never ending.

Things worth reading, curated by Shreyansh & Alok

144 readsTitles are sourced from OpenGraph metadata and may not always be accurate.

Elvis (@elvissun)·25-02-2026

OpenClaw + Codex/ClaudeCode Agent Swarm: The One-Person Dev Team [Full Setup]

Matthew Berman (@MatthewBerman)·25-02-2026

5 BILLION tokens later, OpenClaw is now my company's operating system. I discovered things most people never will. (PS I solved the Anthropic OAuth loophole.) Here’s exactly how it works. 0:00 Intro 0:16 Email Management 5:20 Sponsor 7:02 Inbox Pipeline 9:05 Multiple Prompt Versions (HUGE) 12:28 MD File Breakdown 14:12 Telegram Groups 14:51 CRM System 17:25 Meeting Intelligence 18:45 Knowledge Base 20:51 Content Pipeline 21:53 Security (HUGE) 24:49 Cron Jobs 26:05 Memory 27:55 Notification Batching 28:59 Financial Tracking 29:40 Usage & Cost Tracking 31:01 Full Logging Infrastructure 31:52 OAuth Loophole (HUGE) 32:55 Separating Personal/Work 34:29 Errors & Self-Improvement 35:59 Cost Savings 37:09 Backup & Recovery 37:52 Health Pipeline 38:30 Bee Memory

citriniresearch.com·24-02-2026

THE 2028 GLOBAL INTELLIGENCE CRISIS

Chaos Labs (@chaoslabs)·24-02-2026

mF-ONE: Private Credit, Public Liquidity

Oracle Boar (@bored2boar)·21-02-2026

The math behind Polymarket bots (from theory to execution)

Mikita Ahnianchykau (@mikita_crypto)·21-02-2026

Kelly + Monte Carlo: The Formula for a Perfect Strategy

Lance Martin (@RLanceMartin)·20-02-2026

Prompt auto-caching with Claude

Dougie (@DougieDeLuca)·20-02-2026

Venues, Not Products

Will Manidis (@WillManidis)·18-02-2026

Against Taste

zethes 🐼 (@zethesx)·17-02-2026

Where Polymarket Still Misprices Reality (And How You Can Profit From It)

vitalik.eth (@VitalikButerin)·17-02-2026

Recently I have been starting to worry about the state of prediction markets, in their current form. They have achieved a certain level of success: market volume is high enough to make meaningful bets and have a full-time job as a trader, and they often prove useful as a supplement to other forms of news media. But also, they seem to be over-converging to an unhealthy product market fit: embracing short-term cryptocurrency price bets, sports betting, and other similar things that have dopamine value but not any kind of long-term fulfillment or societal information value. My guess is that teams feel motivated to capitulate to these things because they bring in large revenue during a bear market where people are desperate - an understandable motive, but one that leads to corposlop. I have been thinking about how we can help get prediction markets out of this rut. My current view is that we should try harder to push them into a totally different use case: hedging, in a very generalized sense (TLDR: we're gonna replace fiat currency) Prediction markets have two types of actors: (i) "smart traders" who provide information to the market, and earn money, and necessarily (ii) some kind of actor who loses money. But who would be willing to lose money and keep coming back? There are basically three answers to this question: 1. "Naive traders": people with dumb opinions who bet on totally wrong things 2. "Info buyers": people who set up money-losing automated market makers, to motivate people to trade on markets to help the info buyer learn information they do not know. 3. "Hedgers": people who are -EV in a linear sense, but who use the market as insurance, reducing their risk. (1) is where we are today. IMO there is nothing fundamentally morally wrong with taking money from people with dumb opinions. But there still is something fundamentally "cursed" about relying on this too much. It gives the platform the incentive to seek out traders with dumb opinions, and create a public brand and community that encourages dumb opinions to get more people to come in. This is the slide to corposlop. (2) has always been the idealistic hope of people like Robin Hanson. However, info buying has a public goods problem: you pay for the info, but everyone in the world gets it, including those who don't pay. There are limited cases where it makes sense for one org to pay (esp. decision markets), but even there, it seems likely that the market volumes achieved with that strategy will not be too high. This gets us to (3). Suppose that you have shares in a biotech company. It's public knowledge that the Purple Party is better for biotech than the Yellow Party. So if you buy a prediction market share betting that the Yellow Party will win the next election, on average, you are reducing your risk. Mathematical example: suppose that if Purple wins, the share price will be a dice roll between [80...120], and if Yellow wins, it's between [60...100]. If you make a size $10 bet that Yellow will win, your earnings become equivalent to a dice roll between [70...110] in both cases. Taking a logarithmic model of utility, this risk reduction is worth $0.58. Now, let's get to a more fascinating example. What do people who want stablecoins ultimately want? They want price stability. They have some future expenses in mind, and they want a guarantee that will be able to pay those expenses. But if crypto grows on top of USD-backed stablecoins, crypto is ultimately not truly decentralized. Furthermore, different people have different types of expenses. There has been lots of thinking about making an "ideal stablecoin" that is based on some decentralized global price index, but what if the real solution is to go a step further, and get rid of the concept of currency altogether? Here's the idea. You have price indices on all major categories of goods and services that people buy (treating physical goods/services in different regions as different categories), and prediction markets on each category. Each user (individual or business) has a local LLM that understands that user's expenses, and offers the user a personalized basket of prediction market shares, representing "N days of that user's expected future expenses". Now, we do not need fiat currency at all! People can hold stocks, ETH, or whatever else to grow wealth, and personalized prediction market shares when they want stability. Both of these examples require prediction markets denominated in an asset people want to hold, whether interest-bearing fiat, wrapped stocks, or ETH. Non-interest-bearing fiat has too-high opportunity cost, that overwhelms the hedging value. But if we can make it work, it's much more sustainable than the status quo, because both sides of the equation are likely to be long-term happy with the product that they are buying, and very large volumes of sophisticated capital will be willing to participate. Build the next generation of finance, not corposlop.

