Compartilhamento de conhecimentos e aprofundamentos sobre desenvolvimento de softwares
Big-O, Omega and Theta: what they really mean when comparing algorithms. Measurable Python examples, the counter-example everyone gets wrong, and a real-world case where O(n²) beats O(n log n) based on actual data size.
Continue lendoModern multi-agent with LLMs replacing symbolic reasoning: CrewAI, AutoGen, LangGraph. Role-based patterns and orchestration.
Continue lendoCompetitive multi-agent systems: Contract Net Protocol (Smith 1980), auction theory, sealed-bid vs English vs Dutch.
Continue lendoFIPA-ACL (Agent Communication Language): performatives, ontologies, content. FIPA protocols: Request, Query, Contract Net.
Continue lendoJADE (Java Agent DEvelopment) is the FIPA-compliant standard for Java agents. Setup, first agent, behavior, lifecycle.
Continue lendoBratman's Belief-Desire-Intention mental model: formal representation of mental states for deliberative agents.
Continue lendoFormal agent definition (Russell & Norvig): sensors, actuators, environment. Types: reactive, deliberative, hybrid, learning.
Continue lendoCook-Levin theorem: SAT is NP-complete. Reductions to 3-SAT, vertex cover, clique, hamiltonian path. Practical industry approach.
Continue lendoThe central complexity classes: P (deterministic poly-time), NP (non-deterministic poly-time), the P=NP problem.
Continue lendoMapping reductions and Turing reductions: the technique to prove undecidability without starting from diagonalization.
Continue lendoThe first undecidable problem: halting problem. Turing's diagonalization proof and consequences for programming.
Continue lendoWhat computable means: Church-Turing thesis, equivalence between lambda calculus, partial recursive functions and TM.
Continue lendoThe Turing machine as universal computation model: infinite tape, head, states. Formal definition and variants.
Continue lendoCommunicating results with interactive dashboards: Streamlit for rapid data apps, Dash for enterprise dashboards, storytelling principles.
Continue lendoFrom notebook to production: serialization (pickle, joblib, ONNX), REST API with FastAPI, batch scoring, real-time streaming with Kafka.
Continue lendoCorrect validation avoids fatal optimistic estimates: stratified k-fold, nested CV for hyperparameter tuning, time-series split, group K-fold.
Continue lendo2026 reference tabular models: linear models, random forest, XGBoost, LightGBM, CatBoost. When to choose which.
Continue lendoProduction-ready feature engineering: one-hot, target encoding (with regularization), scaling, feature interactions, datetime features.
Continue lendoSystematic Exploratory Data Analysis: missing pattern detection, outliers (IQR, Z-score, isolation forest), distributions and transformations.
Continue lendoProductive working environment: venv/poetry/conda, JupyterLab vs VS Code notebooks, pandas+numpy fundamentals.
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