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A mother who works at fast-growing AI company Lovable argues that 996 schedules (9am to 9pm, six days a week) are a bad strategy, both for the people working them and for companies that filter out parents and others with real lives outside work. She says boundaries only hold if companies build cultures that protect them and if people stop overriding their own limits.
- Working 996 gets treated as a badge of honor in AI companies, but she argues it confuses long hours with ambition and talent.
- Companies that require 996 as the default borrow against employees' future health and relationships, and that cost never shows up on the balance sheet.
- Excluding parents and caregivers narrows the range of perspectives building the product, and constraints outside work often make people sharper at prioritizing.
Chris Kiehl pushes back on the gloom around Hacker News, arguing that while AI has changed the economics of paid programming, coding for its own sake is still worth doing. He describes his weekend projects, a buggy Breakout clone, Java type-safety experiments, and a frustrating dive into recursion schemes, to show that learning hard things is rewarding without any business payoff.
- Programming for work and programming for enjoyment are different things; AI changing the value of hand-written code at a job doesn't remove the reason to do it for fun.
- Kiehl admits his projects have no clear payoff: a bad Breakout clone, Agda-inspired Java experiments, and recursion schemes that left him unsure what they were even good for.
- The "there is no point" feeling comes from constant exposure to doom-heavy online discussion, and it fades once you log off.
Jake Goldsborough argues that AI coding agents aren't killing software engineering—they're freeing engineers from tedious mechanical work so they can focus on the actual problem-solving that defines the craft. The hard parts that never mattered (regex wrestling, syntax memorization, build system incantations) are disappearing, but the work that requires real judgment and responsibility remains.
- The difficult parts of coding—parsing edge cases, remembering obscure syntax, navigating unfamiliar codebases—were never the point; they were tolls between understanding a problem and solving it, and removing them doesn't diminish engineering skill.
- Agents handle syntax and scaffolding faster, which lets engineers explore domains that were previously too expensive (rewriting programs in different languages, understanding unfamiliar systems, testing experimental ideas), actually tightening the learning loop rather than replacing it.
- The real shift is architectural: engineering moves from "produce every character by hand" to "direction, judgment, verification, and responsibility," and the actual risk isn't that the work disappears but that companies cut people, generated code creates failures at scale, and the junior-to-senior training path collapses.
As AI models get better at coding, engineers need to stop pretending it's not happening and instead identify what they can do better than the models—mainly deep knowledge of their specific codebase and the ability to write clearly about technical decisions.
- AI models fail on "errors of ignorance" (not knowing which module to use, missing company conventions) and "errors of paranoia" (over-engineering simple systems), mistakes only caught by someone deeply familiar with the actual codebase and willing to confidently disagree with the AI.
- LLMs are paradoxically getting worse at writing even as they improve at coding, because writing quality can't be automatically verified and labs prioritize capability over communication—making human-written technical docs increasingly rare and valuable.
- Being a "meat proxy" who just runs prompts through AI and submits the output is worse than not using AI at all, since you're providing zero value and will be replaced once those workflows get automated into enterprise tools.
This article discusses a live session with Alejandro Aboy, a Senior Data and AI Engineer, about the evolving role of data engineers in the context of AI. Alejandro emphasizes that strong data engineering skills, particularly data modeling and orchestration, are crucial for successful AI work and shares practical steps for integrating AI into existing data workflows.
- Data modeling accounts for roughly 80% of the skill transfer from data engineering to AI engineering, according to Aboy.
- Good orchestration/workflow design can handle most AI tasks without needing complex agents.
- Precise column/model descriptions function as prompts for AI, so metadata and documentation quality directly determine AI performance.
- AI still lacks common-sense judgment (illustrated by a debugging agent missing context), so critically reviewing outputs rather than accepting them blindly remains essential.
Career-Ops is an AI-driven tool that simplifies job searches by evaluating offers, generating tailored CVs, and tracking applications in one place. It uses a structured scoring system to help users focus on high-fit opportunities without spamming companies. The system is customizable and designed for efficiency.
- Creator personally evaluated 740+ job offers and generated 100+ tailored CVs using this system before landing a Head of Applied AI role
- Uses an A-F scoring system across ten weighted dimensions to rank job fit and cut wasted applications
- Automates job scanning across Greenhouse, Ashby, and Lever for 45+ companies with batch processing of multiple offers at once
- Generates ATS-optimized CVs as PDFs tailored to specific job descriptions, run through Claude Code with a dashboard for tracking applications
Deep Atlas offers an intensive curriculum designed to compress months of AI and machine learning education into just weeks. With hands-on projects, community learning, and successful alumni, participants can quickly gain the skills needed for a career in AI.
- Deep Atlas compresses months of AI/ML education into a few weeks through an intensive, project-based curriculum.
- The program emphasizes hands-on projects and community learning as core to skill-building.
- Alumni outcomes are cited as evidence the accelerated format leads to real AI career placement.