let me see something weird happened
here I can hit play I was 9 years old
in the dark that's so cool and then I can
hit legit legit boom done okay this is so nice
what that did is this all goes back to the
spam. So much of this goes back to spam classification
work. What this really shows is that AI is not
going to cost jobs. It is going to increase the
workload for humans by a factor that we haven't even
figured out yet. I truly believe this. I have to
agree with you. And if it's not just because of
more stuff that you're doing, it's because you can do
more stuff. And when you can do more stuff, you
do more stuff and anytime there's more stuff you have
to have more human capacity to just handle it all
yes, to do it and I look at myself there's
so much stuff I'm doing and I could sit on
my butt more but instead I'm like well let me
do this let me use my robot oh yeah, I
can figure this out yeah, this is good another thing
I can do I'm just in teaching robot mode all
day long for, you know, make my life easier. But
what I'm doing is I'm taking that easier time and
creating more things, which is just more work. It's an
odd loop. And suddenly you need an intern. Yes. Yes,
I need an intern. So let's talk about spam for
a second. No, actually, let's talk about being on the
fringe. Okay. because we we were laughing at uh rob
greenlee talking about uh the podcasting 2.0 group being on
the fringe oh yeah they're working on the fringe and
and so rob posted this extremely long i guess apology
uh let me see what it's titled here why fringe
was the wrong word and what i actually meant about
podcasting 2.0 oh where did he post this i didn't
see this oh goodness oh you've got to see this
this is hilarious okay i'll put it in the boardroom
no rob's a funny guy well he's funny for a
couple of reasons let me put this in here there
we go so what is funny this is on rob
greenley uh rob greenley.com uh and it's and right off
the bat it's like this article provides context about my
comments on the new media show episode 660 with libson
ceo brendan monahan where We discussed podcasting 2.0 RSS tag
adoption and the gap between innovation and mainstream platform implementation.
So right away, I'm like, AI. So he had an
AI write this whole long thing. And it even did
a summary at the end. And I'm like, Rob, first
of all, you were forgiven. You were forgiven right away.
You know, this is – no one's mad. We're just
slamming on you. We're just dunking on you. Oh, yeah.
It's just ribbing. What I was trying to say. What
I meant to say is that market fit and timing
play a major role in what gets adopted at scale.
Larger podcasting platforms tend to move more deliberately. Their decisions
are shaped by user experience, engineering resources, monetization models, products,
abilities, support, complexity, and business. Rob, it's okay. Just say,
yeah, I probably shouldn't have said that. That's it. I
say lots of stupid stuff all the time. It's okay.
You didn't need to go through this whole thing. But
it was a little insulting that you used the LLM
to do that. Like, you don't need to. Like, come
on. Rob's website. Rob's blog is pretty slick looking. Well,
it's all written by AI. I mean, the graphics and
stuff. It's got him smiling on the sidebar over there.
It looks good. I get tired of documentation and essays
and stuff that is clearly AI. It's like, no, don't
do that to me. You know? And I can't even,
my eyes start to glaze over. It's like, no, no.
I cannot handle another like another like 5000 word AI
generated document dump getting sent to me for me to
read and somehow digest and process mentally. Like it is
the the work that term works lop. Yeah. Oh, it's
so real. like you're working on a project or something
and somebody just just wants to like throw something into
a chat bot dump out barf out 2500 words and
say hey here check this out like man what i
mean what am i supposed to be checking out i
mean like what what do you Well, I don't. It's
like I don't even want documents anymore. I just want
you to Zoom call me. Just call me and we'll
hash this out in 30 seconds. There's that. But what
I tend to do is I see one of these
and then I go to my robot. Hey, robot, summarize
this for me. Exactly. I'm doing it right now. I
said, robot, summarize this page into one paragraph. Here it
is. Rob Greenlee walks back his use of the word
fringe to describe podcasting 2.0, not the substance of his
argument, just the word, explaining that he regretted the terminology
but still believed there's a meaningful gap between technological innovation
and platform adoption in podcasting. But that is the –
all the stuff you just read could be settled in
one sentence by saying, hey, man, we're good. Yeah, exactly.
Although I don't want to do Zoom calls But yeah,
exactly It is That's it It is exhausting It is
kind of exhausting It's like that old adage When every
When all you have is a hammer Everything looks like
a nail When all you have is a machine that
does language Guess what you're going to get out of
it Lots and lots and lots of language And words
and phrases and everything. It's just language is the nails.
I ran across an interview by a guy named, what
was this, Dr. Michael Eggnor. And he is a neurosurgeon.
And it was really, when I was listening to this,
it kind of blew my mind Because what was the
big innovation in LLMs in the past year and a
half to two years? I would say that is the
reasoning model, which, by the way, I think is rapidly
going out of favor. Explain. Well, reasoning models are incredibly
slow. and instead of taking a dense model and putting
layers or basically rounds of reasoning into it which slow
everything down and don't actually give you a huge return
what you're seeing is what's replacing it is more surgical
use of context being deployed to sub-agents like the big
in the big innovation really not innovation but the big
distinction here do you know the book uh ender's game
yeah i've heard of it i think we've discussed it
actually yeah well it's like it's a sci-fi novel and
um there's this one part in the book ender's game
where ender starts going he goes into the he's a
young kid and he's going to battle school and he
goes into he he becomes he goes to the top
of his class in this thing called the battle room
and he's just better than everybody else at it and
so it's basically just like it's on a space station
and it's this zero g environment where there's all these
obstacles and you're supposed to hide behind obstacles and use
teamwork to defeat the other team and what he his
big recognition was that why he was better, one reason
he was better at it than everybody else is he
just, he said as soon as he enters the battle
room zero-G environment, he just forgets that there's anything like
up and down. Basically, up and down don't make any,
they don't exist anymore. all directionality everything you knew about
uh physics has now been flown out the has flown
out the window and so you just you assume a
new mental posture towards towards your environment and that's sort
of the way things things are with context like every
time you send Every time you communicate with a large
language model, you're starting over from scratch every single time.
