In the AI Era, Winning Means Choosing the Right Problems, Not Working Harder

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A software engineer argues that motivation, discipline, consistency, and obsession are merely execution tools — worthless without sound judgement about which problems deserve pursuit. He contends that professional identity should be anchored in a problem-solving approach rather than a job title, making it resilient to technological change. With AI dramatically lowering the cost of building, he says the scarce and valuable skill has shifted from fast execution to correctly identifying, contextualising, and validating real-world problems. He positions AI as a hypothesis machine useful for research, prototyping, and simulating stakeholders, while emphasising that human judgement remains essential for testing ideas against reality. The proposed workflow moves from selecting a meaningful domain and observing real problems, through AI-assisted thinking and rapid building, to measuring real-world signals before deciding to scale or stop.

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How One Word — 'Trade-off' — Defined a Computer Engineer's Entire Career

A computer engineering student in South Korea was introduced to the concept of 'trade-off' by Professor Sang Lyul Min during an introductory course that also served as his first exposure to C programming. With limited internet access at the time, the student had to look the term up in a Korean-English dictionary, gradually grasping its deeper meaning. The word came to encapsulate a core principle: that every engineering decision involves a compromise, distinguishing the discipline from pure science or mathematics. As the author progressed through his career — from algorithms courses weighing time against space complexity to reading academic papers — the concept of trade-off resurfaced repeatedly. Decades later, he considers it the single most defining word in engineering, one he was effectively handed for life on his first day of college.

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How Apex Code Can Automate Gladly Task Creation Directly from Salesforce

Developers can use Salesforce Apex to integrate with Gladly's task API, automatically creating customer-linked follow-up tasks when events like appointment rescheduling occur. Gladly tasks are customer-specific records that sit on a customer's timeline alongside conversations and emails, requiring both task details and customer identification in the API request. The integration uses a wrapper class pattern in Apex to manage the nested JSON request body, making the code easier to maintain as the integration evolves. Gladly's POST endpoint accepts fields such as assignee, task body, due date, and customer contact information to create or attach tasks to existing customer profiles. Building in error handling from the start is emphasized as critical, since skipping it often leads to complications during real production incidents.

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Developer builds custom Django command to alter Milvus schemas without data loss

A developer at a company using Milvus as its vector database needed to modify collection schemas in production without losing existing customer data. At the time, Milvus lacked a native ALTER command, with the only official workaround being a manual data migration to a new collection. To streamline this process, the developer wrapped the migration logic into a reusable Django management command that any team member could run safely. The command accepts a database name, collection name, new schema definition, and batch size, then creates a temporary collection, copies data in batches, drops the old collection, and renames the temporary one. Field removals and additions with default values are handled automatically, though updating existing field values is not currently supported.

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