Avner Sorek

[Avner Sorek @AvnerSo]

After 5 manual features quality is at 98%. After 10 AI features (we do more now!), quality is at 82%.

[Avner Sorek @AvnerSo]

This is one of the main things people miss about AI generated code.

If we take this analogy and use it for code quality ...

[Mitchell Hashimoto @mitchellh]

I've got an agent in a loop optimizing a renderer with the goal to minimize frame times (and tests to measure). It got times down from 88ms to 2ms and allocations down from ~150K to 500. Sounds good, right? Wrong. This is exactly why agent psychosis is a big fucking problem.

As an experiment, I rewrote the Ghostty core render state in Go, with access to identically laid out data structures as Ghostty and the exact same validation tests. I made a purposely naive renderer (simple, correct, but slow). 88ms per frame with 150,000 allocations (horrendous, lol)!

I then kickstarted a Ralph loop to bring the frame times down. I told it it can't modify input data structures or the public API or tests (they're correct), but it can do anything else it wants. It got to work.

It has worked for about 4 hours. I've spent around $350 on this experiment so far. The results?

88ms => 1.5ms
150K allocs => ~500 allocs

Incredible right? Nope.

My hand-written renderer I ported has frame times (same benchmark) of ~20us (0.020ms) and 0 allocations in the update path.

This is the problem with psychosis and lacking systems understanding. If you don't understand the system, you're going to accept that this is an incredible result. If you understand the system, you'll see better solutions immediately and can do roughly 75x better on throughput.

The people who blindly trust agent output are in the former camp. They're sheeple, overdrinking from a fountain of mediocrity.

Standard disclaimer: I use AI all the time. I like AI. The point I'm making is to not blindly accept results. Think. Analyze. Learn.

[Avner Sorek @AvnerSo]

Considering the AI code preserves 98% of the original quality, and the manually written code preserves 99.8% ...

[Ayaan 🐧 @twtayaan]

🚨 S3 is no longer just Object Storage.

Yesterday (April 7, 2026), AWS officially launched Amazon S3 Files.

This is the biggest update to S3 in 20 years.

It can:
→ Mount S3 buckets as native file systems
→ Provide sub-millisecond file access
→ Handle POSIX permissions (UID/GID) natively
→ Connect to Lambda, EC2, and EKS directly
→ Eliminate the need for s3fs or data staging

Your AI agents can read/write to S3 like a local disk, while your data team access the same objects via API.

DevOps just got a massive upgrade.

Source: https://t.co/gwGIhDlInU

[Avner Sorek @AvnerSo]

MF Seuss

[Avner Sorek @AvnerSo]

@1962buzi @grok זה אמיתי?

[Real News IL @RealNewsIL]

לפרצוף של אוכלי ואוכלות המוות

[Itai @ItaiBoublil]

היא לא טועה

[Avner Sorek @AvnerSo]

If you know what you're doing - you can have AI keep a great standard of quality in your code.

Same as it always was - teams that didn't spend time on quality made low quality products and spent a lot of time on maintenance.

The insistence on quality comes from experience.

[Uncle Bob Martin @unclebobmartin]

In the last three days I have:
1. Designed and implemented a complete JVM language that Codex believes (whatever that means) would be ideal for AIs to use regardless of what humans think about it. It compiles down to JVM bytecode.

2. Designed and build, from scratch, a wiki with it's own internal web server and fully described by Gherkin style acceptance tests.

3. Made significant updates to the computer strategy of the Empire game.

4. Produce the crap4java and mutate4java tools that I used to help build the wiki.

5. Conceived of and implemented the differential mutation strategy used in both my clojure and Java mutation tools.

And for every one of those projects I implemented a strict TDD, ATDD, Crap, and Mutate workflow that forced coverage into the high 90s, kept Crap below 8, and split any files with more than 50 mutation sites.

My poor laptop had all 16 (8 hyperthreaded) cores burning at 100%. The fan was raging the whole time. I was hopping from window to window overseeing the entire campaign. It was exhausting!

