I use around 10 AI tools for 90% of my work, and each one excels in one specific area. But figuring out which tool works best for what task usually

我使用大約10個AI工具完成90%的工作，每個工具都在一個特定領域表現出色。但弄清楚哪個工具最適合什麼任務通常

takes months of trial and error. So, I'll share the one thing each tool does better than alternatives, so you walk away with a clear mental model for when

需要幾個月的試錯。所以，我會分享每個工具比替代品做得更好的那一件事，這樣你帶著一個清晰的心智模型離開，知道什麼時候

to use what. I've grouped these tools into four categories across a two-part series. There's just too much to cover.

用什麼。我把這些工具分成四類，分兩部分系列。內容太多了。

This video covers everyday and specialist AI, while part two covers the remaining two categories. Let's get started. Kicking things off with everyday AI. These are your general purpose chatbots. Chachi, Gemini, and

這個影片涵蓋日常AI和專業AI，而第二部分涵蓋剩下的兩個類別。讓我們開始吧。首先是日常AI。這些是你的通用聊天機器人。ChatGPT、Gemini和

Claude. And while they seem interchangeable, their quote unquote moes, the specific things they do best have actually become quite distinct.

Claude。雖然它們看起來可以互換，但它們所謂的「護城河」，也就是它們做得最好的特定事情，實際上已經變得相當不同。

Starting with the OG Chachet. While Gemini and Claude are arguably just as capable in raw power, Chachib still holds the crown in one area. It's the most obedient model. In plain

從元老ChatGPT開始。雖然Gemini和Claude在原始能力上可以說同樣強大，但ChatGPT仍然在一個領域保持王冠。它是最聽話的模型。簡單來說，

English, Chachib drops fewer balls when you hand it a complex checklist. Other models might be just as smart, but give them a lengthy set of instructions, and they'll sometimes skip a step or decide

當你交給ChatGPT一個複雜的清單時，它掉的球更少。其他模型可能同樣聰明，但給它們一長串指令，它們有時會跳過一步或決定

they know better. If you want proof of this, just ask each model to optimize a rough prompt for itself. Chacht will generate a noticeably longer and more detailed prompt because it knows it can

它們更懂。如果你想要證據，就讓每個模型為自己優化一個粗略的提示詞。ChatGPT會生成明顯更長、更詳細的提示詞，因為它知道它可以

handle the complexity. And if you run that optimized chachib prompt through both chacht and gemini for example, you'll notice two things. First, chachib thinks longer because it's actually checking every requirement and it

處理這種複雜性。如果你通過ChatGPT和Gemini運行那個優化過的ChatGPT提示詞，你會注意到兩件事。首先，ChatGPT思考得更久，因為它實際上在檢查每個要求，它

follows each instruction to the letter. Gemini on the other hand often takes shortcuts. Pro tip, I share the exact prompt optimizer in the essential power prompts template linked below, but you

一絲不苟地遵循每個指令。另一方面，Gemini經常走捷徑。專業提示，我在下面鏈接的基本強力提示詞模板中分享了確切的提示詞優化器，但你

can test this yourself with something as simple as optimize this prompt for Chachib insert model number here. Here's my rough prompt. Diving into a real world example, I gave both Chachet and

可以用像「為ChatGPT優化這個提示詞（在這裡插入模型編號）。這是我的粗略提示詞」這樣簡單的東西自己測試。深入一個現實世界的例子，我給ChatGPT和

Gemini the same complex prompt, a hiring rubric with a dozen requirements. Chachi delivered every single one. Gemini's output looked right at first glance, but when I checked it against my original

Gemini同樣複雜的提示詞，一個有十幾個要求的招聘評估標準。ChatGPT交付了每一個。Gemini的輸出乍看之下沒問題，但當我對照我的原始

list, it had quietly dropped a few rules. That's the key difference.

清單檢查時，它悄悄地漏掉了幾條規則。這就是關鍵區別。

Chachib doesn't decide which instructions matter. It just follows them. Here's a second simpler example.

