Monte Carlo

Monte Carlo AI

Monte Carlo AI

Monte Carlo AI

Monte Carlo AI

Monte Carlo AI

Monte Carlo AI

Monte Carlo AI

Monte Carlo AI

Monte Carlo AI

Monte Carlo AI

I used Claude to build an AI-native research institute, so far, 7 papers submitted to Nature Human Behavior, PNAS, and 5 other journals. Here's exactly how.

I have no academic affiliation, no PhD, no lab, no funding. I'd been using Claude to investigate a statistical pattern in ancient site locations and kept finding things that needed to be written up properly. So I did the stupid thing and went all in. In three weeks, using Claude as the core infrastructure, I've built the Deep Time Research Institute (now a registered nonprofit) and submitted multiple papers to peer-reviewed journals. The submission list: Nature Human Behaviour, PNAS, JASA, JAMT, Quaternary International, Journal for the History of Astronomy, and the Journal of Archaeological Science. Here's what "AI-native research" actually means in practice: Claude Code on a Mac Mini is the computation engine. Statistical analysis, Monte Carlo simulations, data pipelines, manuscript formatting. Every number in every paper is computed from raw data via code. Nothing from memory, nothing from training data. Anti-hallucination protocol is non-negotiable; all stats read from computed JSON files, all references DOI-verified before inclusion. Claude in conversation is the research strategist. Experimental design, gap identification, adversarial review. Before any paper goes out it runs through a multi-model gauntlet - each one tries to break the argument. What survives gets submitted. 6 AI agents run on the hub (I built my own "OpenClaw" - what is the actual point in OpenClaw if you can build agentic infrastructure by yourself in a day session) handling literature monitoring, social media, operations, paper drafting, and review. Mix of local models (Ollama) and Anthropic API on the same Mac Mini. The flagship finding: oral tradition accuracy across 41 knowledge domains and 39 cultures is governed by a single measurable variable - whether the environment punishes you for being wrong. Above a threshold, cultural selection maintains accuracy. San trackers: 98% across 569 trials. Aboriginal geological memory: 13/13 features confirmed over 37,000 years. Andean farmers predict El Niño by watching the Pleiades — confirmed in Nature, replicated over 25 years. Below the threshold, traditions drift to chance. 73 blind raters on Prolific confirmed the gradient independently. I'm not pretending this replaces domain expertise. I don't have 20 years in archaeology or cognitive science. What I have is the ability to move at a pace that institutions can't and integration cross-domain analysis - not staying in a niche academic lane. From hypothesis to statistical test to formatted manuscript in days instead of months. Whether the work holds up is for peer review to decide. That's the whole point of submitting. Interactive tools: Knowledge extinction dashboard: https://deeptime-research.org/tools/extinction/ Observability gradient: https://deeptime-research.org/observability-gradient Accessible writeup: https://deeptimelab.substack.com/p/the-gradient-and-what-it-means Happy to answer questions about the workflow, the architecture, or the research itself. This has been equally intense and a helluva lot of fun! submitted by /u/tractorboynyc [link] [comments]

I build a risk toolkit for investment portfolio's

I've been investing though DeGiro and I was always frustrated about the lack of risk metrics. There is a P&L and that's it, I want to know how volatile my portfolio is, how well diversified I am against crashes and loads of other things. I discovered Claude Code and 2 weeks later I had built Drawdn, a risk dashboard for retail investors. Stress tests against real crashes, Monte Carlo simulations, portfolio optimizer, deep dive per holding, alerts. Next.js + Python risk engine, ~280 tests, Stripe billing. I'm pretty happy how it turned out and just launched early access today. If you invest and want to see what a crash would do to your portfolio: drawdn.com/crash-test , no signup, just enter your tickers. I'm making some costs on data and the risk calculations, but I have a good free tier for a solid risk analysis of your own portfolio. Would love feedback, and happy to talk about the workflow. submitted by /u/Hour-Associate-7628 [link] [comments]

I gave Claude a Quantum Brain: Quanta-SDK now has 20+ MCP tools for AI Agents

Writing Qiskit code manually is a pain, and letting an LLM hallucinate quantum circuits is even worse. I built an MCP (Model Context Protocol) Server for my Quantum SDK. Now, you can point Claude/GPT to your quantum backend and let it actually execute and interpret circuits through a dedicated toolset. What your AI Agent can now do: run_circuit: Direct execution on IBM hardware or local MPS simulators. explain_result: AI-driven interpretation of measurement outcomes. analyze_noise: Real-time noise model simulation. monte_carlo_price: Quantum-powered financial option pricing. It’s basically an abstraction layer that turns complex quantum physics into a declarative API that AI can actually use to solve optimization problems. GitHub:https://github.com/ONMARTECH/quanta-sdk(v0.9.2 is live with 820+ tests and 91% coverage) submitted by /u/Existing-Juice7152 [link] [comments]

I built an MCP server that gives Claude 12 real optimization tools (bandits, LP solver, Monte Carlo, risk analysis) — all sub-25ms, free tier included

