Calvin Janitra Halim

Eu tenho um piquenique com meu esposa. Banner and profile picture by

[Calvin Janitra Halim @CalvinJH]

またこの時期が来ました！今回の冬コミでは、自分もCJHJという名義で2曲出していますので、ぜひチェックしてみてください！暇がある方は是非今週の土曜日に私たちのブースを見に来てください！東7ホールのw19aです！

[Desktop Notes @DesktopNotes]

冬コミNew Albumのサンプルです。Youtubeの方にlong versionも上げているので是非試聴してみてください！
https://t.co/Kgm1pLxgH0

[Angelo @angelodotsui]

a good representation of how misinformation spread

[kakiraちゃん | 技術書典20【そ04】個人開発AIプロダクトのアイデア原石 @kakira9618]

🎉AIにゲームを作らせるゲーム Made In AI を公開しました🎉

テーマを入れるだけで、AIがその場でミニゲーム本体を生成、その場で遊べます。

テーマは自由。「猫」「餅」「釣り」など。何が出るかは遊ぶまでわかりません。

PC・スマホ対応、無料です:
https://t.co/31BDnZ6FAC

[Chaofan Shou @Fried_rice]

Claude code source code has been leaked via a map file in their npm registry!

Code: https://t.co/jBiMoOzt8G

[Andrej Karpathy @karpathy]

- Drafted a blog post
- Used an LLM to meticulously improve the argument over 4 hours.
- Wow, feeling great, it’s so convincing!
- Fun idea let’s ask it to argue the opposite.
- LLM demolishes the entire argument and convinces me that the opposite is in fact true.
- lol

The LLMs may elicit an opinion when asked but are extremely competent in arguing almost any direction. This is actually super useful as a tool for forming your own opinions, just make sure to ask different directions and be careful with the sycophancy.

[kakiraちゃん | 技術書典20【そ04】個人開発AIプロダクトのアイデア原石 @kakira9618]

【PlayPage リリース！🎉】

あらゆるWebページを、Geminiパワーでその場で「アプリ化」して遊べるChrome拡張「PlayPage」を公開しました！

アプリ例：Wikipedia記事のゲーム化 / 英文をクイズ化 / AtCoder問題のVisualizer生成 / 統計・数学シミュレータ等

コンテンツ理解のお供に是非！

↓デモ動画

[Pedro Silva @pedrosilva]

When you want too report Cloudflare is down but Down Detector uses Cloudflare.

[ちいかわ💫アニメ火金 @ngnchiikawa]

⬛️

[ナガノ @ngntrtr]

🐬

[daniel habib @DannyHabibs]

Try it in your browser: https://t.co/1X1z5drktK

(Works on phones and laptop webcams too.)

[お買いものパンダ【楽天公式】 @Rakuten_Panda]

秋の頭皮ケア。マッサージ器買ってみたよ。 #お買いものパンダ

[Calvin Janitra Halim @CalvinJH]

Very much relatable

[Andrej Karpathy @karpathy]

Continuing the journey of optimal LLM-assisted coding experience. In particular, I find that instead of narrowing in on a perfect one thing my usage is increasingly diversifying across a few workflows that I "stitch up" the pros/cons of:

Personally the bread & butter (~75%?) of my LLM assistance continues to be just (Cursor) tab complete. This is because I find that writing concrete chunks of code/comments myself and in the right part of the code is a high bandwidth way of communicating "task specification" to the LLM, i.e. it's primarily about task specification bits - it takes too many bits and too much latency to communicate what I want in text, and it's faster to just demonstrate it in the code and in the right place. Sometimes the tab complete model is annoying so I toggle it on/off a lot.

Next layer up is highlighting a concrete chunk of code and asking for some kind of a modification.

Next layer up is Claude Code / Codex / etc, running on the side of Cursor, which I go to for larger chunks of functionality that are also fairly easy to specify in a prompt. These are super helpful, but still mixed overall and slightly frustrating at times. I don't run in YOLO mode because they can go off-track and do dumb things you didn't want/need and I ESC fairly often. I also haven't learned to be productive using more than one instance in parallel - one already feels hard enough. I haven't figured out a good way to keep CLAUDE[.]md good or up to date. I often have to do a pass of "cleanups" for coding style, or matters of code taste. E.g. they are too defensive and often over-use try/catch statements, they often over-complicate abstractions, they overbloat code (e.g. a nested if-the-else constructs when a list comprehension or a one-liner if-then-else would work), or they duplicate code chunks instead of creating a nice helper function, things like that... they basically don't have a sense of taste. They are indispensable in cases where I inch into a more vibe-coding territory where I'm less familiar (e.g. writing some rust recently, or sql commands, or anything else I've done less of before). I also tried CC to teach me things alongside the code it was writing but that didn't work at all - it really wants to just write code a lot more than it wants to explain anything along the way. I tried to get CC to do hyperparameter tuning, which was highly amusing. They are also super helpful in all kinds of lower-stakes one-off custom visualization or utilities or debugging code that I would never write otherwise because it would have taken way too long. E.g. CC can hammer out 1,000 lines of one-off extensive visualization/code just to identify a specific bug, which gets all deleted right after we find it. It's the code post-scarcity era - you can just create and then delete thousands of lines of super custom, super ephemeral code now, it's ok, it's not this precious costly thing anymore.

