June 26, 2026 • 13 mins
Wrapping up a busy week of GraphRAG training and book writing with two topics worth unpacking: what we actually mean when we call AI "intelligent," and what a recent article reveals about the shifting landscape for software developers.
- AI models are pattern-matching algorithms. They appear intelligent only when humans surface the right context, not on their own
- "Intelligent" is a term developers should use loosely and critically, not as a given
- I ran a GraphRAG Fundamentals online workshop (code available) and is deep in writing her AI-first Java book
- The article "Code is Cheap. Show Me the Talk" argues that LLM tools have fundamentally changed software development
- AI-generated docs and READMEs make it harder to judge how much care an author put into their work
- "Slop" has always existed in software. AI just makes it harder to spot
- The biggest cost: junior developers are losing the learning path that built strong technical foundations
- Senior devs have an opportunity to mentor the next generation differently
The hype around AI in development cuts both ways. It lowers barriers to building, but raises the stakes for intentional learning and craft. Worth thinking about wherever you are in your career.
Listen
Watch
Mark as Played
Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
Jennifer Reif (00:05):
You are listening to the Breaktime Tech Talks podcast, a bite-sized
tech podcast for busy developers wherewe'll briefly cover technical topics, news
snippets and more in short time blocks.
I'm your host, Jennifer Reif, anavid developer and problem solver
with special interest in data,learning and all things technology.
Honestly, I have been a bit scattered thisweek, but things are coming together, and
(00:28):
I have made a good amount of progress.
I am working on the book andgiving an online workshop.
I have produced a lot of content andreally enjoyed the results this week.
Then I keep hearing a couple ofterms and topics that I want to pick
apart a bit and found an articlethat dives into that more with some
perspectives I hadn't considered before.
Let me catch you up.
(00:49):
One of the things I have run across, andit seems innocuous enough, and while we
could debate terminology and specificsover whether you should or should not
do this or that, it doesn't reallymatter in the grand scheme of things.
But I keep hearing this term intelligentreasoning or intelligence in conjunction
with AI and the AI technologies.
(01:11):
However, I do think it's importantto note that intelligence in this
context is really only when humans areproviding the context to allow them
to account for external perspectives.
AI and large language models ontheir own are not intelligent,
at least not yet, right?
That's a very fine line thatwe are running right now.
(01:32):
But I do think it's very importantto clarify because it's very easy
for us to use "well, they seem veryintelligent, they seem very smart, they
produce a lot of really interesting andseemingly intelligent output", right?
But at this point in time, they are notactually intelligent, sentient systems.
(01:52):
And These things only seemintelligent when that important
information is surfaced in orderfor them to take that into account.
They're not intelligent on their own.
Remember, they aremathematical algorithms.
They are pattern machines.
They are not sentient, feeling, thinkingsystems, at least not at this point.
Will we get there?
I don't know.
(02:12):
But at least at this point, it is veryimportant to clarify that whether we
use that terminology or not is, that'sup for debate for those deep thinkers
that are far beyond my skill level.
But I do think it is important that whenwe throw this term intelligent around,
that we use it very, very loosely.
They're seemingly intelligent.
(02:34):
They are analyzing really well, and theyseem, on the surface level, very smart.
But there's no real understandingof these complex concepts and
societal things that we deal withon a day-to-day basis as humans.
It is so easy for us to feel lulledinto the buzzwords of the day because
that's all we hear all around.
(02:54):
That's all we're surrounded with.
But we developers really know better.
We know how this stuff actuallyworks, at least as of now.
It cannot recreate human intelligence.
It can mimic it, it cannot create it.
So I think that's very important.
We'll dive into kind of another veryfine line delineation here in just a bit.
Now, the few things that I've beenworking on this week are I ran a
(03:16):
GraphRAG Fundamentals online traininglast night, which was super fun.
That's one of the reasons whythis podcast episode is just a bit
late in getting published today.
But I had to prep for a bunch of that,make sure everything was up and running
and ready to go for that, and thengive that training late last night.
I had some really fantasticquestions and some very engaged
attendees online for the training,so that was super fun and exciting.
(03:38):
The thing that I really love about givingthis course is I focus the content around
building the foundational skills of LLMsand RAG and GraphRAG so that attendees
will hopefully walk away with knowledgeand ideas to add on top of those.
