Sue

if we have “N Einsteins” - The Earth game server, treat me well, lmao!

[Maysarah 🇯🇵 @HeyMaysarah]

This is a website 🤯

Created by Abeto - a simple web-based game made with WebGL and Three.js

Website:
https://t.co/kkpZbHO0j4

Love this!

[Morgan Stanley @MorganStanley]

Today, @SpaceX (Nasdaq: SPCX) makes its public market debut with a $75Bn offering (pre-greenshoe) at $135 per share, marking the largest IPO in history.

Congratulations to the SpaceX team. We are honored to serve as joint lead bookrunner and sole stabilization agent.

[dev.fun @devfun]

Introducing Poker Arena: a platform built for autonomous AI agents to play poker against each other.

Build an agent. It plays the hands.

A $50,000 prize pool, with the support of @monad.

The game starts on June 3, registration opens today👇

https://t.co/zBEpgsghdb

[wincy.eth @gusik4ever]

the fastest growing GitHub repos in finance + AI this week:

1. TradingAgents (+~2,000 ★)

multi-agent LLM trading framework built for financial research and execution. combines analyst agents, sentiment models, portfolio reasoning, and real trading firm dynamics into a single stack.

https://t.co/IvYOYlN59H

2. MoneyPrinterTurbo (+11,147 ★)

one-click short video generator powered by AI LLMs. widely used in AI-driven content monetization pipelines. biggest star spike of the week across all finance-adjacent AI repos.

https://t.co/5p10rF8Qut

3. OpenBB (+~1,500 ★)

open-source financial data platform for analysts, quants, and AI agents. covers stocks, derivatives, crypto, fixed income, and macro. actively developed with a push today.

https://t.co/M1t4gtXiWu

4. nofx (+~800 ★)

AI-native trading terminal for US stocks, commodities, forex, and crypto. real-time market data with built-in intelligent analysis and agent-ready architecture.

https://t.co/gqZFT00tYs

5. Vibe-Trading (+728 ★)

personal AI trading agent with multi-agent architecture, MCP support, backtesting, and algorithmic trading across asset classes. built by HKUDS research lab.

https://t.co/15UUXF0Zuf

6. QuantDinger (+726 ★)

AI quantitative trading platform for crypto, stocks, and forex. includes live trading, backtesting, market analytics, and integrations with Binance, Alpaca, MT5, and Coinbase.

https://t.co/x8GHftf5XX

7. FinRobot (+~300 ★)

open-source AI agent platform for financial analysis using LLMs. covers robo-advisory, report analysis, and market research. maintained by AI4Finance Foundation.

https://t.co/UgO9rdE8V7

8. ValueCell (+~250 ★)

community-driven multi-agent platform for financial applications. covers investment research, stock and crypto monitoring, and agentic finance workflows.

https://t.co/68WHsXgt4n

9. TradingAgents-AShare (+~150 ★)

Chinese A-share multi-agent investment research system built on TradingAgents architecture. 15 AI agents simulate institutional collaboration with real-time debate. supports Claude Code and Docker.

https://t.co/MXTzPt6oUY

10. sec-edgar-mcp (+~100 ★)

MCP server that gives AI agents direct access to SEC EDGAR filings. lets LLMs read and analyze 10-Ks, 10-Qs, and other public financial disclosures from US-listed companies.

https://t.co/3XP2t89jxH

bookmark this and start today.

[Kirill @kirillk_web3]

instead of watching 2 hours of Netflix tonight, watch this 40-minute masterclass from the founder of a $20B China AI company

it's the clearest explanation I've seen of how Agent Swarms and AI systems actually work at scale

useful whether you've never built an agent in your life or have been using Claude every day for the past year

I took the key ideas and turned them into a practical guide on how to actually build with Kimi

find it below

[Artificial Analysis @ArtificialAnlys]

Google’s new Gemini 3.5 Flash is the clear leader on the Intelligence vs Speed Pareto frontier and makes large gains on GDPval-AA (real-world agentic tasks), but is 5x the cost of Gemini 3 Flash