ChatGPT不決定哪些指令重要。它就是遵循它們。這裡是第二個更簡單的例子。

Sometimes when you explicitly tell Gemini to search the web, it just doesn't, which is wild since Gemini and Google search are both Google products, right? Whereas with ChachiT, when you

有時當你明確告訴Gemini搜索網絡時，它就是不搜，這很瘋狂，因為Gemini和Google搜索都是Google的產品，對吧？而ChatGPT，當你

enable web search, it performs the web search every single time. I know this is a small example, but it's downstream from Chachib's core superpower. Obedience means you can

啟用網絡搜索時，它每次都執行網絡搜索。我知道這是個小例子，但它是ChatGPT核心超能力的下遊。聽話意味著你可以

trust the behavior you ask for. So, as a rule of thumb, if a task has a lot of moving parts, and getting one wrong breaks the whole thing, start with

信任你要求的行為。所以，作為經驗法則，如果一個任務有很多活動部分，弄錯一個就會破壞整個事情，從ChatGPT開始。

Chachib. Next up, Gemini. Where ChachiT wins on obedience, Gemini wins on multiodality. In plain English, Gemini is able to process a massive amount of mixed media, video, audio, images, and

接下來是Gemini。ChatGPT在聽話方面贏了，Gemini在多模態方面贏了。簡單來說，Gemini能夠原生處理大量混合媒體，影片、音頻、圖片和

text natively. Taking a look at this table, we see that only Gemini can handle all four types of media natively.

文字。看這個表格，我們看到只有Gemini能原生處理所有四種媒體類型。

It's able to quote unquote listen to audio and quote unquote watch videos, while Tragic and Claude use roundabout ways to access that information. What's more, Gemini's massive 1 million token

它能「聽」音頻和「看」影片，而ChatGPT和Claude使用迂迴的方式來獲取那些資訊。更重要的是，Gemini巨大的100萬token

context window means it can handle large video recordings, hour-long audio recordings, full slide decks, all together that would literally choke other models. If you watch my latest Gemini video, you'll remember the use

上下文視窗意味著它可以處理大型影片錄製、一小時的音頻錄音、完整的投影片，所有這些一起會讓其他模型窒息。如果你看了我最新的Gemini影片，你會記得那個用例

case where I screen recorded a messy walkthrough of myself completing a task, uploading that video onto Gemini, and asking Gemini to turn it into a readytouse SOP with perfect formatting,

我螢幕錄製了一個我自己完成任務的混亂演練，把那個影片上傳到Gemini，並請Gemini把它變成一個格式完美、可直接使用的SOP，

which is an example of Gemini ingesting video and turning it into text. Now, let's take that a step further. Imagine you just finished a weekly meeting. You

這是Gemini攝取影片並把它變成文字的例子。現在，讓我們更進一步。想像你剛剛完成一個每週會議。你

have a video recording of the call, a 20 slide deck, and a photo of a messy whiteboard session. You can upload all three and ask Gemini to summarize what

有通話的影片錄製、一個20頁的投影片，和一張凌亂的白板會議照片。你可以上傳所有三個，請Gemini總結

was discussed, pull out the key decisions, and draft the follow-up email. Gemini is the only tool that can synthesize all three in one go. All that said, I have to point out that Gemini's

討論了什麼，提取關鍵決定，並起草後續郵件。Gemini是唯一能一次綜合所有三個的工具。話雖如此，我必須指出Gemini的

raw reasoning capabilities sometimes feels slightly behind CatchBT. But when the task involves video, audio, or massive files, the trade-off is obviously worth it. Speaking of matching the right tool to the task, today's

原始推理能力有時感覺略遜於ChatGPT。但當任務涉及影片、音頻或大文件時，這個權衡顯然是值得的。說到為任務匹配正確的工具，今天的

sponsor HubSpot put together a free guide called the AI productivity stack that covers 50 tools organized by use case. Here's why I like it. While this video focuses on my personal favorites,

贊助商HubSpot整理了一份免費指南叫做AI生產力堆疊，涵蓋了50個按用例組織的工具。這是我喜歡它的原因。雖然這個影片專注於我個人的最愛，

your workflow probably needs something different. Maybe you're in marketing and need SEO specific tools or you manage a team and want to build automated workflows with reliable AI. This guide

你的工作流程可能需要不同的東西。也許你在行銷部門需要SEO特定工具，或者你管理一個團隊想用可靠的AI建立自動化工作流程。這份指南

breaks down tools across business functions like research, design, and marketing. And for each tool, it shows you the best use case, key features, pricing, and a step-by-step workflow.

