KP

🦀 Building data infra @e6data. Fullstack developer in past life. Writes @

[KP @thisizkp]

High Agency is how one thrives in the AI era.

https://t.co/5VKVLSsnnj

[KP @thisizkp]

@PacktPublishing not able to access my pre-order. It's not showing up in the owned content cc @mactsouk

[KP @thisizkp]

@mactsouk @PacktPublishing Excited to read this. And preordered. All the best

[e6data @e6data]

There’s more hiding inside open lakehouse architectures than most teams realize.

Catch @vishnuv9248's talk on June 18 at 11:30 AM PDT, then drop by booth 548 to meet the e6data team.

Cost, governance, portability, and the freedom to run anywhere.

[e6data @e6data]

Reminder!
Samyak and Aayush are running a hands-on “Building a SQL engine from scratch” workshop this Friday, 1:30–5:30 PM, at Sahaj Software office, Koramangala.

[Gergely Orosz @GergelyOrosz]

Things I really dislike about Fable:

1. Anthropic collects my prompt history, stores it, and does whatever they want with it for 30 days. No opt-out

2. They can nerf their most expensive model without telling me, billing me the same amount, wasting my time. Whenever they want

[Dillon Mulroy @dillon_mulroy]

the only fable i'm looking forward to

[Arpit Bhayani @arpit_bhayani]

Would you let AI play your fav video game while you just stare at the screen? No! You play because you get joy, because it challenges you, because you love it.

Same applies to AI + Enginnering. What an amazing take by @hnasr. Watch this, folks!!

And yes, do not let AI rob you of the journey.

[John Carmack @ID_AA_Carmack]

I admire Fabrice Bellard. He is almost certainly a better overall programmer than I am.

[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.

[e6data @e6data]

Alerts tell you what changed. But they rarely tell you why.

This blog breaks down how we built a causal attribution engine that separates WHERE the delta came from, WHY it happened, and THE STORY an AI agent can verify with SQL.

Read here: https://t.co/ouWIdjRhP7

[Mitchell Hashimoto @mitchellh]

I strongly believe there are entire companies right now under heavy AI psychosis and its impossible to have rational conversations about it with them. I can't name any specific people because they include personal friends I deeply respect, but I worry about how this plays out.

I lived through the great MTBF vs MTTR (mean-time-between-failure vs. mean-time-to-recovery) reckoning of infrastructure during the transition to cloud and cloud automation. All those arguments are rearing their ugly heads again but now its... the whole software development industry (maybe the whole world, really).

It's frightening, because the psychosis folks operate under an almost absolute "MTTR is all you need" mentality: "its fine to ship bugs because the agents will fix them so quickly and at a scale humans can't do!" We learned in infrastructure that MTTR is great but you can't yeet resilient systems entirely.

The main issue is I don't even know how to bring this up to people I know personally, because bringing this topic up leads to immediately dismissals like "no no, it has full test coverage" or "bug reports are going down" or something, which just don't paint the whole picture.

We already learned this lesson once in infrastructure: you can automate yourself into a very resilient catastrophe machine. Systems can appear healthy by local metrics while globally becoming incomprehensible. Bug reports can go down while latent risk explodes. Test coverage can rise while semantic understanding falls. Changes happens so fast that nobody notices the underlying architecture decaying.

I worry.

[Sam Lambert @samlambert]

thanks to AI i create and abandon projects 4x faster

[KP @thisizkp]

@shrirambalaji they missed the point that compilers are deterministic

[Swanand @_swanand]

I would argue that it's much better to work through his book:
https://t.co/2uQmVMHej8

[皓樂芒 @howlemont]

美国数学家Richard Hamming是图灵奖得主，计算机先驱，纠错码之父。

他说自己很早在Los Alamos见过Feynman费曼、Oppenheimer奥本海默等。他承认自己当时很嫉妒，凭什么大家都是物理人，你们这些人是大牛人？

他后来在贝尔实验室Bell Labs继续观察Shannon香农、这些人，为什么有些人做到了，而其他人只是差点做到了？

“运气”只能解释一半。具体做成哪一个题，当然有运气。
Hamming自己也承认，他和Shannon香农同在贝尔实验室Bell Labs，同一时期一个做coding theory，一个做information theory，确实有运气成分。