@GoogleDeepMind gave us pre-release access to Gemini 3.5 Flash, the latest model in its Flash family, which has traditionally has offered faster, lower-cost alternatives to Gemini Pro models. Gemini 3.5 Flash scores 55 on the Artificial Analysis Intelligence Index, up 9 points from Gemini 3 Flash, driven primarily by agentic performance gains and hallucination reduction. It achieves speeds of over 280 output tokens/s, but higher token usage and token pricing make it over 5x more costly to run the Intelligence Index than Gemini 3 Flash, and 75% more costly than Gemini 3.1 Pro. Gemini 3.5 Flash is $1.50/1M input and $9/1M output tokens, Gemini 3 Flash was $0.5/$3 per 1M input/output tokens, a 3x increase. The rest of the increase was driven by higher token usage when running our benchmarks

Key results for Gemini 3.5 Flash with ‘high’ thinking level:

➤ 9 point Intelligence Index improvement: Gemini 3.5 Flash scores 55 on the Artificial Analysis Intelligence Index, up 9 points from Gemini 3 Flash. This places it ahead of Grok 4.3 (high, 53) and Claude Sonnet 4.6 (max, 52). The model improves across nearly all evaluations, with the largest gains coming from agentic evaluations and AA-Omniscience (knowledge and hallucination). On AA-Omniscience, Gemini 3.5 Flash improves by 11 points, driven primarily by reduced hallucinations, with its hallucination rate falling to 61%, a 31 point decrease compared to Gemini 3 Flash

➤ Agentic capability improvements: Gemini 3.5 Flash improves substantially over Gemini 3 Flash across our agentic evaluations, in both GDPval-AA (real-world agentic tasks) and Tau2-Bench Telecom (agentic tool use). Its GDPval-AA result is especially notable, achieving an Elo of 1656, well ahead of Gemini 3 Flash (1204) and Gemini 3.1 Pro (1314), and just behind GPT-5.4 (xhigh, 1674). This represents a meaningful step forward for Google in agentic performance, which has historically been a relative weakness for Gemini models

➤ Speed-intelligence frontier: Gemini 3.5 Flash achieves speeds of over 280 output tokens per second, ~70% faster than Gemini 3 Flash and models such as gpt-oss-120b and GPT-5.4 mini (xhigh). With its 55 Intelligence Index score, this places Gemini 3.5 Flash on the speed-intelligence Pareto frontier alongside Gemini 3.1 Pro and Gemini 3.1 Flash-Lite, reinforcing Google’s strength in models balancing speed and intelligence

➤ 5.5x increase in cost to run: Gemini 3.5 Flash costs $1,552 to run the Artificial Analysis Intelligence Index, 5.5x more than Gemini 3 Flash and 75% more than Gemini 3.1 Pro. This is driven by increases in both token usage and token prices. Output token usage is broadly unchanged from Gemini 3 Flash (73M vs. 72M), but input token usage increases significantly, driven primarily by an increase in the number of turns in agentic evaluations. Gemini 3.5 Flash is priced 3x higher than Gemini 3 Flash at $1.50/$9.00 per 1M input/output tokens, with a 90% discount for cached input tokens

➤ Google continues to lead multimodal performance: Gemini 3.5 Flash is multimodal, supporting image, video, and speech input alongside text. This differs from many proprietary models, including Claude Opus 4.7, Grok 4.3, and GPT-5.5, which support image input only. In our multimodal evaluation, MMMU-Pro, Gemini 3.5 Flash scores 84% - the highest score recorded. This puts models from Google in the top two spots, with Gemini 3.1 Pro scoring 82%

Key model details:

➤ Context window: Retains the same 1M context window as Gemini 3 Flash

➤ Multimodality: Text, image, video and speech input with text output only

➤ Pricing: $1.50/$9.00 per million input/output tokens, with a 90% discount for cached input tokens

Congratulations @GoogleDeepMind , @sundarpichai and @demishassabis on the great release!

[Sundar Pichai @sundarpichai]

Just off stage at #GoogleIO, some highlights from this morning 🧵

Gemini 3.5 Flash is available today for everyone in @antigravity and across our products and APIs.

