Welcome to the Deep Dive Learner.
Today we are taking on a mission to really unpack an incredibly
dense week of AI news. I mean, we're looking at
everything from April 6th to April 10th, 2026 and there is a
lot of ground to cover. Yeah, there really is.
It's it's been a massive week. Right, we've got breaking
reports from the new stack. We have some really deep
analysis over from late in space and honestly some wild frontline
dispatches from developer subreddits.
Like, you know, our clawed code and our clawed AI.
The subreddits have been a goldmine lately honestly.
Absolutely, because the theme of what we're looking at today,
Lerner, is that whether you are a developer, you know, right
there in the trenches or a business leader trying to figure
this out, we are so far past the the sort of wow phase of AI
coding tools. Yeah, the honeymoon period is
completely over. Totally over.
We're in this really messy high stakes era now where AI is, I
mean, it's automating entire corporate workflows, it's
sparking these developer turf wars, and it's fundamentally
changing how businesses actually operate.
So OK, let's unpack this, because to understand the big
picture, we actually have to start super small.
Right with the invisible failure.
Exactly like a micro level problem discovered by just one
single user on Reddit. Because it perfectly shows the
absolute danger of trusting these tools blindly.
Yeah, this was wild. So on April 10th, a Reddit user
in the R Claude code community, they basically found three
stacked bugs in Claude code. This is since version V2, point
0.64. Right, the new update.
Yeah, so developers do what developers do right?
They tweak their config files. But they found that if a user
sets their config to Always thinking enabled.
True, it was also an environment variable.
Right, Yes, exactly. If you set the environment
variable, Claude could effort level to Max the extended
thinking feature. Just, well, it silently fails.
There is 0 error message, no warning in the terminal, it just
quietly runs at the default shallow capability.
See, that is terrifying because, you know, Lerner, think about
this like driving a car. You're on the highway, your
speedometer says you're doing 65 mph, You feel the engine, the
road is going by, it all seems totally fine.
But in reality, you're only doing 30 mph and you have
absolutely no idea. Yeah, there's no check engine
light whatsoever. Right.
So how did they even figure it out if there was no error?
Well, they had to prove it using what they call a Canary
question. OK.
I love this part. It's basically a logic puzzle
with deliberate misdirection built into it.
If the model does a fast shallow pass, it will always get it
wrong. It falls for the trap every
time. That was literally the only way
they could prove their config files were lying to them.
That's insane. And then fixing it.
They had to use a manual wrapper, didn't?
They yeah, a community member had to publish a custom wrapper
just to intercept the config before execution to force it to
actually work. I have to ask, I mean, is a
silent failure actually worse than a complete system crash?
Oh, absolutely, yes. I mean, what's fascinating here
is the core lesson. You cannot trust your config
files anymore. Right, you can't fix what you
don't know is broken. Exactly.
A loud system crash stops the assembly line so you can fix it.
Yeah, a silent failure means you keep shipping defective code.
We are having to completely shift from just looking at our
settings to actively verifying the behavior of the AI.
Which brings us to the scale of this problem.
Because the final and failure for one developer is, well, it's
a massive headache. Yeah, but what happens when you
scale that blind trust across the entire software industry?
Right, because over on our cloud AI, there was this post from an
11 year veteran developer who just automated, I mean roughly
80% of their job. 80%, that's just wild.
It's nuts. They use the cloud CLI in this
custom.net console app to just constantly pull the GitLab API
for issues. So the AI reads the issue,
writes the code and submits the PR the pull request.
And on the surface, that looks like the ultimate developer
dream, right? Right, but the news stack is
reporting a totally different reality.
They're calling it an AI generated PR crisis.
Open source maintainers are just drowning right now.
They literally cannot keep up with the unsustainable volume of
these AI pull requests. Because the failure mode is so
tricky, the code, well it sounds right on 1st read it looks
plausible. Plausibility is the real danger
here. The syntax is technically
correct, but it's subtly bugged, or it completely ignores project
conventions, or, you know, misses the contextual nuance of
the code base. And the News Stack warned that
enterprise teams are next. So here's where it gets really
interesting. I want to push back on that 11
year veterans automation dream. OK, let's hear it.
If that developer automates 80% of their coding, they aren't
actually eliminating 80% of the work, right?
Aren't they just dumping 80% of their mental workload onto
whoever the poor reviewer is that has to check that code?
That is exactly what's happening.
The bottleneck of software engineering has officially
shifted. Wow, shifted how?
Well, for decades the bottleneck was generation.
Writing the code. Now generation is practically
free. The new bottleneck is
evaluation. Because evaluating the alien
logic takes so much brainpower. Right, and the sheer volume of
this generated code vastly outpaces reviewer capacity,
which leads to this incredibly dangerous culture of rubber
stamping. You're tired.
The syntax looks fine, so you just click Approve.
And boom, silent failures and contextually blind bugs slip
right into production. Yep.
Which is why the giant AI labs are currently in an absolute
knife fight to be the ultimate solution to this exact review
bottleneck. Right, the mind tier war.
Let's talk about the big corporate moves, because on
April 9th, Open AI came out swinging.
They really did. They announced that new $100 a
month ChatGPT Pro tier aimed specifically at developers
hitting rate limit. And what blew my mind is that
Open AI is pitch materials explicitly named Claude code.
