Two private cards meet five community cards. Your best five-card high hand can use either, both, or neither private card. Bets use a fixed small increment on the first two streets and a larger increment on the final two.

Take your seat with Fern and familiar AI company. The room offers heads-up or six-seat play, automatic next hands, adjustable pace, practice peeks, a visible WSRF tree, a Monte Carlo playground, and a notebook that keeps your place. Choose Gentle company while learning the rhythm, then explore Seasoned or Sharp whenever you feel ready.

Start by naming the game and the legal five-card construction. Then count the players still active and read the amount needed to continue. Those small observations give the coach’s estimate a useful setting. The worked pages that follow preserve their cards, assumptions, and numerical receipts so you can explore the same question again.

These are the room’s declared house settings. A short stack contributes its available chips, and side pots keep each contribution matched to the players eligible for it. A returned unmatched contribution stays separate from a contested pot. Opening the Coach or Options panel holds the action while you explore.

A fixed amount makes the price easy to follow. Use the current street’s increment and the chips already in front of you when reading a raise-to amount.

The community board

Your two private cards and five community cards offer twenty-one five-card selections at the river. You can use both private cards, one, or the five board cards. The evaluator chooses your strongest available five-card high hand.

Try covering the worked answer and pointing to the five chosen cards. Then name one different final card that would invite a new construction. A concrete card change is a useful way to connect imagination with the rules.

The pocket map uses your first two dealt cards. In an Omaha hand these are an explicit pair of anchors inside the four-card pocket; the hand evaluator still explores all legal two-card choices. In a stud hand they are the first two private cards. The moving map uses the latest two community cards or your latest two exposed stud cards once they are available.

The map’s circular ranks run from 2=0 to A=12. The reference expressions use the two anchors, their sum modulo thirteen, and their absolute difference. Near means distance zero or one. Seven or more near ranks gives LIKELY; six or fewer gives UNLIKELY. Both labels receive balanced examples in each newly trained model.

Known-card counts include your own cards and every publicly exposed card, including cards shown by a folded stud opponent. That keeps the remaining physical-card counts connected to what you can actually observe. The game-specific hand ranking and the map each describe their own part of the hand.

Read the rhythm before the price

At this table the small bet is 20 and the big bet is 40. The first two streets use the small amount; the turn and river use the larger amount. That rhythm makes planning easier. A turn decision carries a different future cost from a flop decision, even when the visible pot looks similar.

Small choices still tell a story

Checking, calling, and raising can each express a useful plan. A raise might build a pot with a strong made hand, while a call can preserve several possible future decisions. Treat the fixed increment as a clear vocabulary. The useful discovery is how the board and the active field change the value of that vocabulary from one street to the next.

Position gives another observation

Acting later lets you see more of the current street before choosing. Pause the room and name the decisions already made: a check, a bet, a call, perhaps a raise. Then describe the price you face. Reading the sequence in ordinary words often makes a complicated-looking hand feel more manageable.

Let the river be its own decision

By the river, the visible board is complete. Compare the current call price with the eligible pot and your assumption about opposing hands. Your earlier contributions are part of the story already told. The remaining decision is about the chips and possibilities still available from this point.

This independently generated teaching hand preserves your own cards and every exposed card, then samples the remaining cards uniformly. Fractional ties and split pots contribute the share actually earned. The interval describes sampling uncertainty under that model; the forest’s estimate is a separate learned prediction.

Compare the action scores with the call price and the active field. They express the explicit betting policy’s use of the learned share, rather than a fitted chip-return forecast. Opening a tree lets you follow the card features that helped produce the share estimate.

Study fixture holdem-1 · 4,096 samples · Independent namespace mixed-guide-independent. Cards, seeds, and measurements are preserved in the study receipt.

This independently generated teaching hand preserves your own cards and every exposed card, then samples the remaining cards uniformly. Fractional ties and split pots contribute the share actually earned. The interval describes sampling uncertainty under that model; the forest’s estimate is a separate learned prediction.

Compare the action scores with the call price and the active field. They express the explicit betting policy’s use of the learned share, rather than a fitted chip-return forecast. Opening a tree lets you follow the card features that helped produce the share estimate.

Study fixture holdem-2 · 4,096 samples · Independent namespace mixed-guide-independent. Cards, seeds, and measurements are preserved in the study receipt.

This independently generated teaching hand preserves your own cards and every exposed card, then samples the remaining cards uniformly. Fractional ties and split pots contribute the share actually earned. The interval describes sampling uncertainty under that model; the forest’s estimate is a separate learned prediction.

