nDeso

menanam, merawat & memetik!

[stellar protocol @stellarprtcol]

🤯 Larry Page ternyata udah ngomongin ini sejak 2007... dan sekarang rasanya prediksi itu mulai kejadian.

Kasus terbarunya datang dari Stripe. 🧵👇

Kabarnya, Claude Fable 5 berhasil membantu memigrasikan 50 juta baris kode Ruby hanya dalam 1 hari.

Padahal pekerjaan sebesar itu biasanya bisa makan waktu berbulan-bulan dan melibatkan satu tim engineer penuh. 😳💻

Dengan biaya input yang disebut sekitar $10 per 1 juta token, pekerjaan yang dulunya identik dengan satu kuartal pengembangan kini diklaim bisa ditangani oleh satu engineer dengan bantuan AI.

Yang bikin makin menarik, video di atas memperlihatkan Larry Page pada tahun 2007.

Di detik 0:06, dia bilang kalau masa depan AI bukan soal algoritma yang makin rumit, tapi soal kekuatan komputasi (compute). ⚡

Salah satu analogi yang dia pakai juga masih relevan sampai sekarang:

🧬 Seluruh DNA manusia bisa dikompresi jadi sekitar 600 MB—lebih kecil dari ukuran sistem operasi Windows.

Pesannya sederhana tapi dalam:

Kalau kecerdasan bisa direpresentasikan dalam informasi yang relatif kecil, maka seiring compute terus berkembang, kemampuan AI juga bakal ikut melesat.

Dan melihat perkembangan model AI generasi terbaru sekarang... rasanya prediksi itu makin sulit buat diabaikan. 👀🚀

https://t.co/pind1RuqRz

[Gia Macool @GiaMMacool]

Good men have been taught how to “treat a woman.”

They’ve been raised with competence, respect for hierarchy, and the expectation to always show respect.

Women have not been taught how to treat men and that’s exactly why so many get away with constant disrespect.

Our society holds men to a higher standard while not demanding the same from women

Men aren’t the problem.
The double standard is.

[Andrew Ng @AndrewYNg]

“Loop engineering” is a hot buzzphrase after mentions of it by Boris Cherny (Claude Code’s creator) and Peter Steinberger (OpenClaw's creator) went viral on social media. Loops are now a key part of how we get AI agents to iterate at length to build software. In this letter, I’d like to share my 3 key loops, shown in the image below, for building 0-to-1 products. These loops guide not just how I build software, but also how I decide what software to build.

Agentic coding loop: Given a product specification and optionally a set of evals (that is, a dataset against which to measure performance), we can have an AI agent write code, test its work, and keep iterating until the code is bug-free and meets its specification. This idea of closing the loop took off around the end of last year, and it has been a game changer in enabling coding agents to work longer productively without human intervention. For example, over the weekend, I was building an app for my daughter to practice typing, and my coding agent could easily work for around an hour, using a web browser to check what it had built multiple times before getting back to me, without needing my intervention.

The engineering loop executes quickly. Every few minutes, the coding agent might build and test a new version of the software. I hear frequently from developers who are finding new ways to engineer more effective engineering loops. This is an active area of invention!

Developer feedback loop: In this loop, a developer examines the current product and steers the coding agent to improve it. Last year, a lot of developers (including me) were acting as the QA (quality assurance) function for our coding agents, manually finding bugs and then asking the agent to fix them. But with coding agents much more able to test their own code, the amount of time we need to spend on this function has decreased significantly. This allows us to make higher-level product decisions, such as what key features to offer, where the UI needs improvement, and so on.

The developer-feedback loop operates over time intervals between tens of minutes and hours — that's how frequently a developer might review a product and give feedback. In the case of the typing app, I changed my mind a few times about the visual design, what cat costumes she can unlock as she learns (she loves cats), and the user flow for a grown-up to log in and steer the child's learning experience.

When a developer has a clear vision for what to build, it is still a lot of work to translate that vision into a specification for a coding agent to implement. Further, after the developer has seen an implementation, they might update (or perhaps clarify) the spec to steer it toward what they want. If you find that the system repeatedly runs into certain problems, building a set of evals for the agent becomes useful.

AI-native teams are increasingly using AI to help shape product direction, for example, automating the gathering and analysis of usage data, summarizing written and verbal customer feedback, or carrying out competitive analysis. However, for pretty much all the products I’m involved in, I see humans as having a significant context advantage over current AI systems — we know a lot more than the AI system about the users and the context the product has to operate in — and thus humans play a critical role. Many people describe this human contribution as “taste,” but I prefer to think of it as humans having a context advantage, since that gives us a clearer path to helping AI systems get better. This also speaks to why this step can’t be automated: So long as the human knows something the AI does not, human-in-the-loop is needed to to inject that knowledge into the system.