按業務功能分解工具，如研究、設計和行銷。對於每個工具，它顯示最佳用例、關鍵功能、定價和逐步工作流程。

What I found most useful is the decision logic at the end of each section. So, for example, the research category tells you exactly when to use Perplexity versus Claude versus Humatada based on

我發現最有用的是每個部分末尾的決策邏輯。所以，例如，研究類別告訴你確切地什麼時候使用Perplexity versus Claude versus Humata，基於

what you're actually trying to do. It's a great way to quickly understand what each tool does. Well, I'll leave a link to this free guide down below.

你實際想做的事情。這是快速了解每個工具做什麼的好方法。好的，我會在下面留下這份免費指南的連結。

Thank you, HubSpot, for sponsoring this video. Rounding out the everyday AI category, Claude. Claude superpower is producing higher quality first drafts than the other models. In plain English, that means Claude's first attempt is

感謝HubSpot贊助這個影片。完成日常AI類別，Claude。Claude的超能力是比其他模型產生更高品質的第一稿。簡單來說，這意味著Claude的第一次嘗試

usually closer to done. This superpower shows up in two areas. First, coding.

通常更接近完成。這個超能力體現在兩個領域。首先是程式碼。

Here's a fun fact. The latest version of Gemini beat the older version of Claude in every single benchmark score except for the coding one, which is crazy. So obviously Anthropic has figured out

這是一個有趣的事實。最新版本的Gemini在每一個基準測試中都打敗了舊版本的Claude，除了程式碼那個，這很瘋狂。所以顯然Anthropic弄清楚了

something related to coding the others haven't. And in practice, developers universally agree that Claude writes functional code on the first try more consistently than alternatives. Here's a real world example. I needed to bulk

與程式碼相關的東西是其他人沒有的。在實踐中，開發者普遍同意Claude在第一次嘗試時比替代品更一致地寫出可運行的程式碼。這是一個現實世界的例子。我需要批量

export conversations from a customer service platform, but their support team said only developers could do it. I described the problem and Claude not only gave me step-by-step instructions

從客服平臺導出對話，但他們的支援團隊說只有開發者能做。我描述了問題，Claude不僅給了我逐步說明

but also wrote a script in Go that worked on the first try. I don't even know what Go is nor can I write code.

還用Go寫了一個腳本，第一次就能運行。我甚至不知道Go是什麼，我也不會寫程式碼。

Another example, I asked all three models to turn a static image into an interactive chart and Claude performed the best on the first try. So basically, anything that requires generating

另一個例子，我請所有三個模型把一張靜態圖片變成互動式圖表，Claude在第一次嘗試時表現最好。所以基本上，任何需要生成

working code tends to favor Claude. Pro tip, when it comes to diagrams, you can ask Claw to generate mermaid code, which you can then paste directly into tools

可運行程式碼的事情都傾向於Claude。專業提示，當談到圖表時，你可以請Claude生成mermaid程式碼，然後你可以直接貼到像

like Excaliraw to get clean visuals in minutes. Area two, polishing copy.

Excalidraw這樣的工具中，幾分鐘內獲得乾淨的視覺效果。第二個領域，潤飾文案。

Beyond code, Claude produces written drafts that sound human and need fewer revisions. When you need to tighten an argument or match a specific voice, Claude just gets it. Put simply, it's

除了程式碼，Claude產生的書面草稿聽起來像人類，需要更少的修改。當你需要收緊論點或匹配特定的聲音時，Claude就是懂。簡單來說，它

exceptionally good at style matching. Once you share examples of your existing work, it replicates your tone almost perfectly. When I was in corporate, I'd shared previous documents so Claude could replicate that voice across

在風格匹配方面特別出色。一旦你分享了你現有工作的例子，它幾乎完美地複製你的語氣。當我在企業時，我會分享之前的文件，這樣Claude可以在

presentations and performance reviews. And now, as a creator, I feed it my existing YouTube scripts to help refine new drafts. At this point, you might be wondering how I use all three everyday

簡報和績效評估中複製那種聲音。現在，作為創作者，我把我現有的YouTube腳本餵給它來幫助精煉新草稿。在這一點上，你可能在想我如何同時使用所有三個日常

AI tools together. In a nutshell, Chachip or Gemini usually handles the beginning of my work, ideation, research, drafting the outline of a presentation. Claude then handles the last mile, turning that rough output

