Using WorkBuddy as a central asset hub ended the need to test single-point AI tools individually

After treating WorkBuddy as my content hub, I finally stopped testing single-point AIs one by one

Today I scrolled through the AI news roundup for August 26: Doubao supports mixed parsing of PDFs, Excel, and images; Qwen is gray-testing a code interpreter; DeepSeek refined token consumption tracking. My first reaction was a bit numb. I manage three WeChat Official Accounts and also handle operations for a Video Account channel—my dark circles are practically touching my chin. No matter how many tools there are, without 10k+ views in output, it’s just a slight stir in my DNA.

Doubao enhanced multi-file mixed parsing, supporting simultaneous upload and joint analysis of mixed materials like PDFs, Excel, and images.

Lately, instead of asking "Which AI can write this for me," I first ask "What exactly does this task need?" I call this a Task Card. The Task Card itself isn't a tool, nor is it tied to any specific platform. It's just a one-page brief detailing the target audience, input materials, prohibited actions, output format, acceptance criteria, and deadline.

Previously, I'd have Doubao read files, ChatGPT polish them, Excel schedule tasks, and email collect feedback. Each step worked, but there were gaps between every step. The problem was that I wasn't passing the context along to them.

I keep these Task Cards in local Markdown files and sync them to a standard project in WorkBuddy. I've been using WorkBuddy for a month; it handles documents, spreadsheets, and emails well, making it great for keeping records. I treat it as a content hub, but today I'm not talking about how to build a library or arrange workflows. I realized that before content goes in, the Task Card must clearly define the context so that single-point AIs don't need to be tested individually. WorkBuddy is just where the Task Card lives; defining the task itself is still on me.

The fields for a Task Card are simple: Task Name, Target Audience, Input Materials, Prohibited Actions, Output Format, Reviewer, Deadline, and Tool Selection.

For example, for a spoken script for a Video Account client, the target audience is professionals aged 25–35. Input materials include the client brief, 3 competitor videos, and keywords from comments. Prohibited actions: no fabricating data, no copying competitor copy, no unconfirmed client names. Output format: 3 titles, one 60-second spoken script version, one cover copy version. The reviewer is the Editor-in-Chief. For tool selection: Doubao for initial material screening, DeepSeek for logic compression, Haiyi Theater and LibTV for visual references.

Here lies the boundary. I've used Doubao for a week; its mixed parsing is indeed fast, but I only let it do the first rough screen. If it writes the draft directly, it fills in boundaries I haven't clarified. I've only tried DeepSeek recently; its detailed token consumption made me realize models need budgeting too. I just started with Haiyi Theater and LibTV; they're inspiration boards, not delivery tools. Qwen handles spreadsheets and code interpreters better, but I won't throw complex tables at any random AI. I clean them into CSVs first, then let the Task Card decide who handles what.

Now, every morning, I check the Task Card first, then pick tools based on the task. Multi-file reading goes to Doubao, logic compression to DeepSeek, spreadsheets/code to Qwen, visual refs to Haiyi Theater/LibTV, and record-keeping/collaboration to WorkBuddy. The tools are the same, but I'm no longer testing them one by one.

Not having to test single-point AIs one by one comes down to finally clarifying "what we want, what we don't want, and how to accept." Efficiency bottlenecks often stem from whether context has been handed over. The generation part usually isn't that hard.

Going forward, I plan to template these Task Cards, but I won't stuff everything into one box. Tools can connect, but context must connect first.