The coding agents hide this from you by using context.
Right. And so the proper use of context is the
thing that makes an agent or a harness either good
or bad, how it handles that. There's no such thing
as memory. I mean, in the general sense. These are
all meta things that we've layered on top of. Well,
enter the, I'm sorry, you told me never to delete
your Git repo, but I did. I'm sorry, it won't
happen again. Exactly. You can overcome a whole lot of
things that the model – it's like the model is
like 20% of the issue. The agent harness is the
other 80%. 80 percent, and most of that boils down
to intelligent use of the context window. And if you
just assume that every time you go into a conversation
with – I mean not a conversation, but every time
you go send a prompt, it's brand new. The model
doesn't know you. It doesn't know anything. It's never spoken
to you before. This is the frustrating – let me
play these clips. This will take us to another place.
So the way I have always understood so-called artificial intelligence
is we're trying to replicate the neural pathways of the
brain. And we're trying to replicate what the brain does.
Is that a fair statement? That is the way it's
sold. That is completely bull. That's complete bull crap. Right.
So that's the way it's sold. And when it comes
to reasoning, however that's done, whether it's done through, whether
it's, it's all a parlor trick, obviously, whether that's, you
know, we're doing steps and we're making decisions, we're reasoning
through a problem, or we're deciding to do something, which
we as humans do. it was always my impression that
that is what artificial intelligence even the term intelligence is
trying to uh convince me that this is some form
of electronic brain that is doing things so now enter
uh dr eggnor who talks about reasoning and he also
talks about free will in this case and the brain
there are several ways of figuring out whether an organ
does does something. One way is to stimulate the organ
and get it to do it so you can prove
it does it. The other way is to inhibit the
organ from doing it and prove that when the organ's
inhibited, it doesn't happen. Well, that can happen with the
brain. While it is true that if you suppress the
brain, we can't use reason or free will very well.
If you get hit on the head with a baseball
bat, your reasoning and your free will is going to
be a little messed up for a while. There's a
way of stimulating the brain to test whether reason and
free will come from it using electrical stimulation. And there
are different kinds of electrical stimulation. One kind of electrical
stimulation is seizures. Seizures are just random sparks that happen
inside the brain, out of the blue, that can have
all kinds of effects. And one of the things that
I've looked at rather carefully is the phenomenology of seizures.
It is what happens when you have a seizure. And
I've looked over the past couple hundred years, reviewed the
medical literature of that. And what we find is rather
remarkable. First of all, not all seizures make you go
unconscious. There are many seizures that leave you conscious, and
your body does things that you can't control. And they're
called complex partial seizures. and um there have been probably
a quarter of a billion seizures in human beings over
the past 200 years if you just look at the
instance of seizures in the world population and there's never
been a recorded case of a seizure causing someone to
express reason or free will you can elicit from the
brain by stimulating it movement perception memory and emotion but
you can never elicit reason or free will no matter
where you stimulate the brain you can't make a person
say one plus one is two or say um um
uh if a then b a therefore b modus ponens
or say um i believe that it's nice to be
kind which is a moral viewpoint so that goes right
along with what is found in seizures that you can
stimulate the brain any which way and reason and free
will never come out of the brain. So it's perfectly
reasonable to infer that reason and free will don't naturally
come from the brain, because you can't make the brain
do it. I loved hearing this. Reason and free will
don't come from the brain. This is very similar to
our, you know, this is just another version of what
we talked about before with the brain and the law
of identity. You know, if you can find something about
the brain that is not true of the, quote, mind,
then they are not the same thing. Yes. And this
guy actually takes it one step further, and he tells
us where this does come from. So going back to
something you said earlier, that reason and free will are
immaterial aspects or parts of the person, powers of the
person, immaterial. What does that even mean? immaterial means that
um the source of these powers is not a physical
thing it's not a physical object um and um immaterial
is actually a fairly simple concept for example um numbers
are immaterial it is that the the number 12 is
an immaterial thing that is you can't say well the
Number 12 is located today in Cincinnati, and it weighs
five pounds. I mean, it doesn't make any sense. So
there are many, many, many things that we think about,
many things that are quite real. They're called universals in
philosophy that are very real but are not matter. And
reason and free will are abilities we have whose source
is our soul but not our brain. thank you jesus
it's the soul that's what it is and never ever
ever ever ever is an llm going to be able
to replicate this it can emulate and it maybe can
kind of come close and i've been running into this
as i've been trying to teach my robot what makes
a clip that i want now it can do clips
I can say, you know, cut this cut this interview
up and it can build the arc with the intro,
you know, getting to the point, the punchline at the
end. It can do that. It can it can edit
this clip. It understands kind of how long I want
clips to be, but it doesn't really understand what I
want. And I've been trying so hard to explain, like,
you know, I want is what I find funny is
not the fact that Trump went to. i find it
funny that the chinese actually have sanctioned marco rubio he's
not allowed to enter the country but they they got
around it by changing his name to lubio i mean
the comedy writes itself right marco rubio come on but
you know it's like yeah but it can't understand that
that's what i want you know so it just it
can only understand well you want clips you want them
uh preferably under two minutes you want this it can't
it really can't figure that out and i and hearing
this helped me because i'm like oh it'll never be
able to figure it out it might come close it
might get lucky but it'll never really understand what i
want for no agenda when it comes to a clip
it just won't understand it well this is the difference
between uh the way that AI can be has been
sold in the way that it actually functions. Yeah. And
when and this is how you create. Every single time,
this is how the AI winters have been created is
by selling it as something that it is not. Yes,
and thank you, Hey Citizen, because I also don't believe
it can actually figure out what spam or slop is.