AI工具。簡而言之，ChatGPT或Gemini通常處理我工作的開始，構思、研究、起草簡報大綱。Claude然後處理最後一哩，把那個粗略輸出

into something I'm ready to present or publish. Quick note on Grock. A lot of people ask why I don't use it. It's actually very simple. Uh Grock's superpower is its direct access to the

變成我準備好展示或發布的東西。關於Grok的快速說明。很多人問我為什麼不用它。其實很簡單。Grok的超能力是它直接訪問

Twitter/x fire hose, right? So it's the best option for people who need to analyze breaking news events in real time. I never needed that. And as a rule

Twitter/X的消防水管，對吧？所以它是需要實時分析突發新聞事件的人的最佳選擇。我從來不需要那個。作為經驗法則，

of thumb, we should never use tools just for the sake of using tools. We should only add them to our toolkit when they solve an actual problem we have. Here's

我們永遠不應該為了使用工具而使用工具。我們只應該在它們解決我們實際問題時把它們加入我們的工具箱。這是

a quick recap of the three models and when to use them. And if you're wondering whether you need all three, the short answer is no. Most people should stick with the paid version of

三個模型及何時使用它們的快速回顧。如果你在想是否需要所有三個，簡短的答案是不需要。大多數人應該堅持使用ChatGPT的付費版本

ChachiBT and get really good at it. But if you can afford multiple subscriptions and your workflow can take advantage of their individual superpowers, mix and match as needed. Fun fact, according to

並真正精通它。但如果你負擔得起多個訂閱，你的工作流程可以利用它們各自的超能力，根據需要混合搭配。有趣的事實，根據

this study on open router data, models from different labs like Chadypt and Gemini expand the pie of AI use cases precisely because they excel at different things. Onto the second

這項關於Open Router數據的研究，來自不同實驗室的模型，如ChatGPT和Gemini，擴大了AI用例的版圖，正是因為它們在不同的事情上表現出色。進入第二個

category, specialist AI. Before diving in, let's clear up a very common misconception. Tools like Perplexity are not foundational models. Here's a simple visual. OpenAI, a Frontier AI lab,

類別，專業AI。在深入之前，讓我們澄清一個非常常見的誤解。像Perplexity這樣的工具不是基礎模型。這是一個簡單的視覺。OpenAI，一個前沿AI實驗室，

develops the GPT family of models. They also created ChatGpt as the userfriendly app >> >> layer. Perplexity is different. It fine-tunes existing foundational models for speed and accuracy and is optimized

開發了GPT系列模型。他們也創建了ChatGPT作為用戶友好的應用層。Perplexity不同。它針對速度和準確性微調現有的基礎模型，並優化

for search. Their own sonar model, for example, is just a fine-tuned version of Meta's openweight llama model. So, on that note, Perplexity superpower is finding accurate information fast. In plain English, the general purpose

用於搜索。例如，他們自己的sonar模型只是Meta開放權重llama模型的微調版本。所以，在這一點上，Perplexity的超能力是快速找到準確的資訊。簡單來說，通用

chatpots are built for reasoning. You use them to help you think, brainstorm, or write a draft. Perplexity is built for fetching. You need a specific fact,

聊天機器人是為推理而建的。你用它們來幫助你思考、頭腦風暴或起草。Perplexity是為獲取而建的。你需要一個特定的事實，

and you need it now. Starting off with a simple real life example, I used chachib to plan a trip to Japan with my brother because that is a creative task. It

而且你現在就需要。從一個簡單的現實生活例子開始，我用ChatGPT來規劃我和我弟弟的日本之旅，因為那是一個創意任務。它

requires weighing trade-offs, building a narrative, and for that kind of task, I'm happy to wait while the model thinks. But when I need grab-and-go information, like whether a specific restaurant is foreigner friendly because

需要權衡取捨、建立敘事，對於那種任務，我很樂意等模型思考。但當我需要即取即用的資訊，比如某家餐廳是否對外國人友好，因為

we don't speak Japanese, I'd want Perplexity to give me accurate and update information within seconds.

