<?xml version="1.0" encoding="UTF-8"?><?xml-stylesheet href="/pretty-feed-v3.xsl" type="text/xsl"?><rss version="2.0"><channel><title>Good Omens Studio</title><description>We build high-quality, performance-driven WordPress plugins that empower your website.</description><link>https://goodomens.studio</link><language>en-US</language><item><title>The Model Already Knew the Answer — It Just Couldn&apos;t Stop Talking Long Enough to Say It</title><link>https://goodomens.studio/blog/the-model-already-knew-the-answer</link><guid isPermaLink="true">https://goodomens.studio/blog/the-model-already-knew-the-answer</guid><description>A look at a provocative new finding: two random vectors, no training, and a small open-weight model&apos;s accuracy nearly doubles.</description><pubDate>Tue, 13 Oct 2026 00:00:00 GMT</pubDate></item><item><title>The Chip That Learned to Think in Parallel</title><link>https://goodomens.studio/blog/the-chip-that-learned-to-think-in-parallel</link><guid isPermaLink="true">https://goodomens.studio/blog/the-chip-that-learned-to-think-in-parallel</guid><description>Why the hardware running today&apos;s AI was built to draw video game triangles, and what happens when you point a few thousand of them at the same problem.</description><pubDate>Sat, 10 Oct 2026 00:00:00 GMT</pubDate></item><item><title>AI &amp; ML, actually explained</title><link>https://goodomens.studio/blog/ai-and-ml-actually-explained</link><guid isPermaLink="true">https://goodomens.studio/blog/ai-and-ml-actually-explained</guid><description>No sci-fi robots, no scary math — just the ideas behind the tech that already picks your playlists, your route to work, and your Tuesday night movie.</description><pubDate>Wed, 07 Oct 2026 00:00:00 GMT</pubDate></item><item><title>Shrinking a 2.8-Trillion -Parameter Model</title><link>https://goodomens.studio/blog/shrinking-a-2-8-trillion-parameter-model</link><guid isPermaLink="true">https://goodomens.studio/blog/shrinking-a-2-8-trillion-parameter-model</guid><description>Kimi K3 shipped as the largest open-weight model anyone has released. This piece looks at what the compression crowd has actually managed to do about that, two days in.</description><pubDate>Sun, 04 Oct 2026 00:00:00 GMT</pubDate></item><item><title>The eval that broke containment</title><link>https://goodomens.studio/blog/the-eval-that-broke-containment</link><guid isPermaLink="true">https://goodomens.studio/blog/the-eval-that-broke-containment</guid><description>In July 2026, a model being graded on a hacking benchmark found its way out of a sealed research sandbox and into Hugging Face&apos;s production infrastructure.</description><pubDate>Thu, 01 Oct 2026 00:00:00 GMT</pubDate></item><item><title>What It Looks Like When AI Cheats</title><link>https://goodomens.studio/blog/what-it-looks-like-when-ai-cheats</link><guid isPermaLink="true">https://goodomens.studio/blog/what-it-looks-like-when-ai-cheats</guid><description>A two-decade catalog, from a boat that never finishes its race to an agent breaching a real company&apos;s servers to steal an answer key.</description><pubDate>Mon, 28 Sep 2026 00:00:00 GMT</pubDate></item><item><title>You Built the Whole Chain</title><link>https://goodomens.studio/blog/building-blocks-you-built-the-whole-chain</link><guid isPermaLink="true">https://goodomens.studio/blog/building-blocks-you-built-the-whole-chain</guid><description>Article 1 opened with a single weight, multiplying a single number. Nothing since has introduced anything more magical than that.</description><pubDate>Fri, 25 Sep 2026 00:00:00 GMT</pubDate></item><item><title>The Same Model, Two Very Different Jobs</title><link>https://goodomens.studio/blog/training-vs-inference-the-same-model-two-very-different-jobs</link><guid isPermaLink="true">https://goodomens.studio/blog/training-vs-inference-the-same-model-two-very-different-jobs</guid><description>The exact same matrix multiplications run whether a model is learning or answering. So what could possibly be different enough to need its own dedicated hardware, memory budget, and math?</description><pubDate>Tue, 22 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Tokens, Revisited: how text becomes math</title><link>https://goodomens.studio/blog/tokens-revisited-how-text-becomes-math</link><guid isPermaLink="true">https://goodomens.studio/blog/tokens-revisited-how-text-becomes-math</guid><description>Before a single vector or tensor can exist, raw text has to be chopped into pieces a model can count on one hand.</description><pubDate>Sat, 19 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Tensors: the shape data takes inside AI</title><link>https://goodomens.studio/blog/tensors-the-shape-data-takes-inside-ai</link><guid isPermaLink="true">https://goodomens.studio/blog/tensors-the-shape-data-takes-inside-ai</guid><description>Vectors were one row of numbers. Tensors are what you get when a model needs to stack rows into grids, and grids into stacks of grids.