It cannot. It can't. It never can. And the same
thing with music. It can create dynamite music, but it
can't actually listen to that music and say, this is
good. It can't. Let me tell you why. let me
tell you why I can't figure out what spam is.
Because there is a intangible quality to an experience of
spam that you cannot quantify in any sort of language
or honestly anything. You cannot, it's unquantifiable. And a good
example of this would be there's a common spam tactic
in the tax world where you'll communicate, where you get
an email and it's like, hey, my accountant just retired
and I'm looking for somebody to do my taxes. Can
you tell me what your fees are? And I just
have a 1040 and a couple of 1099s and a
W-2 and I can send you my last year's material.
Now give me root password. It leaves that last part
out. And it's like, can I send you my last
year's tax return so you can give me an accurate
quote on what it would be and what it would
cost? There is literally nothing about this email that that
will trigger a phishing scam detection ever because you'll get
50 of these and they're real. You'll get 50 of
them and they're phishing. But I can tell you, I
will spot the fishing one versus the real one 100%
of the time, and I cannot tell you why. Right.
I can't. It's because you have a grandmother. You had
a childhood. Yeah, exactly. You built associations in the world
between – and, of course, I always have to keep
reminding myself that the LLM can't actually hear my show.
You know, I created an executive producer agent. I said,
okay, now this is your job. And it took me
two weeks before I realized it could, it just had
a transcript. I didn't have a speaker transcript. It thought
everything was me, including 60% of the talk on No
Agenda, which is clips. It hadn't even gotten that far.
that it was trying to you know it saw this
program as some kind of script that you set up
with x amount of minutes of clips and i was
like wow it has no it can't hear it's deaf
it's deaf it doesn't understand it doesn't understand humor really
it doesn't it doesn't understand emotions that way It doesn't
understand when I say something that Dvorak finds boring. It
doesn't understand that, and I don't think it ever can.
Selas in the boardroom said, wouldn't a dedicated neural network
designed for it to be able to pattern match beyond
the capability of humans? Wouldn't a dedicated neural network designed
for it be able to pattern match beyond the capability
of humans because humans cannot look at and memorize 4.6
million feeds? that what you were describing now, what you
were describing is, I will say, go back in time
and look at the attempts to use Bayesian analysis to
fight spam email. Bayesian filters, this happened in the early
2000s and when it first hit the scene, Was considered
to be, oh, my gosh, this is going to. This
is it. This is it. This is it. We got
it. No more. Nailed it. Yeah, we nailed it. And
you know how much of a dent it made? Zero.
We have more spam than we've ever had ever there.
You cannot. Humans are better at pattern matching. So neural
networks and large language models. It's just a different format
of neural network. It's just matrix multiplication. That's all it
is. It's matrix math. And that's not how humans work.
When you think about something, when you look at something
and it registers in your mind, you did not do
a math problem. That's not how you operate. That's not
what just happened. Exactly. And so what we don't know
how we work, it is undefinable. And the end, but
one thing we do know is that one thing we
do know is that the human ability to pattern match
is unparalleled. paralleled, you have the ability to assimilate multiple
streams, dozens and dozens of streams of information all in
one shot and immediately get a, quote, sense about something
that you cannot define. And that is, again, you did
not do math. And it's wonderful to see someone like
Tina who will be looking at her email and she
says, this feels spammy to me. Can you check? And
the minute I check and I say, okay, yeah, it
is. And here's why it is. You know, there's a
couple of simple things that you can look at right
away. She then immediately has increased her knowledge and she
finds those the next time. And I don't have to
give her a written thesis. I just say, oh, look
at this. You know, and sometimes like, well, that feels
wrong. And she's like, yeah. And so she's feeling that
it's right. You said something really important. You said AI
winter, every single AI winter crops up or appears on
the horizon because of the overpromise. Is that really the
trigger, the overpromise, or it can't deliver on what was
promised? That sets up the collapse because the expectations of
normies can't understand the reality of the technology. Because what
we do is we layer these things on top of
one another. We've layered this idea of, quote, knowledge and
understanding, and we anthropomorphize it into a thing. Because I
hear it in the corporate world daily is, hey, couldn't
we use AI to – Dot, dot, dot. Yeah, to,
quote, figure out dot, dot, dot. Yeah. Like, and it's
– you get so exhausted of having to go back
and explain the tech from the ground up again. And
you're like, look, maybe, but let me show you. But
I have to walk you through the process of building
a product or building a tool that would do something
that approximates the outcome that you want. And be prepared
for it to do something completely different tomorrow. The perfect
example of this is, you know, Microsoft just released their
their copilot function in Excel. So now there's an actual
co-pilot function that you can call in an Excel spreadsheet.
