The Githubers·21wBe brutally honest
A data scientist seeking career advice requests feedback on their GitHub profile to improve their chances of landing a role at FAANG or other prestigious companies. They've organized their projects and improved the design, but want honest critique on what else needs improvement to make their profile more competitive.
MIT News·16w“MIT Open Learning has opened doors I never imagined possible”
Munip Utama leveraged MIT's MicroMasters Program in Data, Economics, and Design of Policy to strengthen his work at Baitul Enza, a nonprofit supporting disadvantaged students in Indonesia. Coming from a lower-middle-class background, he gained rigorous training in data analysis, economic reasoning, and evidence-based policy design. He now applies these skills to design case-based learning modules, mentor youth researchers, and improve program effectiveness through data-driven approaches. Financial assistance and income-based pricing enabled him to complete the program while managing resource constraints at his volunteer-run organization.
Code Like A Girl·19wI Scraped 10,000 Reddit Posts to Find Out Why Data Analysts Are Panicking
A data analyst scraped 10,000 Reddit posts from data analytics subreddits using Python's PRAW API to analyze career anxiety in the field. The analysis revealed that automation fear and skill overload are the top concerns, with analysts worried about AI replacing jobs while simultaneously feeling pressure to master multiple technologies. Engagement analysis showed job market saturation generates the most discussion, while sentiment tracking from 2017 to present revealed fluctuating confidence levels, with a notable dip in 2017 and instability from 2024 onward due to AI advancements.
Hacker News·20wLMArena is a cancer on AI
LMArena, a popular AI model leaderboard, is fundamentally flawed because it relies on casual internet users who prioritize superficial qualities like formatting, length, and emojis over factual accuracy. Analysis shows 52% of votes were questionable, with users consistently choosing confident-looking but incorrect answers over accurate ones. The system rewards models that game human attention spans rather than those that provide truthful responses, creating perverse incentives that push the entire AI industry toward optimizing for appearance over substance. This structural problem stems from using unpaid, unvetted volunteers with no quality control, making the leaderboard's influence on model development actively harmful to building reliable AI systems.