Buy or Sell? $INTC Earnings October 29th after the bell.

It is becoming f*cking dangerous: a model that never writes a single word now judges every move your Claude AI agent makes -> Create a folder named JEV LAB on your PC and launch Claude Code inside it -> Install the Jev CLI: npm install -g jev-axi -> Grab a key at console.typesafe.ai and run: jev-axi config set apiKey YOUR_KEY -> Run jev-axi setup safety so every command Claude runs gets checked first. Dangerous downloads get denied, routine tests pass for free -> Run jev-axi setup git-hooks to stop any commit that sneaks in a password or API key -> Feed it anything your agent reads: jev-axi guard < email.txt Hidden "ignore your previous instructions" tricks get flagged in under half a second -> Run jev-axi setup supervise --project to catch the moment your agent quits early or gets stuck in a loop The whole setup takes around 5 minutes Turn Claude into an agent with a second opinion: every command screened, every page checked for prompt injection, every commit scanned for secrets. For a fraction of a cent per check Bonus: pipe a broken build into jev-axi triage and it finds the root cause for you Bookmark this article and check repo below: github.com/shiftynick/jev-ax…

🚨 ESTOS 10 REPOS DE TRADING ESTÁN CREANDO MILLONARIOS EN SILENCIO… Y SON 100% GRATIS > 10 repos open source > 435K+ estrellas en GitHub > $0 en licencias > 1 stack: pensar, probar, ejecutar 10 repos de trading open source que deberías tener guardados: AGENTES DE IA QUE ANALIZAN: 1. TradingAgents (111K ⭐) → github.com/TauricResearch/Tr… un equipo de agentes de IA: analistas, investigadores alcistas y bajistas, trader y gestor de riesgo que debaten cada operación 2. ai-hedge-fund (64K ⭐) → github.com/virattt/ai-hedge-… agentes que analizan acciones como lo harían Buffett, Munger o Burry. Educativo: no opera con dinero real BOTS QUE OPERAN: 3. freqtrade (55K ⭐) → github.com/freqtrade/freqtra… el bot de cripto gratis más usado: backtesting, optimización de estrategias y control desde Telegram 4. Hummingbot (20K ⭐) → github.com/hummingbot/hummin… bots de market making y arbitraje en exchanges de cripto MOTORES DE BACKTESTING: 5. NautilusTrader (30K ⭐) → github.com/nautechsystems/na… motor escrito en Rust: la misma estrategia corre en backtest y en vivo 6. Lean de QuantConnect (22K ⭐) → github.com/QuantConnect/Lean el motor de trading algorítmico de QuantConnect, en Python y C# IA PARA QUANTS: 7. Qlib de Microsoft (49K ⭐) → github.com/microsoft/qlib plataforma de inversión cuantitativa con IA, de los datos al modelo 8. FinRL (16K ⭐) → github.com/AI4Finance-Founda… aprendizaje por refuerzo aplicado a trading LA BASE: 9. ccxt (44K ⭐) → github.com/ccxt/ccxt una sola API para más de 100 exchanges de cripto y mercados de predicción 10. machine-learning-for-trading (21K ⭐) → github.com/stefan-jansen/mac… todo el código del libro de Stefan Jansen: de conseguir datos a operar en vivo Guárdalo antes de que sea tarde 🔖

ex-Citadel Quant just revealed his Opus 5.5 & Fable 5.5 trading system this shouldn't be f*cking public... It turns Claude into a 24/7 research & trading swarm of AI agents: one researches, one trades the edge, one can kill any position Send it to Opus 5.5 , and enjoy. Then read how to build Bloomberg terminal for ~300$:

Three prompts turn Grok + Opus 5.5 into a Citadel-level research desk that works for you 24/7 Ken Griffin spent 36 years and billions building this machine. you build a copy in one evening, for free one agent hunts for ideas. one makes the calls. one tears every idea apart before real money touches it and none of them holds the keys to the vault. risk limits are hard-coded, no model can override them. that's what every blown-up bot was missing no code, no quant background. drop three ready prompts into three agents, set your risk limits, done all three are in the paper below. worth saving

ANTHROPIC JUST DROPPED THE BEST 1-HOUR AI AGENT CRASH COURSE THAT YOU’LL FIND It covers: - building /skills, /loops, and graphs - ⁠agent dreaming & orchestration - reverse engineering with Claude agents