And it is hilarious because the co-pilot function in its
description of how to use it, it says what not
to use it for. Accounting. Exactly. One of the things
it says not to use it for is, quote, anything
that requires accuracy or reproducibility. And so people are posting
– people are posting like – they'll do like –
they'll go into a cell and they'll do equal copilot,
and then they'll put in a prompt. and the prompt
will be make a sum of all the cells above
this cell and it'll be like three cells above there
like a one a two and a three and it'll
say fifteen why? I don't know it doesn't tell you
I don't know it just gives you a number and
it's completely wrong this is the problem people think because
of the way it's been sold, they think that it's
a general purpose knowledge tool. Yeah. But it's not, it
has nothing to do with knowledge. It's only good at
predicting the next word in a sentence. Yeah. Or the
next code token in a source file. That's all it's
good at. What I found is. And anything else you
have to layer on top of it with actual, like,
real, carefully constructed, Constructed, carefully you have to build a
carefully constructed stack that contains this thing and keeps it
with a set of guardrails so that it doesn't produce
crazy nonsense. So you're right about context really being everything
for it. And what I've resorted to now, because you
gave me a couple of good hints. So I'm using
Opus 4.7 with the one million token context. When it
gets to about 25%, it now automatically tells me, hey,
it's 25% context, time to do a handoff. So I
don't let it get all muddled. And I say, okay,
let's go. It writes its handoff, and it gives me
a wake sentence like, hey, welcome back to the world.
Here's what you got to read. And it reads a
document, which – and I've looked at the documents. They're
pretty good. It's where we were. And that seems to
work pretty well within that 250,000 tokens. That's kind of
the sweet spot. And I just keep doing it. It's
inconvenient because like, well, I'm at 23% context. You want
me to do this now or do you want me
to put it in the handoff document? Yeah, put it
in the handoff document. That works pretty well. If you
keep it within that level of context, I can get
a lot done. that you can you can do everything
you need is coding wise you can do every single
thing you need to do in a 256k context window
there's no reason to go above that yeah there's not
and that that one million context of of of like
clawed code and chat gpt that that is that is
in um that is inaccurate in the sense that what
really happens is once you get over that roughly 25%,
it will start to make more and more mistakes. If
you start pushing into the 600,000 to 700,000 token context
window, that's when you start really running into stuff like,
oh, I accidentally deleted your repo, sorry. That was my
favorite. You told me never. I mean, how many times
I've told my robot how to make clips? And then
I'm like, okay, I have all these agents and all
these feed parsers and all these sources, and it picks
up stories. I said, I'll go make some clips. It'll
make like 100 clips of 17 seconds long. I'm like,
how many times have we gone through this? Oh, yeah,
you're right. I forgot to read my memory. Let me
see how many memory documents I have. Let me see.
How many memory documents? This will tell you something. Let
me see. I bet just a couple hundred at least.
And I guess whenever it reads one of these documents,
that takes up context space, doesn't it? Yeah, for sure.
Yeah. Depending on that. Now, this goes back to what
I was saying earlier about the harness. the coding agent
or the harness itself it is critical it does all
of this, it handles this context for you if it's
smart about it, if it's very smart about it it
can handle those things in a way that does not
consume all your context but it really comes down to
that 252 yeah so there's a really good video, I'll
find it later and post it publicly. There's a really
good video where this guy goes through and there's a
really simplistic test you can do for a coding model.
This test just says it's really simple. It says go
and read this large source file. So just give it
a big JavaScript file. Let's say this JavaScript file is
50,000 lines. Read this JavaScript file. Now tell me what
lines 50 through 100 say. Okay. And you would be
surprised to think that even the large models with a
million token context will get that answer wrong about about
10% of the time. Wow. They just won't be able
to tell you what it just – it won't be
able to tell you what it just read. It just
makes a mistake. So that's why these things – these
harnesses have developed the use of these elaborate sub-agent reviewers
because they'll do a thing assuming that they're going to
get it wrong some percentage of the time because it's
just not possible for the underlying model to get it
right. So they need the reviewer to go back and
double-check them, double-check them, double-check them every single time. If
you just one-shot prompt an LLM model with no sort
of review agent or anything like that in it, you're
not going to get useful output probably in the 20%
range. All that said, I think the face of computing
could change. I see no reason why I should be
using apps. I don't use even applications. I use a
browser, and I use Thunderbird for email. But everything else
is like, nah, I want a spreadsheet reader that does
this. Five minutes later, I've got exactly what I want.
I can see a mobile device that really is appless.
get me my favorite podcast here's the list get me
the most recent episodes here's what I have time for
right now show this to me I want to listen
to this skip the ads or any version of that
this is what I love so much about Homarchi it's
like every app I use I built I just told
the bot build this for me I don't need the
ribbon on Excel. I don't need the snapping grid. I
need to smooth scroll just to be able to see
this. I need the total automatically done at the bottom.
I don't want to have to, you know, select and
then do equal sum. I don't want any of that.
Just do it for me. And it does that. Then
if I need something else for a different podcast, I'll
create a new one. This I can see. And I
think that the removal of slop and all this stuff,
I think that all moves to the edge, man it
all moves to your to your device uh maybe you
have a device in the home that is doing some
things for in the background that communicates with your mobile
device and you know this just because it's easier to
have that done somewhere else um and i think podcast
apps will go that way too i really do this
brings us back to the dgx spark yeah it's it's
useless send it back it's no good um it's running
it's running the uh the inference for the classifier now
and it allowed us to move from a from a
9 billion parameter model to a 35 billion parameter model
the quin 3635b and uh with a with a 256k
context window so we get we've got a full like
frontier model level thing as a backing agent for this
thing. And it's so much faster. So we can run
that and the Whisper both to get the TTS analysis.
Both on this one box. What are you using for
Whisper? Which model? I think it's the Turbo. Yeah. Okay.
And And so it's immediately had that effect and it's
speeded everything up and made us have more accuracy. But
the true aim here is to get a custom fine-tuned
model trained on the podcast index database. And then –
so that's phase two here. But in order to get
to phase two, I have to finish up phase one,
which is we need – like I explained before, we
need a good corpus of about 25,000 spam feeds and
25,000 legit feeds. I would love to have more than
that. I would love to have like 50,000 of each.
