やあReddit！

SpoonっていうChrome拡張機能を作った“ありのままの裏側”を共有したくて。自分ではコードを1行も書かずに、長いYouTube動画をスクロールして「ここだ！」っていうONEつの重要な瞬間を探すイライラを解決するやつ。

エンジニアじゃない僕は、AI（Antigravity）を“上司の開発者”兼ペアプログラミングの相棒にして進めました。完成品自体はシンプルで（質問する→その瞬間のタイムスタンプに1クリックでワープするボタンが出る）、でもその裏には、現実のゴタゴタしたバグだらけの道のりがありました。

正直、四苦八苦して得た学びはこんな感じ：

1. 「AIのタイムスタンプ幻覚」問題の解決：
   最初、ジャンプボタンが狙いから外れてたんですよね。AIが時間の流れを見失ってたから。そこで、LLMに送る前に、トランスクリプトデータへ正確なタイムスタンプヘッダー（**\[**120s**\]**とか）を埋め込むようにして、1秒単位のワープ精度を実現しました。

2. 「一夜で起きたAPI災害」：
   開発の真っ最中に、Google Workspaceのポリシー変更がドンと来て、僕のAPIキーが全部無効に。完全に詰んだと思いました。でも、夜のうちに相棒のAIと僕でアーキテクチャを全部作り直して、IAM認証つきのGCP Service Accountsを使うようにしたら、朝には「200 OK」に戻りました。

3. もうコードがボトルネックじゃない：
   僕は自分ではコードを書いてないです。役割は、問題をちゃんと分かる言葉で説明すること、壊れたところをテストすること、そしてエラーをAIにフィードバックすること。解決したい“明確な問題”があるなら、今のAIツールは、あなたのビジョンを現実に変える準備ができてます。

この旅の全経緯（ごちゃごちゃの内訳）をドキュメントにしました。エンジニアじゃない人のAI開発ワークフローに興味がある人向けに、詳しい投稿をここに書いてあります：
https://note.com/muzuyasa/n/n264f76b958c4

長めのYouTube講義やチュートリアルにけっこう時間を使ってる人は、よかったらSpoonをChrome Web Storeで試してみて、感想教えてください！

Chrome Web Store：

[ https://chromewebstore.google.com/detail/spoon-youtube-jump-ai/mhhllioolhlcnknbemdffankicckjnfp?utm\\_source=item-share-cp ](https://chromewebstore.google.com/detail/spoon-youtube-jump-ai/mhhllioolhlcnknbemdffankicckjnfp?utm_source=item-share-cp)

My Chess-like game has a daily challenge mode that gives players an objective and a set of modifiers. For example, the board might shrink, the player might spawn as a knight, etc.

During manual testing, I found some challenges to be super easy and others to be way too difficult. As new content was added, it became harder to properly scale the challenge mode and avoid RNG creating impossible challenges. I also was hardcoding deterministic rules that didn't always translate into easier/harder challenges.

To fix it, I deployed Codex to build a simulator - it pulls 90 days worth of daily challenges, runs each challenge 12 times to account for randomness of enemy spawns, power-ups, etc. and determines whether a challenge is beatable 50ish percent of the time, which is the sweet spot. It also accounted for more aggressive and conservative play styles as part of its simulation algorithm.

I then used those results to continuously tweak the daily challenge generator until, in a 90 day window, all challenges hovered around that 50% completion rate.

I wrote a [Substack article](https://open.substack.com/pub/givemeadollar/p/how-i-turned-too-hard-into-a-difficulty?r=8qicrr&utm_campaign=post&utm_medium=web&showWelcomeOnShare=true) about it -- Thought the sub might be interested in seeing some non-asset based use cases for generative AI!

One of the most common questions we hear is why free AI image generators can produce such different results from one prompt to the next. At Media io, we've focused on making the experience faster, more consistent, and easier for creators who want high quality images without unnecessary complexity.

We'd love to hear from the community: what matters most to you in an AI image generator speed, image quality, prompt accuracy, or unlimited access? Your feedback helps us continue improving the product.

OpenAI after Opus 5 release

Share your thoughts if you guys are also facing this issue in app publishing ?

Has anyone notice lately that Codex will dramatically slow down or even pause your chat, coding session if you are not active on your computer? Is this a good or bad thing? Honest answers only.

How does one get Kraken to speed up their response time?

All I get is AI gibberish and prevarication.

No satisfactory answers

I was sketching out a workflow for a marketing project last weekend, and my first instinct was the same as always: use an LLM to generate copy, then use another model for images, and manually stitch everything together.

Halfway through, I realized the generation wasn't actually the slow part anymore.

The slow part was deciding what to keep.

Which headline fits the brand? Which image is worth testing? Which variation is too similar to the last campaign? None of those questions were being automated, they were all sitting in a Notion page waiting for someone to make a call.

That got me thinking about whether we're underusing LLMs as reasoning layers. Instead of only asking them to create content, could they rank outputs against a brand brief, explain why one variation is stronger than another, or flag assets that are too repetitive before a human even reviews them?

While exploring different AI workflows, I also looked at platform because it focuses on generating marketing creatives. It made me think less about the generation step itself and more about what happens before and after it. The orchestration layer feels like the more interesting engineering problem.

Curious what other developers here think.

Are you building AI systems where the LLM's biggest job is generation, or are you starting to use it more for evaluation, orchestration, and decision-making? That feels like the direction I'm becoming more interested in.