Agencias de modelos virtuales en OnlyFans/Fanvue

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📈 ¿Cómo generan ingresos estas agencias?

• Uso de modelos AI + humanos: Combinan modelos virtuales (generados por IA usando herramientas como GANs y Fooocus) con modelos reales para generar contenido exclusivo y escalar operaciones automatizando mensajes y publicaciones (fanpromgmt.com, aiinfluencercompany.com).

• Monetización directa: Cobran suscripciones, contenido premium y mensajes personalizados. Un caso destacado, Aitana López, generó entre €3 000 – 10 000 mensuales con contenido AI (Wikipedia).

• Upsell de cursos y licencias: Se venden programas educativos de alto costo ($200–300) para enseñar a otros a crear estas agencias .

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🔍 ¿Qué resultados muestran?

• FanPro MGMT afirma ingresos de $3 M al mes y más de $120 M generados hasta la fecha (fanpromgmt.com).

• Forbes reporta que agencias como FanPro, Lalaland.ai y Deep Agency están probando modelos automatizados de contenido con IA, en crecimiento (Forbes).

• Modelos individuales virtuales como Aitana y Miquela consiguen decenas de miles de dólares al mes (euronews).

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⚖️ Ventajas vs Riesgos

Ventajas

• Escalabilidad 24/7: No dependen de disponibilidad de modelos reales, y los bots manejan interacciones .

• Control total: Se pueden diseñar modelos con estética perfecta y sin “escándalos” reales .

• Reducción de costos: Ahorro en logística, producciones y pagos recurrentes a modelos reales .

Riesgos y desafíos

• Ética y legalidad: Algunos usan imágenes deepfake sin permiso, lo que ha provocado acciones de plataformas como OnlyFans y Fanvue (Business Insider).

• Transparencia: Riesgo de engañar a usuarios haciéndoles creer que el avatar es humano .

• Competencia y saturación: Rápido crecimiento del sector, pero también creciente vigilancia regulatoria .

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🧭 Pasos para iniciar

1. Estudia casos de éxito: Analiza perfiles como Aitana López o Lil Miquela (Wikipedia).

2. Aprende tecnología: Cursos como el de Udemy para Fanvue OnlyFans enseñan a usar GANs, Fooocus, Discord, etc. (Udemy).

3. Define tu nicho & marca: Crea una identidad potente (personalidad, estética, historia).

4. Cumple las reglas: Es vital respetar los términos de plataformas y no usar deepfakes sin consentimiento (aiinfluencercompany.com, Business Insider).

5. Automatiza interacciones: Bots de mensajes, chat AI, programación de contenido y análisis de datos .

6. Optimiza y diversifica: Renueva contenido, prueba múltiples plataformas como Fanvue y diversifica ingresos.

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🕵️‍♀️ Otras agencias y recursos

• The Clueless: Empresa detrás de Aitana López; modelo AI rentable en Europa.

• Lalaland.ai, Deep Agency, The Diigitals: Firmas emergentes mencionadas en Forbes (fanpromgmt.com, Wikipedia, Forbes).

• Cursos accesibles: Udemy y plataformas como Fiverr (servicios de diseño AI desde ~$10) (Fiverr.com).

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🧭 ¿Vale la pena invertir?

Depende de varios factores:

• ¿Tienes conocimientos técnicos o acceso a talento creativo y tecnológico?

• ¿Puedes cumplir con políticas de contenido AI sin infringir derechos de terceros?

• ¿Estás dispuesto a dedicar tiempo a branding, estética y engagement, más que a producción física?

Si la respuesta es sí, este nicho emergente es prometedor. El crecimiento exponencial aún está en fases tempranas, las plataformas están afinando sus reglas, y el público muestra interés, incluso fascinado por este tipo de contenido.

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Si te apetece, puedo profundizar en puntos como:

• Comparativa entre plataformas (OnlyFans vs Fanvue).

• Estrategia de adquisición de suscriptores (funnel desde Instagram/Meta).

• Proveedores y costes de creación de modelos AI.

• Alternativas más éticas y transparentes.

Cuando quieras, le damos ese toque conspiranoico con posibilidades ocultas 🔍. Avísame 😊

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• Business Insider
• WIRED
• The Guardian

The details of the 'One Big Beautiful Bill Act' (OBBBA), signed into law on July 4, 2025.

 I’ll dig into the details of the 'One Big Beautiful Bill Act' (OBBBA), signed into law on July 4, 2025. I’ll focus especially on the provisions related to tax cuts, exemptions (such as Social Security and overtime), and any other significant measures that lead to direct savings for individuals and small businesses.

I’ll also look into credible sources such as Tax Foundation, government releases, and legislative records to verify all the financial impacts of the bill. I’ll report back shortly with a clear summary and structured breakdown of these benefits.

President Trump signs the "One Big Beautiful Bill Act" into law during a July 4, 2025 ceremony at the White House. This landmark legislation – often nicknamed the “One Big Beautiful Bill” – packages together major tax cuts with increased federal spending on defense and other priorities. Passed narrowly by Congress along party lines, it was quickly signed by Mr. Trump as a key achievement of his second term. The new law encompasses sweeping tax relief for individuals and businesses, along with policy changes aimed at boosting economic growth and funding national security.

Largest Tax Cuts Made Permanent

• Extension of 2017 Tax Rates: The bill makes permanent the individual income tax cuts from the 2017 Tax Cuts and Jobs Act, which were originally set to expire at the end of 2025. This means the current seven tax brackets stay at their lower rates (preventing across-the-board increases of 1–4% that would have hit in 2026) and the higher standard deduction from 2017 remains in place. By avoiding the expiration of these tax cuts, middle-class families collectively avert an estimated $400 billion tax increase that would have otherwise occurred in 2026.

• Small Business and Estate Tax Relief: Key provisions benefiting small businesses and wealthy individuals are also locked in. The 20% Qualified Business Income (QBI) deduction for pass-through businesses (small businesses, partnerships, S-corporations, etc.) is made permanent rather than sunsetting in 2025. Additionally, the lifetime estate and gift tax exemption – the amount individuals can pass on without incurring estate tax – is boosted to $15 million per person and made permanent (up from roughly $12 million under prior law). This allows families to transfer more wealth tax-free to heirs. Together, these measures constitute some of the largest tax cuts in U.S. history for individuals and small businesses, cementing tax relief that was previously temporary.

Tax Relief on Overtime, Tips, and Social Security

• No Federal Tax on Overtime Pay: The law effectively eliminates federal income tax on overtime earnings for many workers. From 2025 through 2028, employees earning under $150,000 can claim a special deduction for overtime income – up to $12,500 of overtime pay per year is deductible (i.e. tax-free) under this provision. This incentive rewards workers who put in extra hours. (The deduction begins phasing out for higher incomes and is scheduled to expire after 2028.)