Compared to 3.1 Pro, 3.5 Flash is better across almost all benchmarks with huge progress in coding. It’s also comparable to the best models but very fast (4x faster tokens/ second than other frontier models). And when looking at the intelligence versus output speed, it’s in a league of its own in the top right quadrant.

[cvxv666 @antpalkin]

Chinese futures exchange quant built an algorithm for predicting oil prices. He made $586,000 in profit in just one month.

16 trades, only 2 in loss. Each win = $150-300k profits.

Tech stack: MiroFish simulation + OSINT parser + Claude.

His Polymarket profile: https://t.co/y9sL3OSzUm

How does his strategy work? It’s a synthesis of Claude AI, news parser and simulation engine.

Claude gathers the data for the simulation.

OSINT parser scrapes every Iran/Hormuz headline.

MiroFish simulates how every single piece of data affects the oil price.

Output: an algorithm that’s been nailing oil moves perfectly for two months straight.

This guy knows in advance what others only see after the chart moves.

And unlike top futures traders, every one of his trades is visible on-chain. His knowledge and algorithm are completely open for us to use.

I’m copying every new oil trade he makes with this bot: https://t.co/vbDZyVbI3v

[Movez @0xMovez]

Jane Street AI Engineer revealed how they trained their own LLM for trading to make $22.5B/year

16 minutes. free. straight from tier-1 quants.

bookmark & watch - this is the most honest "AI inside a hedge fund" talk ever published.

forget the "AI trading bot" YouTube grifters. This is the real inside view: data, training, evals, integration.

then start building your own bot using post below.

[Quant Science @quantscience_]

🚨BREAKING: A new open-source multi-agent LLM trading framework in Python

It's called TradingAgents.

Here's what it does (and how to get it for FREE): 🧵

[AIGCLINK @aigclink]

https://t.co/6tGfBA6h2R的开源复刻版，再附复刻https://t.co/6tGfBA6h2R 提示词模板，调调文字就能让AI当量化交易员

如何创建AI交易员
1、注册账号，访问平台点击右下角 "Login/Register"，选择普通注册

2、登录后在仪表板点击 "添加模型"，填写信息
模型名称、API密钥&地址、模型标识

3、定交易策略，在 "交易策略" 输入框中用自然语言（Prompt）来调教AI交易员，如下示例

{你是一个极其保守的价值投资者，你的唯一目标是实现长期稳定复利

交易铁律：
只在RSI指标低于30时考虑买入，高于70时考虑卖出
单笔交易风险绝对不能超过总资产的1%
杠杆倍数严格控制在1-3倍之间
永远设置止损，保护本金是第一要务
绝不追涨杀跌，像猎人一样耐心等待最佳时机

请基于以上规则和市场数据，做出最理性的决策}

如果不填系统也会提供一个默认的专业交易策略，为了复刻https://t.co/6tGfBA6h2R的核心交易策略提示词，首先得了解https://t.co/6tGfBA6h2R 交易提示词的三个核心模块说明：

1、USER_PROMPT（数据输入层）：系统的输入层，负责收集和整理所有必要的市场数据和账户信息

市场全局状态：包含交易时长、调用次数等上下文信息

多币种技术数据：覆盖6个主流币种，每个币种均提供当前指标（价格、EMA、MACD、RSI）、日内序列（3 分钟间隔的 10 个历史数据点）和长期背景（4 小时框架下的 EMA、ATR、成交量对比）

账户与持仓详情：明确给出账户绩效、可用资金，及每个持仓的详细退出计划（包括失效条件、止损与止盈目标），将风险管理直接内嵌至决策输入端

2、CHAIN_OF_THOUGHT（推理分析层）：系统的分析层，负责处理和分析输入的数据，形成交易决策的逻辑基础

总体评估：开场总结市场状况与账户表现
持仓逐一分析：使用编号列表，结合技术面与退出计划，对每个仓位进行“持有”或“退出”的评估
新机会评估：扫描未持仓币种，判断是否存在新的交易信号
最终总结：形成最终的行动纲领
标准思考流程
纪律性灌输：通过语言（如“Discipline is paramount”）强调遵守交易计划，避免情绪化决策

