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Stats/ML/Quant

semak-semak
Joined March 2011
Photo by Probably Approximately Correct (@PAClearning)Photo by Probably Approximately Correct (@PAClearning)Photo by Probably Approximately Correct (@PAClearning)Photo by Probably Approximately Correct (@PAClearning)Photo by Probably Approximately Correct (@PAClearning)Photo by Probably Approximately Correct (@PAClearning)Photo by Probably Approximately Correct (@PAClearning)Photo by Probably Approximately Correct (@PAClearning)Photo by Probably Approximately Correct (@PAClearning)Photo by Probably Approximately Correct (@PAClearning)
Probably Approximately Correct retweeted
I once tried to explain Bayesian reasoning at a dinner party. By dessert, everyone had updated their beliefs about inviting me again.
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never fucking quit. life flips fucking fast.
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semua antek antek pemerintah mah gakada niat baik sama sekali buat rakyat jadi harus dicancel abisabisan masih trelalu banyak rakyat bego yang vollovv dan ngevans sama tokoh tokoh seperti ini
Acaranya sepi, masa lalunya dikorek korek, akhirnya geter juga sampe jam 1 pagi upload klarifikasi. Lanjut cancel gak?
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Probably Approximately Correct retweeted
Aku dah cukup gede di taun 98. Udah beberapa kali cerita di sini. Highlight aja ya: 1. Temen sekolah ku ada yang jadi korban kerusuhan. Ditemukan terbakar ber3 bersama adik dan mamanya dalam lemari. 2. Rumahku dijadikan pengungsian temen2 kantor mama yang ga bisa pulang. Akomodasi kurang. 1 single bed bisa diisi 3 orang dewasa. 3. Hari ke tiga makanan lauk abis di rumah untung ada tukang bakso jualan. 3A. Temen kantor mama orang manado ada yang memberanikan diri pulang. Di jalan disetop massa mau ditel@nj@ngi karena disangka cina. Dia sampe keluarin KTP buat nunjukin punya fam Manado. 5. Saya melayat temen saya tadi sengaja naek motor ga pake helm jadinya. Nunjukin kulit gelap rambut keriting biar ga disangka cina. Lewatin salemba senen matraman yang masih bau asap
Ceritakan kejadian seram tahun segitu please, biar wawasan gue terbuka. Cerita pribadi dari kakek atau om tante kamu
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Probably Approximately Correct retweeted
kalo sampe lengser duaduanya i giveavvay gibson vlying v gitar pertamaku dan 100 kaos band yang aku design sendiri
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Ex-hedge fund lawyer here. Helped set up these multi-manager multi-strategy hedge funds. There are a few distinctions here: 1. These platforms achieve economies of scale by maintaining a central ‘book’ that aggregates and offsets positions coming from all pods so they don’t pay duplicative fees. The best multi-strat funds have an algo that allocates capital very efficiently across multiple pods as a result. It’s similar to BigLaw’s ‘staffing model’ just that BigLaw never had a sophisticated algo to deploy associate capacity. 2. They prune extremely quickly if a PM doesn’t perform, usually by experiencing a sharp drawdown. It’s called a ‘stop-out’. The PM walks out the door quickly. BigLaw pruning isn’t as brutal once you become an EP. 3. They have strict non compete non solicitation on their PMs. In fact the platform’s anchor investors usually mandate the platform to include those terms into PMs’ contracts to protect the platform’s value, and if the PM sets up their own shop, they must let the platform take a percentage of the new shop’s equity. Whereas the US ABA bans non-compete (Rule5.6) 4. These platforms don’t always hire the PMs fully. Some platforms like Citadel and Point72 just buy strategies/signals from smaller managers (this is called ‘alpha capture’ and deploy their own capital to pursue those opportunities and split profit with those managers. That way they don’t maintain the overhead. We seldom hear BigLaw licensing some lawyer’s templates/knowhow without hiring them. 5. These platforms mostly scale by deploying capital efficiently so PMs don’t need to set up their own fund. In BigLaw, you can’t really deploy work or relationship the same way so eventually it becomes winners take all rather than fully performance based. In short, these platforms have much more durable value than BigLaw and can survive the departure of rainmaker PMs.
Once had a friend who worked in hedge funds describe to me the multi-manager “pod shop” structure. Each portfolio manager in the hedge fund is assigned capital and analysts and they pursue their own strategies independently. The fund handles the centralised risk management and operations, technology and analytics. The structure enables the diversification of risk and pooling of shared resources. Modern Biglaw seems pretty similar in practice. Firms diversify across practices to reduce risk e.g. a restructuring practice is a great hedge for a downturn affecting PE and M&A. Each equity partner runs a team that they feed with their “book” that is semi-autonomous in a department. They do however cross collaborate and work with specialist lawyers who are a shared resource (and of course bring in their own work). And it’s much less brutal and volatile.
