I love all the attention DSPy is getting right now; soon, DSPy will be the only framework worth using. I only use DSPy btw.

engineers who are high IQ but low EQ continue to report terrible results from coding models, while CTOs with people skills rip ahead because the AI likes them

Apparently the median user *really* prefers the way AIs write now, where the text is constantly broken up with bulleted lists and blockquotes, and this is why it's so hard to get AIs to stop doing it.

every coding agent looks like a senior dev until you ask it to use an ORM. a new paper shows that adding a real database and architecture rules drops agent pass rates by 30%, with cross-file consistency hitting a brutal 8%. we didn't build autonomous engineers, we built a machine that writes single-file flask apps and panics the second it touches a data layer.

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canadians be like "thank you!" to the bus driver

Vibecoding is when you let LLMs generate code and you accept it as long as the end result appears to be working. You don't even review the code, you don't care. When you rigorously ensure that generated code is up to a standard, it's not vibecoding anymore. It's programming.

A friend of mine had her embryos screened by Herasight and they found one with an IQ score in the 99.99th percentile

my engineer friends here, are you guys still using an IDE? hearing more and more than lots of developers simply don’t even use one anymore i guess lots of my former customers still are, considering they’re in air-gapped deployments what do you say ?

It is taken as given that AI coding will continue to improve. Right now AI generated code has a lot of quality and performance issues. But surely in two years it will exceed the capabilities of senior developers. But will it? LLM AI's will never exceed the "intelligence" of the training data RL. Where is the training data for the future going to come from? It won't be @StackOverflow, that is dying. If it is @github, half of that (or more) will be AI generated code. LLMs can't improve by training on the output of other LLMs. There is the possibility that Agentic Coding based on LLMs may not improve significantly from here.

"Hashmaps are fast" Ohh no they are not! Hash math is incredibly expensive. Avoid them if you can. Sometimes O(n) can be cheaper than O(1). Sometimes O(n^2) is faster than O(1). Time complexity only matters if you size of n is big enough for it to matter.

The vibes in SF feel pretty frenetic right now. The divide in outcomes is the worst I've ever seen. Over the last 5yrs, a group of ~10k people - employees at Anthropic, OpenAI, xAI, Nvidia, Meta TBD, founders - have hit retirement wealth of well above $20M (back of the envelope AI estimation). Everyone outside that group feels like they can work their well-paying (but <$500k) job for their whole life and never get there. Worse yet, layoffs are in full swing. Many software engineers feel like their life's skill is no longer useful. The day to day role of most jobs has changed overnight with AI. As a result, 1. The corporate ladder looks like the wrong building to climb. Everyone's trying to align with a new set of career "paths": should I be a founder? Is it too late to join Anthropic / OpenAI? should I get into AI? what company stock will 10x next? People are demanding higher salaries and switching jobs more and more. 2. There’s a deep malaise about work (and its future). Why even work at all for “peanuts”? Will my job even exist in a few years? Many feel helpless. You hear the “permanent underclass” conversation a lot, esp from young people. It's hard to focus on doing good work when you think "man, if I joined Anthropic 2yrs ago, I could retire" 3. The mid to late middle managers feel paralyzed. Many have families and don't feel like they have the energy or network to just "start a company". They don't particularly have any AI skills. They see the writing on the wall: middle management is being hollowed out in many companies. 4. The rich aren’t particularly happy either. No one is shedding tears for them (and rightfully so). But those who have "made it" experience a profound lack of purpose too. Some have gone from <$150k to >$50M in a few years with no ramp. It flips your life plans upside down. For some, comparison is the thief of joy. For some, they escape to NYC to "live life". For others still, they start companies "just cuz", often to win status points. They never imagined that by age 30, they'd be set. I once asked a post-economic founder friend why they didn't just sell the co and they said "and do what? right now, everyone wants to talk to me. if i sell, I will only have money." I understand that many reading this scoff at the champagne problems of the valley. Society is warped in this tech bubble. What is often well-off anywhere else in the world is bang average here. Unlike many other places, tenure, intelligence and hard work can be loosely correlated with outcomes in the Bay. Living through a societally transformative gold rush in that environment can be paralyzing. "Am I in the right place? Should I move? Is there time still left? Am I gonna make it?" It psychologically torments many who have moved here in search of "success". Ironically, a frequent side effect of this torment is to spin up the very products making everyone rich in hopes that you too can vibecode your path to economic enlightenment.

Gentle reminder on how, in the recent DS4 fiesta, not just me but every other contributor found GPT 5.5 able to help immensely and Opus completely useless.

if ORMs are wrong why do they feel so right

I have seen intelligent people destroy their careers by never learning to play dumb. Conveniently, game theory explains why that happens: In most high-stakes hierarchies, influence is perceived as a zero-sum game. If you appear "too intelligent," you are perceived as a threat. That can lock you out of important networks. Let the others feel superior for a while and hide your intelligence until deployment. Unfortunately, most people will never utilize this strategy. It necessitates acting strategically, not egoistically.

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