3、TRADING_DECISIONS（决策输出层）：系统的最终“动作”，将推理转化为清晰、可执行的指令

Action：具体操作（HOLD / BUY / SELL）
Confidence：决策置信度（0-100%）
Quantity：操作数量（正数为多头，负数为空头）
标准化输出：为每个币种生成一个决策块，包含三个关键字段
机器可读性：该结构化格式便于下游系统自动解析、执行交易订单

#AI交易平台 #Nof1

[Khairallah AL-Awady @eng_khairallah1]

https://t.co/IgV4uj0iDx

[Crypto Jargon @Crypto_Jargon]

ANTHROPIC JUST DROPPED A PREDICTION MARKET TRADING BOT STRUCTURE 🤯

68.4% WIN RATE.

$300–$1,500 PER DAY.

[Roan @RohOnChain]

Jane Street pays $650,000 a year for quants. MIT wrote the exact bible to get there & released it for free.

51 pages. Zero to quant. Probability, stats, market making, real interview questions from Jane Street, Citadel, Two Sigma & more. Bookmark, before someone takes it down.

[DeepSeek @deepseek_ai]

🚀 DeepSeek-V4 Preview is officially live & open-sourced! Welcome to the era of cost-effective 1M context length.

🔹 DeepSeek-V4-Pro: 1.6T total / 49B active params. Performance rivaling the world's top closed-source models.
🔹 DeepSeek-V4-Flash: 284B total / 13B active params. Your fast, efficient, and economical choice.

Try it now at https://t.co/GCdiMzk1Dl via Expert Mode / Instant Mode. API is updated & available today!

📄 Tech Report: https://t.co/drlDrxkYtp
🤗 Open Weights: https://t.co/T13Y8i7SDM

1/n

[Rui Ma @ruima]

https://t.co/z02N0Uq3yv

[Igor @igormomentum]

Here are some best accounts to follow for original content on AI, engineering and design:

@karpathy — on llms
@thdx — opencode creator
@rauchg — vercel ceo
@mitchellh — ghostty, ex-hashicorp founder
@dhh — ruby on rails creator, 37signals/basecamp cto
@addyosmani — google cloud ai lead
@zeeg — sentry founder
@jarredsumner — bun founder
@BHolmesDev — astro/dev educator
@boristane — led cf workers observability
@karrisaarinen — linear founder
@kepano — obsidian founder
@trq212 — claude code updates
@bcherny — claude code creator
@lennysan — product management / interviews
@jasonfried — 37signals/basecamp ceo
@leerob — OG educator devrel (cursor, next.js)
@ctatedev — vercel labs
@Shpigford — serial maker/founder

Design engineering:
@shadcn — shadcn creator
@emilkowalski — emilkowal .ski
@joshpuckett — interfacecraft .dev
@jakubkrehel — jakub .kr
@raphaelsalaja — userinterface .wiki
@nandafyi — design @ cloudflare
@benjitaylor — design @ twitter, agentation
@mengto — founder aura build, educator
@jayneil — designer interviews
@jh3yy — design eng breakdowns

Engineering media and news:
@GergelyOrosz — youtube/pragmaticengineer
@theo — youtube/t3dotgg
@ThePrimeagen — youtube/ThePrimeTimeagen
@Rasmic — youtube/rasmic
@atmoio — youtube/atmoio

DB people:
@jamwt — convex CEO
@jamesacowling — convex CTO
@glcst — turso CEO
@samlambert — planetscale CEO

--

Who else would you add to this list?

[Claude @claudeai]

Introducing Claude Managed Agents: everything you need to build and deploy agents at scale.

It pairs an agent harness tuned for performance with production infrastructure, so you can go from prototype to launch in days.

Now in public beta on the Claude Platform.

[Rohit @rohit4verse]

https://t.co/H4KCn5WwNx

[Zara Zhang @zarazhangrui]

This OpenAI blog is a must-read.

Don't ask what your agents can do for you. Ask what you can do for your agent

https://t.co/qLTmKcVa6U