So once they have those foundationalbuilding block things, you can
build on top of those and abstractcertain things away and make it
(04:01):
cleaner and easier and more efficientfor your particular use case.
But understanding those foundationalconcepts I feel is super important.
I also am still working on the book.
I'm coming up on a deadline at the endof the summer for the rest of the book.
So we've got about half of it done.
We have the other half remaining,and the deadline is at the
end of the summer for that.
So I have a very intense writingschedule, but I am still enjoying
(04:24):
the process, at least most days.
The current chapter I'm workingon is talking about collections.
This is a AI first Java book, soit's focusing on Java language and
learning Java in this age of AI.
And so this chapter is focusingall around learning collections.
I have found throughout my writingof all of the chapters I've covered
(04:45):
so far, I found some preferences forhow I like to approach topics and
concepts, but I am still figuring outthe flow, and I feel like the, the flow
for each chapter is a bit different.
Sometimes I will dig into certainconcepts more, or I'll move faster
over certain things, or I want to digin and gloss over something or not.
(05:06):
And so I'm finding that each chapter'sjust a little bit different in how
deep I go, how much I feel is importantfor an early learner, and the types
of examples and how many examplesthat I provide throughout the chapter,
depending on how complex the topic is.
It changes and adapts as I go.
And so every chapter, while it hassimilar themes and, and outline
(05:28):
structure, is differing a little bit inhow detailed and, and my approach to it.
Now, the big questions for each chapterare what to cover, when do I cover
those things, and what to leave out.
Those are the tough things, and those,again, I battle with every chapter.
I do feel like, as a whole for myworkload, events do seem to have slowed
(05:48):
just a bit, at least for the summer, whichis really good with all the other things
that are sitting on my plate right now.
However, fall forecasting islooking rather booked up already.
So we'll see where that leads.
It'll be super exciting, butit'll be very busy as well.
I do also have some ideas for a couple ofexperiments, but I just really haven't had
a lot of time to devote to them just yet.
I'm hoping to get to those eventually.
(06:08):
They're just sitting on theback burner for right now.
First up, I'd like toexplore code-based analysis.
This was after an event thatI attended a few weeks ago.
I had some folks doing some code-basedanalysis tooling and trying to do some
cool stuff with AI, things like jqAssistant and similar things like that.
I'd like to dig intothat a little bit more.
I've always wanted to play with jqAssistant, and I've dabbled here and
(06:30):
there on a very surface level, but nowI have some ideas of how I can explore
that a little bit better, thanks tothe folks that I interacted with.
So I would love to play around with that.
I also really want to streamlinesome of the manual bits of
putting together content.
If you've listened to some other episodeson my channel, you may have heard me talk
about some automation practices or littletidbits that I'm trying to reduce some
(06:51):
of the boilerplate I have for creatingthings that I really enjoy creating.
For instance, I lovewriting tech blog posts.
I like doing this podcast.
I really enjoy writing the book andother types of technical content.
I really wanna focus more on thosebits and less on the pre- and
post-processing steps that I do for that.
I'm hoping to try to automatesome of those bits just a little
(07:12):
bit more, hopefully, maybeusing AI to help me do that.
But that way, I can focus on thecontent writing and design of the
content itself rather than thosemanual pre- and post-processing bits.
I'm not sure exactly how thiswill take form just yet, but I
am noodling on some ideas there.
The article I wanna cover for thisweek is called "Code is Cheap.
(07:33):
Show Me the Talk" by Kailash Nadh.
And I hope I pronounced that correctly.
The TLDR for this blog post is "softwaredevelopment as it has been done for
decades is over. LLM coding toolshave changed it fundamentally for the
better or worse." Now, that statementfeels just a little bit dramatic to
me, but the core of it is kind of true.
(07:53):
Things are definitely different in thesoftware development industry, and the
practices and the way that we approach it.
The author talks a bit about hisbackground in technology, his career,
and the general tech industry history,at least from his perspective.
Then he goes into a bit about opensource software, how you could tell
a good repository from a bad one.
(08:15):
For instance, if you opened up a GitHubrepository and you were trying to look
at how much care and time has the authorinvested in this open source repository,
you can look at the documentation, youcan look for a good README, and other
types of hints that would tell youthat the author cared and invested the
time and the effort in those, what isoften seen as less important tasks.
(08:39):
I'm using air quotes aroundthat, less important, right?