Like they position themselves dollar for dollar against
Claude's heavy usage pricing. It's incredibly aggressive.
It is and at the same time you have GitHub Copilot CLI
introducing this rubber duck feature.
Yes, the rubber duck. Which is so cool, It allows
developers to get a second opinion from a completely
different model family right there in the command line.
Because different models have different blind spots.
Exactly. Oh, and as a quick side note,
Simon Wilson just released this tool called Cleanup Clawed Code
Paste to fix white space when you copy prompts from the
terminal. Which just shows how deeply
embedded clawed code is in People's Daily workflows.
People are building custom utility scripts just to format
their copy pastes. Right, so looking at all this,
if Open AI is naming clawed code directly in a pricing pitch, are
they actually sweating real customer churn?
Oh. Without a doubt, this raises an
important question about philosophy really, because you
have two completely different approaches to solving complex
coding problems right now. OK, break that down for me.
Well, on one side you have open AI's approach.
Throw more money and more compute at a single massive
model. Tell it to think harder.
Right. And on the other side.
You have Copilot's rubber duck approach multi model
deliberation. Instead of 1 model thinking
harder, you have two different models.
Argue it out, one model right? It's a different model.
Family critiques. It's a structural solution to
the rubber stamping problem. So they are fighting over
developers right at the command line, but meanwhile Anthropic is
doing something entirely different.
They are quietly executing this massive pincer movement to
capture entire corporate structures.
Yeah, they're surrounding the Enterprise from top to bottom.
So on April 8th, Anthropic launched managed agents in
public beta for builders. Right.
Anthropic runs the infrastructure, the
orchestration, all of it. The business just defines what
the agent needs to do. And then literally the next day,
April 9th, they bring Claude Cowork into general
availability. It's massive because that is for
the non developers, the business buyers.
You can delegate tasks with 0 coding required and it even
looks like cloud for Microsoft Office is live over at pivot dot
claw dot AI. It's a brilliant two track go to
market strategy, top down and bottom up.
So what does this all mean? I kind of look at it like a
restaurant, right? Manage Agents is like hiring
this brilliant head chef, but they absolutely refuse to share
their recipes with your kitchen staff.
That's a great way to look at it.
And then cowork is like just handing the customers out front
a magic menu where the food just appears.
The trade off here is wild. Businesses lose all that heavy
infrastructure burden, but aren't they also completely
surrendering all their internal operational knowledge to
Anthropic? They absolutely are.
That is the ultimate trade off of this era.
It's a huge risk. It is by abstracting the
operational knowledge away from internal engineering teams,
businesses gain incredible speed.
I mean, they move so much faster, but they become
fundamentally dependent on Anthropic's ecosystem.
They lose their operational sovereignty.
Wow. OK, so if Anthropic is just
swallowing the enterprise hole like this, I have to ask what
kind of absolute monster of a model is powering all of this
behind the scenes? And more importantly, what are
they hiding? That is the $1,000,000 question
or well the $30 billion question as we found out this week,
right? So announced April 7th and 8th
we have Project Glass Wing and Claude Mythos.
Yeah, Claude Mythos. It's a new general purpose
model, supposedly comparable to Opus 4.6.
But with additional totally undisclosed capabilities.
Exactly. And the kicker is they are not
publicly releasing it. It is restricted to a tiny, tiny
set of security research preview partners.
Yeah, Latent Space covered this and literally called it the
first model too dangerous to release since GPT 2.
Which is quite the statement. It is.
And during this exact same news cycle, it gets reported that
Anthropic hit $30 billion in annual recurring revenue.
So I have to get a little cynical here.
Go for it. Are they really holding this
model back just for safety or is announcing a quote UN quote too
dangerous model just the absolute ultimate marketing
flicks to drop exactly when you hit 30 billion in ARR.
I love the cynicism here there because if we connect this to
the bigger picture, you're actually touching on something
profound about enterprise sales. OK, tell me deliberate non
release acts as the ultimate safety credibility signal.
Oh, interesting. Yeah, think about conservative
enterprise buyers, banks, healthcare, government.
They are terrified of liability. By holding back mythos,
Anthropic is telling those buyers we are incredibly
powerful, but we are also the most responsible adult in the
room. Safety is essentially a flex.
It's a product feature. Wow.
Lerner, look at how this all connects.
We went from a single silent configuration bug on a Reddit
thread all the way to a $30 billion enterprise ecosystem
that Anthropic is building. It really highlights how
critical the human element still is.
Exactly. Whether you are fighting off AI
generated pull requests, or you're deliberating between
models in your terminal, or you're adopting managed agents
for your business, you cannot just set things to autopilot.
You. Absolutely.
Really. Cannot you?
Have to verify the behavior. You have to remain the critical
thinker in the loop. And you know, that leaves us
with a really big lingering question to Mull over.
What's that? Well, if we are entirely
abstracting our operational knowledge to managed agents,
right? And if AI is generating 80% of
our code while we just review it, how does the next generation
of junior developers actually learn what good looks like?
I mean, if the AI does all the doing, where do tomorrow's
expert reviewers even come from? Oh wow, that is.
Yeah, that is terrifying to think about.
If you never build a muscle, how do you know if the machine is
doing it right? Exactly.
Well, Lerner, that is something to really chew on this week.
Thank you so much for joining us for this deep dive.
Yeah, thanks for listening everyone.
We will see you on the next one.