Did that workflow slow the process down? Probably. Probably a lot. On the other hand all these projects maintained rigorous semantic stability, with all unit tests, and acceptance tests passing.

I never ran the wiki until it was done. It worked first time. I never compiled a program with AIR-J until it was done. It worked first time. No bugs have been introduced into the Empire game (so far).

And that, boys and girls, is a freaking miracle.

[Avner Sorek @AvnerSo]

@NickScheifler @SearchForRyan Totally. Small tasks split to sub agents with clear contexts makes this work.

[Avner Sorek @AvnerSo]

I see a lot of people still missing out on these basic AI coding practices -

- Use files for plans and requirements, don't rely on context.
- Clear context between smaller tasks
- Always give the agent a feedback loop it can call - even a type check.

[Avner Sorek @AvnerSo]

Iran has reportedly spent fifteen billion USD annually making long range missiles the core of its strategy.

I wonder where we would be if they spent that on GPUs instead ...

[StockMarket.News @_Investinq]

Oracle just told every AI company on earth the same thing.

Your models are worthless.

Not the technology, talent or the billions spent training them.

But the data they were trained on.

Larry Ellison, the man who built Oracle into the backbone of global enterprise just dropped a bombshell.

He said ChatGPT, Gemini, Grok, and Llama, all of them are training on the exact same data.​

The entire public internet, every Wikipedia page, Reddit thread and every news article.

That means they're all converging essentially becoming the same product with different logos.​

Ellison's word for it is commodities.

But here's where it gets dangerous.

He says the real gold isn't public data, It's private data.​

The medical records in hospital systems, the financial data in bank vaults.

The supply chain secrets of every Fortune 500 and guess where most of that data already lives.

Not Google, Amazon or Microsoft but inside Oracle.​

Oracle databases hold most of the world's high value private enterprise data.
So Oracle just launched something called AI Database 26ai.​

It lets the top AI models, ChatGPT, Gemini, Grok, Llama reason directly over a company's private data, without that data ever leaving the vault.​

They're using a technique called RAG, Retrieval Augmented Generation.

The AI doesn't train on your data, it searches it in real time.​

Think about what that means.

A bank could ask AI to analyze every loan it's ever made without exposing a single customer record.

A hospital could have AI diagnose patients using its full medical history without violating HIPAA.​

A defense contractor could let AI reason across classified operations without data leaving a secure environment.​

Ellison is betting this is bigger than the training market. Bigger than the GPU boom.

Bigger than the data center buildout.​

He called it the largest and fastest growing market in history.​

The numbers back the ambition.

Oracle's remaining performance obligations just hit $523 billion.

That's contracted revenue not yet delivered and $300 billion of it comes from OpenAI alone.​

Cloud revenue hit $8 billion in a single quarter, OCI grew 66 percent and GPU revenue surged 177 percent.​

But here's the part nobody's talking about.

If private data becomes the real AI moat, then whoever controls the database controls the future of AI.​

And that's a level of power that should make everyone uncomfortable.

[Eylon Levy @EylonALevy]

RT if you can't wait to visit a free Iran

[Avner Sorek @AvnerSo]

What becomes the real competitive advantage of software companies?

[Aanya @xoaanya]

If everyone has Al

What becomes the real competitive advantage for engineers?

[Aanya @xoaanya]

If everyone has Al

What becomes the real competitive advantage for engineers?

[exQUIZitely 🕹️ @exQUIZitely]

Most people associate the name Jordan Mechner with Prince of Persia - and that's perfectly fine. But what sometimes gets lost in that conversation is his very first published game: Karateka, from 1984.

It was Karateka that pioneered rotoscoped animation in video games. Not only did he introduce an entirely new technique, but he did so while still a student at Yale and solo-developing on the game for two whole years.

Karateka also introduced wordless storytelling. Simply through cutscenes and gameplay, the story unfolded before your eyes. Not a single word was spoken.

So while Prince of Persia will forever be his most famous game, Karateka remains his most important one.