• Tax-Free Tips: Similarly, service industry and tipped workers benefit from a new tax deduction on tip income. From 2025–2028, workers can deduct up to $25,000 of reported tips from their taxable income (if their total income is below $150,000). In effect, most tip earnings up to that cap will no longer be subject to federal income tax during these years. Both the overtime and tip deductions are temporary measures aimed at boosting take-home pay for middle and working-class Americans.

• Reduced Taxes on Social Security Benefits: For retirees, the bill slashes taxes on Social Security. It introduces a new $6,000 senior tax deduction for individuals 65 and older (or $12,000 for married seniors) from 2025 through 2028. This deduction – which phases out for higher incomes above ~$75k (single) or $150k (couples) – will enable an estimated 88% of seniors to pay no federal tax on their Social Security benefits (up from about 64% of seniors previously). In practical terms, most retirees will no longer owe income tax on Social Security payments under this law, significantly reducing tax burdens for middle-income seniors.

Family Tax Benefits and Credits

• Higher Child Tax Credit: The Child Tax Credit is increased and made permanent. The maximum credit per child rises from $2,000 to $2,200 starting in 2025, and going forward the amount will be indexed to inflation so it grows over time. This provides greater annual tax savings for families with children (over 40 million families are expected to benefit). However, the increase is mainly for middle-income households – the portion of the credit that is refundable to those with very low incomes was not expanded. Even so, a permanent boost in the child credit means more money in parents’ pockets each year.

• “Trump Accounts” (Children’s Savings Accounts): The bill creates new tax-advantaged savings accounts nicknamed “Trump Accounts” for children born between 2025 and 2028. For each such child, the government will deposit $1,000 at birth to seed the account. Parents and family can then contribute up to $5,000 per year, and the money grows tax-deferred (no taxes on the interest or investment gains). Funds can later be used for approved purposes like higher education, job training, or buying a first home. This essentially acts as a baby bond or kids’ savings plan, helping families build nest eggs for their children with tax-free growth.

• Other Personal Tax Benefits: The law also continues certain deductions and credits that help families and individuals save money. For example, it preserves the expanded Standard Deduction from 2017 (about $13,850 for single filers and $27,700 for joint filers in 2025) which makes tax filing simpler and reduces taxable income for most Americans. It also keeps in place tax preferences like the credit for dependent care and education (those weren’t repealed). Collectively, these measures ensure that individuals can keep more of their income tax-free or tax-reduced, supporting household savings and spending.

Higher SALT Deduction Cap (State and Local Taxes)

• Temporary SALT Relief: Homeowners in high-tax states get a break via a higher deduction for state and local taxes. The bill raises the SALT deduction cap from the previous $10,000 limit to $40,000 (for taxpayers earning under $500,000) starting in 2025. Married couples filing separately get $20,000 each. This higher cap is temporary – it will be in place for five years and then revert back to $10,000 in 2030. In the meantime, many middle- and upper-middle-income taxpayers (especially in states like New York, California, New Jersey) can deduct a much larger portion of their state/local taxes on their federal return, potentially saving thousands in federal tax for those who itemize deductions.

• Who Benefits: This SALT change, while offering tax relief to some, is somewhat controversial. Analysis shows the primary beneficiaries are upper-income households in high-tax areas. Many moderate-income taxpayers don’t exceed the old $10k cap or they take the standard deduction, so they see little change. In fact, tax analysts estimate that those in roughly the 95th–99th income percentiles would enjoy the biggest boost (about a 0.6% increase in after-tax income), whereas over 80% of taxpayers (the bottom four income quintiles) would see no benefit from the SALT cap increase. Critics argue this provision mainly helps wealthier taxpayers, but it was included to secure support from lawmakers in high-tax states and is set to sunset after 2029.

Other Notable Tax Savings and Economic Measures

• Auto Loan Interest Deduction: To encourage big-ticket consumer purchases, the bill allows a tax deduction for auto loan interest on new cars made in the USA. From 2025 through 2028, buyers of American-assembled new vehicles can deduct up to $10,000 per year in interest on their car loans. Importantly, this is an “above-the-line” deduction (available to both itemizers and standard-deduction filers), meaning anyone financing a new U.S.-built car can save on taxes via their interest payments. (This deduction is phased out for high earners over certain income thresholds, and is scheduled to expire after 2028.)

• Business Expensing and Investment Incentives: The law provides generous tax write-offs to businesses, which also helps small businesses and entrepreneurs save money. It extends 100% bonus depreciation through 2028, allowing companies to fully expense qualified equipment investments upfront. Similarly, 100% of R&D (research and development) costs can be expensed in the year incurred. These provisions let businesses large and small write off new machinery, technology, and other capital costs immediately, reducing their tax bills and incentivizing expansion. The bill also relaxes limits on interest deductions for businesses by measuring the cap using EBITDA instead of EBIT (a higher allowance), meaning more of a company’s interest expense becomes tax-deductible. All told, these measures are designed to spur business investment by lowering the tax cost of expansion and new projects.

• Corporate Tax Rate Unchanged: Notably, the corporate income tax rate remains at 21%, with no increase or new surtax on corporations. President Trump’s prior tax reform (2017) had cut the corporate rate from 35% to 21%, and this new bill keeps that low rate intact. Lawmakers opted not to raise corporate taxes, arguing it keeps American businesses competitive. (Business groups applauded maintaining the current corporate rate and other pro-business tax provisions.) Keeping the rate steady is effectively a tax savings compared to proposals that would have hiked corporate taxes to raise revenue.

• Other Provisions: To partly offset the revenue lost from tax cuts, the bill includes a few revenue-raising measures (which aren’t money-savers for taxpayers, but important to note). For example, it imposes a new 1% fee on foreign remittances – money sent abroad by individuals – which is expected to raise billions over the decade. It also scales back or eliminates some tax credits, such as rolling back certain clean energy tax credits from the 2022 Inflation Reduction Act in favor of fossil fuel production incentives. These moves help narrow the deficit impact. On the spending side, the law authorizes significant funding for national defense and infrastructure – including about $150 billion in additional defense spending (e.g. for a new missile defense system, military technology, and upgrading Coast Guard fleets), as well as $170 billion for border security and immigration enforcement (funding new border barriers, thousands of new ICE and Border Patrol agents, and related facilities). It even provides $12.5 billion to modernize air traffic control systems and upgrade aviation infrastructure. While these spending measures are beyond the tax cuts, they were part of the “one big” omnibus bill and were funded in part by the above-mentioned cuts to other programs and new fees.

