Phawin

Software Engineer

[Phawin @Lifez]

@nutjane Congrat!!!!

[Phawin @Lifez]

@gu174red สมัครครับ

[geoff @GeoffreyHuntley]

Canceling 8 x ChatGPT 500 subs w/@OpenAI.

If you value your time, then OpenAI just isn't it right now.

Poor service availability, and inference is super, super slow. This isn't even about model quality; the problems go far beyond that. @claudeai is 100/100 better choice right now, faster inferencing, better availability and the models are better too.

[Charlie Marsh @charliermarsh]

We're starting to see some agentic Rust forks (or alternative implementations) focused on iteration speed.

More-so than the specific projects, I'm interested in the ideas being explored (and how they could evolve upstream), so wanted to collate a few here...

[VoidZero @voidzerodev]

Introducing tsrs: a Rust port of the TypeScript compiler 🦀

We are not forking TypeScript!

◆ In close collaborating with the TypeScript team
◆ Up to 8x faster than TS 7.0 & ~70% less memory
◆ Runs in WASM, so it works in your browser & on Workers
◆ Soon part of the Oxc toolchain

npx tsrs

https://t.co/qmmxF3YnQy

[Tyler @rezoundous]

I think... I might cancel Codex sub for the first time ever

[Phawin @Lifez]

OpenAI vs Anthropic Asian session 😅

[lauren @poteto]

how do you trust your agents' work more? copy this post/link and give it to your @bot or agent!

1. codebase improvements: this is just good software engineering - better architecture, stronger types and invariants, clear boundaries. agents love to copy what exists and take the shortest path, so make the easy thing the right thing. categorically eliminate problems through code and you won't need to think about it again.
2. static analysis: lint rules, tests, CI, verification (eg TLA+). very token efficient ways to catch issues ahead of time.
3. prose: comments, rules, AGENTS.md, skills - anything that can't be enforced and requires agent judgment. use it minimally, but do use /create-verification-skill.
4. human review: the hardest to scale. at the rate agents land code, it's pretty much impossible to thoroughly review every PR, so spend that effort on the highest leverage point, designing an environment where agents do the right thing by default.

this forms an inverse pyramid: you want most of your effort at the top, least at the bottom. codebase improvements are deterministic, making it more token efficient, and lets you sleep more soundly at night.

install https://t.co/u9xIlN3eN0 and create a new Project:
1. look through my slack/chat messages and code review comments where i've given feedback on PRs, code, architecture
2. use /recall on my past transcripts to learn how i steer agents
3. /poteto-mode use /correct and /architect on past commits to find the most common pitfalls agents fall into, then propose a plan to rearchitect the code i own so it's more agent friendly. answer open questions with prototyping rather than defer to me, and back the plan with real data.
4. before you begin, use /sharpen + /in-your-own-words and confirm this prompt with me

[Phawin @Lifez]

@longtundiary เดือนหน้าไม่อยู่แล้ว 🤣

[Phawin @Lifez]

WHAT/

[Tibo @thsottiaux]

Day 11/

We've landed a series of changes and more compute to very significantly decrease the occurrence of the disruptive "Selected model is at capacity. Please try a different model". When it does happen, it should also eventually just continue by itself and not just block on it.

Let us know if you still see it out there in the wild and we'll take a look.

[Phawin @Lifez]

สงสัยเดือนหน้าได้ลา chatgpt

[Phawin @Lifez]

@ClaudesRH 👀

[Shubham Mishra 🐐 @shubh6200]

AI made Spotify engineers faster at shipping code. But it also exposed a new problem: keeping quality under control at that speed.

Spotify shares what it learned the hard way, from production incidents and code churn to testing, mobile quality, and infrastructure pressure.

The lesson for every engineering team building with AI:

Generating code faster is easy. Shipping reliable software faster is the real challenge.

A must-read for software engineers :

https://t.co/GpJtdwz0tt

[Scott Farquhar @scottfarkas]

True. Most people aren’t using today’s AI to its full potential.

[Phawin @Lifez]

ปรับตัวนิดหน่อย 6.1 sol ก็เหลือๆละ

[Ruby on Rails @rails]

Agents on Rails is back with Stage 3!

We put the best-performing models to the test with 7 new multi-step tasks, each spanning 3–8 tickets. From building Rails apps from scratch to navigating existing (and at times messy) legacy codebases, we wanted to see how well agents handle the kind of work developers actually face: going back and building on their own work.

Some highlights:
- Best overall: @OpenAI GPT-6.1 Sol offers a strong balance of accuracy, speed, and cost (60.8% success, $0.80 & 23m 35s per run)
- Most accurate: @AnthropicAI Opus 5.5 at max effort (65.3%, but it is also the most expensive and amongst the slowest)
- Fastest: @OpenAI GPT-6 Astra at medium effort (16m 36s, 54% success). Astra's max effort is also fast (22m 36s, 62.3% success).
- Cheapest: @OpenAI GPT-6 Luna ($0.19/run, 43.5% success)

We also combined all tasks from all stages into one central leaderboard.

Read the full Stage 3 report from @evilmartians here: https://t.co/JYnLizQKQQ

[Tap @visibletrap]

ชอบประโยคนี้มากเลย

"Don’t let your kids best days financially be while they are living with you"

ถ้าวันนี้ได้หมดทุกอย่างที่ต้องการแล้ว จะเหลืออะไรไว้ภูมิใจตัวเองในอนาคต

https://t.co/dWikCO2o70

[Phawin @Lifez]

@longtundiary จริงด้วยย

[Phawin @Lifez]

ไป Native อีก 1

[Notion @NotionHQ]

iPad people, this one’s for you. Before, our iPad app was mostly a web view. Now, we’re rebuilding it natively!

→ Sidebar and inbox
→ Notion AI and search
→ A new editor (coming soon)

And on iOS 27, you can ask Siri to find or create a page, start an AI chat, or record meeting notes.

[Phawin @Lifez]

เติม Context อีกนิดคือถ้าทำได้ level นั้นการเรียนรู้สกิลใหม่ๆไม่ใช่ปัญหาละอ่ะ

[Phawin @Lifez]

ถ้าทำตามนี้ได้จริง คนๆนั้นจะไม่กลัวโดนแทนที่ด้วย AI

[👩‍🌾0xAirdropFarmer @0xairdropfarmer]

>ใช้เวลา 4 ปีเรียนเขียน Java, Python, JavaScript, Go >ใช้เวลา 2 ปีดีบั๊ก memory leak กับ race condition >ชำนาญ data structure, system design, CI/CD
> อ่าน Stack Overflow หัดถามจนไม่โดน mark not constructive
> อ่าน document ทุกตัวอักษรนานรวมจนนับไม่ได้
> ใช้เวลา 3 วันเขียนฟีเจอร์เล็ก ๆ ที่ทำงานได้จริง
> ผ่านไปหลายปี กำลังจะเป็น ปกม พันล้าน
> ถูกแทนที่ด้วย Claude Code