youtube.com/watch?v=IBFkgyTX…
리차드 파인만이란 이름 값때문에 귀기울렸나? 생각해봐도 단순히 그래서 집중하진 않은 듯.
'기계가 사람보다 똑똑해지나? 지능을 어떻게 정의하냐의 문제'
'기계가 스스로 생각할 수 있을까? 사람처럼 스스로 생각하면 사람처럼 약아지는 문제가 생길거다. ex굿하트의 법칙
Things are not as bad for junior programmers as the scuttlebutt would have you believe.
I just read that Anthropic has kiboshed LeetCode-style interviews because there was zero correlation between LeetCode results and on-the-job performance. When an AI can do a better job of React coding than most programmers, testing for the minutiae of React programming isn't worth the virtual paper.
Now, admittedly, Anthropic is not exactly a pinnacle of high code quality. Last time I checked, Claude Code had 15,500 issues. Compare that with the (similarly sized, but hand coded) JUnit 5, which has 105 open issues. Nonetheless, Antropic is setting a standard for working successfully with AI assistance, and they're being emulated by other employers.
Felix Rieseberg, a hiring manager at Anthropic, recently said: "Tenure no longer automatically correlates with doing better, faster or higher-quality work!" In other words, they are not hiring for experience, they're hiring for competence in the tech they actually use, and given how new that tech is, we're all starting from the same starting line, no matter how long we've spent getting up to that line. Is experience still valuable? Sure. But, more valuable is learning and adapting. Anybody who does that is employable. Anybody accustomed to being told what to do (and what to learn) who is hoping to get by on experience alone, without actively becoming expert on the new tech, is not. It's not that hard: install Claude Code and build something! Doesn't matter if you do that at work, but do it you must.
m.blog.naver.com/tosoha1/224…
컴퓨터와 친하지 않은 와이프도 엄청 쓰는데.. opus 5.5의 엄청남 치고 매출성장 그래프가 가파르지 않네.. 메타 뮤즈앱이 체인저가 될런지.
ai관련 주가는 흐물거리는데 왜 개발자는 밥그릇을 걱정해야되는지.. 다른 화이트칼라 먼저 대체시키고 마지막에 퇴장해야되는거 아
juniq retweeted
github.com/midagedev/mwoyeo
화면 캡처부터 문자 인식, 글자 지우기, 번역까지 애플 온디바이스 AI만으로 돌아가는 앱 "뭐여"
네이티브앱도, 브라우저 일부 화면 번역도, 심지어 아이폰 미러링 화면도 모두 번역됩니다. 3시간만에 만들었고 방금 릴리즈해서 아직 고칠게 좀 보이지만 일단 올려봅니다.
저도 이게 이렇게 잘될지 몰랐
글에 따르면 에이전트의 엄청난 효과를 보고 있는 사람이 아직도 2천만명 정도다. 에이전트에 투자하고 있어서 침투율 여유가 많아 좋아보인다.
하지만, 사실은 에이전트가 풀만한 문제를 가지고 있는 사람이 별로 없는거 아닌가 하는 의심도 든다.
Dusting off this tweet from April because this gap in shared understanding of LLM capability is *widening*. It's now less "two groups of people speaking past each other" and more a sharp funnel.
- Napkin math somewhere around 6B people (~75% of the population) have barely come in contact with LLMs at all.
- Around 1-2B (~20%) are casual and infrequent users of free-tier ChatGPT-like products. This group sees derpy chatbots and treats them a bit like a better Google search, a writing aid, or etc. Maybe an agent tries to book you a flight. I have non-tech friends who (reasonably, imo) say they have not much use for it at all. Even many professionals outside of math&code are in this tier. For example, execs and many other functions spend a lot of their time talking to other people, so while they understand what is happening intellectually, it is still second-hand and a bit abstract.
- Now we get to professional use of frontier-grade LLMs in math&code. Somewhere around 20M people (0.2%) see first-hand that large, complex projects that used to take them weeks/months can now be completed by agents with a prompt. Building apps, copying apps, translating apps, decompiling apps from binaries... This has all happened very quickly and recently - less than 1 year ago, I was writing code manually by hand, typing memorized computer code commands into a code editor character by character, occasionally pressing Tab to autocomplete a little chunk of code.
- And finally we get to the ~5,000 people (~0.00006%) with access to frontier-grade systems internally. The external world has seen the preview. It looks like swarms of thousands of agents collaborating over weeks on software mega projects: minting zero days, running cyber attacks and defenses at machine speeds, discovering new science, advancing the frontier of mathematics. Things that would have taken top professionals in the industry years of work. Meanwhile, human review and comprehension are starting to fall behind. For example, people are still involved in the "archeology" of the OpenAI-HF incident from many months ago. Mathematicians may be poring over the 722 manuscripts on frontier mathematics for a while.
The funnel is driven by a combination of factors:
1. The impact scales with ambition, problem size, and horizon. A question with a paragraph answer barely stresses the system. You need a reservoir of big, difficult problems that you really care about. This aspect drives the consumer / professional dimension of the funnel.
2. The jaggedness of the system (which I have written about a lot separately). Capability peaks in domains that are digital, verifiable and economically valuable. This is because LLM capability emerges from reinforcement learning on verifiable rewards on a curated environment mixture driven by revenue potential. This aspect primarily drives the area (e.g. math&code) dimension of the funnel.
3. Access. Free-tier, paid-tier, internal.
So this is the weirdness of the moment. The general public has mostly not interacted with these systems. When they have, it looks like a derpy chatbot. The majority of professionals still see only a modest uplift. And a small sliver of professionals are experiencing the vertigo of the curve going vertical. And it is all happening at the same time.
gist.github.com/juniqlim/88f…
클린아키텍처 이야기하다가, 예전에 연습할때 작성했던 코드가 생각나서 다시봄.
그땐 도메인 모델이 인프라스트럭처에 오염되는게 싫었기 때문에, 엔티티 DTO를 따로 만들고 격리했다.
특히 db나 프레임웍의 의존성없이 테스트코드가 동작하길 바랬다.