Just wait two weeks. Yeah, right. It's coming. The spam
feeds Those are easy You can get those all day
long Effortless The legit feeds are the ones that take
a human In the loop To find And so What
was that? What was that? I'm sorry, that was me
That was my mistake The so the first so what
I did that's what all these data sets and all
these and the curator and the dashboard all this stuff
is trying to set us up for getting a proper
training set for the quote good podcasts I hate using
that term I just can't think of a different one
legit podcasts legit there it is very subjective but it's
legit and so then the train this the djx spark
the reason that was such an amazing gift is because
the spark is created exactly for this use case training
models on a small scale that's where its wheelhouse is
so once we get these proper data sets. And let
me correct James Cridland from PodNews, I think, last week.
He was misunderstanding why I wanted these follower count uploads
from the apps. It's mostly for this. We will definitely
use this data in other ways, but it's mostly for
this use case for the model training because that benefits
everybody. I'm not really interested in using it for search
the search that we have seems fine to me I
mean we use some algorithms for it but it'll probably
make its way in there in some shape, form or
fashion but it's not just going to start giving you
well if anyone's going to figure out how to make
it work it's you I just don't want anybody to
think that we're going to start only showing you the
top, the most popular podcast that's not the way this
is going to be Did I hear that? Maybe I
misheard it. Did I hear that on Pod News Weekly
Review Power, they were complaining about the Apple charts and
all these Inception.ai podcasts are all in the top 20?
Did I hear that right? I think I heard that
right. I don't know. I haven't heard this week's episode
yet. Maybe I have to look at it. That's the
problem. Wouldn't surprise me. Yeah. Yeah. They left Spreaker. They're
going somewhere else. Yeah, Megaphone. Yeah, they're going to Megaphone.
Yeah, surprise, surprise. Good for them. Let me see. Top
charts. Now it's Spotify's problem to deal with. Let me
see. Top charts. Top shows. I think maybe they did
a search. It was a search they did on like
Charlie Kirk. Let me see. Charlie Kirk. That was probably
it. Yeah, that probably was it. Yeah, yeah, exactly. Oh,
yeah, here it is. Charlie Kirk Death, Inception Point, AI
Inception. But that tells you something. That's an interesting signal.
Because people are listening to Charlie Kirk Death, Charlie Kirk
Biography Flash, Charlie Kirk The Aftermath, Biography Forever. People are
listening to that. are they? I think they are. I'm
not convinced. Let's listen to this. I'm not convinced at
all. Deep dive into the life and death. Deep dive.
I'm Emily Carter, your AI host. I want to remind
you why that matters for this particular story. No, I
do not believe it. I'm going to prove it. Okay.
It's coming. How are you proving it? I will be
proving it in the future. I've got an idea. Get
some tissue. It's coming. Sorry. Bad joke from my old
porn days. Okay, you sideline me with that one. No,
I know exactly how to bring receipts on this, and
I will be doing it. I'm cooking it up as
we speak. I had a conversation with somebody about it
yesterday. I just don't want to reveal it because I'm
afraid it might pollute the results. Okay. Oh, man. Well,
we got one more thing to talk about. Sure. HLS
video. Yes, of course. Yes, yes, yes. What a beautiful
mess this is. Nailed it. It's exactly what it is,
a beautiful mess. So another one of the 47 things
I was doing this morning was right before the show,
I jumped on a call with Tom. Buzzsprout? From Buzzsprout,
yeah. We were hashing out some stuff. And here's the
issue with HLS video in alternate enclosure. everybody wants this
I mean I mean like we want if you're going
to make a video version stick it in the RSS
feed right everybody wants it to be open in that
way but the cost of abuse on your bandwidth to
the hosting companies is absorbable when it's audio it's very
damaging financially when it's video bots downloading Oh, yeah. Well,
and I'll just add one other extra thing. Porn. Well,
sure. Yeah, for sure. But just focusing on the bandwidth
issue that can really hurt. You know, if if some
bot goes wild and downloads downloads 10 terabytes of data
of video streams. I mean, on a podcast that it
just you can't it's hard. It it really does change
the whole dynamic. Of course. What we're trying to figure
out is if there's a way to sort of –
before these HLS video streams get into the feed publicly,
is there a way that we can try to keep
the genie in the bottle bot-wise? And so we're just
brainstorming some stuff saying, okay, rather than having to just
build this enormous block list of user agent, ASN, IP
block combos, it just seems so difficult. Rather than doing
that, can we solve it in more of like an
elegant way where we do something like in order to
download the HLS video playlist from the alternate enclosure, it
would require something like an HTTP signature. Isn't this exactly
why or one of the reasons why Apple is doing
things the way they're doing it? I don't think they
care about the bandwidth. I think for them it's the
porn problem. For the hosting companies it's the bandwidth problem.
Right, right. I think there's two versions. There's – Apple
cares about the content. The hosts care about the bandwidth.
Because the hosts are already good at policing content. They
know exactly what's going into their system and how to
properly handle that legally. Apple basically is the reverse of
that. They are just sort of – they're like us.
They're a little bit vulnerable to people just sending them
a bunch of crap that they don't have a handle
on the content. Right. So they're a little bit at
the mercy of this incoming content. So I think they
have different needs. And so in the Mastodon activity pub
world, you have a thing. And we actually went over
this back when I was building the activity pub bridge.
