After a Month with WorkBuddy: AI Tool Rankings Can't Fix Chaotic Processes

Got up at five today to train legs, completely wiped myself out. After training, I chugged 25g of whey protein and weighed out 180g of chicken breast for breakfast. While changing clothes, I saw a video titled "Tested 102 AI Tools; These Are the Only 10 I'm Keeping in 2026." I watched the beginning and felt this kind of content is like protein powder reviews—talking about ingredients, price, taste—it's lively, but doesn't answer when you drink it daily, how much, or whether it pairs with your training.

My judgment is straightforward: lists help you avoid a few pitfalls, but if you just throw a single sentence into a tool like WorkBuddy, you'll still get burned. This time, instead of expanding on specific generation scenarios, I'll address a more practical issue: workflow. After using WorkBuddy for about a month, my biggest change was turning my previously chaotic training camp delivery ledger into a deliverable pipeline.

Previously, handling autumn training camp recruitment involved scattered information. Student needs were in Feishu groups, training data in Excel, check-in screenshots on Keep, and feedback docs across several links. I'd just started using Bailian less than a week ago and was testing its text summarization and field extraction, but it didn't know why I was recruiting, who I was targeting, or what couldn't be written. Other tools could process materials but wouldn't decide for me which content goes live first or who verifies it. That's where WorkBuddy fits; it's more like a training schedule.

In my tests, WorkBuddy is most stable for three types of tasks. Scattered requirements can be organized into structured task sheets (e.g., who is the reader, where are the materials, output format, prohibitions). Repetitive actions can be fixed into workflows (e.g., summarize training data every Friday, compile student course consumption tables monthly, check material lists before each delivery). Multiple tools can also be chained together, telling them when to act and where to send the output after acting. Using gym equipment as an analogy, Bailian, Keep, and Excel are like specific machines responsible for one movement. WorkBuddy handles rest between sets, reps, weight, and logging. Tools are the same; movements must be standard.

A few days ago I wrote about WorkBuddy getting stuck when periodically summarizing student feedback. Looking back, the problem was my greed. Initially, I threw student training sheets, course consumption records, group chat screenshots, and feedback drafts all into one task, asking it to directly output a one-page Word delivery report. It started with "Progress went smoothly this week." At that moment, I felt it was like a student lifting heavy weights without warming up—looks intense, but lands crookedly.

Later I split it into three steps, and the effect normalized. Step one: Collection only. Put training camp requirements from Feishu docs, course consumption tables from Excel, and check-in data exported from Keep into the same folder. WorkBuddy only identifies file names, source dates, and missing items; no judgment. This takes about 3 minutes. Step two: Cleaning only. I gave it fixed fields: student name, training date, completed exercises, protein intake notes, reason for non-completion. Notes often contain phrases like "Legs destroyed today." Previously it got distracted by this; now I only ask it to extract facts, not summarize emotions. In my tests, about 8 out of 10 check-in notes go straight into the table; the remaining 2 have blurry handwriting or unrelated chat context and need manual supplementation. Step three: Aggregation. Hand the cleaned table to WorkBuddy, requesting three sections: weekly completion volume, at-risk students, next week's suggestions. Prohibitions are clear: don't fabricate course consumption data, don't write non-completions as recoveries; emotional chats aren't training conclusions, and no medical advice allowed.

Pre-delivery checks are similar. Previously, I'd export files, send them, then manually check data, often missing things. Now WorkBuddy doesn't send content for me, but it lists acceptance fields, check items, delivery times, and data recovery forms in the task sheet. Feishu handles notifications, Excel handles ledgers, WorkBuddy handles the rhythm. Originally, compiling training data and delivery lists on Friday nights took me about 90 minutes manually. Now WorkBuddy runs collection and cleaning first, and I do the final review, finishing in about 35 minutes. The saved time is used to re-review student exercise logs. This effort is worth it. The real time saver is that tasks no longer change ad hoc.

I don't like hyping WorkBuddy as an omnipotent assistant. It suits people who already have half a workflow, such as those who regularly handle training camp data, course ledgers, and student follow-ups scattered across documents and chats. WorkBuddy helps put these loose pieces on track. It also suits people willing to write standards. If you define input formats, output fields, prohibitions, and acceptance criteria, it thrives. Defining data flows and task boundaries clearly is more important than expecting AI to become smart on its own; otherwise, errors get amplified.

It's not suitable for people treating AI as a wishing well. Asking it to generate copy, analyze, and send group notices with zero edits via a single sentence will likely lead to disappointment. It's also not for those unwilling to organize materials. Messy input leads to messy output. Unstructured chat content is especially prone to over-completion; it looks smooth, but close inspection reveals it's all hallucinated filler. One thing to admit: WorkBuddy isn't as good as humans at complex judgments. For instance, if a student hasn't checked in for three consecutive days, whether it's due to overtraining or personal life changes, it can only list risks; it can't make decisions for the coach. I prefer viewing it as a process manager, leaving decisions to humans.

Back to that review of 102 AI tools: lists help avoid pitfalls but don't define workflows for you. After using WorkBuddy for a month, my conclusion is that it's not dazzling, even a bit annoying because you must break down requirements clearly. But precisely this annoyance turned my AI tools from a pile of toys into a deliverable pipeline. Whether it stays depends on whether there's a process for it to manage.