20+ GitHub repositories for algorithmic trading with Python. Grab the repo here:

Ex-Jane Street intern openly shared in private quants forum, how he used free REA MCP to reverse-engineer exchange fee structures and built a solo market-making bot doing $27k/month. feed this exact prompt into Claude code and you'll have a working evidence-backed trading engine. Bookmark it right now. 53k upvotes

this is absolutely f*cking insane Black Rock just leaked 5 GitHub repos that give you the trading desk a fund pays millions for: want a full research team arguing over every trade? -> TradingAgents: a multi-agent trading firm in one repo. analysts, researchers, a risk desk github.com/TauricResearch/Tr… want the data terminal without the terminal bill? -> OpenBB: an open data platform for analysts, quants and AI agents github.com/OpenBB-finance/Op… want a bot that actually places the orders? -> freqtrade: free, open-source crypto trading bot with backtesting built in github.com/freqtrade/freqtra… want the engine serious quants run in production? -> NautilusTrader: a Rust-native trading engine where the backtest and the live run use the same code github.com/nautechsystems/na… want all of it in one place? -> QuantDinger: an AI trading OS. research, Python strategies, backtest, paper and live trading github.com/OpenByteInc/Quant… ~282k stars of trading infrastructure. $0 in licences. now the part worth trying. every one of these has the same slow spot: the moment before the order, where something has to say yes or no. that's where Jev goes. it can't write a word. it returns BUY / SELL / HOLD with a probability in 70 to 500 milliseconds. QuantDinger already ships it as the pre-trade gate. and one repo, jev-trader, asks it buy or sell every ~300 ms and quotes a limit order on every single block. the stack in one line: OpenBB brings the data, the agents do the research, Jev makes the call, the bot places the order. the catch: fast is not the same as right. none of this is an ATM. paper trade it first. Jev + these five is the setup to test this month. bookmark and share with a friend

Opus 5.5 + Jev is extremely powerful to build a trading bot. It's literally 200x faster and 400x cheaper than running Claude alone. Send this prompt to Claude and it will tell you exactly how to set up:

Sam Altman (OpenAI CEO) just released a prompt that turns GPT-6 Sol into a one-person company. It's f*cking unreal... Send it to GPT-6 Sol, and thank me later. Then read the full guide for GPT-6 Sol Graph Engineering.

🚨 Wall Street isn't just buying Bitcoin—they are building the plumbing for the entire decentralized economy. Let's look at three publicly traded equities : $SDEV, $CYPH, and $USDE—that give traditional brokerage accounts direct exposure to DeFi infrastructure. 🧵👇 (Compliance Note: This thread is strictly for educational purposes and market analysis. It is NOT financial or investment advice. Always conduct independent due diligence.) 1️⃣ $SDEV (Stablecoin Development Corp) 💰 Current Price: ~$3.66 📈 YTD Return: +41% Want DeFi yields but demand SEC oversight? The Mechanics: A publicly traded company (NYSE American) that holds roughly 10% of the Sky ($SKY) protocol token supply, generating operating revenue by staking its crypto. Long-Term Thesis: Bulls view $SDEV as a regulated bridge, allowing institutional capital to capture decentralized yields from the stablecoin economy without ever leaving their audited, traditional brokerage accounts. 2️⃣ $CYPH (Cypherpunk Technologies Inc.) 💰 Current Price: ~$3.57 📈 YTD Return: +475% The ultimate "picks and shovels" privacy play. The Mechanics: A NASDAQ-listed infrastructure firm that recently launched the world’s largest Zcash ($ZEC) mining fleet. Long-Term Thesis: As financial surveillance and Central Bank Digital Currencies (CBDCs) expand globally, network privacy becomes a premium. $CYPH offers equity investors a regulated backdoor into the physical hardware powering decentralized, privacy-preserving networks. 3️⃣ $USDE (StablecoinX Inc.) 💰 Current Price: ~$15.51 📈 YTD Return: Up massively from a 52-week low of $1.01 A direct equity bet on the synthetic dollar revolution. The Mechanics: Often confused with the crypto token, $USDE is actually a NASDAQ-listed stablecoin infrastructure company heavily tied to the Ethena ecosystem (holding billions of $ENA tokens). Long-Term Thesis: By providing middleware and distribution services for the Ethena digital dollar, $USDE allows stock market investors to capitalize on the adoption of decentralized, TradFi-independent stablecoins without ever having to manage a crypto wallet or smart contract. 🧠 The Big Picture: Why study these long-term? Because they represent three distinct pillars of the new financial architecture—all accessible via traditional stock exchanges: 🔹 $SDEV: Regulated DeFi & Staking. 🔹 $CYPH: Privacy Network Infrastructure. 🔹 $USDE: Synthetic Dollar Middleware. They carry unique, non-traditional risks (like protocol dependencies and mining economics), but the structural innovation here is undeniable. 📊🌐 #TradFi #DeFi #CryptoEducation #Equities

