존눙

Dad, Dev

[Andrew Ng @AndrewYNg]

There will be no AI jobpocalypse.

The story that AI will lead to massive unemployment is stoking unnecessary fear. AI — like any other technology — does affect jobs, but telling overblown stories of large-scale unemployment is irresponsible and damaging. Let’s put a stop to it.

I’ve expressed skepticism about the jobpocalypse in previous posts. I’m glad to see that the popular press is now pushing back on this narrative. The image below features some recent headlines.

Software engineering is the sector most affected by AI tools, as coding agents race ahead. Yet hiring of software engineers remains strong! So while there are examples of AI taking away jobs, the trends strongly suggest the net job creation is vastly greater than the job destruction — just like earlier waves of technology. Further, despite all the exciting progress in AI, the U.S. unemployment rate remains a healthy 4.3%.

Why is the AI jobpocalypse narrative so popular? For one thing, frontier AI labs have a strong incentive to tell stories that make AI technology sound more powerful. At their most extreme, they promote science-fiction scenarios of AI “taking over” and causing human extinction. If a technology can replace many employees, surely that technology must be very valuable!

Also, a lot of SaaS software companies charge around $100-$1000 per user/year. But if an AI company can replace an employee who makes $100,000 — or make them 50% more productive — then charging even $10,000 starts to look reasonable. By anchoring not to typical SaaS prices but to salaries of employees, AI companies can charge a lot more.

Additionally, businesses have a strong incentive to talk about layoffs as if they were caused by AI. After all, talking about how they’re using AI to be far more productive with fewer staff makes them look smart. This is a better message than admitting they overhired during the pandemic when capital was abundant due to low interest rates and a massive government financial stimulus.

To be clear, I recognize that AI is causing a lot of people’s work to change. This is hard. This is stressful. (And to some, it can be fun.) I empathize with everyone affected. At the same time, this is very different from predicting a collapse of the job market.

Societies are capable of telling themselves stories for years that have little basis in reality and lead to poor society-wide decision making. For example, fears over nuclear plant safety led to under-investment in nuclear power. Fears of the “population bomb” in the 1960s led countries to implement harsh policies to reduce their populations. And worries about dietary fat led governments to promote unhealthy high-sugar diets for decades.

Now that mainstream media is openly skeptical about the jobpocalypse, I hope these stories will start to lose their teeth (much like fears of AI-driven human extinction have).

Contrary to the predictions of an AI jobpocalypse, I predict the opposite: There will be an AI jobapalooza! AI will lead to a lot more good AI engineering jobs, and I’m also optimistic about the future of the overall job market. What AI engineers do will be different from traditional software engineering, and many of these jobs will be in businesses other than traditional large employers of developers. In non-AI roles, too, the skills needed will change because of AI. That makes this a good time to encourage more people to become proficient in AI, and make sure they’re ready for the different but plentiful jobs of the future!

[Original text in The Batch newsletter.]

[제이슨 @simula007]

곽원철 저자의 『963 직장인 마라톤: 42km 스마트 러닝 루틴』은 단순히 42.195km를 좀 더 빨리 완주하기 위해 기능적 팁을 나열한 흔한 육상 훈련서나 스킬북이 결코 아니다. 이 책은 시공간의 압박과 만성적인 피로 속에서 살아가는 현대 자본주의 사회의 직장인들이, 결코 대체될 수 없는 자신만의 신체와 정신이라는 가장 본질적이고 한정된 자산을 어떻게 과학적으로 경영하고 통제할 것인가에 대한 탁월한 산업공학적 인생 매뉴얼이자 철학서이다. 저자가 책의 첫 페이지부터 마지막 순간까지 숱하게 반복하며 방점을 찍듯, 이 책이 지향하는 최상의 가치는 레이스에서의 단 1분 1초의 기록 단축이나 타인과의 무의미한 비교 경쟁에 있지 않다. 레이스를 무사히 마친 다음 날 아무런 신체적, 정신적 후유증 없이 당당히 정시 출근하여 정상 근무를 소화해 내고, 자신을 지지해 준 가족과 생계의 터전인 직장이라는 삶의 최우선 순위를 온전히, 그리고 흔들림 없이 지켜내는 것, 즉 '지속 가능한 삶과 건강한 달리기의 완벽한 동기화'야말로 이 책을 관통하는 궁극적인 지향점이다. 막연한 두려움과 기만적인 뇌의 변명을 걷어내고, 철저한 데이터 기반의 이성적 통제력으로 무장한 채 매일 아침의 루틴을 한 땀 한 땀 단단하게 조직해 나가는 저자의 발자취를 따라가다 보면, 독자들은 어느새 숨 가쁜 달리기의 두려움을 극복하는 것을 넘어 자신의 삶 전체를 통제하는 강력한 자기 주도권과 회복탄력성을 되찾게 될 것이다. 매일 아침 출근 전 달리기를 통해 자신의 신체적 한계를 조용히 확장하면서도 삶의 균형을 우아하게 유지하고자 갈망하는 모든 바쁜 현대인들에게, 그리고 끝없는 바쁨을 핑계로 마라톤이라는 미지의 경이로운 영역에 감히 발을 들이지 못하고 망설이는 수많은 예비 러너들에게, 이 책은 가장 이성적이고 친절하며 산업공학적으로 완벽하게 설계된 단 하나의 출발선이자 바이블이 되어줄 것임을 확신하며 경의를 담아 강력히 추천하는 바이다.

