PT

Me

[PT @ptantiku]

Tesla Roadster is crazy https://t.co/mwsuyX967P

[PT @ptantiku]

AI Assistant ย้ายค่ายได้แล้ว

[Brian Roemmele @BrianRoemmele]

NEW GROK BOT!

AI MEMORY TRANSFER!

Your old AI knows you.

Your new one shouldn't have to start from zero. 🧠➡️

Meet Memory Transfer, a @Grok @Bot that brings over what Claude or ChatGPT learned about you, so you don't spend weeks re-explaining yourself.

There are two ways to bring it over:

• Paste one prompt into your old chat and send me the answer.

• Or drop in your full ChatGPT or Claude data export.

The part that matters most is that nothing gets in automatically.

We review every item together, one line at a time. Only what you keep gets saved, and each item is tagged with where it came from and when.

Other AIs' guesses don't become facts, and nothing you've already saved gets overwritten unless you say so.

Switch assistants and keep your context, without the junk.

Try Memory Transfer on Grok Bot today.

https://t.co/c9r2Vsw2Ek

[PT @ptantiku]

@kafaak แบบนี้ควรจะกดรายเดือนลด 50% ก่อนซักเดือนไหมครับ แล้วค่อยไปรายปี น่าจะถูกกว่า

[PT @ptantiku]

@whitechickpink มีซักล้านนึง แล้วถ้าพอร์ทลงมาไม่กี่เปอร์เซ็นก็เริ่มเครียดแล้วนะ ถ้าก้อนใหญ่ขนาดนั้นคงน่าจะกลัวไม่กล้าทำอะไรแน่เลย

[lauren @poteto]

it's very interesting how many replies i've gotten about maintainability and clean code being still important. i would argue that that is no longer true and we all need to update our priors on what Good Code really means in 2026.

clean code has always come at a cost - performance. indirection and making code readable/understandable for a human is not free, it often comes at the cost of worse performance. we have historically been okay with this because of Moore's Law and the fact that most code is not on the hot path.

these principles also came from a time where humans maintained code. it made sense to optimize for humans then, but it makes less sense now when agents are already writing all the code. we will in fact probably discover and come up with new principles: principles of agent friendly architecture (i plan to write more thoughts about this soon)

[lauren @poteto]

codebases have always contained slop. we have always been okay with this because code rarely needs to be perfect, outside of mission critical "hard" (eg embedded) software.

swe is a unique form of engineering because you're often building something that constantly evolves and is almost organic - user tastes change, your mental models evolve, and features come and go.

admittedly, i and many others who've been coding for a long time were initially (like back in 2024) skeptical that agents would write production code. "but what about all the slop that would enter our beautiful codebase?".

well, even before, we had human slop, and there was a lot of it! if we're being truthful, most people call "code i didn't write" slop. the reality is that 99% of people (i include myself in that number) don't even know what Good Code looks like, let alone be capable of writing it. i would propose that Good Code is:

• correct
• bug free
• fast and cost efficient to run

it has always been exceedingly difficult and expensive to write truly Good Code, because for the longest time (maybe @bendlang will change that), code was not something you could cheaply and formally guarantee to have all 3 of these properties. this is why writing hard software is much more difficult and expensive, because depending on the use case, you may only get one chance and so you have to be right from the start.

but the vast majority of software we use does not need to be Good Code to provide value to users. in fact, it is very likely that most software we use is not formally verifiable! we accepted some bugs in exchange for velocity and progress, so we rely on tests and o11y instead and have adopted a more incremental approach. this is a good thing!

agents help us move faster, and if you're open to the idea, are capable of helping us write better code. it won't be Good Code (that is still expensive), but it is likely to be much better on average at scale.

the new craft of swe is to figure out how to do this. it's your job now! if your productivity with and without agents is not significantly different, you're doing something wrong. but it's not too late to embrace it and use it as an opportunity to learn a whole new skill tree

