https://www.playlikeaquant.com/blog-posts daily 0.75 2026-07-07 https://www.playlikeaquant.com/blog-posts/my-laptop-played-poker monthly 0.5 2026-07-07 https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/a728e1d7-b977-4112-ac88-f8d8049f2b78/Poker1+-+Regret+Falls.png Blog Posts - My Laptop Played 3.5 Million Hands of Poker Against Itself. Here’s the Bot it Became. - Make it stand out The whole project in one curve. “Regret” measures how much the bot wishes it had played differently. It spikes early as the bot discovers new ways to bet, then falls for three million hands straight as the strategy settles. By the end it has dropped to 0.076. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/e0d324a8-d5cf-41c9-8233-5ade0527da43/Poker2+-+Richer.png Blog Posts - My Laptop Played 3.5 Million Hands of Poker Against Itself. Here’s the Bot it Became. - Make it stand out Hands trained per second. The richer betting in v3 made each hand about three times more expensive to learn from, which is why parallelism stopped being a nice-to-have. Spreading the work across six cores took the bot from eight hands a second back up to nearly eighty. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/102f4410-f35a-4641-8e58-e4016e02f6c5/Poker3+-+Rebuild.png Blog Posts - My Laptop Played 3.5 Million Hands of Poker Against Itself. Here’s the Bot it Became. - Make it stand out Two ways v3’s world got bigger: one more bet size, and twice as many possible situations to learn. The number of situations the bot can imagine doubled; the number it ever actually meets in real play turned out to be only about a thousand. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/04dfecda-a191-4d65-a49b-2dac9097db93/Poker4+-+Fifth+Bet.png Blog Posts - My Laptop Played 3.5 Million Hands of Poker Against Itself. Here’s the Bot it Became. - Make it stand out The fifth bet size that did not make it. Adding a one-third-pot option made betting sequences so long that the bot trained roughly five times slower for no real gain in strength. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/1e671148-05d2-4215-97f9-2e4f0d3396c9/Poker5+-+Mixes.png Blog Posts - My Laptop Played 3.5 Million Hands of Poker Against Itself. Here’s the Bot it Became. - Make it stand out Every raise the trained bot makes, sorted by size. No single size dominates. This is the one chart that convinced me the “feel like a person” pass had worked: it is neither a calling station nor a maniac who only shoves. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/76e4f011-a43b-4672-8bb4-9be8451a45c8/Poker6+-+Before+Flop.png Blog Posts - My Laptop Played 3.5 Million Hands of Poker Against Itself. Here’s the Bot it Became. - Make it stand out Before the flop, by hand strength and seat. Weak hands on the left are folded; strong hands on the right are played aggressively but with varied sizing. The bot is noticeably more willing to put chips in from the big blind, where it has already posted money in the pot. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/211d80d8-2527-43d5-8568-a7a1c0eaca5a/Poker7+-+Unexploitable.png Blog Posts - My Laptop Played 3.5 Million Hands of Poker Against Itself. Here’s the Bot it Became. - Make it stand out Chips won per hand against three opponents over the full run. The bot beats the random and push-or-fold players comfortably. Against a pure calling station (red) it slides into the red and stays there, and that is not a bug. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/4c9e52f8-5ce1-4ea1-a38f-b3aaec70231d/Poker7.1+-+Stats.png Blog Posts - My Laptop Played 3.5 Million Hands of Poker Against Itself. Here’s the Bot it Became. - Make it stand out Whatever it is, the way you tell your story online can make all the difference. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/83cfa460-5087-471c-8acf-56a2b21f80a0/Poker8+-+Throughput.png Blog Posts - My Laptop Played 3.5 Million Hands of Poker Against Itself. Here’s the Bot it Became. - Make it stand out Hands per second across the full run. No slow leak, no thermal cliff, just a flat line for twelve hours. Boring, in the way you want a long compute job to be boring. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/f822281e-be4a-4ed4-ab74-49a292550bd8/Poker9+-+Hands+Played.png Blog Posts - My Laptop Played 3.5 Million Hands of Poker Against Itself. Here’s the Bot it Became. - Make it stand out How many distinct situations the bot had encountered, over the run. It saturates almost immediately near a thousand and never approaches the 3,200 it could theoretically reach. The bot learns the poker that actually happens, not the poker that could. https://www.playlikeaquant.com/blog-posts/i-built-a-regression-model monthly 0.5 2026-07-07 https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/18d7cd9c-c143-4692-982d-63dfb30c3e90/Adm1+-+8-Panel+Analysis+-+Henry+Anderson.png Blog Posts - I Built a Regression Model on a High School’s Admissions Data. Here’s What it Predicts. - Make it stand out Each panel shows one school’s applicants from this dataset. Notice how cleanly the blue dots separate from the red X’s in some schools (JMU, Georgetown), and how scrambled they are in others (Penn State, Northeastern). That visual gap is the whole story. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/0025dda2-a0e0-4af0-a69c-d6ab26c59b81/Adm2+-+3+Factors+-+Henry+Anderson.png Blog Posts - I Built a Regression Model on a High School’s Admissions Data. Here’s What it Predicts. - Make it stand out Each bar shows how much of an admissions decision at that school is statistically explained by GPA, test score, and decision type (early vs. regular). The gray block is everything the numbers can’t explain: essays, extracurriculars, recommendations, and the intangibles of holistic review. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/a1aa3b8a-1055-4b22-a4c9-e16d60780966/Adm3+-+JMU+v+Northwestern+-+Henry+Anderson.png Blog Posts - I Built a Regression Model on a High School’s Admissions Data. Here’s What it Predicts. - Make it stand out Left: JMU, where the dashed line cleanly separates blue (accepted) from red (denied). Right: Northeastern, where blue and red are scattered all over each other and no line can really separate them. Background shading shows the model’s predicted probability of admission: warm cream means low, cool blue means high. https://images.squarespace-cdn.com/content/v1/69bca85e190591600bbe558d/faf6f686-2f84-4ef1-8bbd-835a4a3d3d52/Adm4+-+Northeastern+-+Henry+Anderson.png Blog Posts - I Built a Regression Model on a High School’s Admissions Data. Here’s What it Predicts. - Make it stand out Several Northeastern applicants with SATs of 1500+ were denied or waitlisted. 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