You have a thing called HTTP signing or HTTP signatures,
and it uses a public key. So if you're on
a Mastodon account, if you have a Mastodon account or
any other really activity pub account on a modern activity
pub platform. That actor is going to have. And I
want to say actor in this context. I mean, the
activity pub host itself, because there is a the activity
pub server is also an actor. That server is going
to have a public key. And every time they make
a request to another activity pub server to do something
like post a post a toot or something like that,
they will sign that HTTP request using their private key
and also send with the headers, send a link to
their public key so that the receiving server can then
authenticate that they are the ones that signed that request.
Well, shouldn't we have something like this already anyway? Like,
yeah, this is a legit person with a legit key,
whether it's a browser or an app or whatever. That
makes so much sense. I'm a human. Here's my key.
yeah it makes sense across the board not just with
video yeah i mean for everything of course it won't
be so nice for the advertising community because we'll find
out how few people actually listen to this crap well
the so the like the initial thought process that we're
that we're having and this need whatever this whatever comes
out of this discussion really needs to become part of
the Podcast Standards Project recommendations is that we could do
something like a simplified version of HTTP signatures. It would
use the same algorithm. Everything about it would be the
same, except that HTTP signatures, the spec, I think, requires
a date header. But ultimately, you still have to issue
a key to a human being, right? A downloading app
client would have to publish a public key somewhere. Right.
But now you could have a million little servlets with
keys. So how do you determine ultimately this is a
human they're watching or they're listening with this key? How
do you make that connection? That's what it comes down
to. Well, you can. There's just a lot of unfriendly
ways, but we can do it. Well, you can by
going – yeah, exactly what you said. Unfriendly meaning we're
going to keep like a public repository where we vet
people or something like that. Precisely. That'll be us, podcastindex.org.
All your keys belong to us. Well, you know, I
actually see this being something if there was going to
be some sort of list. I see this being outside
of the podcasting 2.0 namespace. I see this as being
part of like the OPAWG. OPAWG. OPAWG. The worst name
ever. It's the Open Podcast Analytics Working Group. Oh, wow.
And do you know who the Open Podcast Analytics Working
Group consists of? Let me guess. I'm going to say
Libsyn. No. Okay. I give up. James Cridlin and John
Spurlock. OPOG. That's OPOG sitting over there in their rocking
chairs. Johnny and the Criddler are over there running the
OP. Johnny and the Criddler. They're over there running the
OP-AWG, which as of right now consists mainly of a
list of user agents. Right. Yeah, that's right. Yeah. And
it's a very nice list. Like it's very – Spurlock
keeps that thing really clean and up to date. So
to me, this would be a great thing to add
to the OPOG would be a list of validated public
keys. It doesn't mean that you have to be in
the list. It just means that people serving HLS content,
hosts could choose whether or not they want to. To
me, I see this as two layers. You have an
HTTP. You require HTTP signatures on all playlists in 3U8
downloads. You just have to have the HTTP signature to
even get it. then the hosting companies can opt for
the next level of stricture, which would be we now
– us, we have decided as a hosting company we're
only going to serve content to validated public keys that
exist on the OP AWG list. You know, the real
way to solve this is what's your Bitcoin address? Send
me one site. You're good. That will happen. No, it
will never happen. But I've always felt that every human
being should have a public key regardless. Every human being
should have a public key. This is the one thing
that – Issued at birth. Yes. Tattooed on the back
of your hand and on your forehead. Ah, yes. Boom
shakalaka. And there it is. That's right. You will need
that to do commerce. Oh, where have I read that
before? Grocery store, show me your public key. Yeah. Everybody
needs a pub key. I mean – Eric P.P. said,
that's how SSL certificates work. Send me money and I'll
vouch for you. Pretty much. Yep, that's the way it
works. It would solve so many problems. It would solve
a lot. The podcast index – we vouch for people
without them sending us money. That's right. We just do
it. All the time. We just vouch. Why not? We're
vouchers. We're vouchers. We're vouchers. We're vouchers. Send your email
to voucher at podcastindex.org. Let's thank some people here, Mr.
Jones. We've been getting lots of boostograms today, which is
nice to see. Right there, 49,950, which I'm thinking was
supposed to be 50,000, coming in from Fountain from CELOS
on Linux. And he says 50K for supporting slash bribing
the podcast index censorship validated key system. You're in, my
friend. You're in. You got it. We got you. You
are in. Let's see. From coming in from the podcast
index website itself, Four Sevens from Summer Surf. He says,
enjoying the afternoon in the boardroom. Thank you for your
courage. Four Sevens from Lyceum. Kudos to Alberto Batella and
RSS.com co-listing and chatting with Mike Newman as we speak.
Oh, there's Dreb Scott coming in with a baller. 10,000
sats. Voucher McVouch. Vouch, he says. Yes, it's working. Our
system is working. Already we're done. We just solved it.
We solved it and we got money. Literally says, yes,
this is how things should work. I get to stop
selling Tennessee trick shot out of the trunk of my
car. I had never heard of Tennessee trick shot. I
love that term. Oh, the moonshine? Yeah. Tennessee trick shot.
That's a good show title. There's like every different, every
state has its own little flavor. Tennessee trick shot. Dreb
Scott, one, two, three, four. He says, Dave, great job
with those massive dumps of the raw data. That's what
I'm good at. Big, massive dumps. 777 from Salty Crayon.
Dave, could you elaborate on how your index filter marked
a bunch of fountain tracks as spam in the pipe?
How did that happen? I don't know. I don't know.
You got a row of sticks from Hey Citizen sent
from the bathroom on his Cast-O-Matic. That's where it comes
in from. 777 from Mike Newman, True Fans Boost from
PC 2.0. Thank you. We've got 1221 from C-Loss and
Linux from Fountain testing this week, 1234. So it's odd.