Those are super valuable things,but often in the development space,
they're left and secondary tasks.
Now, those would help you pinpointhow much care and effort and time
somebody had invested in that.
It is a little bit more difficultto tell that now because AI happens
to help with so many of those tasks.
(09:01):
AI can generate really good documentation.
They can generate a really good README.
They can pour in some of thosedetails that a lot of software
developers miss or care less about,and they can cover those pretty well.
So it's less easy for us now tolook at a repository and say, "Oh,
the author really took the timeand care and is really invested in
(09:23):
the success of this repository."
They may still be, but it's at leastharder to tell that from a surface level.
Now, the author also talks aboutthat it used to take effort to write
10,000 lines of good code, that'sthe, the differentiating factor
there, good code, not just code,and build a good technical career.
It took a lot of time, a lot ofinvestment, and some motivation
(09:44):
to have a good technical career.
Now, you can still have agood technical career, right?
You can build those skills,but you do have to work a
little bit harder at it now.
Slop has existed, and I, I lovedthis section in the article.
Slop has existed in software forever.
It just happened to beeasier to spot pre-AI.
The author points out that peoplecan be trained in software with
(10:05):
a few classes, a boot camp, somebooks, or varying levels of degrees.
However, doctors are required to gothrough rigorous training for years.
Whether you agree, disagree, or likeor dislike this point, I find it
really interesting because I neverreally thought about it like this.
Software runs our major financialmarkets, our global financial
(10:26):
markets, our healthcare systems, ourbuilding access, purchasing systems.
If technology shut down today,the whole world would go dark.
But we don't think anything of adeveloper who's self-trained or took
a few classes over one who has adegree or more degrees in the field.
And whether that's a goodthing or bad thing is, is n-
(10:46):
not for me to point out here.
That's not my intention at all here.
But I just do find it interestingthat it's like, wow, we we don't
gatekeep software development asa career at all, which is really
fascinating and really cool.
But on the other hand, it's like, ooh,uh, for a career in an industry that runs
basically everything else, that, that doesallow a lot of room for slop, potentially.
(11:08):
The author's point is that those in anyfield with poor skills or poor motivation
will produce poor results, and thosewith good motivation and empathy will
produce good results, just like any othercareer, with or without AI coding tools.
You get both good and bad nomatter what you do, whether you
have AI tools or no AI tools.
It is just harder to tell how mucheffort a human might have put in
(11:31):
behind the scenes now that you haveAI kind of covering some of those gaps
that were easy to pick up on before.
My favorite section of the entire articlewas Missing the Forest for the Trees.
This section goes into talkingabout pre-AI, hu-humans still
produced bad code with IDEs, syntaxhighlighting, and other helpful tools.
It's easy to get stuck in the hypethat either AI will solve all our
(11:54):
problems, or on the opposite end ofthe spectrum, in the pit of despair
that it's awful and needs to go away.
However, the author points out that it'sincredibly inspiring to see people who
have never had access to build softwarebe able to design and build tools to
help make their lives or work better.
It's not all roses and sunshine either.
Having a lack of respect for data,good design, and personal privacy
(12:18):
are terrible practices, and those doneed some improvement in this AI age.
The biggest human cost that the authorpoints out is to the junior developer
that is left to fend for themselves.
It is less important to be able towrite the syntax, although the author
points out it's still important forreading and understanding the code,
which is still helpful in today's age.
(12:38):
But it's more important to understandarchitecture, design, and to
communicate those concepts, thearchitecture, the design of the code.
New developers need to foster thoseskills and learn in different ways,
and long-term technical folks have anopportunity to help newer developers
build those skills and bring allof us into a stronger community.
I hope this article was helpful.
(13:00):
I found it thought-provokingand interesting.
From online training to bookwriting to deep thinking, I felt
like I made some strides this week.
I got a lot of value fromthe challenge and the topics.
The book deadline is keeping me totask, and I prepped and gave an online
workshop as well, which added somechallenges but was also really fun.
Finally, there is a lot of buzz arounddeveloper careers in the AI era, and
(13:23):
I found an article that raised someinteresting points, even through
the contrast we currently face.
I'll be back on another episode.
As always, thanks forlistening, and happy coding.
You are listening to the Breaktime Tech Talks podcast, a bite-sized
tech podcast for busy developers wherewe'll briefly cover technical topics, news
snippets and more in short time blocks.