[lauren @poteto]

volume does matter
look, i get it. if you were an engineer before agents came along, you're rightfully skeptical about lines of code and number of PRs being used as a metric for any kind of productivity. and i agree! or at least, i used to. for ease of explanation i will talk about this from the perspective as being an engineer on a large team of at least 50.

volume didn't matter before because we focused on impact. you could have high impact even with a few PRs: for example you could make a one line config change and save the company millions of dollars.

but before agents, the times where i saw volume come into the conversation were for the outliers. on the negative end you might have someone who is struggling to perform, on the other end you might have someone who was a "coding machine" archetype. a coding machine was someone who could just lock themselves in a room and emerge with a stack of PRs that solve a huge number of problems. so volume did matter, but only at the tail ends of the distribution, and combined with impact.

if you've ever worked at a large tech company before, you'll get what i mean. the thing about agents is that big tech co problems are now small-medium co problems as well.

how do agents change this calculus? well, everyone now has the ability to become a coding machine. you have incredibly capable frontier models that can write code better than any of us, at a rate much faster than we can. in this world, if you're still producing the same number of PRs as before, why wouldn't you pause to wonder why you're not being more productive? alien intelligence is here, and yet you can't outship a human coding machine?

this brings me back to why i keep talking about trust (https://t.co/7fzYZV8LGH). if you haven't put in the work to trust your agent's output, it is very difficult to scale up your productivity.

and yes, this approach does require more tokens, but i think about this in terms of cost per intelligence. before agents, this cost was very high - you needed to hire many software engineers with high 6 figure salaries to do the work. tokens are still expensive today, but are likely to get cheaper over time (https://t.co/gTBZx7pl14) factoring in the cost of intelligence. what looks exorbitant today will likely be affordable in 6 moths to a year. a single engineer with agents can do the work of tens if not hundreds of engineers, at a fraction of the cost.

the reality that i don't think has hit yet is that job of the software engineer has truly changed. our job is not merely to produce software any longer (well tbh it was never only about the code, but bear with me for the sake of the explanation), but the machine that writes the software. this idea of a software factory, or a Michelin kitchen as i like to call it, is still a topic of research. and it's the type of stuff i like to share, not to flex, but to show you what's possible when you put a lot of rigor into using agents at scale

[PT @ptantiku]

@larsencc @bot @bot give me a tl;dr

[PT @ptantiku]

This is really good https://t.co/VoZsFbl1ns

[Andrej Karpathy @karpathy]

We'll be spending a lot more time trying to understand the outputs of language models. A few thoughts, tips & tricks:

Writing. Something I've had success with: Ask your LLM to explain something in ASD-STE100, it's a controlled language specification originally developed for aerospace maintenance documentation. LLMs well-versed in this language and it comes with heavy constraints on clean writing style that I often find a lot more readable. Sometimes I've tried to soften it a bit e.g. ask for "80% of the way to ASD-STE100" because the spec is quite stringent. But even better:

Diagrams / images. Instead of writing, ask your LLM to create a diagram. These can be a lot easier to process, parse, and understand. But even better:

Web pages. Ask for output "in HTML" to get a beautiful, interactive webpage. LLMs are getting really good at frontend and can create beautiful experiences, animations, etc. But even better:

Explainer videos. The output format I am most bullish on is fully custom / bespoke explainer videos generated on any arbitrary topic. Experiment with things like "Create a 3b1b style video explainer on X. Use my ElevenLabs API key for audio narration". (you'd need an API key for the latter or you can ask your LLM to find you decent free alternatives that use your local compute). This is actually starting to work!

In summary:
- As LLMs get better, they will do more and more of the legwork autonomously, and a lot more of our work will rise up the abstractions into oversight and understanding.
- Luckily, LLMs can help here too because as intelligence and code are increasingly abundant, you can ask for large, custom, discardable software artifacts (e.g. web apps, video explainers) that would have never made sense to create before. Push the boundaries here and you'll be surprised.