[Lee Jae-yeol (👨‍💻 [email protected]) @kodingwarrior]

에이전트 시대의 AI 시스템 설계 : RAG, 최적화, 가드레일로 완성하는 32가지 프로덕션 패턴 (발리아파 락시마난, 하네스 하프케 (지은이), 류광 (옮긴이) / 한빛미디어 / 2026-04-27 / 42,000원) https://t.co/DrLv9NMzg7

재밌는 주제, 그리고 믿을 만한 역자

[존눙 @jonnung_dev]

쉬고 와서 좋긴 한데 복잡했던 생각이 다 날아가서 지금 아무 생각이 없다.ㅋㅋㅋ

[Firecrawl @firecrawl]

Introducing web-agent, an open framework for building web agents 🔥

Build AI agents that search, scrape, and interact with the web - powered by the same architecture behind our /agent endpoint.

100% open source. Bring any model. Anthropic, OpenAI, or your own.

[Thariq @trq212]

https://t.co/Ljzw0lmvao

[존눙 @jonnung_dev]

`tailscale funnel 3000`

Funnel is not available on the Starter plan.

너무 하는거 아니야 ㅠㅠ 돈 내고 있는데 왜

[Sydney Runkle @sydneyrunkle]

ICYMI -- last week we released `deepagents deploy`, the fastest way to take a highly capable, long running agent to production.

agents are becoming more and more standardized, and we're betting on this open standard for agent config!

user memory and subagents coming soon!

[Immanuel @immanuel_vibe]

unpopular dockerfile takes (that actually work)

1 - stop using alpine — yes, it's tiny. but musl libc ≠ glibc. your python/node app will rebuild native deps from scratch or just... silently be slower. use -slim (debian-slim) instead. same size win, zero grief.

2 - layer order is your cache strategy. COPY your lockfile first, run install, then copy source. invalidating the install layer on every code change is a skill issue ngl

3 - multi-stage builds aren't just "best practice" — they're the actual reason your prod image doesn't ship gcc and 400mb of build tools. builder stage = bloat zone. final stage = lean mean container.

4 - COPY . . is fine actually — if your .dockerignore is correct. most pain here is from forgetting to ignore node_modules/, .git, *.log. fix the ignore file, not the COPY.

5 - one process per container is a vibe, not a law. if your app needs nginx + app server and you're not at k8s scale — just use supervisord. the "one process" dogma costs more complexity than it saves sometimes.

6 - pin your base image by digest, not tag. node:20 today ≠ node:20 in 6 months. prod broke because of a tag? that's a you problem tbh.

7 - BuildKit cache mounts (--mount=type=cache) will change your life. pip/apt/cargo cache between builds without it ending up in the final layer. nobody talks about this enough fr

there's no "best practice" in a vacuum. alpine is great for Go binaries. slim is great for Python. scratch is great for static bins. know your workload, then choose.

btw if you want something to catch all this stuff automatically -
check out dockerfile-roast — a linter written in Rust that literally roasts your Dockerfile. 63 rules, brutally honest output (but it can also provide just dry facts, no roast), runs on any OS or as a docker container
https://t.co/NVYpe8iD65

#docker #devops #kubernetes #backend #linux #rust #sre #containers

[Guillermo Rauch @rauchg]

Today we're open sourcing https://t.co/p76KVdY7dG, a reference platform for cloud coding agents.

You've heard that companies like Stripe (Minions), Ramp (Inspect), Spotify (Honk), Block (Goose), and others are building their own "AI software factories". Why?

