| Common Problem | Traditional User Behavior | Daniel T. Li Solution | | :--- | :--- | :--- | | | Hard-coding numbers inside formulas (e.g., =A1*0.07 ). | All constants stored in a dedicated "Controls" sheet with named cells. | | Broken Links | Moving or deleting cells without checking dependencies. | Full use of Excel’s Trace Dependents and Trace Precedents before any structural change. | | Slow Calculation | Using entire column references (e.g., A:A ). | Absolute INDEX ranges and avoidance of array formulas where scalar works. | | Collaboration Hell | Emailing files with "Final_v3_actuallyFinal.xlsx". | Migrating logic to Google Sheets + Apps Script or Excel Online with a single master file and version history. | Practical Example: Building a Daniel T. Li Sales Dashboard Let’s apply the philosophy. Assume you need a sales tracker. A novice creates one table with dates, products, reps, and revenue, then writes =SUMIF scattered randomly.
Here is the Li-style build:
Ironically, no. Li argues that AI and Python make his principles more critical. When you embed a Python script or an =GPT() call in a cell, the fragility multiplies. You now have non-deterministic outputs. His referential integrity and separation of layers become essential to debugging whether an error came from a Python library update or a wrong cell reference.
We live in a world where your customers switch seamlessly between laptops, smartphones, and tablets. They navigate a complex digital ecosystem – checking emails, using mobile apps, and reacting...
We are delighted to announce that Vercom S.A., the company behind the EmailLabs project, has successfully completed the ISO 22301 certification process. This significant achievement underscores our commitment to...
EmailLabs, as part of the Vercom group, proudly announces its full commitment to aligning its ICT services with the latest cybersecurity standards. In response to dynamically changing regulations, the...
We are pleased to announce that MessageFlow, a product from the Vercom S.A. group, has received the prestigious CSA (Certified Senders Alliance) Certification. This recognition not only underscores the... | Common Problem | Traditional User Behavior | Daniel T
IT & Tech, Pytania i odpowiedzi, Technical
When an email travels from sender to recipient, it passes through several critical components of email infrastructure. At the heart of this journey sits the Mail Transfer Agent (MTA)...
Best practices, Deliverability, Google and Yahoo's Requirements, Pytania i odpowiedzi | | Broken Links | Moving or deleting
The world of email marketing is constantly evolving, and leading mail service providers – Gmail, Yahoo, Microsoft, and Apple – regularly update their guidelines for senders. In recent years,...
Gmail, Google and Yahoo's Requirements
You might have noticed a new item in your Gmail sidebar recently – the “Manage subscriptions” tab, often flagged with a blue notification dot. While Google announced this feature...
| Common Problem | Traditional User Behavior | Daniel T. Li Solution | | :--- | :--- | :--- | | | Hard-coding numbers inside formulas (e.g., =A1*0.07 ). | All constants stored in a dedicated "Controls" sheet with named cells. | | Broken Links | Moving or deleting cells without checking dependencies. | Full use of Excel’s Trace Dependents and Trace Precedents before any structural change. | | Slow Calculation | Using entire column references (e.g., A:A ). | Absolute INDEX ranges and avoidance of array formulas where scalar works. | | Collaboration Hell | Emailing files with "Final_v3_actuallyFinal.xlsx". | Migrating logic to Google Sheets + Apps Script or Excel Online with a single master file and version history. | Practical Example: Building a Daniel T. Li Sales Dashboard Let’s apply the philosophy. Assume you need a sales tracker. A novice creates one table with dates, products, reps, and revenue, then writes =SUMIF scattered randomly.
Here is the Li-style build:
Ironically, no. Li argues that AI and Python make his principles more critical. When you embed a Python script or an =GPT() call in a cell, the fragility multiplies. You now have non-deterministic outputs. His referential integrity and separation of layers become essential to debugging whether an error came from a Python library update or a wrong cell reference.