External feedback loop: This includes a wide range of tactics like asking a few friends for feedback, launching to alpha testers, or putting the code into production with A/B testing. These tactics are usually slow, rarely taking less than hours and sometimes taking days or even weeks. This data informs the developer vision, which in turn continues to drive the detailed product spec, which in turn drives the coding agent.

With coding agents speeding up software development, more engineers are starting to play a partial product management role. For many engineers who are growing into this role, the hardest part is shaping the product vision and striking a balance between building (bridging the gap between vision and spec) and getting user feedback to evolve the vision. It is important to do both!

I will write more about how to do this in future posts, but for now, I find it encouraging that engineers are playing an expanded role (just as product managers and designers now do more engineering).

[Original text: The Batch]

[Movez @0xMovez]

Claude Code Creator:

“90% of companies we work with don’t use verification loops - that’s their biggest mistake.

more than 40% of my code is already written by "loops", and I’m sure this share will only grow.”

in a 25-minute talk, Boris Cherny discusses the best coding workflows with Spotify’s Chief Engineer.

This one video will replace 100 YouTube Claude coding setup tutorials.

Watch it today, then read the full guide on loop engineering below.

[Helen Casanova | Seduction Palace @HelenCasanova_]

3: They intentionally create a gap.

The therapist explained that the most powerful habit is counterintuitive. They intentionally create distance. Not emotional distance. Physical distance. Time apart. The therapist explained that desire grows in the gap between closeness and mystery. When there is no gap, there is no desire. The couples who practice this habit create space. They miss each other. And missing each other is the foundation of wanting each other.

[Livsun @L1vsun]

every time you click buy, someone on the other side already solved for two numbers

you don't even know the names of

number one: their theoretical fair value. not the price on your screen. not the analyst target

their own model, updated in real time from order flow, volatility surface, and live inventory

they already know what the asset is "really" worth before you've opened the chart

number two: the probability you have information they don't. market makers call it adverse selection

they literally price the spread based on how likely you are to know something. wide spread means they think you might

tight spread means they think you're just noise

here's what makes this brutal

you spent 4 hours reading the earnings call, studying the chart, checking reddit

the other side of your trade spent 40ms

not reading faster - solving a completely different equation

Bookmark this

you were asking "is this a good buy?"

they were asking "what's the chance this person has real alpha and how much margin do i need if they do?"

two different games running on the same order book at the same time

most retail traders don't know the second game exists

and that's the entire edge right there

[Gia Macool @GiaMMacool]

Women have convinced men that they’re the emotionally unavailable ones.

Reality: Women are the ones who are emotionally unavailable.

Good men keep showing up, trying to get better, only to be rejected, pushed away, and told they’re defective for so long that they finally withdraw and give up completely.

The gaslighting has to stop.

[delost @thedelost]

a quant from AQR said something on a panel that stuck with me

"the hardest part of building a model isn't finding signals. it's killing the ones that don't belong"

that's what this MIT lecture is about

Lasso regression. the method that forces your model to delete its own variables

regular regression uses every factor you feed it
it gives each one a coefficient and tells you they all matter
the problem: most of them don't. they're fitting noise, not signal

Lasso adds a penalty that shrinks weak coefficients to exactly zero
not close to zero. zero. gone. deleted from the model

you start with 500 potential signals
Lasso keeps 6

those 6 survived because they actually explain returns after everything else was stripped out
the other 494 were noise dressed up as edge

this is how quant desks handle the overfitting problem that kills every retail backtest
you test 1,000 factors, your model will always find something that "works"
Lasso is the filter that asks: does it still work when I penalize complexity?

> Lasso regression: 1996, Robert Tibshirani, Stanford
> used at AQR, Two Sigma, Man Group for factor selection
> MIT teaches it in their financial mathematics program
> the lecture is free on YouTube right now

retail builds models that explain the past perfectly and break in two weeks

quant desks build models that delete 99% of their own inputs
and trade only what survives

full breakdown in the video below

[Livsun @L1vsun]

quants aren't better at predicting the market

they're better at reading yours - before you've finished placing the order

when you click buy on robinhood, your broker sells that order to a market maker in milliseconds

that market maker already knows your direction before it hits the exchange

they reprice accordingly. you never get the true spread

this is called payment for order flow - legal since 1984, and market makers pay brokers specifically to see your orders early

retail traders pay roughly $1.4 billion a year in hidden execution costs. average trader never sees a line item for it

renaissance built their edge on exactly this logic. not prediction - precision on systematic pricing errors that everyone else keeps making

medallion fund: 66% gross annual return, 30 consecutive years

not a single negative year in three decades

trading the same instruments and public markets as everyone else - completely different information layer