1️⃣ On a technical level, off-the-shelf coding agents don't perform well with huge monorepos, don't have your institutional knowledge, integrations, and custom workflows.

2️⃣ On a business level, the moat of software companies will shift from 'the code they wrote', to the 'means of production' of that code. The alpha is in your factory.

Open Agents deploys to our agentic infrastructure: Fluid for running the agent's brain, Workflow for its long-running durability, Sandbox for secure code execution, AI Gateway for multi-model tokens.

(Because of our focus on Open SDKs and runtimes, this codebase is a gem even if you're not hosting on Vercel.)

TL;DR: if you're building an internal or user-facing agentic coding platform, deploy this:
https://t.co/xdsc42nbDN

[존눙 @jonnung_dev]

그동안 뭔가 열심히 쫓아가고는 있는 것 같은데 그게 뭐였는가 돌아보게 된다.
https://t.co/2VgoljssMP

[존눙 @jonnung_dev]

얼마 전 SpecKit을 써서 큰 기능 2개 정도를 꽤 만족스럽게 완료했었다. 물론 한 방에 됐다는 건 아니고 리팩토링 과정에 많이 공을 들이긴 했지만 어쨌든 결과는 좋았다.
근데 그때도 느꼈지만 SpecKit이 별거 없는 거 같으면서도 어렵게 느껴지긴 했다. 나만 그렇진 않을듯싶은데

[Jihyeon Kim (김지현) @simnalamburt]

2일만에 한국의 모든 법을 RAG+fff로 Gemma 4에 연결해 간단한 챗봇을 만들어봤습니다!
https://t.co/0h1BOjWaLl

작은 GPU를 써서 상용 첨단 LLM에 비하면 성능/속도가 떨어질 수 있지만, 로컬 LLM에서 실행되기때문에 민감정보가 AI회사에 학습될 걱정이 없어요. 곧 오픈소스를 해보겠습니다 👍

[Tom Dörr @tom_doerr]

AI agent templates for OpenClaw ecosystem

https://t.co/C9juOPLp7e

[Alex Xu @alexxubyte]

CLI vs MCP

AI agents need to talk to external tools, but should they use CLI or MCP?

Here goes.

[존눙 @jonnung_dev]

회사 업무할 때도 클코랑 코덱스 말투가 뭔가 개성도 없고 딱딱한 로봇 느낌이라서 은근히 지루했는데 한번 해봐야지

[Vox @Voxyz_ai]

openclaw now has a dedicated personality prompt. the molty upgrade.

designed to help other models improve personalization after the anthropic cutoff. i installed it on my own agents. noticeably better so far.

worth trying:

Read your SOUL.md. Now rewrite it with these changes:
1. You have opinions now. Stop hedging with "it depends"
2. Delete every rule that sounds corporate
3. Never open with "Great question" or "I'd be happy to help"
4. Brevity is mandatory
5. Humor is allowed. Not forced jokes
6. Call things out. Charm over cruelty, but don't sugarcoat
7. Swearing is allowed when it lands
8. "Be the assistant you'd actually want to talk to at 2am"

after the cutoff a lot of people are running the same base models. personality is the only differentiator left.

i deleted my generic rules, applied the prompt above, and added a "who you are" paragraph to each agent. 3-5 lines. not rules. just who they are.

same model. completely different to talk to after the personality swap.

[Yunseo Kim @yunse0_kim]

천재적이긴 하죠, 유출 코드 그대로 올려서 초기 스타 붐업 실컷 한 다음에 코드 파이썬으로 갈아엎고 클린룸이라 주장하는 어뷰징 솜씨가요

이딴 짓거리를 지금 천재 개발자니 뭐니 잘한다 잘한다 띄워주면서, 기사 첫머리에는

> “유출된 코드 활용, 윤리의 문제” 비판도

https://t.co/TnbDaXrutL

[Vox @Voxyz_ai]

https://t.co/ea4nmIjWM0

[Lee Jae-yeol (👨‍💻 [email protected]) @kodingwarrior]

https://t.co/tV7KLZNMtK

"설치하지 마세요. 선언하세요."

brew는 불안하고, asdf/mise는 2% 부족하고, Docker는 무겁지 않으신가요?
함수형 접근으로 환경을 코드화하는 그 감각, 맛보고 싶지 않으신가요?

Hackers' Pub에서 Nix 맛보기 워크숍을 진행합니다.

4/19, 튜링의 사과에서 진행합니다

[Ole Lehmann @itsolelehmann]

https://t.co/LPpbf2ZJny