Avi Arora

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Avi Arora

Avi Arora

@c0delemons

building benchspan (backed by yc) prev. ml research @ github

Beigetreten Ağustos 2014
270 Folgt471 Follower
Avi Arora
Avi Arora@c0delemons·
Benchspan has also given @SpaceX the right to acquire Benchspan later this year for $60 billion.
SpaceX@SpaceX

SpaceXAI and @cursor_ai are now working closely together to create the world’s best coding and knowledge work AI. The combination of Cursor’s leading product and distribution to expert software engineers with SpaceX’s million H100 equivalent Colossus training supercomputer will allow us to build the world’s most useful models. Cursor has also given SpaceX the right to acquire Cursor later this year for $60 billion or pay $10 billion for our work together.

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Avi Arora
Avi Arora@c0delemons·
Sam Altman stopped by @ycombinator today. Here's everything he said (w/ direct quotes) - new model coming - electricity is the biggest bottleneck right now, not gpus “if you try to build a company that will still be alive in 50 years you will miss” “i think the way we currently measure things in society is going to break” “we’re still 2.5 years from the chatgpt moment for robotics” “at some point we are effectively going to summon aliens, and i think they will be friendly aliens” “the better you do, the more chaos comes your way” “people have seen robots do some pretty impressive things, but they have not seen a truly general purpose robot they can interact with and have it do useful things. that will be the chatgpt moment” “i would bet that society figures some new grand bargain” “society figures out for new technologies where the value chain is and who is liable where” - society and technology coevolve in difficult to predict ways - the models are good enough now to figure out new architectures themselves - value accrues to the top layer with the direct relationship with the consumer
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Avi Arora
Avi Arora@c0delemons·
@garrytan GStack browser should use @benchspan (YC P26), the most advanced detector for indirect prompt injection 🫡
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Garry Tan
Garry Tan@garrytan·
Sneak Preview: GStack Browser will do defense-in-depth on detecting prompt injections from websites
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Avi Arora
Avi Arora@c0delemons·
Prompt injection defense was built for a world that doesn't exist anymore. Chatbots, roleplay style attacks, 'ignore previous instructions' Agents have hands now. And they are reaching into your database. We've developed the most advanced model to defend against the real risk: Indirect prompt injection
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Y Combinator
Y Combinator@ycombinator·
Datost (@datostapp) is an AI data analyst in Slack. It keeps a semantic layer of your business definitions, crm, docs, and codebase so it knows what questions mean. 75.2% on the hardest public text-to-SQL benchmark, where Opus 4.6 scores 33%. Congrats on the launch, @maceock & @jasonhywang! ycombinator.com/launches/Pxg-d…
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Avi Arora
Avi Arora@c0delemons·
We got into YC Ritesh and I were random roommates in college, day dreaming about starting a company people love. 8 years later, we are still roommates, and we’re building Benchspan to fix all the issues around benchmarking agents. DM if you’re in the Bay and want to connect
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Tarek Mansour
Tarek Mansour@mansourtarek_·
We are hosting our first Prediction Market Conference in March 2026. Researchers, economists, policymakers, traders will discuss big questions around prediction markets and knowledge aggregation. Spots will be limited. Reply here with a topic if interested in joining.
Tarek Mansour@mansourtarek_

In 1945, Friedrich Hayek outlined the Knowledge Problem that any society faces: The central economic problem is not resource allocation - it is how to use knowledge that is dispersed among millions of individuals. He argues that information is fragmented, local, dynamic, and often hidden. He explains that no government or central planner can ever fully possess it, which makes them inefficient resource allocators. He proposes markets as the solution: knowledge is decentralized and prices are how society aggregates it. This idea is the intellectual foundation of modern prediction markets. Decades later, in 1988, the University of Iowa launched the Iowa Electronic Markets (IEM), which allowed small size trades on US elections and macro events. The results: even thin, low-capital markets outperformed polls. This was the first credible empirical proof that market prices are effective aggregators of public beliefs. A variety of corporate and policy experiments followed in the 2000s. Google, HP, and Microsoft all tried their own internal versions of prediction markets to forecast product launches and sales targets. DARPA built its own to forecast geopolitical events. The results were consistent: broad participation with monetary incentives led to accurate forecasts. Then, in 2015, Philip Tetlock published Superforecasting. The book, which is the culmination of decades of research into human judgment, shows that groups of curious and humble “forecasters” dramatically outperformed intelligence analysts and domain experts at forecasting. By showing that smart amateurs can outperform experts, Tetlock put into question authority figures and whether we should trust them for predictions about the future. Today, Kalshi is sitting on one of the largest repositories of high quality market data in the world. For the first time, public beliefs across a variety of domains - from economics, to politics and culture - are aggregated at scale through market prices and updated in real-time as new information arrives. Our data contains answers to open questions held about prediction markets - why they outperform traditional belief aggregation methods, how to detect shifts in collective sentiment, and which players drive market accuracy. This proprietary data has been closed to the public. We are launching @KalshiResearch to change that. We invite academics, researchers, economists, philosophers, and interested parties to work with us to study and uncover the fundamentals underpinning belief formation and prediction markets. Like Hayek proposed 80 years ago, prediction markets have the potential to improve society's collective decision making and resource allocation. The goal for Kalshi Research is to fulfill his vision.

