Wyn Van Devanter

[Wyn Van Devanter @wynv]

Software is buggy largely b/c programmers jump straight into writing code. Few write even a sketch of what their programs should do before coding.

Code draws attention to individual pieces instead of the bigger picture of how your program fits together.
https://t.co/s69Q3fx1LE

[Anish Moonka @anishmoonka]

Scientists put kids through 100 hours of reading, then scanned their brains. New wiring had physically grown inside the language regions. Communication between brain areas sped up by a factor of 10. Kids who didn't read showed zero change.

That was a 2009 Carnegie Mellon study. It gets wilder.

In 2013, Emory University scanned 19 students every morning for 19 straight days while they read one novel chapter each night. Mornings after reading, the brain areas responsible for understanding other people's emotions lit up with new connections. So did the region that processes physical sensation. Their brains were simulating what the characters felt, as if it were happening to them. Those changes stuck around for 5 days after they finished the book.

Now flip to scrolling. A massive review published in Psychological Bulletin last September pulled together 71 studies covering 98,299 people. Heavy short-form video use (TikTok, Reels, Shorts) showed a clear pattern: worse attention, weaker self-control, and more anxiety. Consistent across teenagers and adults, across every platform tested. Oxford didn't name "brain rot" its 2024 Word of the Year for nothing.

A 2024 brain wave study found that people hooked on short-form video had weaker activity in the front of the brain, the part that controls focus and impulse control. Separate brain scans showed the same thing: heavy scrollers had less activation in the exact regions that deep reading strengthens.

UCLA neuroscientist Maryanne Wolf has been studying this for decades. Humans were never born to read. There's no gene for it. Reading is something we invented, and it hijacked neurons that were originally meant for recognizing faces. Over time, it built entirely new brain circuits connecting language, vision, and emotion. But those circuits only survive if you use them. Stop reading, and they fade. Wolf's conclusion is simple: screens built for speed produce a speed-wired brain. Books built for depth produce a depth-wired brain.

One honest caveat: most of these studies are snapshots, not long-term tracking. People who already struggle to focus might just prefer short videos. But the same pattern showing up across nearly 100,000 people is hard to shrug off.

The tweet repeats the line seven times. The research backs it up with brain scans, EEG data, and white-matter imaging across tens of thousands of people.

[UI/UX Savior @UiSavior]

Lol 😂😂

[Dr Milan Milanović @milan_milanovic]

Junior and mid-level engineers can no longer push AI-assisted code without a senior signing off at AWS

[Dustin @r0ck3t23]

Dario Amodei just revealed that the AI training bottleneck everyone is worried about doesn’t exist anymore.

The industry spent years obsessed with scraping the open web. More data. More text. More human output to feed the models.

Amodei: “I don’t think data is quite the most central thing anymore.”

The shift is fundamental.

Amodei: “Static data is becoming less important. A lot of the data we use today is RL environments that we train on. Dynamic data that the model creates itself.”

Not scraped. Not licensed. Not written by humans.

Generated by the model through pure trial and error.

When you train on complex math or agentic coding, you don’t feed it a textbook. You give it an environment.

The model experiments. Fails. Adjusts. Tries again.

Amodei: “You’re getting some math problems and the model experiments with trying the math problems.”

It generates its own experience. Millions of iterations. Each one building on the last.

No human required.

This destroys the entire narrative around AI hitting a data wall.

You cannot throttle a competitor by locking down copyright. Cannot slow the race by putting up a paywall.

When a model learns through its own synthetic experience, the open web becomes irrelevant.

The only true bottleneck left is compute.

And this is where the geopolitical stakes become impossible to overstate.

The nation that wins the compute race doesn’t just build smarter models.

It builds models that generate their own intelligence, compounding on themselves, iterating past every limit human knowledge ever imposed.

We are no longer training AI on the past.

We are letting it simulate the future.

The machine has stopped reading the dictionary.

It’s doing the math itself now.

[dára sobaloju @darasoba]

Claude is releasing startups as plugins.

[Aakash Gupta @aakashgupta]

Sequoia just called the end of an entire go-to-market era and most SaaS companies won’t realize what hit them for 18 months.

Product-led growth was built on one assumption: humans would try the software. The entire playbook since 2010 optimized for human discovery. Beautiful landing pages. Frictionless free trials. Viral invite loops. Slack, Dropbox, Zoom, Calendly. $200B+ in market cap created by winning the user’s first 5 minutes.

None of that matters if an agent is picking the software.

Claude doesn’t care about your hero image. It can’t be impressed by your Dribbble awards. It’s reading documentation, parsing user reviews, checking API reliability, and matching features to use case. All the surface-level polish that convinced lazy humans to click “sign up” becomes irrelevant.

The new PLG funnel isn’t landing page → free trial → activation → conversion.
It’s agent query → documentation scan → feature match → recommendation.

Which means the new moat looks completely different. You don’t need the best onboarding. You need the best documentation. You don’t need viral loops. You need structured data that agents can parse. You don’t need a beautiful UI for the first session. You need an API that an agent can actually call.

The companies that won PLG hired designers and growth hackers. The companies that win agent-led growth will hire technical writers and developer relations engineers.

And here’s the part nobody’s pricing in yet: agents don’t have loyalty. They don’t have switching costs. They’ll recommend Supabase today and something better tomorrow if the documentation is cleaner or the pricing is more transparent. The stickiness that made PLG so powerful, the network effects and learned behavior, doesn’t transfer.

Sequoia is telling you the entire distribution layer is being rewritten. The question is whether your product is optimized for human attention or machine parsing. Most are built for the wrong audience.

[Wyn Van Devanter @wynv]

@sama It is quite a privilege to use a tool like this for free so it makes sense

[Posts Of Cats @PostsOfCats]

Bro doesn’t even have to look 😆

[Lenny Rachitsky @lennysan]

Great overview of where product management is headed

- Problem shaping / clarity
- Context curation
- Evals / taste

[Wyn Van Devanter @wynv]

AI Engineering World Fair happening soon!

[Andrej Karpathy @karpathy]

@winterrose @aiDotEngineer I'm not coming I'm scared but I love that it's happening :)

[Wyn Van Devanter @wynv]

@Roman_Trading Just wait until new news like possible legislative activity, SEC easements and more

[Wyn Van Devanter @wynv]

@jeremyphoward A buggy, messy, llm-based application, which is what ChatGPT is, doesn’t mean they didn’t develop ASI not hooked up to that.

[aka @akafaceUS]

Wow! Palisades in LA is COMPLETELY GONE.. This is horrible.

[Wyn Van Devanter @wynv]

@DavidSacks What does that tangibly mean?

[Wyn Van Devanter @wynv]

@brikis98 How much do you have to read per day to read that many?

[Wyn Van Devanter @wynv]

Making the reboot a science

[Vic 🌮 @VicVijayakumar]

they won't tell you this in SRE school but the easiest way to fix a memory leak is to restart your pods on a schedule

[Wyn Van Devanter @wynv]

@kelseyhightower It’s plain weird

[Wyn Van Devanter @wynv]

@engineering_bae Absolutely yes if that’s what they want to do. It’s still a hot field where CS concepts will be more useful than ever with AI doing a lot of the coding legwork. Understanding the depths and architectures will be important.

[Wyn Van Devanter @wynv]

@engineering_bae I would have made a funny photograph