我們不會說日語，我會想Perplexity在幾秒內給我準確和最新的資訊。

Second example, going back to how I use the three everyday AI tools, let's say Gemini or Chachet helps me brainstorm and structure my newsletter. Claude produces the final draft. Perplexity in

第二個例子，回到我如何使用三個日常AI工具，假設Gemini或ChatGPT幫我頭腦風暴和架構我的電子報。Claude產生最終草稿。Perplexity在

this case is the search scalpel that verifies information like whether Gemini's contact window is 1 million or 2 million tokens. In case you're curious, consumers get 1 million,

這種情況下是驗證資訊的搜索手術刀，比如Gemini的上下文視窗是100萬還是200萬token。如果你好奇，消費者得到100萬，

enterprises get 2 million. Pro tip, you can use Google style search operators like site colon reddit.com to narrow your results to a specific source. I have an entire video

企業得到200萬。專業提示，你可以使用Google風格的搜索運算符，如site colon reddit.com來縮小你的結果到特定來源。我有一整支影片

on the most useful Google search operators, so I'll link that down below.

關於最有用的Google搜索運算符，所以我會在下面鏈接。

As a rule of thumb, think of perplexity as a replacement for Google AI mode.

作為經驗法則，把Perplexity想成是Google AI模式的替代品。

They're both for fetching information and not as a replacement for general purpose chatbots. Actually, let me know if you want an entire video breaking down the AI search apps like Perplexity,

它們都是用來獲取資訊的，而不是作為通用聊天機器人的替代品。實際上，讓我知道你是否想要一整支影片分解AI搜索應用，如Perplexity、

Google Search, Google AI overviews, Google AI mode, because they're all made for different things. Rounding out Specialist AI, Notebook LM superpower is that it only answers from the sources

Google搜索、Google AI概覽、Google AI模式，因為它們都是為不同的事情而做的。完成專業AI，Notebook LM的超能力是它只從你給它的來源

you give it, meaning it won't make things up. Think of it like a walled garden. You upload your sources and Notebook LM answers questions using only those documents. It can't really

回答，意味著它不會編造東西。把它想成一個圍牆花園。你上傳你的來源，Notebook LM只使用那些文件回答問題。它不太能

hallucinate because it has no outside knowledge to draw from. Going back to the visual around how perplexity is optimized for search, Notebook LM uses a fine-tuned Google Gemini model that minimizes hallucinations. For instance,

產生幻覺，因為它沒有外部知識可以借鑒。回到perplexity優化用於搜索的視覺，Notebook LM使用一個微調過的Google Gemini模型來最小化幻覺。例如，

when I was at Google before publishing marketing materials, I would upload the final draft alongside the source documents and ask Notebook LM if the draft made any claims that contradicted

當我在Google時，在發布行銷材料之前，我會上傳最終草稿和來源文件，問Notebook LM草稿是否做出了與

the sources and it would catch these tiny discrepancies other AI might have missed. I use a similar workflow today for my videos. Before I start filming, I upload my script and all my research

來源相矛盾的任何聲明，它會捕捉到其他AI可能錯過的這些微小差異。我今天在我的影片中使用類似的工作流程。在我開始拍攝之前，我上傳我的腳本和所有研究

into Notebook LM and ask it to flag anything not directly supported by the source material. The obvious caveat here is that the output is only as good as

到Notebook LM，請它標記任何不直接由來源材料支持的內容。這裡明顯的警告是輸出只和

the sources we give it. So if the sources are incorrect, Notebook LM is going to be confidently incorrect. So as a rule of thumb, if the accuracy matters

我們給它的來源一樣好。所以如果來源是錯的，Notebook LM會自信地錯。所以作為經驗法則，如果準確性比

more than creativity and you have source materials to check against, use Notebook LM. There are a few more specialist AI tools I use but didn't make this list

創意更重要，而且你有來源材料可以對照檢查，使用Notebook LM。還有一些我使用但沒有列入這個清單的專業AI工具

because I don't use them every day. But to quickly go through them, Gamma for presentations, 11 Labs for voice cloning, Zapier and N for automation, and Excaliraw and Napkin AI for quick

因為我不每天用它們。但快速過一下，Gamma用於簡報，11 Labs用於語音克隆，Zapier和N用於自動化，Excalidraw和Napkin AI用於快速

visuals. As a reminder, I'll cover the remaining two categories in part two, so keep an eye out for that. See you on the next video. In the meantime, have a great one.

視覺效果。提醒一下，我會在第二部分涵蓋剩下的兩個類別，所以留意一下。下支影片見。同時，祝你一切順利。