alpha wasn't hidden in some proprietary model. it lived in FINRA dark pool reports, SEC 13F filings, ATS transaction data

all public, free, sitting completely unread because retail never thought to pull it

game was never about being smarter

it was about seeing the full board when everyone else only sees their own hand

[delost @thedelost]

a quant who started as a technical trader explained what changed everything for him

he stopped trying to predict where price goes

he started measuring the probability of each market state and betting only when the math was asymmetric

that shift took him from drawing trendlines to writing models at 22

the realization was simple but brutal: technical analysis gives you a narrative
quantitative analysis gives you a number

one feels right
the other is testable, repeatable, and either works or doesn't across 10,000 trades

the setup on his desk tells the whole story
no TradingView. no candlestick charts. just code, data, and a terminal

he didn't go to MIT
he didn't intern at Goldman
he learned Python, statistics, and probability theory on his own and built something that actually worked

> the math: free in any stats textbook
> the data: free on Yahoo Finance, FRED, exchange APIs
> the code: Python, 200 lines, running on a laptop
> the barrier: not intelligence. just knowing this path exists

most people spend years staring at indicators from the 1970s
wondering why they can't find consistency

the answer was never a better indicator
it was a completely different framework

one that treats trading as a math problem, not a prediction game

full breakdown in the video below

[Info Gempa Dunia @infogempadunia]

Extratropical Cyclone di Selatan Australia Turut Mengubah Pola Hujan di Indonesia Barat

Pada 13 Juni 2026, kondisi atmosfer di Indonesia bagian barat sebenarnya mendukung hujan yang lebih luas dan lebih intens dibandingkan hari sebelumnya. Pasokan kelembapan di sekitar Jawa, Sumatra, Laut Jawa, hingga Selat Karimata juga terpantau cukup baik untuk mendukung pertumbuhan awan hujan di berbagai wilayah.

Namun, pada saat yang sama keberadaan Extratropical Cyclone atau siklon ekstratropis yang cukup kuat di selatan Australia turut memengaruhi pola aliran udara dan kelembapan di kawasan Indonesia. Akibatnya, sebagian pertumbuhan awan dan pasokan kelembapan yang semula berpotensi mendukung hujan lebih luas di Indonesia bagian barat menjadi lebih terkonsentrasi ke arah selatan, menuju sistem badai tersebut.

Dampaknya, hujan tetap terjadi di banyak wilayah, termasuk Lampung, Jawa Barat, Jawa Tengah, dan DIY yang telah diguyur hujan. Namun, cakupan dan perkembangan awan hujan di sejumlah wilayah tidak berlangsung semaksimal yang diperkirakan sebelumnya. Di beberapa daerah, hujan cenderung bersifat lokal, berdurasi lebih singkat, atau diselingi periode cerah berawan.

Sementara itu, kondisi yang berbeda terjadi di Indonesia bagian timur. Aktivitas hujan di kawasan ini tetap berlangsung dan pengaruh siklon ekstratropis relatif lebih kecil dibandingkan wilayah Indonesia barat. Cuaca di Indonesia timur saat ini lebih dipengaruhi oleh kombinasi angin timuran dari Australia, pola angin regional, serta keberadaan sirkulasi siklonik di timur Filipina yang membantu mendukung pertumbuhan awan hujan di sejumlah wilayah.

Fenomena ini menunjukkan bahwa cuaca Indonesia tidak selalu ditentukan oleh satu sistem cuaca saja. Dalam waktu yang sama, wilayah barat dan timur Indonesia dapat dipengaruhi oleh mekanisme atmosfer yang berbeda, sehingga menghasilkan kondisi cuaca yang juga berbeda.

Salam
@infogempadunia

#info #cuaca

Referensi

1. World Meteorological Organization (WMO). Extratropical Cyclones

2. Australian Bureau of Meteorology (BoM). Extratropical Lows and Southern Ocean Weather Systems

3. NOAA JetStream. Mid-Latitude Cyclones and Weather Systems

[Info Gempa Dunia @infogempadunia]

Sabuk Awan Raksasa Membentang dari Sumatra sampai Australia

Jika kalian melihat pantauan satelit pada hari ini (13 Juni 2026), ada pemandangan yang sangat mencolok di sebelah selatan khatulistiwa. Sebuah sabuk awan raksasa tampak memanjang dan membentang sangat luas, menghubungkan Samudra Hindia di barat Sumatra, melewati Pulau Jawa, hingga membelah daratan Australia. Fenomena luar biasa ini bukan sekadar pemandangan estetik dari luar angkasa, melainkan sebuah sistem cuaca skala besar yang menjadi biang kerok utama di balik hujan yang diperkirakan akan mengguyur wilayah Sumatra bagian selatan dan Jawa hari ini.