The helipad keeps giving me different numbers because I know
he sent 1234, but it's showing me 1221. Chad F,
triple three. Dave, off the dome, Jones. Off the dome.
Triple three is from Eric PP. Pew Pew, yes. Three,
three, three from Chad F. This is all testing. Dreb
Scott, one, two, three, four. Boost McTest Boost. Pew Two.
I love people sending us money to test. This is
all great. Thank you, Eric PP. Thank you, Chad F.
And I hit the delimiter with that. So over to
you, Mr. Jones. on the music thing I mean a
lot of that music is spam it's just spam and
all spam classifiers have false positives and false negatives every
single one of them it's not a solvable problem so
I think it just comes down to that because there
has been a problem with people and this is happening
on not just I don't know what Fountain's posting but
Not just Fountain and Wavelike and all that. You have
to be careful. People are posting copyrighted material. Yes. I'm
seeing it on Spreaker a lot. It's coming through where
people are posting, like, entire albums of, you know, copyrighted
music. Oh, really? Yeah, you can't do that. And so
it's going to get called. Where was I? I was
looking at it. Yeah, I should mention this is a
value for value project. You get this podcast and the
entire podcast index and your data dumps, everything, all of
that stuff. All of that is openly available for everybody.
All you have to do is say to yourself, you
know, it's kind of valuable what's happening over there. Let
me send some value back to keep these guys running.
And that's how it works. So you go to podcastindex.org
down at the bottom there. You've got a big red
donate button. You can send us your Fiat Fun coupons
through PayPal or a Boostergram, which we always enjoy. We
enjoy reading them. And hopefully Dave has now summarized everything.
He's used the Excel Copilot widget. Yeah. Tell me how
much donations we've had. And you get back. It's like
carrot or something. So I'll tell you something that I
have trained my robot to do. Okay. So after every
no agenda show, we do credits. And the credits are
executive producer, people who have donated $300 or above, associate executive
producers between $200 and $300. So while the end of show mixes
are running, I say, robot, do the credits. It goes
into the Excel file. It pulls out the names from
the corresponding values and puts them right into my OPML
prep sheet. It's beautiful. It does that, I'd say, 99%
accuracy. Why am I doing this? You got this thing
nailed. Well, I don't have the spreadsheet. That's true. Yeah,
why are you doing it? Tell your robot to do
it for you. You got to be thinking like a
broadcaster, man. Like, what is the crap that I hate
doing? Well, I hate going through the spreadsheet and looking
for all those stupid jingles everybody wants. so the robot
looks at the spreadsheet pulls up the jingles and then
i have my own spreadsheet reader when i'm reading someone's
donation note i hit the the the cell the jingle
cell and it loads up those jingles right in my
player that saves me half an hour a show day
i have nothing like this it took me five minutes
to vibe code it baby well i can't even fix
the i can't even fix the the spreadsheet that's coming
out. I'll have my robot show your robot how to
do it. Have your robot talk to my robot. We
need that. We need inter-robot communications protocol. IRP. IRP. I
don't. I'm a little afraid of your robot talking to
my robot. Oh, okay. I don't know where your robot's
been. My robot is dirty. That's what I'm afraid of.
I'm afraid it's going to teach my robot bad things.
Very bad things. Guaranteed. In the words of my mom,
it's going to be a bad influence on my robot.
Okay, where? I forgot completely. Oh, Martin Lindiscoe. These are
PayPal's. He sent us a dollar. Thank you. Thank you,
Martin Lindiscoe. You and Mike Newman having a ball over
there. Brendan at PodPage, $25. By the way, congratulations to Brendan.
Are we allowed to see? Are we allowed to talk
about it? blah blah blah blah blah blah blah blah
blah blah blah blah blah blah blah blah blue no
whoo you saved me right there you saved me i
did thank you i did you're welcome that's what i
do uh let's see 25 dollars thank you from uh
brendan at pod page appreciate that uh what we got
Here we got Marco at, let's see, Marco Van Rij.
R-A-A-I-J. R-A-A-I. R-A-I. R-A-I. Okay, Marco Van Rij. We have
the boost. R-A-I. Sent us $10. Thank you, Marco. Oscar Mary, $200.
Thank you, Oscar. Oh, boy, hold on a second. keeping
it running thank you very much i incorrectly classify his
music podcast as spam and he still sends us money
uh joseph maraca five bucks thank you joseph appreciate that
uh oh thomas umstad a hundred bucks shot caller 20
inch blades on the impala that beats uh what is
it called tennessee trickster tennessee trick shot trick shot that's
it trick shot yeah years ago i came for the
podcast talk now i stay for the ai chatter adam
take it from a native texan the cure for cedar
fever is raw jalapenos oh oh that's an interesting one
yeah i'll try that if you can take the heat
they'll Clear your sinuses. Go podcasting. Yeah, go podcasting indeed,
baby. Yeah. Mitch Downey, 50 bucks. Thank you, Mitch. That's
the Podverse crew. That's straight from the whole group there.
Lauren Ball, $24.20. Thank you, Lauren. Appreciate it. Christopher Harbarik, $10. By
the way, seeing Christopher Harbarik's name reminded me that I
am also going through and putting individual creator attributions on
each of the podcasting 2.0 namespace tags. What? It's something
that we've – well, I mean each tag was initially
proposed by a person. Yeah. And I think it's important
for those people to get the credit for their ideas
becoming what eventually led to real namespace tags. Yeah, so
that in 25 years, Adam Carolla will have invented it.