Impact and Reception

• Immediate Savings for Taxpayers: The combined tax cuts in the One Big Beautiful Bill are projected to boost take-home pay for many Americans. Supporters point out that a typical family of four could see several thousand dollars in higher after-tax income per year due to the extended tax cuts, larger child credit, and no taxes on overtime or Social Security benefits. The White House touted the bill as “the largest tax cut in history for working- and middle-class Americans” and a fulfillment of promises to let people “keep more of their money.” Indeed, roughly two-thirds to three-quarters of all taxpayers are expected to get a tax reduction of some size. Key groups like seniors, families with kids, and small business owners see significant financial benefits.

• Debate and Criticism: Despite the tax relief, the bill has been polarizing. Opponents note that many of the benefits skew toward higher-income individuals (e.g. the wealthy gain from estate tax changes and SALT deduction, and investors benefit from extended tax breaks), while the bill also makes steep cuts to Medicaid, SNAP (food stamps) and other social programs to curb spending. The nonpartisan Congressional Budget Office estimated the law will increase the federal deficit by roughly $3 trillion over 10 years. Polls showed a majority of Americans opposed the bill, with critics calling it a “giveaway to the wealthy” that could harm the poor. President Trump and Republican leaders, however, argue that economic growth spurred by tax cuts and deregulation will offset these concerns. The bill’s passage on July 4, 2025 marked a historic moment of sweeping fiscal change, delivering immediate tax savings to millions, but also igniting a fierce debate about priorities and fiscal responsibility in the years to come.

Sources: Key details were drawn from the official text and analyses of the One Big Beautiful Bill Act, nonpartisan research groups (Tax Foundation, CBO), and news reports (Reuters, AP, FactCheck.org) covering the bill’s passage. These sources provide a comprehensive view of the bill’s tax provisions and their expected impact.

🧠🧿 Parásitos y Demonios: ¿Dos formas de la misma energía invasora?

 

¿Y si los demonios no fueran simplemente entidades espirituales, sino también parásitos que habitan cuerpo y mente?

La analogía entre parásitos y demonios no es nueva, pero empieza a tomar fuerza con descubrimientos modernos en neurología, psicología y tradiciones ancestrales. A continuación, te presento un resumen impactante de cómo ambos operan de forma sorprendentemente similar:

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1. Ambos roban energía del huésped

• Parásitos físicos drenan nutrientes del cuerpo, causando fatiga, niebla mental y enfermedades crónicas.

• Entidades demoníacas o negativas se alimentan del miedo, la ansiedad, la adicción y otras emociones de baja vibración, drenando la energía espiritual del ser humano.

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2. Manipulan pensamientos y conducta

• 🧬 Caso científico: Toxoplasma gondii es un parásito capaz de alterar el comportamiento de animales y humanos, induciendo riesgos y decisiones impulsivas.

• 😈 Visión espiritual: Tradiciones religiosas describen cómo los demonios incitan a pensamientos autodestructivos, deseos compulsivos y pérdida del juicio.

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3. Permanecen ocultos por largo tiempo

• Parásitos pueden vivir años en el cuerpo sin ser detectados, mimetizándose entre síntomas comunes.

• Demonios operan desde lo sutil: patrones negativos repetitivos, tristeza constante, insomnio, e incluso "vocecitas internas" con intenciones destructivas.

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4. Sustento bíblico y religioso

• ✝️ Cristianismo: La posesión demoníaca va acompañada de enfermedad, comportamiento errático y sufrimiento, igual que los efectos de una parasitosis avanzada.

• 🕉️ Hinduismo y budismo: Existen entidades que se alimentan de emociones tóxicas, igual que los parásitos prosperan en entornos corporales ácidos y debilitados.

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5. Tradiciones chamánicas y ocultismo

• 🌿 Chamanes indígenas asocian enfermedades crónicas con energías parasitarias o entidades espirituales pegadas.

• 🌌 Plantas maestras como la ayahuasca se usan para purgar parásitos físicos y entidades espirituales negativas.

• 🕯️ Exorcismos y limpiezas energéticas muchas veces reflejan protocolos de desintoxicación: ayuno, silencio, oración, y expulsión de energías invasoras.

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📚 Fundamento científico:

Según estudios en el campo emergente de la neuroparasitología, algunos parásitos son capaces de alterar el sistema nervioso y el comportamiento del huésped. (Referencia: National Institutes of Health, PMC: “Neuroparasitology – A New Horizon”)

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🔍 Conclusión:

¿Y si los demonios fueran más reales de lo que creíamos, solo que no siempre visibles?
El cuerpo y el alma pueden ser invadidos. Ya sea por un protozoo o por una energía oscura, el resultado es el mismo: pérdida de control, debilitamiento, y destrucción lenta desde adentro.

Quizás sea hora de desparasitar… el cuerpo y el alma.

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🧿 ¿Has sentido alguna vez una fuerza invisible drenándote energía o manipulando tus decisiones? Cuéntanos tu experiencia.

#ParasitesAndDemons #Neuroparasitology #Exorcism #EspiritualidadOculta #CuerpoYAlma #TradicionesChamánicas #Ayahuasca #ToxoplasmaGondii #DesintoxicaciónEspiritual

Como desactivar “iPhone May Be Too Close”

 Ese mensaje que te apareció en la pantalla —“iPhone May Be Too Close”— es una función de Salud Visual en iOS que detecta si estás sosteniendo tu iPhone demasiado cerca de tus ojos, algo que puede contribuir a la miopía con el tiempo. Esta opción forma parte de las funciones de Tiempo en Pantalla (Screen Time) o Salud Visual (Screen Distance) en las versiones más recientes de iOS (iOS 17 en adelante).

🔧 Para desactivar esta función, sigue estos pasos:

1. Abre la app de Ajustes (Settings).

2. Baja y selecciona Tiempo en Pantalla (Screen Time).

3. Pulsa en Salud Visual (Screen Distance).

4. Verás un interruptor llamado Distancia de la pantalla (Screen Distance).

5. Desactiva esa opción.

🧠 Información adicional:

• Esta función utiliza la cámara TrueDepth (la misma del Face ID) para medir la distancia entre tu cara y la pantalla.

• Es especialmente útil para niños, por lo que puede estar activada si tienes configuraciones de control parental o Apple Family Sharing.

¿Cómo pueden otros ver mensajes borrados en WhatsApp?

 En teoría, cuando borras un mensaje "para todos" en WhatsApp, este debería desaparecer de ambos lados de la conversación (emisor y receptor). Sin embargo, hay formas en que otras personas pueden seguir viendo esos mensajes, dependiendo de ciertas condiciones. Aquí te explico cómo es posible y cómo podrías intentar ver lo que alguien borró en tu chat:

🔍 ¿Cómo pueden otros ver mensajes borrados en WhatsApp?

1. Notificaciones de terceros o apps de registro:

   • Algunos usan apps como Notification History, WAMR, o Notisave (solo para Android), que guardan las notificaciones entrantes antes de que se borren.

   • Si alguien tiene instalada una app así, podrá leer el mensaje original aunque tú lo borres después.