Di dunia meteorologi, sabuk awan raksasa yang memanjang ini sering disebut sebagai Frontal Cloud Band atau pita awan frontal. Kemunculannya menandakan adanya aktivitas cold front (front dingin) yang ikut terseret oleh pusaran angin atau sistem tekanan rendah di samudra bagian selatan. Ketika pusat pusaran tersebut bergerak, ia menarik "ekor" awan yang sangat panjang dan tebal, hingga membentuk jalur konvergensi (zona pertemuan angin) raksasa yang memotong wilayah Indonesia dan Australia sekaligus.

Terbentuknya sabuk awan ini sebenarnya dipicu oleh "tabrakan" dua massa angin yang sifatnya berbeda jauh. Ada massa angin dingin dan kering yang bertiup kuat dari arah kutub selatan bergerak naik ke utara, lalu menabrak massa angin hangat dan super lembap yang ada di wilayah tropis Indonesia. Karena angin dingin sifatnya lebih berat dan padat, ia langsung menyusup ke bawah dan memaksa angin hangat khas tropis kita terangkat secara drastis ke atas atmosfer.

Nah, angin hangat yang membawa banyak uap air tadi langsung membeku dan mendingin secara massal di atas sana. Proses inilah yang menciptakan bentangan awan mendung sepanjang ribuan kilometer dan menyuplai pasokan hujan tanpa henti ke wilayah Sumatra dan Jawa hari ini.

Salam
@infogempadunia

#info #cuaca #dunia

Referensi :
Berry, G., Reeder, M. J., & Jakob, C. (2011). A automated method for identifying fronts and tracking their evolution in the Southern Hemisphere. Journal of Climate.

Reeder, M. J., Smith, R. K., Deslandes, R., Tapper, N. J., & Mills, G. A. (2000). Central Australian Fronts Experiment (CAFE). Meteorological Applications

Keenan, T. D., & Carbone, R. E. (1992). A preliminary morphology of precipitation systems in Australia's North and the Associated Convergence Zones.

Catto, J. L., Jakob, C., & Nicholls, N. (2012). The cold fronts that affect southern Australia and their relationship to the large-scale circulation.

[Prof. Dr. Erma Yulihastin @EYulihastin]

Update: hujan menyambangi dari tanggal 11 hingga 17 Juni 2026 terjadi karena gelombang Rossby atmosfer sedang aktif dengan lokasi konvergensi di perairan selatan Jawa (lihat gambar).

[Gia Macool @GiaMMacool]

Feminists scream marriage turns women into “slaves for room & board.”

Yet they:

• Slave for a boss
• Let their bodies get used by men who pump & dump in casual hookups
• Simp to government for handouts…

…All doing alone 🤔

They reject sharing life with one man, but happily serve many men doing more.

No wonder they’re miserable.

Don’t let these ladies fool ya’ fellas.
feminism is real slavery.

And it’s why marriage isn’t even an option for them. Because smart men don’t commit to someone expecting a soft life without reciprocity.

[Quant Science @quantscience_]

This paper unlocks every algorithm used by hedge funds.

151 trading strategies.

Get it here (361 page PDF):

[Roan @RohOnChain]

if you've ever thought about building your own hedge fund, MIT professor just gave away the entire playbook in 2 hours:

- how hedge funds actually make money
- how they smooth their returns to look smarter than they are
- how the entire quant system actually works

[Livsun @L1vsun]

a citadel options trader told me the one concept they test first in every quant interview

and it's been sitting on a free website for years

not a hedge fund textbook, not a $3,000 prep program. a free course syllabus - options greeks, volatility, quizzes - publicly available, almost nobody applying has ever opened it

concept is expected value across a probability distribution

retail looks at a chart and asks which direction. quant looks at expected payout across every possible outcome and asks if that number beats the cost of the trade - completely different question

options pricing is just EV made rigorous

fair value of any position = sum of (each outcome's probability x its payoff), discounted back. that formula is in every intro stats course and every free options curriculum these firms post publicly

citadel's first round isn't a stock pitch or a DCF

it's a market-making problem: "set me a bid and ask on a coin flip"

if you can solve that fast and size it correctly, you can price any derivative on earth

prep is documented in 6 categories: probability, greeks, volatility, mental math, coding, microstructure

firms don't want you pattern-matching to old trades. they want raw EV instinct - and that's in free courses that have been online for years

entry-level quant traders at these firms start at $300k. senior traders clear $650k+

most people never make it past round 1. not because they weren't smart - because nobody told them what the test was actually measuring

Bookmark this

they kept you reading charts while they were drilling expected value at 2am