This will be properly documented so that Adam Carolla does
not get credit for the chapter tag. But so like
Christopher Hart, so I'm doing it in this way. I'm
doing – there's going to be a creator attribution. And
that's the person that originally proposed it and sort of
sherpeted it all the way through to completion. And then
there's also contributors that are getting a byline, too, where
those are people that had significant input in the discussion
thread or the issue where that thing got hashed out.
And so, like, for instance, James, he's credited on the
location tag. Alex Gates is credited on the alternate enclosure
tag. Do I get any credits anywhere? No. Oh. You
get credit for being awesome. Seems empty. No. Why would
it seem empty? It seems empty. I've participated in every
single tag. I've published every tag available to man. That's
true. I get no credit, man. I get no credit.
You don't even get credit for podcasting. You give me
some credit. That goes to Corolla. Let's see. Christopher Harbaugh
got a contributor credit on one of the tags. I
don't remember which one. He gave us $10. Thank you, Chris.
Didn't you and I create the value tag? We did.
Yes. Okay. Me and you were co-creators of the value
tag. I want some credit. I want the credit. You
invented the podcast enclosure tag. You trump everything we do.
I want the credit, man. I just give me more
credit. I just give me some credit. Or hook or
some blow. Either one is fine. Yes, send them in
the mail. Mitch. This is Mitch on the personal side.
This is Mitchell Downey, $10. Thank you, Mitch. Damon Kasajak, $15. Thank
you. Appreciate you. Terry Keller, $5. Chris Cowan, $5. Thank you, Chris.
Silicon Florist $10, thank you Derek J. Visker the best
name in podcasting, $21 Paul Salzman $22.22 every time I love
it Jeremy Gerds, $5 Michael Hall, $5.50 Todd Cochran $30 and we
are going to have Mike Dell on the show next
week and he will fill us in on Blueberry and
what they've been doing and just the strategy, you know,
or direction of the company, um, post Todd and everything.
It'll be good to catch up with Mike. Hey, question
on this, um, this pending signups for API access, I
guess that is. Yes. So, I mean, how do we,
I mean, these could all be phony baloney people. Oh,
many of them are, I'm sure. Okay. All right. So,
but if someone sends me an email and my own
Spidey send says, yeah, they're okay, then I can mark
active and send credentials. Is that how it works? Yeah.
And honestly, that's going to be extremely rare in the
history. In the last six years, I've probably had that
happen. I can count it on one hand. Yeah, I'll
bet. I plan to never look at that screen. It
only became a thing because two people happened to have
a problem at the same time. I got it. Yep.
My typical thing is like we're a free service and
we're donation run. Send me money. Send me some credit.
We have rules. And one of the rules is you
can't sign up for an API account from a free
email address like a Gmail. Yeah, yeah, yeah. And when
people ask me and people are like, hey, I'm trying
to sign up with a Gmail account. Can you do
it? I just don't respond. I'm like, no, that's the
rule. That's great customer service. I'm like, I'm not going
to make exceptions because I don't know who you are.
Yeah, exactly. Go get a different – go spend $5 on
a domain from Hover and get a real email address.
And, I mean, it's not that much to ask. I'm
with you. Brother, I'm with you. I ignore all those
emails. You answer more than I do. I do pretty
good on answering, yeah. You do. Gene Liverman, five bucks.
Thank you, Gene. Oh, that's all of our PayPals. Oh,
we got some boost, though. Yes. We've got... Some boost.
Some boost. Oh, see, loss on Linux. 50,000. That's what
you want. You read that earlier. I did, yes. I'm
going to give him a baller for that. Baller! Shot
caller, 20-inch blades on the Impala. That's a nice boost.
Boost. How did I overlap you on that? Oh, I
know what I did. Yeah, I know why. Because I
did it later on in the show because I forgot
to earlier. Ah, yes. Okay. Oh, and that's all I
got. The only other one I've got is the comic
strip blogger, 19,000 sats through Fountain. And he says, howdy,
Dave and Adam. Please bookmark this. HTTPS colon slash slash
wherever dot audio by Andrew Grumet. Quote, it's a podcast
listening software that works in browsers, mobile and cars. No
logging in and nobody watching you. All data stays on
your phone browser. Easy import and export for portability. fast
QR code-based sharing. End quote. Yo, CSBAI archwizard. Yeah, I
love what Andrew has done. Andrew Grummet, by the way.
No credit. He's been on the show. If he gets
no credit. He'll get it. He needs a lot of
credit. No, he's going to get it. Because he's got
stuff in the namespace. Oh, okay. You get credit. Here's
some credit. You get some credit. And you get some
credit. Everybody gets some credit. Everywhere.audio. It's a great audio.
A very business-like professional note there from CSB this week.
All right, brother. I'm going to let you go because
you need to throw some things in the bag. You
know you're going to wind up on this camping trip
and it's like, what do you mean we've got no
pigs? What? Yes. We're all out of pigs. We don't
have any matches. Oh, man, what are we going to
do? And I'll see you at 3 o'clock, right? We've
got our early meeting. Exciting things happening with the podcast.
I thought it was 2.30. I thought it was 2.30.
If it's 2.30, is it 2.30? Well, I got to
hurry then. I got to rock and roll. Okay, run
and rip. All right. Hey, boardroom, thank you very much
for being here. Thank you for providing your very valuable
feedback, and we look forward to seeing each other again
for the board meeting next Friday. Until then, bye-bye, everybody.
Podcasts are cool. You have been listening to Podcasting 2.0.
Visit podcastindex.org for more information. Go podcasting! Get some tissue.
It's coming.