I'm your host, Jennifer Reif, anavid developer and problem solver
with special interest in data,learning and all things technology.
Honestly, I have been a bit scattered thisweek, but things are coming together, and
(00:28):
I have made a good amount of progress.
I am working on the book andgiving an online workshop.
I have produced a lot of content andreally enjoyed the results this week.
Then I keep hearing a couple ofterms and topics that I want to pick
apart a bit and found an articlethat dives into that more with some
perspectives I hadn't considered before.
Let me catch you up.
(00:49):
One of the things I have run across, andit seems innocuous enough, and while we
could debate terminology and specificsover whether you should or should not
do this or that, it doesn't reallymatter in the grand scheme of things.
But I keep hearing this term intelligentreasoning or intelligence in conjunction
with AI and the AI technologies.
(01:11):
However, I do think it's importantto note that intelligence in this
context is really only when humans areproviding the context to allow them
to account for external perspectives.
AI and large language models ontheir own are not intelligent,
at least not yet, right?
That's a very fine line thatwe are running right now.
(01:32):
But I do think it's very importantto clarify because it's very easy
for us to use "well, they seem veryintelligent, they seem very smart, they
produce a lot of really interesting andseemingly intelligent output", right?
But at this point in time, they are notactually intelligent, sentient systems.
(01:52):
And These things only seemintelligent when that important
information is surfaced in orderfor them to take that into account.
They're not intelligent on their own.
Remember, they aremathematical algorithms.
They are pattern machines.
They are not sentient, feeling, thinkingsystems, at least not at this point.
Will we get there?
I don't know.
(02:12):
But at least at this point, it is veryimportant to clarify that whether we
use that terminology or not is, that'sup for debate for those deep thinkers
that are far beyond my skill level.
But I do think it is important that whenwe throw this term intelligent around,
that we use it very, very loosely.
They're seemingly intelligent.
(02:34):
They are analyzing really well, and theyseem, on the surface level, very smart.
But there's no real understandingof these complex concepts and
societal things that we deal withon a day-to-day basis as humans.
It is so easy for us to feel lulledinto the buzzwords of the day because
that's all we hear all around.
(02:54):
That's all we're surrounded with.
But we developers really know better.
We know how this stuff actuallyworks, at least as of now.
It cannot recreate human intelligence.
It can mimic it, it cannot create it.
So I think that's very important.
We'll dive into kind of another veryfine line delineation here in just a bit.
Now, the few things that I've beenworking on this week are I ran a
(03:16):
GraphRAG Fundamentals online traininglast night, which was super fun.
That's one of the reasons whythis podcast episode is just a bit
late in getting published today.
But I had to prep for a bunch of that,make sure everything was up and running
and ready to go for that, and thengive that training late last night.
I had some really fantasticquestions and some very engaged
attendees online for the training,so that was super fun and exciting.
(03:38):
The thing that I really love about givingthis course is I focus the content around
building the foundational skills of LLMsand RAG and GraphRAG so that attendees
will hopefully walk away with knowledgeand ideas to add on top of those.
So once they have those foundationalbuilding block things, you can
build on top of those and abstractcertain things away and make it
(04:01):
cleaner and easier and more efficientfor your particular use case.
But understanding those foundationalconcepts I feel is super important.
I also am still working on the book.
I'm coming up on a deadline at the endof the summer for the rest of the book.
So we've got about half of it done.
We have the other half remaining,and the deadline is at the
end of the summer for that.
So I have a very intense writingschedule, but I am still enjoying
(04:24):
the process, at least most days.
The current chapter I'm workingon is talking about collections.
This is a AI first Java book, soit's focusing on Java language and
learning Java in this age of AI.
And so this chapter is focusingall around learning collections.
I have found throughout my writingof all of the chapters I've covered
(04:45):
so far, I found some preferences forhow I like to approach topics and
concepts, but I am still figuring outthe flow, and I feel like the, the flow
for each chapter is a bit different.
Sometimes I will dig into certainconcepts more, or I'll move faster
over certain things, or I want to digin and gloss over something or not.
(05:06):
And so I'm finding that each chapter'sjust a little bit different in how
deep I go, how much I feel is importantfor an early learner, and the types
of examples and how many examplesthat I provide throughout the chapter,
depending on how complex the topic is.
It changes and adapts as I go.