   • iPhone no permite este tipo de apps tan fácilmente por seguridad.

2. WhatsApp Web o sesiones paralelas:

   • Si alguien tiene abierta una sesión en WhatsApp Web, y está activo el navegador, puede ver el mensaje antes de que lo borres.

   • A veces WhatsApp Web no se actualiza tan rápido, y el mensaje permanece visible si no se refresca.

3. Copia de seguridad antes del borrado:

   • Si alguien hizo una copia de seguridad (backup) antes de que borres el mensaje, y luego restaura esa copia, verá el mensaje como si nunca se hubiera borrado.

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👀 ¿Cómo ver tú lo que otro borró en la conversación?

Aquí algunas opciones para intentar recuperar o ver el mensaje eliminado:

1. App de registro de notificaciones (Android)

• WAMR o Notisave pueden registrar mensajes de WhatsApp antes de que sean eliminados.

• Funcionan solo si estaban instaladas antes del borrado.

• No funciona con mensajes que estaban silenciados o no generaron notificación.

2. Widgets o historial del sistema (solo algunos Android)

• Algunos Android guardan el historial de notificaciones, y puedes acceder desde Ajustes > Notificaciones > Historial de notificaciones (Android 11+).

• Aquí podrías ver el mensaje antes de que fuera eliminado.

3. Backup de WhatsApp

• Si sabes que el mensaje se borró después del último backup de WhatsApp (Ajustes > Chats > Copia de seguridad), podrías:

  1. Desinstalar WhatsApp.

  2. Reinstalarlo y restaurar desde ese backup.

  3. Ver si el mensaje sigue ahí.

⚠️ Pero esto solo sirve si el mensaje estaba antes de la copia de seguridad.

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🧠 Trucos que algunos usan (y debes tener en cuenta):

• Si tú borras un mensaje "para mí", no se borra para la otra persona.

• Si ves "este mensaje fue eliminado", la otra persona lo borró "para todos".

• Algunas personas toman screenshots antes de que se borre algo sospechoso.

🧠 Chess Programming: Chapters 1-6

 

🧠 Chess Programming – Chapter I: Getting Started

This is the first entry in a six-part tutorial about teaching computers how to play chess — and, by extension, other perfect information games like Go, Checkers, and Othello.

Chess is often called the Drosophila melanogaster (fruit fly) of artificial intelligence — meaning it's a prime subject of study due to how much insight it offers into strategic thinking. Much like fruit flies have helped genetics researchers unlock complex biological mechanisms, chess has guided AI researchers toward major milestones — including the historic victory of IBM’s Deep Blue over Garry Kasparov, widely considered one of the greatest players of all time.

This series will walk you through the most successful techniques used in top chess programs, including Deep Blue. By the time we reach the final installment (originally scheduled for October 2000), I will have published a working Java chess engine along with downloadable source code. So if you're looking for examples, they’re coming — just bear with me.

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♟️ Perfect Information Games

Chess falls under the category of perfect information games — meaning both players always have full knowledge of the current game state. If you look at the board, you know exactly where every piece is, and what your opponent can do. Other games in this category include Checkers, Go, Go-Moku, Backgammon, and Othello. Poker doesn’t qualify, since your opponent's hand is hidden.

Although this series focuses on chess, many of the concepts you'll learn can be applied to other perfect information games. However, specifics like how to generate moves or evaluate positions will always depend on that game’s unique rules.

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🧩 What a Chess Program Needs

To play a decent game of chess, a computer must be able to:

1. Store the board in memory and track the game’s current state.

2. Generate legal moves based on chess rules, to play fairly.

3. Decide on the best move from all possible legal options.

4. Evaluate moves to choose the most intelligent one.

5. Provide a user interface (which this series won’t cover in detail).

Throughout this tutorial, we’ll explore all the components except the user interface, which is relatively generic for a 2D game. Let’s now introduce the core ideas behind each key component.

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🧱 Board Representation

In the early days of computing, memory was limited. Chessboards were often represented using an 8x8 array where each square was stored as one byte — empty squares were 0, and pieces were assigned integer values (e.g., black king = 1). These compact methods were perfect for the limited systems of the 1970s and 1980s.

Later, more advanced techniques emerged — particularly with the arrival of 64-bit machines. The bitboard method was developed (possibly first in the Soviet Union) where each of the 64 bits in a word represents one square on the chessboard. For example, a bitboard could track every square occupied by black pawns, or every square a queen on e3 can reach.

Bitboards allow for incredibly fast operations because logical operations like AND, OR, and NOT can handle them in a single CPU cycle. Many calculations that appear repeatedly in a game can be simplified this way.

For more on bitboards, stay tuned for Chapter II.

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🔄 Move Generation

Generating legal moves may seem simple at first, but in chess, it's quite complex. Each piece has different movement rules, and the game has special conditions for castling, en passant captures, pawn promotion, and not leaving the king in check.

Because of this complexity, move generation is one of the most demanding parts of a chess engine. However, much of the work can be optimized with pre-processing. I’ll present efficient data structures in Chapter III that drastically improve speed.

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🔍 Search Techniques

Choosing the “best” move from legal options isn’t straightforward. The computer must predict the consequences of each — often looking several moves ahead. A basic search algorithm called minimax assumes both players play optimally and analyzes move trees to determine outcomes.

However, minimax can quickly become inefficient. Its complexity is O(bⁿ) — where b is the number of possible moves, and n is the number of moves (plies) analyzed. To manage this, smarter search techniques like Alpha-Beta pruning, NegaScout, Iterative Deepening, and MTD(f) are used.

These methods — along with structures like transposition tables and the "killer heuristic" — will be covered in Chapter IV and Chapter V.

One major challenge is the horizon effect, where the program can only “see” danger a few plies deep. For example, it might throw away a bishop to delay a queen capture beyond its search depth — thinking it “saved” the queen, only to lose it later. Solutions like quiescence search and singular extensions (used in Deep Blue) address these issues.

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🧠 Evaluation

Ultimately, the engine needs to judge whether a position is good or bad — ahead or behind. The evaluation function is key. In chess, material balance (having more or stronger pieces) is often a primary factor. But in other games, like Othello, this can be misleading — you might actually want fewer pieces on the board until the end.

Building a solid evaluation function is tricky. Chapter VI will explore how top programs — such as Chess 4.5, Cray Blitz, and Belle — approach this task.

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🔚 In Closing

We’ve just laid out the foundational elements needed for a chess engine. Next time, we’ll focus on how to represent chess boards in code, including arrays, bitboards, and other methods.

Get ready to build the core — piece by piece.

—

🖋 François Dominic Laramée, April 2000 (Paraphrased by ChatGPT)

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🧠 Chess Programming – Chapter II: Data Structures

In the first article, we reviewed all the essential components of a chess-playing program. Now, it’s time to zoom in on one of the most foundational elements: how the chessboard is represented in memory.