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Alex Svanevik 🐧
Alex Svanevik 🐧@ASvanevik·
anyone building an aggregator for prediction markets?
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PJ
PJ@Prithvir12·
Prediction Market Weekly Update Notional Volume 1. @Opinionlabsxyz $1.53b 2. @Polymarket $1.15b 3. @Kalshi $1.11b 4. @Trylimitless $33m 5. @MyriadMarkets $4.05m 6. @predictdotfun $0.463m Total: $3.8b WoW: +8% Open Interest 1. @Kalshi $313m 2. @Polymarket $296m 3. @Opinionlabsxyz $65m 4. @MyriadMarkets $0.945m 5. @Trylimitless $0.928m 6. @predictdotfun $0.01m Total: $677m WoW: +2.7% Transactions 1. @Polymarket 6.07m 2. @Kalshi 5.2m 3. @MyriadMarkets 0.627m 4. @Opinionlabsxyz 0.45m 5. @Trylimitless 0.33m 6. @predictdotfun 0.07k Total: 12.68m WoW: +10% Users 1. @Polymarket 219k 2. @Opinionlabsxyz 29k 3. @Trylimitless 27k 4. @predictdotfun 3.2k 5. @MyriadMarkets 1.9k Total: 282k WoW: -0.7% I post these every Friday. Lmk what other data would be interesting to add. h/t @datadashboards for the @Dune dashboard. Use @tradefoxai to trade all these markets from a single interface.
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Phosphen
Phosphen@phosphenq·
Jane Street for the poor real money is made in silence, look for ghost markets where you'll be the only mm // on low liquidity markets spreads reach 10-40c find an empty order book set your prices: buy ~$0.30 / sell ~$0.60 wait for a random trader to hit the limit orders ImJustKen: top-tier trader on the platform admits it's manual work, not magic Profile: @ImJustKen?via=01" target="_blank" rel="nofollow noopener">polymarket.com/@ImJustKen?via… > "nobody believes it, but i trade manually, i have a bunch of orders in the book, most trades are when people hit my limit orders" Live Setup (MrBeast Views): Market: polymarket.com/event/of-views… right now the market for the next MrBeast video views is empty liquidity and hype will inevitably come with the video release your task is to take over the order book now and sell entry to the crowd later btw nfa
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shafu
shafu@shafu0x·
Things with we need to solve about Prediction Markets - not permissionless - low liquidity - oracles - analytics - aggregators - off-chain CLOB - no good prediction market AMM - no defi on top - separated from the "news" - liquidity fragmentation - capital inefficient - hard to compare markets
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Avi Arora
Avi Arora@c0delemons·
We just release our Prediction Market Dominance Index Finally there is a single place to track volume, liquidity, turnover, open events, and much more for both @Kalshi and @Polymarket
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Avi Arora
Avi Arora@c0delemons·
@igor_mikerin great article man! you should also look into cross market arbitrage. there is about 500 duplicate markets between Kalshi and Polymarket that you can find arbs between i’ve been working on a scanner for these over at oddpool.com and we find more than 50 everyday >3%
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MrKangaroo
MrKangaroo@MrKangarooX·
polymarket tip #34 (thread) this trader turned $1,000 into $1,120.46 in just two years on @Polymarket here’s how he did it 👇
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