And so every chapter, while it hassimilar themes and, and outline
(05:28):
structure, is differing a little bit inhow detailed and, and my approach to it.
Now, the big questions for each chapterare what to cover, when do I cover
those things, and what to leave out.
Those are the tough things, and those,again, I battle with every chapter.
I do feel like, as a whole for myworkload, events do seem to have slowed
(05:48):
just a bit, at least for the summer, whichis really good with all the other things
that are sitting on my plate right now.
However, fall forecasting islooking rather booked up already.
So we'll see where that leads.
It'll be super exciting, butit'll be very busy as well.
I do also have some ideas for a couple ofexperiments, but I just really haven't had
a lot of time to devote to them just yet.
I'm hoping to get to those eventually.
(06:08):
They're just sitting on theback burner for right now.
First up, I'd like toexplore code-based analysis.
This was after an event thatI attended a few weeks ago.
I had some folks doing some code-basedanalysis tooling and trying to do some
cool stuff with AI, things like jqAssistant and similar things like that.
I'd like to dig intothat a little bit more.
I've always wanted to play with jqAssistant, and I've dabbled here and
(06:30):
there on a very surface level, but nowI have some ideas of how I can explore
that a little bit better, thanks tothe folks that I interacted with.
So I would love to play around with that.
I also really want to streamlinesome of the manual bits of
putting together content.
If you've listened to some other episodeson my channel, you may have heard me talk
about some automation practices or littletidbits that I'm trying to reduce some
(06:51):
of the boilerplate I have for creatingthings that I really enjoy creating.
For instance, I lovewriting tech blog posts.
I like doing this podcast.
I really enjoy writing the book andother types of technical content.
I really wanna focus more on thosebits and less on the pre- and
post-processing steps that I do for that.
I'm hoping to try to automatesome of those bits just a little
(07:12):
bit more, hopefully, maybeusing AI to help me do that.
But that way, I can focus on thecontent writing and design of the
content itself rather than thosemanual pre- and post-processing bits.
I'm not sure exactly how thiswill take form just yet, but I
am noodling on some ideas there.
The article I wanna cover for thisweek is called "Code is Cheap.
(07:33):
Show Me the Talk" by Kailash Nadh.
And I hope I pronounced that correctly.
The TLDR for this blog post is "softwaredevelopment as it has been done for
decades is over. LLM coding toolshave changed it fundamentally for the
better or worse." Now, that statementfeels just a little bit dramatic to
me, but the core of it is kind of true.
(07:53):
Things are definitely different in thesoftware development industry, and the
practices and the way that we approach it.
The author talks a bit about hisbackground in technology, his career,
and the general tech industry history,at least from his perspective.
Then he goes into a bit about opensource software, how you could tell
a good repository from a bad one.
(08:15):
For instance, if you opened up a GitHubrepository and you were trying to look
at how much care and time has the authorinvested in this open source repository,
you can look at the documentation, youcan look for a good README, and other
types of hints that would tell youthat the author cared and invested the
time and the effort in those, what isoften seen as less important tasks.
(08:39):
I'm using air quotes aroundthat, less important, right?
Those are super valuable things,but often in the development space,
they're left and secondary tasks.
Now, those would help you pinpointhow much care and effort and time
somebody had invested in that.
It is a little bit more difficultto tell that now because AI happens
to help with so many of those tasks.
(09:01):
AI can generate really good documentation.
They can generate a really good README.
They can pour in some of thosedetails that a lot of software
developers miss or care less about,and they can cover those pretty well.
So it's less easy for us now tolook at a repository and say, "Oh,
the author really took the timeand care and is really invested in
(09:23):
the success of this repository."
They may still be, but it's at leastharder to tell that from a surface level.
Now, the author also talks aboutthat it used to take effort to write
10,000 lines of good code, that'sthe, the differentiating factor
there, good code, not just code,and build a good technical career.
It took a lot of time, a lot ofinvestment, and some motivation
(09:44):
to have a good technical career.
Now, you can still have agood technical career, right?
You can build those skills,but you do have to work a
little bit harder at it now.
Slop has existed, and I, I lovedthis section in the article.
Slop has existed in software forever.
It just happened to beeasier to spot pre-AI.
The author points out that peoplecan be trained in software with
(10:05):
a few classes, a boot camp, somebooks, or varying levels of degrees.
However, doctors are required to gothrough rigorous training for years.