Surprisingly, the core ideas used today haven’t changed much in over 30 years. This isn’t due to stagnation — it’s because early developers made some clever decisions that still hold up. Good board representation is the bedrock of any chess engine. Without it, none of the more complex features (like move generation or searching) could function efficiently.

In this chapter, we’ll cover both the primary board structures and three powerful supporting data structures. Two of them help accelerate search, while the third optimizes move generation.

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🔡 Keep It Simple (At First)

A general tip: use the simplest data structure that does the job. Fancy techniques can improve performance, but when you’re just starting or working on a new game, basic, well-structured systems are often enough. You can always optimize later once the core engine is working.

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📦 Basic Board Representations

Back in the 1970s, memory was extremely limited, so chess programmers had to be economical. The go-to method was an array of 64 bytes, with each byte representing a square. The value stored in each byte indicated the piece type (e.g., 0 for empty, 1 for black king, etc.).

There are a few well-known refinements of this technique:

• 🧱 SARGON-style padding:
  The early SARGON program wrapped the board with two layers of “fake” squares, filled with invalid values. This hack made it easy to detect out-of-bounds moves. For example, a bishop could slide in a direction until it hit one of these fake squares, stopping the move. Knights — with their unique L-shape — required two padding layers to avoid memory errors.

• 🔁 MYCHESS-style piece mapping:
  MYCHESS flipped the logic: it tracked the square of each piece, rather than what piece was in each square. For example, one byte stored the location of the white king, another stored the location of the queen’s knight pawn, etc. But this had a major flaw: you couldn’t promote a pawn to a new queen unless the original queen had already been captured. Later versions solved this.

📝 Today, these techniques may only be useful in environments with extremely tight memory (e.g., old cell phones, embedded systems). For most games, simpler and more modern representations will work better.

📚 For more on vintage chess engines, see David Welsh’s 1984 book: “Computer Chess.”

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🧠 Bitboards: The Power of 64 Bits

For 64-square games like chess or checkers, one brilliant technique stands out: bitboards. Developed by the KAISSA team in the Soviet Union during the late 1960s, bitboards map each square to a single bit in a 64-bit word.

For example:

• One bitboard could represent all squares occupied by white pawns.

• Another might show all squares threatened by a queen on e3.

• Yet another could show the squares under attack by black knights.

By using logical operations like AND, OR, and NOT, bitboards can rapidly compute results in just a few clock cycles.

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✅ Why Bitboards Are Awesome

Let’s say we want to know if the white queen is threatening the black king:

1. Load the bitboard showing the queen’s position.

2. Use it to index into a pre-computed database of queen attack patterns.

3. AND the result with the black king’s position bitboard.

If the result isn’t zero, the king is under attack. Fast, elegant, and done in just a few cycles — assuming the data is already in the cache.

Here’s another example: to find all possible moves for white knights, combine their attack bitboards and remove any square already occupied by white pieces (using a NOT on the white occupancy bitboard). No looping through all pieces or squares — just fast, logical math.

📖 For more, read about CHESS 4.5 in Peter Frey’s “Chess Skill in Man and Machine” (1977, 1981 editions).

💡 Note: Not all personal computers are truly 64-bit (especially older ones), so you may not always get the full performance benefit. But even in emulated environments, bitboards remain efficient and widely used.

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🔁 Transposition Tables

In chess, you can reach the same board position through different move orders — a phenomenon called transposition. For example, 1. e4 then d4 leads to the same position as 1. d4 then e4.

Rather than analyzing the same position twice, chess engines store previously computed evaluations in a transposition table, which acts like a cache. If a new move leads to a known position, the program can reuse old data.

🧠 Benefits of Transposition Tables:

• Speed: Especially during endgames, where many paths converge to the same state. Transposition tables allow reuse of work — often saving 90% or more of the search effort.

• Depth bonus: If a position has already been analyzed to 6-ply, but the current search only needs 4-ply, the deeper analysis can be reused for more accuracy.

• Opening integration: Many engines preload their transposition tables with content from their opening books, allowing them to continue using this knowledge even after the book “ends.”

⚠️ Drawback:

• Memory-hungry. To be useful, the table must store thousands (preferably millions) of entries, each taking about 16 bytes. In tight memory environments, this is a challenge.

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🧪 Hashing and Keys: Zobrist’s Method

How do you quickly generate a unique key for each chess position?

Enter Zobrist hashing (1970):

1. Assign a random N-bit number to each possible piece-square combo (e.g., black rook on h4).

2. Start with an empty hash value.

3. For each occupied square, XOR its number into the hash.

To update the hash after a move:

• XOR the moving piece's old position.

• XOR the captured piece (if any).

• XOR the new position.

💡 Bonus: This method also allows a secondary “hash lock” key to detect collisions — two different boards producing the same hash.

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📈 History Tables

History tables help guide move ordering during search. Inspired by the “killer move” heuristic, they track which moves tend to cause early cutoffs.

• Implemented as a simple 64x64 table.

• Each time a move proves effective, its counter is incremented.

• Moves with higher history values are considered earlier in future searches.

It’s a cheap, yet surprisingly effective way to improve performance.

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⚙️ Preprocessed Move Databases

Move generation is expensive. But we can preprocess a lot of it:

The technique proposed by Jean Goulet in his 1984 thesis (Data Structures for Chess, McGill University) suggests:

• Precompute “rays” of movement for every piece on every square, assuming an empty board.

• Store these rays in a small database.

• During gameplay, check each ray until it hits a piece or edge of the board.

This turns move generation into a near-instant lookup and only takes a few dozen kilobytes — almost nothing compared to transposition tables.

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🔚 Wrapping Up

Today, we explored how chess boards are stored, manipulated, and optimized for speed. Bitboards, transposition tables, hashing, and move databases all contribute to a powerful and flexible chess engine.

All of these tools will be demonstrated in the sample engine that accompanies this series.

Next time, we’ll dive deeper into Move Generation itself — one of the trickiest but most crucial parts of the engine.

—

🖋 François Dominic Laramée, June 2000 (Paraphrased by ChatGPT)

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♟️ Chess Programming – Chapter III: Move Generation

Previously, we introduced a technique for speeding up move generation using precomputed data structures. Now, we dive deep into the mechanics of how computers generate chess moves — and the different strategies programmers have used over the years.

This chapter focuses on understanding the options and trade-offs between generating all possible moves at once or just a few at a time. It also revisits some early experimental approaches like forward pruning, explains why they failed, and compares modern approaches that work better in today’s engines.

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⚔️ The Challenge of Move Generation

For humans, not all legal moves are equally appealing. Even beginner players learn not to hang their queen. But coding that kind of judgment into a computer is extremely difficult — especially since so much human intuition is subconscious.

