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Member since 2017-07-15T03:50:57Z. Last seen 2025-04-09T16:00:01Z.
2754 blog posts. 128 comments.
Sukh Greeting https://youtu.be/gWnCXEKDIA0
Intro Flashcards & Real Object https://youtu.be/yqj-oGqFW3w
Song https://youtu.be/bSivQVMw7Dw
Interaction with kids https://youtu.be/-lkm7WV_dqg
周四晚美國科技股急挫，當下的One Billion Question必然是「今次堅定流？」
殺倉看似突然而來，細心看仍是有跡可尋。科技股Big 6中的蘋果和Tesla，因上月宣布股份拆細而突然變得相當「親民」，Tesla更因此成為環球散戶的熱炒股，有統計指南韓股民空群而出力掃Tesla，累購股權達0.89%，晉身大股東之一；不少中小型科技股趁強勁業績股價melt up，例如視像會議程式公司Zoom（ZM）績後就大升四成，連帶其他雲計算股，績前已偷步炒起，破頂再破頂。
另一邊廂，美國30年長債債息靜靜回升至貼近1.5厘，市場當時仍視為資金沽債買股的良好訊號，甚少人留意其實債息上升對科技股估值有影響，尤其是仍處於初創階段，有P/S（市銷率）而無P⁄E（市盈率），甚至單單販賣前景的夢想型股票，估值衝擊更大；同一時間波幅指數VIX亦蓄勢反彈，美股三大指數由慢升變勁升已久，出現三數日急調整，本來就有此可能性，上周筆者亦提出，要預先做好對沖。
適當時間對沖 最佳示範
近月愈來愈多人講「對沖無用」論：今年3月大市回升以來，已經被美股扮轉勢不知騙了多少次，假如次次買Put買保險，可謂買保險都買到窮，與其終日疑神疑鬼，還不如一注坐到尾更好？
的而且確，全年每月對沖成本高昂，的確不可行，但如何選擇適當時候對沖，就考驗基金經理對宏觀風險的洞察力、市場論述的理解程度和對投機值博率掌握度，當大眾仍瘋狂於科技泡泡浴中，套用鱷王達里奧金句，站在舞池狂舞中，仍請緊盯出口動靜。
隨着美國總統大選日子漸近，市場情緒化的程度只會更厲害，再加上聯儲局貨幣政策，已出到容許通脹飛升這招，對於好友而言，可以說是幣策利好已接近完全出盡，未來兩個月，美股是有可能繼續慢升急調整，不過大選行情的論述愈近選舉就愈明顯，同時美股累升幅度高，純粹長倉的值博率愈來愈低亦是不爭事實，未來兩個月，美股出現有波幅無升幅的狀況，機會並不低。
愈近泡沫市的尾段，煙花往往是愈燦爛，升幅亦可以更癲更勁，不過逃生門亦只有一剎那開啟，全身而退絕不容易，這正正是非理性泡沫最吸引同時亦是最矛盾之處。
777 tlarkworthy 14 hrs 217
https://observablehq.com/@tomlarkworthy/hacker-favourites-analysis
Unable to load the content https://observablehq.com/@tomlarkworthy/hacker-favourites-analysis
August 29, 2020 A coworker of mine asked:
The short answer is do roll your own crypto, but don't use it in production until it's vetted by professionals. The long answer below might take a few years to hash out.
Making mistakes is an unavoidable part of the learning process. I've been rolling crypto for Google production for years, but my code is not bug free and will never be. I found that the cheapest way to learn from mistakes is to learn from other people’s mistakes. I recommend taking Cryptography I, doing CTFs, and solving crypto challenges. This won't take long, and very quickly you'd be pretty dangerous because you'd be able to find many crypto bugs.
But it's just the beginning. When I got to Google, I thought I knew crypto because look at all the bugs I found! I was so wrong. It took me years to learn the tradecraft from the real experts which fortunately my employer has plenty.