但Einstein爱因斯坦、Shannon香农这类人可以反复做出好东西。一次可以说是撞上了，反复撞上，就要看准备工作、胆量和选择了。

机会会飘过很多人身边，但只有少数人已经在脑子里预留了接口。

他讲，重要问题不是结果影响听起来多大。比如时间旅行、传送、反重力，结果影响当然巨大，但手里没有合理的入口，也只能供人幻想。

一个真正该上的科题，要同时有分量和入口，如果做成，它会改变一些东西；而你现在又能找到一条可以攻进去的路。

很多聪明人输在这里。他们每天忙忙碌碌，设计问题很精致，方法很专业，可心里其实知道，这些东西就算做完，也很难通向更大的东西。

Hamming每周五中午以后给自己留“Great Thoughts Time”，只聊大问题，比如计算机会怎样改变科学？

这听起来像偷懒，其实是给自己留10%的雷达时间。

你如果一周五天都在处理眼前小事，很容易把效率误认为方向。你会变成一个很勤奋、很可靠、很会交付的人，然后十年后发现，自己一直在小问题上越做越熟。

Hamming还有一个观察：

关着门工作的人，完全隔绝噪音，短期内很舒服，貌似产出更高；长期看，你会错过那些不成体系、没法写进报告、但能告诉你“问题变了”的信号。

而开着门工作的人，虽然经常被打断，但更可能知道世界的新动向。一流工作需要深度，也需要暴露在真实问题流里。

他讲，成名后的危险，也很像今天的创业者、研究者和内容创作者。

一旦做出一个大东西，人会不愿意再种小种子，只想一上来就抱大树。Hamming说Shannon香农在信息论之后，可能就被“下一次必须同样伟大”这件事困住了。

早期的伟大而会把人冻住。因为你不愿意再做那些小、丑、未成形、别人看不上的起点。

但大东西通常就是从这种小起点长出来的。

还有一点很多人不爱听。

做出来还不够，你要会把它讲出去。

Hamming说科学家讨厌“sell”这个词，觉得好东西应该自然被世界看见。可现实是，所有人都在忙自己的事。你写得不清楚，讲得不清楚，会议上不敢开口，别人就会翻过去。

所以表达不是包装，是研究的一部分。

一个想做一流工作的人，至少要会三种表达：
写清楚，在正式场合里讲清楚，在混乱的场合里也能讲清楚。
很多“事后诸葛亮”三周后写报告证明自己早就看对了，但时过境迁了。

才华放晚了也会变成旁白。

把“伟大”从天赋、环境、运气这些大词拽回到具体的日常动作：

你有没有固定时间想大问题。
你手里有没有10到20个真正重要、且可能进攻的问题。
你遇到一个机会时，能不能立刻看出它碰到了你哪一个老问题。
你做一个项目时，有没有顺手把它变成一类问题的方法，而不止交一个答案。
你有没有把自己的缺点拿来当借口。
你会不会和大的体系合作，借力秘书、同事、老板、听众、组织流程打战役，而不是一生耗在小型战斗里。

讲真，你可以说自己缺运气，缺资源，缺年轻，缺老板支持。那你有没有准备好？有没有选对问题？有没有留出想大问题的时间？有没有勇气押上去，把成果讲到别人愿意停下来听？

说到最后，很多所谓怀才不遇，可能只是长期没有管理自己。

[Joran Dirk Greef @jorandirkgreef]

Where others use AI to “create”, at TigerBeetle we’re more excited about how to use the machines to “destroy”.

Some see AI as a way to “type faster” or “increase productivity”, but at TigerBeetle I tell the team we’re already “too productive” (through TigerStyle), we want calm focused work not burnout… and we know that all the huge gains come from understanding anyway.

So “destruction” aka testing, i.e. as a foil or sparring training partner, is where we see it’s at with AI.

Not to create. That stays with the humans so we don’t atrophy our understanding, which is more valuable than “LOC”.

But to increase quality through defense in depth in testing. But even there, the gains with AI are marginal, a few percent, compared to the 90% power of our DST, which again, came from systems thinking.

So I encourage our team to “keep playing the violin yourself”, to keep practicing, keep training, keep investing in understanding.

[Joran Dirk Greef @jorandirkgreef]

I asked the TigerBeetle team yesterday:

“What are the things that accelerate us existentially, by orders of magnitude?”

Everyone said:

“Exponential quality”
“First principles understanding”
“Systems thinking”
“A methodology that’s 2nd order remarkable”

Guess what nobody said?

[Uncle Bob Martin @unclebobmartin]

What we are losing with AI is syntax -- and good riddance. The less our brains are occupied by semicolons and braces the better. There are much more important things for us to consider and manage.

[Darshak Rana ⚡️ @thedarshakrana]

https://t.co/Ctz4zbfTG1