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GONGG Titik Kumpul demo dari Mahasiswa Trisakti adalah ISTANA NEGARA⚠️⚠️ PANJANG UMUR PERJUANGAN #INDONESIASADAR
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HARI INI KAWAN-KAWAN DARI TRISAKTI SEDANG MENUJU KE ISTANA NEGARA, AYO KITA KAWAL SEPERTI AKSI SEBELUMNYA! PANJANG UMUR PERJUANGAN!! NAIKKAN ALGORITMA #INDONESIASADAR
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🚨INFO : #DEMO 28 OKTOBER 2026 AYO TEMAN-TEMAN KITA RAMAIKAN DEMO HARI INI. JANGAN SAMPAI DEMO HARI INI DI SABOTASE KOMDIGI. AYO KITA BANTU TEMAN-TEMAN KITA YANG HARI INI TURUN KE JALAN DENGAN MERAMAIKANNYA DI MEDIA SOSIAL👊🏻👊🏻👊🏻
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you can "speedrun inner work" by going straight to the final boss and forgiving your parents
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Buat rekan2 yg di layanan primer: Silakan diakses Buku Saku Skrining Kesehatan Jiwa berdasarkan siklus hidup 📕Ibu Hamil & Pascapersalinan repository.kemkes.go.id/book… 📗Anak & Remaja repository.kemkes.go.id/book… 📘Dewasa repository.kemkes.go.id/book… 📙Lansia repository.kemkes.go.id/book…
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“𝘓𝘦𝘵 𝘵𝘩𝘦 𝘺𝘰𝘶𝘯𝘨 𝘮𝘢𝘯 𝘨𝘰 𝘢𝘯𝘥 𝘩𝘶𝘯𝘵, 𝘪𝘧 𝘩𝘦 𝘬𝘪𝘭𝘭𝘴 𝘢𝘯 𝘦𝘭𝘦𝘱𝘩𝘢𝘯𝘵, 𝘩𝘪𝘴 𝘱𝘰𝘷𝘦𝘳𝘵𝘺 𝘦𝘯𝘥𝘴... 𝘪𝘧 𝘵𝘩𝘦 𝘦𝘭𝘦𝘱𝘩𝘢𝘯𝘵 𝘬𝘪𝘭𝘭𝘴 𝘩𝘪𝘮, 𝘩𝘪𝘴 𝘱𝘰𝘷𝘦𝘳𝘵𝘺 𝘦𝘯𝘥𝘴.” — 𝘈𝘧𝘳𝘪𝘤𝘢𝘯 𝘗𝘳𝘰𝘷𝘦𝘳𝘣𝘴
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Action-oriented people are more likely to achieve difficult goals. Readiness is a result, not a requirement.
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What drives Prabowo’s approval? Based on our August 2026 national survey, my regression model explains 54% of the variance. Satisfaction with MBG is the strongest predictor (β = 0.21), and the relationship cuts both ways: those satisfied with MBG tend to approve of Prabowo, while those dissatisfied tend to disapprove. And 58% are now dissatisfied with MBG. And calls for the President to admit policy failures show the strongest negative association with approval. Programmes create disappointment. Denial turns it into distrust.
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[TWIT KELAS PACMANN] Pengumuman!🌟 Kelas baru, lebih cepat, lebih murah: Quantitative Finance with LLM oleh @pacmannai Anda dapat pelajari materi Quant dalam 16x pertemuan Offline dan Online di Jakarta, dalam 2 bulan. Harganya mulai dari Rp 3 jutaan! Berikut kurikulumnya:
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i love her
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Replying to @PAClearning
Dalam kuliah, saya selalu menekankan kepada mahasiswa untuk mempelajari apa pun, karena kita tidak tahu ilmu apa yang akan berguna bagi kita di masa depan. Open your mind, istilah Londo-nya. Saya berikan contoh-contoh sederhana, semisal manfaat saya belajar mengganti ban mobil di pagi hari, malamnya ban mobil meletus di jalan tol, langsung praktik.
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Replying to @PAClearning
Zaman sekarang itu enak klau mau cari ilmu. Zaman dlu hrus bli buku, cari tmpt kursus masih mahal dan sulit. Zaman skarang ilmu di Youtube berjibun, tempat kursus online banyak dan bisa disesuaiakan kantong. AI juga udah banyak membantu. Buat portpolio, jangan mikirin ijazah aja.
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I’m in love with this sentence: “A person's capacity for growth is directly linked to how much truth they can handle about themselves without running away.”
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