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A fifth-year CS PhD student at Brown University shares surprising lessons from their research scientist job search, including that only one or two papers matter and that interviews often focus on solving team problems rather than past research.
The author shares that they stopped comparing AI models and focused on workflow design, leading to improved output. They argue that workflow has more leverage than model choice for most practical use cases.
A user shares a personal experience of an AI agent incurring a $220 cost overnight without their knowledge, highlighting potential hidden costs of AI agents.
The author recounts encountering a job application that requested SAT scores, and discusses the history and validity of hiring assessments, questioning the use of decades-old test scores as a proxy for cognitive ability.
The author developed the cat.mowen.cn product through Vibe Coding and earned over $10,000 in revenue, sharing the development cadence and iteration details since June.
A software engineer reflects on his past job at a startup that may have been created fraudulently by a venture capitalist who was later sued by the SEC for excessive fees. He investigates whether his entire career move was based on fraud.
A developer shares their weekend project of building a low-level infix language that compiles to WebAssembly, and offers a personal ranking of AI coding tools from contextual autocomplete to frontier models.
After three months using Hermes Agent, the author shares insights on memory management and profile optimization, finding that less memory and fewer profiles lead to better results.
A personal reflection on how AI tools boost productivity but also raise expectations, leading to more work and psychological fatigue rather than free time.
The author finds local coding agents useful for small tasks but requires constant supervision to prevent errors and scope creep, describing an iterative workflow of small fixes, tests, and manual diffs.
A user describes how Google's AI overview can analyze Instagram profiles and find all interactions, raising privacy concerns about permanent online footprints.
The author reflects that trying Anthropic's new Fable model did not improve their productivity over existing Claude models, comparing it to incremental smartphone upgrades.
The author shares their personal journey of adopting a new coding workflow using a single Codex agent with /goal mode, which they find superior to multi-agent setups with newer models like GPT-5.5 and Opus 4.8.
The author shares their experience running Qwen3.6 35B-A3B locally on an ASUS Zenbook Pro 14, achieving 27 TPS at 32k context, marking a personal milestone towards fully local AI for privacy.
A former Atlassian engineer shares a detailed retrospective on eight years of work before being laid off, covering the technical systems he built including an Open Service Broker, an Envoy-based control plane called Sovereign, and large-scale proxy infrastructure on AWS.
A personal account of using AI agents to manage tasks, emails, and calendars, significantly improving productivity for someone with ADHD.
The author recounts a personal experience where their reasoning test scores dropped significantly after two years of daily AI tool usage, raising concerns about long-term cognitive trade-offs for short-term productivity gains.
The author argues that the biggest AI productivity gain comes from optimizing workflows rather than chasing the best models, suggesting simpler setups lead to more output and less context switching.
A developer shares a personal experience of their AI agent producing hallucinated data and the lessons learned about verification and prompt specificity.
The author shares their experience of using Don's DBS Skill to assist in writing WeChat public account articles, using AI to diagnose and optimize titles, structure, etc., ultimately achieving 100k+ reads.