Kris (@KrisAbdelmessih)·17-02-2026

A Visual Appreciation For Black-Scholes Delta

Raoul Pal (@RaoulGMI)·17-02-2026

The Universal Code - Everything is Compute

jordy (@jordymaui)·17-02-2026

I wasted 80 hours and $800 setting up OpenClaw - so you don't have to.

Avid (@Av1dlive)·16-02-2026

How to Design Using AI in 2026

Eyad (@eyad_khrais)·16-02-2026

The claude code tutorial level 2

Ray Dalio (@RayDalio)·15-02-2026

It’s Official: The World Order Has Broken Down

Kris (@KrisAbdelmessih)·15-02-2026

A Visual Primer For Understanding Options

Junfan Zhu 朱俊帆 (@junfanzhu98)·14-02-2026

🎛️ 102. In-Context Learning=Weight Update: Not Emergence, but Inference-Time Update as Fine-Tuning

Eyad (@eyad_khrais)·14-02-2026

The complete claude code tutorial

Andrew Warner (@AndrewWarner)·14-02-2026

Opus 4.6 Replaces a $120K Marketing Agency

James Ross (@jamesrosst)·14-02-2026

Instant Resolution Capital: Finance’s Temporal Collapse

GEE-yohm LAMB-bear (@guil_lambert)·14-02-2026

Now that this challenge has concluded, here's how to frame this challenge under the view that LP positions = options (& why LVR/edge benchmark is not what real traders worry about): - You sell a straddle, hedging the delta after each spot price move. The edge benchmark is measuring the theta minus gamma-bleed. The basic assumption from LVR is that you need to hedge after every trade, so the best performing hooks want to limit trades getting to the AMM to only those coming from benign/non-arbitrage flow. But hedging after every trade is one extreme end of the spectrum. The other end is not hedging at all, and the "final" loss is IL when the position is closed: |---------------------| 𝙻𝚅𝚁 𝙸𝙻 𝚑𝚎𝚍𝚐𝚎 𝚊𝚏𝚝𝚎𝚛 𝚍𝚘𝚗'𝚝 𝚎𝚟𝚎𝚛𝚢 𝚝𝚛𝚊𝚍𝚎 𝚑𝚎𝚍𝚐𝚎 Most funds would never hedge after every trade, not even Citadel or other HFT firms doing cross-arbitrage between very similar products like SPX, SPY, and /ES. So constraining the solution space to everytrade/LVR misses how real funds handle risk. Somewhere between LVR and IL is where most funds need to manage risk, leading to more elegant mechanism designs for better AMMs: - hedge at fixed time intervals (markouts) - hedge at fixed delta (reverse gamma scalping) - hedge before every trade (propAMMs) - hedge with TradFi vs. perpetual options - hedging dynamics when LPs are arbitrageurs, arbitrageurs are LPs

How to build a product people can’t stop using

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