Algorithmic trading is the domain of secretive Hedge Funds. Python unlocked these secrets for everyone (even Goldman Sachs has an open-source tool). Here are 17 Python libraries that open the hedge fund black box (📚bookmark this for later):

STOP DOWNLOADING LLMS YOUR MACHINE WAS NEVER GOING TO RUN LLMFIT is a new open-source tool that scans your hardware first, then tells you exactly which models will actually run well on your setup. It inspects: → RAM → CPU → GPU(s) → VRAM / unified memory Then it scores every model across four dimensions: → quality, based on parameter count and quantization → speed, estimating tokens per second for your exact backend → fit, matching memory usage to your hardware → context window support for your use case Each model then gets a clear label: Perfect, Good, Marginal, or Too Tight. It also picks the best quantization automatically, stepping down until something fits instead of leaving you to guess GGUF sizes by hand. Coverage is broad too! 100's of models from Meta, Mistral, Qwen, DeepSeek, and more. It works with Ollama, llama.cpp, MLX, and LM Studio out of the box. And that’s not even it! There’s also a lightweight TUI, a web dashboard, and REST API endpoints if you want to plug it into orchestrators or deployment pipelines. This should be step zero before every local model download :) 100% free and open-source. macOS, Linux, and Windows. Repo in 🧵↓

Look at your open hand and roll your fingers into a fist. That simple movement traces a precise mathematical spiral. Sacred geometry isn't a distant, abstract mystery; it is the literal, scientific blueprint of human life. The mathematical constant known as Phi dictates the structural proportions of your face, arms, torso, and the lengths of the bones in your fingers. Even deep inside your cells, every double helix of DNA measures 34 by 21 angstroms, producing a ratio that closely mirrors this exact geometric constant. From the spherical boundaries of our cells to the spirals of a pinecone or a sunflower, nature builds life using efficient, repeating structural patterns. You aren't just walking through the universe—you are a living, breathing mathematical equation. #Biology #Mathematics #SacredGeometry #Nature #Science

a quant built 17 trading strategies in Python and just put them all on a repo for free momentum, breakout, stat arb, options, Monte Carlo, it's all there

Leaked Army plans show another military invasion likely by end of year: report rawstory.com/donald-trump-wa…

AI memory is getting f...cking illegal 10 open-source GitHub projects that stop agents from starting from zero EVERY new session 01 Mem0 ▸ github.com/mem0ai/mem0 → 66K+ stars 02 Hindsight ▸ github.com/vectorize-io/hind… → retain → recall → reflect 03 memU ▸ github.com/NevaMind-AI/memU TURN MEMORY INTO KNOWLEDGE 04 Cognee ▸ github.com/topoteretes/cogne… → documents + code + conversations → knowledge graph 05 Graphiti ▸ github.com/getzep/graphiti → remembers how facts change over time 06 OpenViking ▸ github.com/volcengine/OpenVi… MAKE THE AGENT STATEFUL 07 Letta ▸ github.com/letta-ai/letta → memory + identity across sessions 08 Letta Code ▸ github.com/letta-ai/letta-co… REMEMBER ACROSS THE STACK 09 OpenMemory ▸ github.com/mem0ai/openmemory 10 Agent Memory Benchmark ▸ github.com/vectorize-io/agen… the loop: experience → remember → connect → retrieve → act → update 3 builds I'd actually test: coding: Hindsight → Cognee → Letta Code personal agent: Mem0 → Graphiti → Letta company brain: Cognee → Graphiti → Hindsight and this is where the sh...t gets interesting: bigger context isn't the same as memory a model can hold 1M tokens and still meet you for the first time every morning these projects change that save this before your agent forgets it ⭣

Some guy made a python library of self-improving algorithmic trading AI agents Then gave it away for free:

How to make your own algorithmic trading system in Python (a complete roadmap):