Because of this, most modern engines rely on brute force: examine every legal move and look ahead as far as possible. You don’t need clever reasoning if you can analyze every outcome fast enough. So, optimizing move generation is about enabling fast, exhaustive search.

Three main strategies exist:

1. Selective Generation: Only generate “likely good” moves.

2. Incremental Generation: Generate moves one at a time as needed.

3. Complete Generation: Generate all legal moves upfront, then search them.

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🚫 The Fall of Forward Pruning (Selective Generation)

Claude Shannon, in 1949, suggested two basic ways to build a chess-playing program:

• Analyze all moves and all responses.

• Analyze only a few “best” moves and their most probable replies.

Initially, the second option (selective search) seemed logical. After all, that’s how people play. But programs using this technique never reached high-level play. They’d miss critical tactical threats and blunder in key moments.

Why it failed: Even a “95% accurate” best-move selector will make at least one mistake in nearly every 40-move game. A 90% accurate one will go a full game without error in only about 1.5% of cases.

In the mid-1970s, the Northwestern team (Slate & Atkin) demonstrated that it was more reliable to search all legal moves, even if it cost more processing time. The results were stronger engines, and forward pruning largely vanished from chess programming.

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♟️ Botvinnik’s Vision: A Grandmaster’s Plan

In the Soviet Union, Mikhail Botvinnik tried to build a program that “thought” like a grandmaster — planning deeply with only a few candidate moves. His system aimed to simulate high-level strategic ideas.

Though it offered interesting insights into chess theory, the engine never reached competitive performance. The complexity and fragility of the approach made it impractical.

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📜 Complete Move Generation: The Standard Method

Once forward pruning was out, most programs adopted a simpler, universal method:

1. Generate all legal moves.

2. Sort them based on likely usefulness (e.g., captures, checks).

3. Search them one by one until a cutoff is found or all moves are evaluated.

In older programs like Sargon, this involved scanning the board one square at a time and computing moves on the fly — slow but necessary due to memory constraints.

Today, engines use precomputed move databases (as discussed in Chapter II), which dramatically improve speed. These databases make complete generation faster and more efficient, especially when combined with transposition tables. If even one move hits a cutoff due to prior search results, the engine can skip evaluating the rest.

Complete generation is also more flexible across different games (e.g., Othello, Go-Moku), making it a better choice for engines that support multiple games.

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➕ Incremental Move Generation: One at a Time

Some advanced engines use a different strategy: generate a few moves at a time, hoping to find a cutoff early and avoid calculating the rest.

Reasons to use this method:

• Memory efficiency: Programs in the 70s lacked room for full databases.

• Chess-specific complexity: Move legality is tricky due to castling, en passant, and king safety.

• Early refutations: Often a single capture refutes an entire line. So, generating captures first can quickly prune bad paths.

• Quiescence Search compatibility: Captures dominate the positions explored in quiescence (covered in Part V), so isolating them early is helpful.

For instance, CHESS 4.5 used:

• A set of bitboards that track all attacking pieces.

• Efficient queries to check whether a piece attacks or is attacked.

• “Rotated bitboards” to manage complex attack directions (see James Swafford’s work online for technical details).

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🧠 Ordering Moves to Speed Up Search

The order in which moves are searched dramatically affects performance. Good move ordering can shrink the size of the search tree to its square root!

💡 Problem: The best ordering means searching the best move first… but if you knew that already, you wouldn’t need to search!

Heuristics to improve move ordering:

• Captures first: Losing your queen is rarely good.

• Pawn promotions: Major material swings.

• Checks: Often force responses.

• Killer moves: Past moves that caused cutoffs at the same depth.

• History heuristic: Moves that worked well in the past (regardless of piece type).

All of these help Alphabeta pruning (see Chapter IV) work more effectively.

Important note: These methods delay “bad” moves but do not discard them. This is not forward pruning — every move is still eventually considered.

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❗ When Is a Move Illegal?

Some moves are technically illegal — like moving into check. But instead of filtering them out during generation (which is expensive), most engines allow them and only validate legality during search, when needed. This lazy validation saves time when early cutoffs happen.

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✅ Why I Chose Complete Generation

In my Java engine, I generate all legal moves at once. Why?

• It makes the code cleaner and easier to follow.

• I want to extend the engine to other games, not just chess.

• I have enough memory for precomputed databases.

• There are already other programs (e.g., Crafty, Galahad) using incremental generation, so that ground is covered.

Sure, it may not match Deep Blue’s performance — but the simplicity and portability are worth it for this tutorial series.

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🔜 Next Chapter: Search Algorithms

Now that we can generate moves, we’ll look at how to search through them intelligently. In Chapter IV, we’ll start with basic search methods like Minimax and Alphabeta — the core of every strategy AI.

Stay tuned — things are about to get deep.

—

🖋 François Dominic Laramée, July 2000 (Paraphrased by ChatGPT)

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🤖 Chess Programming – Chapter IV: Basic Search

Welcome to Part IV — the brain of the chess engine: the search algorithm. Now that the engine can generate legal moves, it must figure out which one is best.

This chapter introduces the Minimax algorithm and its faster cousin Alpha-Beta pruning, which lie at the heart of all competitive chess engines. These strategies apply to most two-player, turn-based games of perfect information.

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🔍 Why Search?

Because humans are too complex — and computers aren't good at guessing.

Rather than trying to understand a position the way a human would (e.g., “control the center,” “attack the weak pawn”), a computer just calculates millions of possibilities very quickly. If a computer examines all legal moves, all responses, and all replies a few moves deep, it can spot tactics like forks, mates, or blunders — without “understanding” anything.

For instance, a knight fork (attacking two valuable pieces at once) is easy for a human to spot. But a program that explores three moves deep can “discover” it purely by evaluating the material gain it causes, even if the programmer didn’t explicitly teach it what a fork is.

So in short: search depth = tactical strength.

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👴 The Minimax Algorithm

Minimax is the most basic two-player game search method. Here’s how it works:

1. Assign a numeric value to each board state.

   • Positive = good for Player 1 (Max)

   • Negative = good for Player 2 (Min)

2. Max wants to increase the score. Min wants to decrease it.

3. Assume both players play perfectly.

4. Simulate every move for Max, every reply for Min, and so on.

5. Choose the path where Max gets the best worst-case result.

Let’s imagine a simple tree with two moves for each player. Max chooses between move A and B. For each move, Min has two replies. Max picks the move that minimizes Min’s advantage — and maximizes his own.

That’s Minimax.

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⏳ The Problem: Exploding Complexity

Minimax’s major flaw is its exponential growth. The number of board positions increases dramatically with each move (“ply”) you look ahead.

In chess:

• Average branching factor: ~35 moves per turn

• Depth of 8 plies: 35⁴ ≈ 1.5 million positions

• Depth of 10: 35⁵ ≈ 50 million

• Depth of 12: ~1.8 billion positions

This becomes unsustainable fast. So, we need something smarter.

