Why Most AI Tools Miss the Mark and How to Pick the Ones That Actually Boost Your Productivity

1. Observations

Observation 1: How much real benefit lies behind the "spectacle"?

Recent AI model releases are abundant, with countless AI products claiming SOTA or "world's first" status, while many media outlets use flashy headlines to attract attention.

Few people pause to ask whether AI truly provides tangible help.

Observation 2: Same planet, different eras

People still read papers manually, but some upload papers to advanced models like DeepSeek, ChatGPT, Gemini, or Claude for Q&A.

Others use Tencent Ima knowledge base, Google NotebookLM for Q&A, audio podcasts, video explanations, flashcards, or test banks.

Some create custom agents for personalized learning, becoming "super individuals" with efficiency far beyond the average.

Many still rely on traditional methods, seeing little AI benefit, or use AI only via simple chat prompts, struggling with complex tasks.

Observation 3: Mismatch between AI tools and use cases

Even after attending many AI conferences, the author notes that most users apply AI only to specific work scenarios and revert to traditional methods for other tasks where AI could improve efficiency.

Examples include a programmer using Cursor for coding but drawing UML diagrams by hand, or a teacher using DeepSeek for questions but not AI‑generated PPTs.

Observation 4: "Fish and dragons" and contradictory advice

The early AI era is crowded with mixed signals: some media claim prompts are obsolete, while others hype complex prompt techniques.

In practice, users with clear thinking and strong expression achieve better AI results; poor prompt engineering leads to subpar outcomes.

Learning a few key prompt‑engineering tricks can noticeably improve AI effectiveness.

Observation 5: The most efficient solution may not be a standard product

Many AI products can be replicated by writing prompts, attaching knowledge bases, and using tools like Cherry Studio, Claude, Gemini, or ChatGPT.

When you master prompt engineering and have access to the latest models, custom solutions often outperform off‑the‑shelf AI products.

Claims of "long‑term memory" are usually backed by external storage, and many platforms make it hard to export key data.

The author has accumulated over 100,000 words of personal data, but notes that more data does not always mean better AI extraction.

2. Recommendations

Recommendation 1: Choose stronger or more suitable models

Different large models resemble people with varying personalities; many only know DeepSeek, but numerous domestic and foreign models exist.

Domestic models are closing the gap with foreign ones, especially on long inputs.

If possible, prioritize Claude for coding and Gemini for text creation; otherwise, test multiple domestic models and pick the best performer.

Example: switching from an underperforming domestic model to Kimi‑K2 yielded a clear improvement.

Recommendation 2: Use more efficient AI tools

Select the tool that best fits each scenario, and replace it when a better option appears.

For writing, a free AI voice input method (DaTi) doubled typing speed; the author bought a high‑quality microphone to improve it further.

For coding, iFlow CLI (free) is preferred; for higher demands, Claude Code or Gemini CLI are alternatives.

IDE choice: Cursor, with occasional use of Trae or Qoder; for IDEA plugins, Cline or Augment Code.

For rapid learning, NotebookLM supports Q&A, audio podcasts, video summaries, flashcards, mind maps, and question generation.

Recommendation 3: Master best practices

Even with the same model and tool, results vary widely based on prompt quality.

When generating PPTs, users who clarify their ideas and provide high‑quality references achieve far better outputs than those who give a single vague sentence.

Similarly, clear prompts lead to higher‑quality AI‑generated code, as shown by side‑by‑side screenshots of two coding sessions.

Recommendation 4: Build your own fleet of intelligent agents

Create agents that first explain concepts in simple terms, then provide mnemonics and visual aids.

Examples include converting articles into "knowledge‑card agents" for core points and actions, or turning articles into visual web‑page agents for faster reading.

Recommendation 5: Adopt an AI‑First mindset

Changing thinking is the hardest but most important step; merely using more AI tools does not automatically make one a "super individual".

When faced with repetitive tasks, first consider whether AI can alleviate the burden.

Use AI to generate PPTs for science communication, often achieving higher quality than manual effort.

For specialized platforms like Kousi, download documentation as Markdown, build a knowledge base, and query it with natural language to improve efficiency.

When discussing frameworks such as LangChain or LangGraph, the author suggests pulling the open‑source code, opening it in Cursor, and letting AI generate a complete technical solution.

3. Final Thoughts

As people become more rational about AI hype and choose reliable tools, AI will deliver genuine productivity gains.

Building personal AI agent fleets and embracing an AI‑first approach will turn AI into a practical ally for work, study, and life.