Whether you agree, disagree, or likeor dislike this point, I find it
really interesting because I neverreally thought about it like this.
Software runs our major financialmarkets, our global financial
(10:26):
markets, our healthcare systems, ourbuilding access, purchasing systems.
If technology shut down today,the whole world would go dark.
But we don't think anything of adeveloper who's self-trained or took
a few classes over one who has adegree or more degrees in the field.
And whether that's a goodthing or bad thing is, is n-
(10:46):
not for me to point out here.
That's not my intention at all here.
But I just do find it interestingthat it's like, wow, we we don't
gatekeep software development asa career at all, which is really
fascinating and really cool.
But on the other hand, it's like, ooh,uh, for a career in an industry that runs
basically everything else, that, that doesallow a lot of room for slop, potentially.
(11:08):
The author's point is that those in anyfield with poor skills or poor motivation
will produce poor results, and thosewith good motivation and empathy will
produce good results, just like any othercareer, with or without AI coding tools.
You get both good and bad nomatter what you do, whether you
have AI tools or no AI tools.
It is just harder to tell how mucheffort a human might have put in
(11:31):
behind the scenes now that you haveAI kind of covering some of those gaps
that were easy to pick up on before.
My favorite section of the entire articlewas Missing the Forest for the Trees.
This section goes into talkingabout pre-AI, hu-humans still
produced bad code with IDEs, syntaxhighlighting, and other helpful tools.
It's easy to get stuck in the hypethat either AI will solve all our
(11:54):
problems, or on the opposite end ofthe spectrum, in the pit of despair
that it's awful and needs to go away.
However, the author points out that it'sincredibly inspiring to see people who
have never had access to build softwarebe able to design and build tools to
help make their lives or work better.
It's not all roses and sunshine either.
Having a lack of respect for data,good design, and personal privacy
(12:18):
are terrible practices, and those doneed some improvement in this AI age.
The biggest human cost that the authorpoints out is to the junior developer
that is left to fend for themselves.
It is less important to be able towrite the syntax, although the author
points out it's still important forreading and understanding the code,
which is still helpful in today's age.
(12:38):
But it's more important to understandarchitecture, design, and to
communicate those concepts, thearchitecture, the design of the code.
New developers need to foster thoseskills and learn in different ways,
and long-term technical folks have anopportunity to help newer developers
build those skills and bring allof us into a stronger community.
I hope this article was helpful.
(13:00):
I found it thought-provokingand interesting.
From online training to bookwriting to deep thinking, I felt
like I made some strides this week.
I got a lot of value fromthe challenge and the topics.
The book deadline is keeping me totask, and I prepped and gave an online
workshop as well, which added somechallenges but was also really fun.
Finally, there is a lot of buzz arounddeveloper careers in the AI era, and
(13:23):
I found an article that raised someinteresting points, even through
the contrast we currently face.
I'll be back on another episode.
As always, thanks forlistening, and happy coding.
Popular Podcasts
Stuff You Should Know
If you've ever wanted to know about champagne, satanism, the Stonewall Uprising, chaos theory, LSD, El Nino, true crime and Rosa Parks, then look no further. Josh and Chuck have you covered.
Dateline NBC
Current and classic episodes, featuring compelling true-crime mysteries, powerful documentaries and in-depth investigations. Follow now to get the latest episodes of Dateline NBC completely free, or subscribe to Dateline Premium for ad-free listening and exclusive bonus content: DatelinePremium.com
Betrayal Weekly
Betrayal Weekly is back for a new season. Every Thursday, Betrayal Weekly shares first-hand accounts of broken trust, shocking deceptions, and the trail of destruction they leave behind. Hosted by Andrea Gunning, this weekly ongoing series digs into real-life stories of betrayal and the aftermath. From stories of double lives to dark discoveries, these are cautionary tales and accounts of resilience against all odds. From the producers of the critically acclaimed Betrayal series, Betrayal Weekly drops new episodes every Thursday. If you would like to share your story, you can reach out to the Betrayal Team by emailing them at betrayalpod@gmail.com and follow us on Instagram at @betrayalpod and @glasspodcasts. Please join our Substack for additional exclusive content, curated book recommendations, and community discussions. Sign up FREE by clicking this link Beyond Betrayal Substack. Join our community dedicated to truth, resilience, and healing. Your voice matters! Be a part of our Betrayal journey on Substack.