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✂️ Alpha-Beta Pruning: Minimax on Steroids

Alpha-Beta speeds things up by pruning branches that can’t possibly affect the final decision.

How it works:

• Track the best score Max can guarantee (alpha)

• Track the worst score Min can allow (beta)

• If a branch results in a worse score than these limits, it gets cut off early

For example:

• If we already know Max has a move worth +5

• And we try another move that leads to a guaranteed -2 no matter what…

• We can stop analyzing it. It’s already worse than the +5 we had.

In best-case scenarios, Alpha-Beta reduces the number of nodes from millions to just a few thousand — with zero loss of accuracy.

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🔄 Move Ordering: The Key to Alpha-Beta’s Power

Alpha-Beta only works well if we analyze the best moves first. Otherwise, we waste time on suboptimal branches.

But there’s a paradox: if we knew which move was best, we wouldn’t need to search!

Here’s how we fake it:

1. Try moves stored in the transposition table first.

2. Prioritize captures, checks, and promotions — they often cause cutoffs.

3. Use the killer move heuristic: moves that previously caused early cutoffs.

4. Track successful moves in a history table and try them earlier.

These aren’t guaranteed, but they massively improve efficiency.

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🔁 Iterative Deepening: Searching in Layers

This technique feels backwards — but it works.

Instead of searching straight to depth 6, we search:

• First to depth 1

• Then 2

• Then 3...

• Until we reach 6

It sounds wasteful — repeating previous work over and over. But due to how search trees grow, each extra ply requires far more effort than all the previous ones combined. So repeating shallower searches adds minimal cost — but gives you move ordering data from earlier layers.

In fact, Iterative Deepening with Alpha-Beta (IDAB) is so effective that it outperforms plain Alpha-Beta with even perfect static ordering.

Add in a transposition table, and you’ll often reuse results from earlier layers — making IDAB not just smarter, but faster.

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🧠 What This Means for Play Style

Minimax-based programs are cautious. Here’s why:

Let’s say a program sees a bold move that might lead to checkmate — unless the opponent finds a tricky refutation 8 plies deep. A human might risk it. But a computer, assuming perfect play from the opponent, avoids it entirely.

As a result, engines often choose safe lines — even boring ones — to avoid risk. They won’t gamble on the opponent missing something.

Also, computers aren’t great at deception or long-term traps — unless the depth of search is enough to see the payoff.

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🔚 Coming Up Next

In Chapter V, we go beyond basic search. Topics include:

• Horizon effect (and how to fix it)

• Quiescence search

• Null-move heuristics

• Aspiration search

• MTD(f) — one of the most efficient search algorithms in existence

• Deep Blue’s secret weapon: Singular Extensions

This is where engines get seriously strong.

—

🖋 François Dominic Laramée, August 2000 (Paraphrased by ChatGPT)

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🔍 Chess Programming – Chapter V: Advanced Search

We’ve explored how Minimax and Alpha-Beta allow a chess engine to make decent decisions — but there’s still a long way to go. Rigid, fixed-depth search has major weaknesses. This chapter introduces advanced search enhancements used by top-tier programs to make their play stronger and faster.

These techniques help engines:

• Avoid blunders just beyond their search limit,

• Handle chaotic positions more intelligently,

• And make better use of processing time.

Let’s get started.

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🌪️ The Problem with Fixed Depth

Suppose your engine always searches 5 plies deep. Here are three ways that causes trouble:

1. Early Termination:
   If a checkmate or stalemate is visible at ply 3, there’s no need to search further. But without awareness, the engine might waste time evaluating meaningless extensions beyond a decisive moment.

2. False Positives:
   Say the engine captures a pawn on move 5. At first glance, this looks great. But it doesn’t realize that the move exposes its queen, which will be captured at ply 6 — just outside its search depth. The engine incorrectly assumes the queen is safe.

3. The Horizon Effect:
   This is when the engine “hides” a disaster by pushing it just beyond its vision. For example, it might sacrifice a rook to delay the capture of its queen to ply 6, falsely believing it has avoided the loss. On the next turn, it “realizes” the queen is still gone — but now it's also down a rook.

These problems demand a smarter, more adaptive form of search.

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🤫 Quiescence Search: Waiting for Calm

Chess positions are only safe to evaluate if they’re quiet — meaning no big material swings are likely to happen immediately. Evaluating a volatile position will almost always lead to errors.

Quiescence Search (QS) is the solution:

• After reaching the search depth limit, the engine doesn't immediately evaluate.

• Instead, it continues searching only “non-quiet” moves (e.g., captures, promotions, and sometimes checks).

• Once it finds a quiet position — no more captures or tactical explosions — it finally runs the evaluator.

Example:

• After a 5-ply search, the engine finds a knight capturing a rook.

• Instead of evaluating right away, it checks if the capturing knight can itself be captured.

• If yes, the position is not quiet and search continues.

This selective extension fixes the horizon effect in most cases — but QS can be expensive, eating up 50% or more of total processing time. Use it wisely.

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👻 The Null-Move Heuristic

Here’s an unexpectedly powerful idea: let your side skip a move.

At first glance, that sounds insane. But in most positions, doing nothing should make things worse — or at least not better.

How it works:

• Your engine evaluates the current position, but pretends your side passed the turn.

• If, even after that, your position is still strong enough to cause a beta cutoff, you can skip evaluating all your legal moves.

Benefits:

• Huge time savings: Instead of analyzing 35+ branches, you only examine 1 “null” branch.

• Strengthens QS: If skipping a move leads to disaster, you can be sure the position is not quiet and needs deeper analysis.

Risks:

• In rare positions (e.g., zugzwang), skipping a move might actually improve your chances.

• But these cases are so infrequent that the trade-off is worth it.

Null-move pruning can cut search time by 20% to 75%, with just a few lines of extra code.

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🧪 Aspiration Search and MTD(f)

🫧 Aspiration Search

Instead of searching with extreme bounds (–∞ to +∞), you “guess” the score range and search within a small window — say, ±100 points around the expected value.

If the result falls inside the window, great — it’s faster due to more pruning.

If not, it’s a fail-high or fail-low, and you retry with a wider window.

It’s a gamble that usually pays off.

🔄 MTD(f) – Memory-enhanced Test Driver

A researcher named Aske Plaat took this further. What if the search window is zero?

That’s right — each search guesses one specific value, and it either fails high or low. The engine then adjusts its guess and tries again, gradually zeroing in on the correct score. Like a binary search through the score space.

Combined with a transposition table, this is incredibly efficient:

• Each guess is fast.

• Fewer nodes are explored.

• It works well with “coarse” evaluators (where position scores aren’t ultra-precise).

📝 MTD(f) is compact (10 lines of code), simple to implement on top of Alpha-Beta, and extremely fast — making it one of the most effective search methods available.