They say you can become a better programmer by reading good code. Unfortunately, I've learned the hard way that this rule usually does not work in crypto, for 3 reasons:
Because of side-channel and other constraints, crypto code is usually far from obvious. Imagine coding without the if statement; Crypto code usually has many subtle details that look unnecessary, but once changed or removed totally destroy security; and Bad crypto code usually looks and produces results indistinguishable from good crypto code. There's no shortcut rather than learning the fundamentals. It’s not hard, but it takes time. A couple of resources that you might want to check out:
Cryptography Engineering by Schneier and Ferguson: this was the book that got me started; Cryptograph I and the crypto book by Dan Boneh and Victor Shoup: this is hands down the best materials on applied crypto; An Introduction to Mathematical Cryptography: if you are worried that you don't know enough math to do crypto, this book will help you put rest to that fear; Tink: this is Google’s recommended crypto library (full disclosure: my team owns it). Tink provides high-level APIs, so you won't be able to learn much from it if your goal is to write your own AES, but it has pretty good info on common crypto techniques. The funny thing is that after spending years studying these resources, you still don’t have a free pass to roll all the crypto in the world. You'd realize and appreciate that crypto is a deep and vast field of study with a very long food chain. Sitting on top are cryptanalysts who
propose and solve hard mathematical problems such as integer factoring, discrete log, shortest vector, closest vectors, etc; or design and break efficient heuristic one-way functions such as AES, ChaCha20, SHA3 or Blake; and at the bottom are software engineers who want to encrypt some data. Along the chain, you will find people who
take the one-way functions and build fundamental primitives such as AEAD, MAC, digital signatures, or public key encryption; take the primitives and build protocols such as TLS; take the protocols and build APIs such as OpenSSL, Bouncy Castle, JCE, Golang Crypto, libsodium or Tink; and take the APIs and build applications such as end-to-end encryption, storage encryption, user authentication, etc. The division is not always clear cut, as there are people who wear multiple hats. The thing is, unless you get to the very top, there are always good reasons not to roll your own crypto. The reasons are usually non-obvious, unknown unknowns. I’ve seen a distinguished engineer encrypting data with AES-ECB arguing that they weren’t rolling their own crypto, because they didn’t implement AES from scratch but called OpenSSL.
So if you want to roll your own crypto, make sure you understand where you are in the crypto food chain and what are the reasons preventing you from moving up. Study and eliminate said reasons. Good luck and have fun!
Discussion on Hacker News: https://news.ycombinator.com/item?id=24320998#24321319.
https://en.m.wikipedia.org/wiki/Sartor_Resartus
我看到他二零零二年台北國際書展的演講，提及影響他最大的三本書，分別是歌德的《浮士德》、倉田百三的《出家人及其弟子》，以及卡萊爾（Thomas Carlyle）的《衣裳哲學》（Sartor Resartus）。那演講主要談的，是今天很多人應該聞所未聞的帶有後現代小說風格的《衣裳哲學》。除了《出家人及其弟子》，《浮士德》和《衣裳哲學》我都看過，且每隔一段日子，我就會懷着探望老朋友的心情重看。
Category: Artificial intelligence Posted Jul 17 fish on a bicycle
The concept: When we look at a chair, regardless of its shape and color, we know that we can sit on it. When a fish is in water, regardless of its location, it knows that it can swim. This is known as the theory of affordance, a term coined by psychologist James J. Gibson. It states that when intelligent beings look at the world they perceive not simply objects and their relationships but also their possibilities. In other words, the chair “affords” the possibility of sitting. The water “affords” the possibility of swimming. The theory could explain in part why animal intelligence is so generalizable—we often immediately know how to engage with new objects because we recognize their affordances.
The idea: Researchers at DeepMind are now using this concept to develop a new approach to reinforcement learning. In typical reinforcement learning, an agent learns through trial and error, beginning with the assumption that any action is possible. A robot learning to move from point A to point B, for example, will assume that it can move through walls or furniture until repeated failures tell it otherwise. The idea is if the robot were instead first taught its environment’s affordances, it would immediately eliminate a significant fraction of the failed trials it would have to perform. This would make its learning process more efficient and help it generalize across different environments.
The experiments: The researchers set up a simple virtual scenario. They placed a virtual agent in a 2D environment with a wall down the middle and had the agent explore its range of motion until it had learned what the environment would allow it to do—its affordances. The researchers then gave the agent a set of simple objectives to achieve through reinforcement learning, such as moving a certain amount to the right or to the left. They found that, compared with an agent that hadn’t learned the affordances, it avoided any moves that would cause it to get blocked by the wall partway through its motion, setting it up to achieve its goal more efficiently.
Why it matters: The work is still in its early stages, so the researchers used only a simple environment and primitive objectives. But their hope is that their initial experiments will help lay a theoretical foundation for scaling the idea up to much more complex actions. In the future, they see this approach allowing a robot to quickly assess whether it can, say, pour liquid into a cup. Having developed a general understanding of which objects afford the possibility of holding liquid and which do not, it won’t have to repeatedly miss the cup and pour liquid all over the table to learn how to achieve its objective.
108 MindGods 5 hrs 49
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