Final layer of defense is GPT5 Pro, which I go to for the hardest things. E.g. it has happened to me a few times now that I / Cursor / CC are all stuck on a bug for 10 minutes, but when I copy paste the whole thing to 5 Pro, it goes off for 10 minutes but then actually finds a really subtle bug. It is very strong. It can dig up all kinds of esoteric docs and papers and such. I've also used it for other meatier tasks, e.g. suggestions on how to clean up abstractions (mixed results, sometimes good ideas but not all), or an entire literature review around how people do this or that and it comes back with good relevant resources / pointers.

Anyway, coding feels completely blown open with possibility across a number of "kinds" of coding and then a number of tools with their pros/cons. It's hard to avoid the feeling of anxiety around not being at the frontier of what is collectively possible, hence random sunday shower of thoughts and a good amount of curiosity about what others are finding.

[ちいかわ💫アニメ火金 @ngnchiikawa]

☀️

[Hideyuki Tanaka @tanakh]

今書いておかないと時期を逃しそうなんでなんか書いておく。

AIが怖い。これはAIが賢くて怖いとかそういうんでなくて、人間のほうが原因で怖いというか。

ちょっと前にTypeScriptとwasmの性能差についてのベンチマーク記事があった。

[Odyssey @odysseyml]

Introducing AI video you can watch and interact with, in real-time!

Powering this is a new world model that imagines and streams video frames every 40ms(!). No game engine in sight.

We call it interactive video, and it's free for anyone to try right now (GPUs permitting)!

[Google DeepMind @GoogleDeepMind]

Video, meet audio. 🎥🤝🔊

With Veo 3, our new state-of-the-art generative video model, you can add soundtracks to clips you make.

Create talking characters, include sound effects, and more while developing videos in a range of cinematic styles. 🧵

[Kinopee @kinopee_ai]

Gemini 2.5 Pro、お前エージェントがやっていいことと悪いことの見分けもないのか？
.env を閲覧する権限もないのに、削除するか？

[ナガノ @ngntrtr]

🐈

[Andrew Ng @AndrewYNg]

Some people today are discouraging others from learning programming on the grounds AI will automate it. This advice will be seen as some of the worst career advice ever given. I disagree with the Turing Award and Nobel prize winner who wrote, “It is far more likely that the programming occupation will become extinct [...] than that it will become all-powerful. More and more, computers will program themselves.”​ Statements discouraging people from learning to code are harmful!

In the 1960s, when programming moved from punchcards (where a programmer had to laboriously make holes in physical cards to write code character by character) to keyboards with terminals, programming became easier. And that made it a better time than before to begin programming. Yet it was in this era that Nobel laureate Herb Simon wrote the words quoted in the first paragraph. Today’s arguments not to learn to code continue to echo his comment.

As coding becomes easier, more people should code, not fewer!

Over the past few decades, as programming has moved from assembly language to higher-level languages like C, from desktop to cloud, from raw text editors to IDEs to AI assisted coding where sometimes one barely even looks at the generated code (which some coders recently started to call vibe coding), it is getting easier with each step.

I wrote previously that I see tech-savvy people coordinating AI tools to move toward being 10x professionals — individuals who have 10 times the impact of the average person in their field. I am increasingly convinced that the best way for many people to accomplish this is not to be just consumers of AI applications, but to learn enough coding to use AI-assisted coding tools effectively.

One question I’m asked most often is what someone should do who is worried about job displacement by AI. My answer is: Learn about AI and take control of it, because one of the most important skills in the future will be the ability to tell a computer exactly what you want, so it can do that for you. Coding (or getting AI to code for you) is a great way to do that.

When I was working on the course Generative AI for Everyone and needed to generate AI artwork for the background images, I worked with a collaborator who had studied art history and knew the language of art. He prompted Midjourney with terminology based on the historical style, palette, artist inspiration and so on — using the language of art — to get the result he wanted. I didn’t know this language, and my paltry attempts at prompting could not deliver as effective a result.

Similarly, scientists, analysts, marketers, recruiters, and people of a wide range of professions who understand the language of software through their knowledge of coding can tell an LLM or an AI-enabled IDE what they want much more precisely, and get much better results. As these tools are continuing to make coding easier, this is the best time yet to learn to code, to learn the language of software, and learn to make computers do exactly what you want them to do.

[Original text: https://t.co/HdI3Jb9HmF ]