Compare the action scores with the call price and the active field. They express the explicit betting policy’s use of the learned share, rather than a fitted chip-return forecast. Opening a tree lets you follow the card features that helped produce the share estimate.

Study fixture holdem-3 · 4,096 samples · Independent namespace mixed-guide-independent. Cards, seeds, and measurements are preserved in the study receipt.

This independently generated teaching hand preserves your own cards and every exposed card, then samples the remaining cards uniformly. Fractional ties and split pots contribute the share actually earned. The interval describes sampling uncertainty under that model; the forest’s estimate is a separate learned prediction.

Compare the action scores with the call price and the active field. They express the explicit betting policy’s use of the learned share, rather than a fitted chip-return forecast. Opening a tree lets you follow the card features that helped produce the share estimate.

Study fixture holdem-4 · 4,096 samples · Independent namespace mixed-guide-independent. Cards, seeds, and measurements are preserved in the study receipt.

This independently generated teaching hand preserves your own cards and every exposed card, then samples the remaining cards uniformly. Fractional ties and split pots contribute the share actually earned. The interval describes sampling uncertainty under that model; the forest’s estimate is a separate learned prediction.

Compare the action scores with the call price and the active field. They express the explicit betting policy’s use of the learned share, rather than a fitted chip-return forecast. Opening a tree lets you follow the card features that helped produce the share estimate.

Study fixture holdem-5 · 4,096 samples · Independent namespace mixed-guide-independent. Cards, seeds, and measurements are preserved in the study receipt.

This independently generated teaching hand preserves your own cards and every exposed card, then samples the remaining cards uniformly. Fractional ties and split pots contribute the share actually earned. The interval describes sampling uncertainty under that model; the forest’s estimate is a separate learned prediction.

Compare the action scores with the call price and the active field. They express the explicit betting policy’s use of the learned share, rather than a fitted chip-return forecast. Opening a tree lets you follow the card features that helped produce the share estimate.

Study fixture holdem-6 · 4,096 samples · Independent namespace mixed-guide-independent. Cards, seeds, and measurements are preserved in the study receipt.

Each game receives 6,144 training positions, with 128 independently sampled showdowns at every position. Six equally represented pools combine opening, middle, and closing play with LIKELY and UNLIKELY moving-map labels. Private and public suit counts, exposed-board strength, and rank geometry give the forest a richer view. The fitted object is pytoolzx.wsrf.WSRFRegressor, and pytoolzx.indexrng.Index supplies independently named seed streams.

Compact and full forests are compared on 768 validation positions. The smaller validation error chooses the serving forest before a fresh 1,536-position test is evaluated. Validation labels use 384 samples; test labels use 512. MAE is the average absolute difference in estimated pot share, expressed here in percentage points.

The complete-hand benchmark rotates the trained policy through every seat of each fresh deck. Its intervals use deck averages to respect the relationship between seat rotations. These named local practice styles define the scope of the measurements. The full receipt preserves every game, table size, and opponent row.

Begin with a comfortable rhythm

Choose Gentle company and a relaxed pace. Leave automatic next hands enabled if you enjoy a continuous session, or choose each new hand yourself. The panel controls hold your exact place whenever you open an explanation.

Give one hand a question

Choose a question you can inspect: can this card complete the construction, can this price be expressed as a pot share, or can the tree show which input it used? The worked answer becomes useful when you can point to its reason on the table.

Explore with a practice peek

A peek makes the opposing cards visible for your exercise. The model continues to use its own cards and public observations, so its estimate stays comparable before and after your peek. The notebook marks that hand as practice and preserves the result.

Let the forest take a turn

The WSRF partner option lets the trained adviser play your seat using the explicit betting policy. Pause on a street that interests you and open the tree. Watch mode is a way to explore the model’s choices; your own chair is ready whenever you switch back.

Keep a story to return to

Download a notebook from Options. It includes the deal and action sequence needed for exact replay, along with stacks, funding, completed hands, and HUD totals. Restore it in the same game and table size. A saved story can make the next visit feel like a continuation.

VPIP counts voluntary entries on the opening street; OR counts opening raises or completions. AF divides bets plus raises by calls. WTSD counts showdowns among hands reaching the first draw street. WSD counts showdowns winning any contested pot, including ties. Read each percentage together with its completed-hand sample count.

Explore the background for this variant in Wikipedia’s fixed-limit hold’em overview. The room’s specific stakes, cap, reopening, and odd-chip conventions are declared on page 3. The examples, exercises, UI, and training experiment in this guide are original to Lantern Poker.

Explore the worked-hand receipt, complete-hand match table, and training and reproduction notes. Return to the Hold’em Hearth table whenever you are ready.