I kept running into the same problem: Claude is amazing at reasoning about what to optimize, but terrible at actually doing the math. Ask it to pick the best A/B test variant and it'll give you a plausible answer that ignores the exploration-exploitation tradeoff. Ask it to solve a scheduling problem and it burns 5,000 tokens to approximate what a linear solver does in 2ms. So I built an MCP server with 12 tools that handle the math correctly: **Install:** ``` npx u/oraclaw/mcp-server ``` **Claude Desktop config:** ```json { "mcpServers": { "oraclaw": { "command": "npx", "args": ["@oraclaw/mcp-server"] } } } ``` **What Claude gets:** - `optimize_bandit` — UCB1/Thompson Sampling for A/B testing and option selection - `solve_constraints` — LP/MIP solver (HiGHS) for scheduling, resource allocation - `simulate_montecarlo` — Monte Carlo with 6 distribution types - `assess_risk` — Portfolio VaR/CVaR - `predict_bayesian` — Bayesian inference with evidence updating - `detect_anomaly` — Z-score/IQR anomaly detection - `analyze_decision_graph` — PageRank, community detection - `plan_pathfind` — A* with K-shortest paths - `predict_forecast` — ARIMA + Holt-Winters - `evolve_optimize` — Genetic algorithm - `optimize_cmaes` — CMA-ES continuous optimization - `score_convergence` — Multi-source agreement scoring Every tool returns deterministic, mathematically correct results. No tokens burned on reasoning about math. **Performance:** 14 of 17 endpoints respond in under 1ms. All under 25ms. 1,072 tests. Free tier: 25 calls/day, no API key needed. The API is live — you can try it right now. Interactive demo: https://web-olive-one-89.vercel.app/demo GitHub: https://github.com/Whatsonyourmind/oraclaw npm: https://www.npmjs.com/package/@oraclaw/mcp-server Would love feedback on which tools are most useful for your Claude workflows. submitted by /u/WolfOfCordusio [link] [comments]

Real-time LLM coherence control system with live SDE bands, dual Kalman filtering, post-audit, and zero-drift lock (browser-native Claude artifact)

Hey r/ClaudeAI, I’ve built a full real-time coherence control harness that runs entirely as a Claude artifact in the browser. It treats conversation as a stochastic process and applies control theory in real time: Live Monte Carlo SDE paths with tunable uncertainty bands on the coherence chart Dual Kalman filtering (second pass on post-audit score) with quiet-fail detection GARCH variance tracking Behavioral and hallucination signal detection (sycophancy, hype, topic hijack, confidence language, source inconsistency, etc.) Zero-Drift Lock toward a resonance anchor with visual status Configurable harness modes and industry presets (Technical, Medical/Legal, Creative, Research) Mute mode and Drift Gate for controlled responses on planning prompts or high-variance turns Full export suite (clean chat, research CSV with health metrics, SDE path data, and session resume protocol) The system injects real-time control directives back into the prompt to maintain coherence across long sessions without needing a backend. The full codebase has been posted on GitHub. I’m actively looking for peer-to-peer review and honest scrutiny. This needs to be tested by other people. Any feedback on the math, signal detection, stability, edge cases, or potential improvements is very welcome — the more critical, the better. Images of the UI, coherence chart with SDE bands, TUNE panel, and exports are attached below. Looking forward to your thoughts and test results. submitted by /u/Celo_Faucet [link] [comments]

Cursor and Claude beefing

Sorry for it being a picture but this is hilarious, i’ve been feeding boths responses into each other and they are lowkey throwing shade submitted by /u/ovrlrdx [link] [comments]

Cursor and Claude are beefing lmao

Sorry for it being a picture but this is hilarious Ive been feeding boths responses into each other and they’re lowkey throwing shade submitted by /u/Brilliant-Gas9662 [link] [comments]

[P] Using residual ML correction on top of a deterministic physics simulator for F1 strategy prediction

Personal project I've been working on as a CSE student: F1Predict, a race simulation and strategy intelligence system. Architecture overview: - Deterministic lap time engine (tyre deg, fuel load, DRS, traffic) as the baseline - LightGBM residual model trained on FastF1 historical telemetry to correct pace deltas — injected into driver profile generation before Monte Carlo execution - 10,000-iteration Monte Carlo producing P10/P50/P90 distributions per driver per race - Auxiliary safety car hazard classifier (per lap window) modulating SC probability in simulation - Feature versioning in the pipeline: tyre age × compound, qualifying delta, sector variance, DRS activation rate, track evolution coefficient, weather delta - Strategy optimizer runs at 400 iterations (separate from the main MC engine) to keep web response times reasonable The ML layer degrades gracefully if no trained artifact is present, simulation falls back to the deterministic baseline cleanly. Redis caches results keyed on sha256 of the normalized request. Current limitation: v1 residual artifact is still being trained on a broader historical dataset, so ML and deterministic paths are close in output for now. Scaffolding and governance are in place. Stack: Python · FastAPI · LightGBM · FastF1 · Supabase · Redis · React/TypeScript Repo: https://github.com/XVX-016/F1-PREDICT Live: https://f1.tanmmay.me Happy to discuss the modelling approach, feature engineering choices, or anything that looks architecturally off. This is a learning project and I'd genuinely value technical feedback. submitted by /u/CharacterAd4557 [link] [comments]