aki.ts🦔

デザイン苦手、typescript,python 使い(´⊙ω⊙`)エンジニアやプログラミング学習してる方基本フォロー返します #React #python #Nextjs #typescript #AWS #Bedrock #CBs #LINE #キミガタリ #Mastra #AISDK #リアコエ

[aki.ts🦔 @HedgehogPython]

仲間と一緒にMastra本を出版します!
基礎解説から、実務で使えるハンズオン3本、複数クラウドへのデプロイまで網羅しています。
本日Amazonで予約開始しました。Mastraを使ってAIエージェントを作りたい方、ぜひ手に取ってみてください！
https://t.co/uKEXeH6tWa

#Mastra実践入門

[aki.ts🦔 @HedgehogPython]

@ru__ru__ru__ru 単一ループかサブエージェント構成かについては、評価用のOSSがAnthropicから出ているので、実際に使って試してみるのがおすすめですねhttps://t.co/0BMg3u3shj

[aki.ts🦔 @HedgehogPython]

サブエージェントの実装思考も時代遅れか？😅

Anthropicいわく「モデルが進化した今、安易なマルチAgent化より単一ループ＋Skillsの方が速くて高品質」。
https://t.co/n45uSMytJK

[みのるん @minorun365]

あらあら、もう公式がAWS Bedrockって言っちゃった😅
僕らユーザーもそう表現できたら便利だとずっと思いつつ、公式名称に合わせて使ってるのに…
https://t.co/nn6RrCZi2A

[みのるん @minorun365]

なんと、 #みのるん式 がAmazonで和書102位という過去最高のランキングを記録している…！！
ありがとうございます🥹
https://t.co/9ZiIRhFHA9

[みのるん @minorun365]

きょうの資料です！ #JAWSUG_Tokyo

Kiro Crewで「みのるん式」仕事術やってみた
https://t.co/CvKj2W5flQ

[モミー 【少年ジャンプ＋編集】 @momiyama2019]

21歳のドイツ在住、スペイン人作家さんによる読切「夏の嵐がやってくる！」を、本日ジャンプ＋で掲載しました。

この空気感、ぜひ読んでみて欲しいです。
海外版ジャンプルーキー！のMANGA PlusCreatorsの投稿作品です。（投稿作はスペイン語でした）
https://t.co/UKtUx2QkgK

[みのるん @minorun365]

なんと！！デブサミ福岡でもベストスピーカーに選出いただきました。ありがとうございます🥹 #devsumi
https://t.co/dF1YkNCS0j

[aki.ts🦔 @HedgehogPython]

現地でお会いしましょう🦔

[馬渡慎之助 @hensyunoouma]

【告知】

11月5日（木）15:15〜19:00
書籍『MastraによるAIエージェント開発・運用［実践入門］』の著者3名にブースを展開していただきます！

ブース名
『仕事でどう使う？生成AI活用相談室』

日々の業務に生成AIをどう取り入れるのか。ビジネス活用から本づくりにおける活用事例まで、具体事例を用いてご相談にお答えします。

https://t.co/99AeiXvpRE
ぜひお越しください。

※目安です。
・ハリネズミさん（@HedgehogPython） ：15:15〜16:40
・伊野瀬さん（@i_inose0304）：16:40〜18:05
・みのるんさん（@minorun365）：18:05〜19:30

[Kento.Yamada @ymd65536]

バックエンドの知識地図
https://t.co/a4sIqUQ6yu

[じんの @yjinn448208]

書影も出ました！！いよいよ感があってドキドキしています！！​😂
https://t.co/Lg3BpYiUZa

[Felix Rieseberg @felixrieseberg]

Hi, I'm Felix. I'm an eng lead working on Claude. I love watching people take flight and do things they never thought possible.

We often describe that as "being more ambitious" when using AI, which is tricky advice to follow. I wrote a tutorial:

https://t.co/vNPPs5FWm8

[aki.ts🦔 @HedgehogPython]

最近やってる動画作成コスト削減のやり方😅

https://t.co/j1VkB2aai7

[AI SDK @aisdk]

MCP Events are now available in AI SDK.

Build agents that react to updates from MCP servers. Discover events and subscribe to them with AI SDK, while Vercel Connect handles subscription storage, renewals, and verified delivery.

[aki.ts🦔 @HedgehogPython]

@web_se 😅...

[aki.ts🦔 @HedgehogPython]

最高😀

[TypeSafe AI @typesafeai]

we announced Jev 1.14 on 10.14 at 1:14pm PT 🔥

a new and improved model, along with experimental features for our dreamers:

• images
• a new box type
• longer context

surprise, Jev was already able to see you! 👁️

BUT our bar for production-grade intelligence is quite high, so builders beware, these are preview features.

enter the ~*~ danger zone ~*~ at your own risk. 🚨

[Anthropic @AnthropicAI]

New on our Engineering blog: Creating in-house source control.

In May, we merged around 2,500 code changes per day. In September, we merged more than 30,000 per day. As a result, we became bottlenecked by developer infrastructure built for human (rather than agent) scale.

Here, we share how we built a new version control system—and fully migrated our monorepo from git—in five months: https://t.co/wzNkwHs7IN

[Google Antigravity @antigravity]

Gemini 4 Argon is here! It is our frontier model built for complex workflows across real-world software engineering, tested across Google's own codebases on real production challenges. There, we have seen an improvement in both developer velocity, with a 35% increase in agentic code, and code quality, with a 30% decrease in post-submit rollbacks.

Rolling out now to Google AI Ultra subscribers, try it in the Antigravity app and the Antigravity CLI. Get inspired with what you can build today ↓

[しょうへい｜デイトラ代表 @showheyohtaki]

AIへの指示用に、"動画のカメラワークと切り替えの名前" をまとめました！

たとえばClaudeやCodexで動画を作るとき、「いい感じにおしゃれにして」だけでは、狙った演出はまず出ない。

「FPV」「ラックフォーカス」「ホイップパン」と名前で頼めば、思い通りの演出を再現できます。

保存推奨です🔖

[aki.ts🦔 @HedgehogPython]

@ma_tsukada おめでとうございます🎉

[Andrej Karpathy @karpathy]

Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.

- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.

The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.

So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.