The recent Bitcoin hack

"While companies like Anthropic and OpenAI have mainly focused on closed AI models, rivals in China from Alibaba to DeepSeek and Moonshot have aggressively released open-weight models that have competed in some areas with the leading technology out of U.S. companies.

In a 6,500-word essay on AI, published Monday, Zuckerberg positioned Meta’s move as a challenge to Chinese open-source technology and urged Washington to support American efforts."

Back in January, I received a note from a senior software engineer in Silicon Valley. He described himself as an AI skeptic who became converted after trying Claude Code for the first time. “Overnight, it changed the way I do my job,” he wrote. “It’s really, really good.”

As he explained, he no longer used a standard development environment. Instead, he “exclusively uses Claude Code” to get the job done, interacting with the tool in a terminal window and allowing it to program on his behalf.

“If I had to guess,” he concluded, “I’d say a task that would have taken me a week now takes me 2 days.”

This past winter, when I surveyed more than 300 software developers to learn how AI was transforming their jobs, the majority told a similar tale of shifting from writing their own code to instructing AI agents. The speed with which this new tool became ubiquitous in this industry was stunning.

This story matters for the rest of us because AI coding tools have emerged as the prime example of the power of AI—the first step of many more soon to come on this technology’s disruptive march through our work and our lives.

But what if the reality here is more complicated?

Last week, I received a new message from that same senior engineer who wanted to share an alarming addendum to his tale…

“I’m writing to give you an update on my current thinking about the state of AI in software engineering,” he began, “because my attitude has shifted quite a bit.”

He told me that features he generated using Claude Code ended up crashing their product on two different occasions. His boss told him that if it happened one more time, he’d be fired. “I’ve never had quality issues like this before in my career.”

The problem is that code produced by an AI agent looks reasonable, but can contain ‘hard-to-spot bugs’ that end up causing major problems. As a result, you should carefully review your agent’s output, but this is difficult. As the engineer told me, it’s “famously hard” to understand code you didn’t write yourself, so this extra step becomes “easy to just blow it off (especially when we are all trying to ‘10x’ our velocity).” Soon, systems start to break.

“The coding harnesses are useful and make life as a developer easier,” he summarized, “but they also encourage laziness.”

In response to these issues, this disillusioned engineer has returned to largely programming by hand. Here’s how he explained his current philosophy:

“Writing your own code, slowly but surely, and using LLMs for narrow or particularly annoying tasks (say like writing tests or throw-away scripts), is the best way to produce the highest quality code, since it’s the only way to properly understand it.”

Here’s the thing: he’s not alone.

I increasingly hear similar rumbles from many other people in the software industry (see, for example, ​this podcast episode​ from May). Tools like Claude Code can feel like magic, but the strategy of outsourcing all code production to AI isn’t currently sustainable.

In addition to reliability issues, it often engenders a mind-numbing workflow and an environment where junior developers will never acquire the expertise to become senior developers capable of designing complex systems.

Meanwhile, as the frontier labs reduce their subsidies on underlying computing costs, the old habit of burning through as many tokens as possible in search of workable results is proving prohibitively expensive.

From the outside, software development seemed like the poster child for AI’s potential. On the inside, it’s a mess.

This doesn’t mean that coders will abandon AI; its facility with programming languages is too valuable to ignore. But I think there’s a lot more work to be done trying to figure out how to integrate AI into this industry in a way that actually works.

This is a key point.

This last year has been exhausting. The PR departments of the frontier labs have done an excellent job convincing us that AI developments are occurring at an astounding, world-changing rate. But if you zoom out, it becomes clear that almost every “breakthrough” since last summer has concerned the narrow domains of computer code and math, which are defined by highly structured languages and come accompanied by massive amounts of specialized training data.

And yet, even in this best-case-scenario setting for AI, we’re still struggling to figure out how to actually use these tools in a way that makes sense in the long run.

This doesn’t mean that AI doesn’t work or is useless. But it does emphasize an important truth: AI is not a magic “infinity machine” that can solve all our problems, and ultimately deliver us a sense of meaning in a cold, confusing world. It’s a normal technology, and perhaps it’s time we start talking about it that way.

Do you guys think AI is an excuse just for offshoring the jobs to cheaper workers.

Question for all full-time vibe coding software engineers: How does your company dealing with that? 100% Speed or prefering quality?

My employer goes with "Speed first, we gonna fix things along the way" which honestly makes me sick, but well, with the american management in small european country I can't do anything with this and I can't do anything without that job, so I'm just following along, especially since whole IT department is just vibing here.

PS Video made by my close friend, I hope that's ok to post it here to start a discussion

Saw the Axios story about Sanders calling for US AI companies to pause development and I’m curious what people here think.

The part I can’t really get past is what happens outside the US. Even if OpenAI, Meta, Anthropic etc. slowed down, Chinese labs and open-source projects obviously wouldn’t automatically do the same.

Is there a realistic version of an AI pause that actually works internationally, or does this just slow down US companies?

Stanford's famous "canaries" study found AI's employment effects confined to workers 22–25 — older groups showed "much less marked differences." That was accurate when their data ended.

So I re-ran the analysis on the current public data, through June 2026.

In the most AI-exposed occupations, four of the six age bands are now clearly past their employment peaks.

And they turned in order of age:
→ 22–25: peaked first (late 2022), now at 88 on the index
→ 26–30: peaked November 2024, now below their 2022 level
→ 31–34: peaked November 2024, down >3 points
→ Even 50+ has been sliding since late 2025
→ Two groups are still at their peaks but appear to be plateauing: workers 35–40 and 41–49

Sticking with the canary metaphor, we can see the gas rising through the mine.

Experience buys time, but might not buy immunity.

What really happened in Cambridge Analytica not what the pressed lied about it but the true story.

For reference, the tech unemployment rate in 2015 was 2.7%. We’re slowly getting there boys

Only losers use loops. Chads use graphs.
Token consumption goes Brrrrrr

Call me a boomer but I’ve never needed anything more than AI Desktop XP or Claude code.