Génesis 3:15, según la versión más usada en español (Reina-Valera 1960):
Y pondré enemistad entre ti y la mujer, y entre tu simiente y la simiente suya; esta te herirá en la cabeza, y tú le herirás en el calcañar.
Este versículo forma parte de la maldición que Dios pronuncia contra la serpiente después de la caída de Adán y Eva.
Resumen rápido: El creador gana dinero mensual + más ranking + fans más fieles. Tú ganas estatus visible, acceso exclusivo a lives/contenido/comunidad y más poder de regalo/impacto en sus lives.
Los beneficios exactos de “privilegios personalizados” dependen de lo que ese creador concreto haya activado. Si quieres, dime el nombre del creador y puedo ayudarte a revisar qué ofrece específicamente.
Fact check this”🚨BREAKING: The State of Florida is launching a study on Ivermectin to see if it can potentially cure cancer."
Mostly true, with important caveats on timing, framing, and expectations.
Florida has directed state funding toward research evaluating ivermectin (and other generic/repurposed drugs) for potential use in cancer treatment or related effects. This is not a brand-new “breaking” announcement in August 2026; the relevant push was publicly highlighted in September 2025.
Bottom line: The core claim is grounded in real Florida state funding priorities for studying ivermectin in the cancer context and an upcoming University of Florida trial. The “BREAKING” urgency and “cure cancer” wording overstate the status—research is exploratory, results are not yet available from the main efforts, and it remains unproven as a cancer treatment. Always rely on established oncology care and discuss any experimental approaches with qualified physicians.
Y después de esto derramaré mi Espíritu sobre toda carne, y profetizarán vuestros hijos y vuestras hijas; vuestros ancianos soñarán sueños, y vuestros jóvenes verán visiones.
(Versículo 29 continúa: “Y también sobre los siervos y sobre las siervas derramaré mi Espíritu en aquellos días.”)
Este es el pasaje clásico que Pedro cita en Hechos 2:17 el día de Pentecostés.
Chess engines do not assign official ratings the way FIDE, USCF, or online platforms do. Official ratings come from game results against other humans under a rating system (Elo/Glicko). Engines are used to estimate a player’s strength by analyzing move quality. These estimates correlate with real ratings but are imperfect.
There is a clear, consistent correlation: stronger players make moves closer to the engine’s top choices, even when facing opponents of similar rating. Studies of tens of thousands of games (Lichess and Chess.com data) show average precision rising steadily with rating and ACPL falling (roughly from well over 100 at low club level toward the 20–40 range at master/GM level).
Practical accuracy for individuals:
Engine-based estimates are useful for tracking your own progress, spotting trends, comparing groups of players, and historical analysis. They give a solid approximate picture of strength, especially with many games under similar conditions. They are not precise enough to replace official ratings or to claim an exact Elo number from analysis alone. For the most reliable rating, nothing beats a large sample of rated games against other humans.
Yes. Modern chess engines routinely use neural networks, and the strongest ones rely on them heavily.
Neural networks do not replace search; they improve the quality of the evaluation and/or the move ordering that search relies on. The combination of deep learning evaluation + efficient search is what produces the extremely high playing strength seen in current top engines.
Computer chess programs (engines) are written as specialized software that combines efficient data structures, search algorithms, and position evaluation. They are almost always written in high-performance languages (mainly C++ for top engines like Stockfish, sometimes Rust) because speed is critical—strong engines examine tens of millions of positions per second.
A typical engine has these main parts:
Board Representation How the position is stored in memory. Modern engines almost universally use bitboards (64-bit integers, one bit per square). This allows very fast operations with bitwise logic (AND, OR, shifts) for attacks, occupancy, etc. Older methods included mailbox arrays or 0x88.
Move Generator Efficiently produces all legal (or pseudo-legal) moves from a position. This must be extremely fast because the search calls it constantly. Special handling is needed for castling, en passant, promotions, and pinned pieces.
Search The “thinking” part. Classical engines use a form of minimax improved by alpha-beta pruning and many enhancements:
Pure neural-network engines (Leela Chess Zero style) usually use Monte Carlo Tree Search (MCTS) guided by the network instead of pure alpha-beta.
Evaluation Function Assigns a score to a position (usually in centipawns).
Supporting Systems
Most engines are built iteratively:
Tuning evaluation parameters is often automated (methods such as Texel tuning or SPSA).
| Style | Search | Evaluation | Hardware focus | Example |
|---|---|---|---|---|
| Hybrid / Classical | Alpha-beta + enhancements | NNUE (or classical) | CPU | Stockfish |
| Neural-network focused | MCTS | Deep neural net | GPU | Leela Chess Zero |
Stockfish remains the strongest overall because its extremely efficient search + fast NNUE evaluation scales extremely well on ordinary CPUs.
In short: writing a chess engine is a classic exercise in performance-oriented programming, search algorithms, and (nowadays) machine learning. The strongest programs are the result of decades of incremental refinement by communities of programmers rather than a single breakthrough.