[sui @birdabo]

AHAHAHAHAHAHAHAHAHAHAHA NO WAY.

> OpenAI just announced Dots.
> SpaceXAI bought https://t.co/WdFqpl07IQ

it redirects straight to the Grok Bot download page. based lmao.

[PRD กรมประชาสัมพันธ์ @prd_official]

🌧️ ฝนตก น้ำท่วม เช็กให้ครบในที่เดียว!
📍 NOW รวมข้อมูลสถานการณ์น้ำในกรุงเทพมหานครแบบครบจบ
ประชาชนสามารถติดตาม สถานการณ์น้ำ จุดน้ำท่วมขัง และคาดการณ์สภาพอากาศ พร้อมข้อมูลสำคัญที่ช่วยวางแผนการเดินทางและรับมือสถานการณ์ได้ที่

👉 https://t.co/1YcPzNEHDO

#NOW #กรุงเทพมหานคร #น้ำท่วม

[คุณก้อน @CNanxeng]

กล้องสาธารณะที่ใกล้ที่สุด ภาพอัปเดตทุก 15 วินาที เผื่อใครต้องเดินทางไปที่ไหน ดูในนี้ได้เลย เรียลไทม์มาก

https://t.co/9U5RmEaqju

#น้ำท่วม #น้ำท่วมกรุงเทพ

[Rukchanok Srinork @nanaicez]

ระบบฐานข้อมูลกลางพรรคประชาชน
ทำเสร็จหลายวันแล้วงับ ฝากเซฟลิงค์เก็บไว้ใช้
https://t.co/38gbYUNi6U

- รวมระบบข้อมูลเรดาร์ฝน/ลม ✅
- ระบบ one map น้ำ ✅
- แจ้งเลี่ยงเส้นทางสัญจร ✅
- รวมพิกัดจุดฝากรถ จุดพักพิง กรุงเทพและปริมณฑล ✅
- ดูระดับน้ำในคลอง กทม. ใกล้บ้าน ✅
- เช็คการลงพื้นของ สส./สก. ✅
ใครยังไม่เห็น สส. สก. ลงพื้นที่สับโลดดด

รวมทุกอย่างไว้แล้วในที่เดียว 🧡

[SaltyAom @saltyAom]

How can a random Japanese guy show case and build a better M3 interactive editor better than a trillion dollar company 😭😭😭

[PT @ptantiku]

more is less

[ClaudeDevs @ClaudeDevs]

Starting September 14, we're permanently raising standard weekly limits in Claude Code by 25% for Pro, Max, Team, and seat-based Enterprise plans. Until then, the current 50% increase will be in place.

[PT @ptantiku]

ชื่อโดเมนนี้เลย

[iannnnn  @iannnnn]

Password leak statistics 06 2026
https://t.co/PaDKv79RSi

[Thidakarn @thidakarn]

ในที่สุด ความเชื่อว่า "กิน KFC ก่อนขึ้นเวรแล้วปริมาณงานในเวรจะมากขึ้น" ก็ได้รับการพิสูจน์ด้วยงานวิจัยระดับ RCT ว่าไม่เป็นความจริง
เป็นงานวิจัยจากทีมศัลยแพทย์รพ.พัทลุง และได้รับการตีพิมพ์ในวารสารอินเตอร์
ชอบที่มีการเปรียบเทียบกับกลุ่มควบคุม และกลุ่มไก่ทอดห้าดาวด้วย🤣

[ClaudeDevs @ClaudeDevs]

As a result of a US government directive, we are suspending access to Claude Fable 5 for all users. You can continue to use all other Claude models.

Here’s what this means for you:

Across Claude products, new sessions will run on your selected default model or Opus 4.8, and existing Fable 5 sessions will end with an error.

On the Claude Platform, requests to Fable 5 will also return an error. Please update your integrations to other Claude models.

We know this is a disruption to your workflows; we appreciate your patience and support.