Original MTD(f) paper:
Aske Plaat – MTD(f)

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🧠 Singular Extensions (Deep Blue’s Secret)

Deep Blue, IBM’s famous engine that defeated Garry Kasparov, used a clever trick:

If one move is way better than all the others at a certain node, extend the search one ply deeper for that move only.

This ensures that:

• You confirm the move’s superiority.

• You’re not being fooled by a tactic hidden one move deeper.

This strategy:

• Boosts tactical sharpness,

• Helps detect forced wins or traps,

• And gives an edge in key moments.

But there’s a trade-off: extending even one ply can double the size of that subtree. It requires serious hardware. Deep Blue had it. Most engines don't.

Still, a simplified version of this concept can be implemented in most programs.

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🧠 Wrapping Up

With these advanced techniques, a chess engine gains:

• Speed (via null-move and aspiration),

• Clarity (via quiescence),

• Efficiency (via MTD(f)),

• And tactical accuracy (via singular extensions).

If you’re building a competitive engine, these methods will get you there.

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🔜 Coming Next: Evaluation Functions

In Chapter VI, we finally reach the “soul” of the chess engine: the evaluation function — how the engine scores a position. We’ll break down concepts like:

• Material balance

• Mobility

• King safety

• Pawn structures

• Development

• And more

It’s the most creative — and controversial — part of chess engine design.

—

🖋 François Dominic Laramée, September 2000 (Paraphrased by ChatGPT)

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🧮 Chess Programming – Chapter VI: Evaluation Functions

Congratulations! After months of theory, we're now at the heart of the chess engine’s intelligence: the evaluation function.

While move generation and search are mechanical and follow the rules, the evaluation function is where the engine thinks. It analyzes a position and decides whether it's winning, losing, or balanced — and why.

This chapter introduces the most common factors used to evaluate a chessboard and highlights how subjective and experimental this part of programming really is.

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⚖️ Material Balance: The Backbone of Evaluation

The simplest — and most important — factor is material balance, i.e., who has more and stronger pieces.

Typical piece values (in centipawns):

• Pawn = 100

• Knight = 300

• Bishop = 325

• Rook = 500

• Queen = 900

• King = ∞ (but its safety is scored indirectly)

The formula is:

Material = Σ (Number of pieces × Value of piece)

Many top engines rely on material difference alone and still play decent chess. According to the creators of CHESS 4.5, an advantage of 1.5 pawns outweighs most positional advantages.

👉 So yes, material is king — but there's more to the story.

Some refinements:

• Reward trades when ahead (simplifies the position).

• Penalize early material sacrifices unless part of a known opening gambit.

• Apply a “contempt factor”: a small optimism bias that discourages draws when slightly behind.

Note: In games like Othello or Go-Moku, material balance is irrelevant — every game needs a custom approach.

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🧭 Mobility and Board Control

In chess, one way to pressure your opponent is to limit their mobility — the number of legal moves they can make.

To measure this:

• Count the number of legal moves for each side.

• Compare totals.

However, pure mobility scores are misleading. Many legal moves are garbage (e.g., moving the rook back and forth). Worse, a mobility-focused engine might waste time chasing meaningless checks.

Refinements:

• Penalize “bad bishops” blocked by same-colored pawns.

• Penalize knights near the edge (less reach).

• Reward rooks on open or semi-open files.

Board control is a deeper concept:

• A square is controlled if you attack it more than your opponent.

• Controlling the center is a known positional advantage.

But board control is expensive to calculate in real time — especially without bitboards. My engine skips it. Yours might not.

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🚀 Development and Initiative

Early in the game, you want to:

• Move minor pieces (bishops, knights) into play.

• Castle the king for safety.

• Delay moving the queen until later.

Good evaluation functions reward:

• Pieces off the back rank

• Openings that don’t block your rooks

• King already castled

• Avoiding early queen moves

But this factor fades fast. After 10–12 moves, almost all engines enter the middlegame where development scoring becomes irrelevant.

In games like Checkers, keeping back-rank pieces in place is good — so development heuristics might be reversed.

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🧱 Pawn Structure: The Soul of Chess

Many grandmasters have said, “Pawns are the soul of chess.” Here's why.

Good pawn structure = long-term strength. Bad structure = target practice for the opponent.

Common factors:

• Doubled/Tripled Pawns:
  Two or more friendly pawns on the same file. Penalize — they block each other and can't protect neighboring files.

• Pawn Rams:
  Two opposing pawns blocking each other. Creates gridlock — neutral but worth tracking.

• Isolated Pawns:
  No friendly pawns on adjacent files. Vulnerable to attack.

• Passed Pawns:
  No enemy pawns in front or on adjacent files. Very strong, especially in the endgame. Reward heavily.

• Too Many Pawns:
  Eight pawns restrict rook activity. Sometimes it's better to have an open file.

Advanced:

• Reward passed pawns with rook support from behind — they’re lethal.

• Track pawn chains for defense or attack coordination.

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🛡️ King Safety and Tropism

Early in the game, the king should be hidden — usually behind castled pawns.

In the endgame, though, the king becomes a fighting piece.

Scoring king safety involves:

• Checking if the king has castled (reward it).

• Evaluating how many enemy pieces are nearby.

• Assessing pawn cover around the king.

Tropism = How close enemy pieces are to your king. The closer they are, the higher the threat.

This is tricky to evaluate, but even a basic scoring of proximity (distance to king square) can help.

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⚖️ Assigning Weights: The Black Art of Evaluation

Now that you’ve got all these features — how do you combine them?

Answer: Trial and error.

There is no universal formula. Every engine uses a custom combination of weights and priorities, often refined through:

• Self-play matches

• Genetic algorithms (in fast engines)

• Manual tuning by the programmer

Three rules of thumb:

1. Keep it simple:
   A fast evaluator lets you search deeper. Don’t overload it unless necessary.

2. Balance for play style:
   Want a defensive engine? Emphasize king safety. Want aggression? Boost mobility and development.

3. Search is stronger than smarts:
   Adding one ply of search is often better than all the heuristics in the world.

If you're not aiming to win world championships, a basic material + safety evaluator with 5–6 supporting features will do wonders.

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🏁 Final Words

This concludes the Chess Programming series. We’ve covered:

• Game structure

• Board representations

• Move generation

• Search algorithms

• Optimization techniques

• Evaluation strategy

You now have all the tools to write a complete chess engine — and if you followed along, you’ll find the author’s Java code available alongside this final chapter.

Of course, there’s more:

• Opening books

• Endgame tablebases

• Parallel search

• Neural networks

But those are for another time.

If this journey inspired or helped you, find the author at GDC or E3 — and say hello.

—

🖋 François Dominic Laramée, October 2000 (Paraphrased by ChatGPT)

#chessprogramming #chess #ajedrez #programming