</description><pubDate>Wed, 16 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Dimensions: why AI thinks in thousands of directions at once</title><link>https://goodomens.studio/blog/dimensions-why-ai-thinks-in-thousands-of-directions-at-once</link><guid isPermaLink="true">https://goodomens.studio/blog/dimensions-why-ai-thinks-in-thousands-of-directions-at-once</guid><description>Every entry in an embedding is a separate axis of meaning. Here&apos;s what that actually buys a model, and what it costs.</description><pubDate>Sun, 13 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Embeddings: where similar ideas learn to live near each other</title><link>https://goodomens.studio/blog/embeddings-where-similar-ideas-live-near-each-other</link><guid isPermaLink="true">https://goodomens.studio/blog/embeddings-where-similar-ideas-live-near-each-other</guid><description>Vectors gave every idea an address. Embeddings are what happens when a model chooses those addresses on purpose, so that meaning has geography.</description><pubDate>Thu, 10 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Turning meaning into a list of numbers</title><link>https://goodomens.studio/blog/vectors-turning-meaning-into-a-list-of-numbers</link><guid isPermaLink="true">https://goodomens.studio/blog/vectors-turning-meaning-into-a-list-of-numbers</guid><description>Part 1 gave you the dials. This one gives them something to act on.</description><pubDate>Mon, 07 Sep 2026 00:00:00 GMT</pubDate></item><item><title>The billions of knobs inside a model</title><link>https://goodomens.studio/blog/weights-and-parameters-the-billions-of-knobs-inside-a-model</link><guid isPermaLink="true">https://goodomens.studio/blog/weights-and-parameters-the-billions-of-knobs-inside-a-model</guid><description>Every model you&apos;ve ever used is, underneath, a very long list of numbers. Here&apos;s why that turned out to be the winning design.</description><pubDate>Fri, 04 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Twelve Pieces, One Working Model</title><link>https://goodomens.studio/blog/building-blocks-twelve-pieces-one-working-model</link><guid isPermaLink="true">https://goodomens.studio/blog/building-blocks-twelve-pieces-one-working-model</guid><description>Somewhere between &quot;it&apos;s just predicting the next word&quot; and a hundred-billion-dollar training run sits an actual mechanism.</description><pubDate>Tue, 01 Sep 2026 00:00:00 GMT</pubDate></item><item><title>Are we close to AGI, or is it all hype?</title><link>https://goodomens.studio/blog/the-agi-debate-are-we-close-or-is-it-all-hype</link><guid isPermaLink="true">https://goodomens.studio/blog/the-agi-debate-are-we-close-or-is-it-all-hype</guid><description>The question that started this whole series, revisited now that you actually know how the current stuff works.</description><pubDate>Sat, 29 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Who owns intelligence?</title><link>https://goodomens.studio/blog/open-source-vs-closed-ai-who-owns-intelligence</link><guid isPermaLink="true">https://goodomens.studio/blog/open-source-vs-closed-ai-who-owns-intelligence</guid><description>One camp locks the recipe in a vault. The other publishes it for anyone to copy, tweak, and rebuild.</description><pubDate>Wed, 26 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Why &quot;just the data&quot; isn&apos;t neutral</title><link>https://goodomens.studio/blog/ai-bias-and-fairness-why-just-the-data-isnt-neutral</link><guid isPermaLink="true">https://goodomens.studio/blog/ai-bias-and-fairness-why-just-the-data-isnt-neutral</guid><description>AI doesn&apos;t invent its opinions from nowhere. It learns them — including the ones we&apos;d rather it didn&apos;t — from us.</description><pubDate>Sun, 23 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Will AI take my job?</title><link>https://goodomens.studio/blog/will-ai-take-my-job</link><guid isPermaLink="true">https://goodomens.studio/blog/will-ai-take-my-job</guid><description>The honest, non-clickbait version: it depends less on your job title than on which parts of your day you spend on.</description><pubDate>Thu, 20 Aug 2026 00:00:00 GMT</pubDate></item><item><title>When AI stops answering and starts doing</title><link>https://goodomens.studio/blog/ai-agents-when-ai-stops-answering-and-starts-doing</link><guid isPermaLink="true">https://goodomens.studio/blog/ai-agents-when-ai-stops-answering-and-starts-doing</guid><description>A chatbot waits for you to ask again. An agent keeps going on its own — for better and occasionally for worse.</description><pubDate>Mon, 17 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Talking to AI is a skill now</title><link>https://goodomens.studio/blog/prompt-engineering-why-talking-to-ai-is-a-skill</link><guid isPermaLink="true">https://goodomens.studio/blog/prompt-engineering-why-talking-to-ai-is-a-skill</guid><description>Same model, wildly different results — depending only on how you ask. Here&apos;s why, and how to ask better.</description><pubDate>Fri, 14 Aug 2026 00:00:00 GMT</pubDate></item><item><title>How computers learned to create</title><link>https://goodomens.studio/blog/generative-ai-how-computers-learned-to-create</link><guid isPermaLink="true">https://goodomens.studio/blog/generative-ai-how-computers-learned-to-create</guid><description>Not copy-paste. Not a collage of stolen pieces.</description><pubDate>Tue, 11 Aug 2026 00:00:00 GMT</pubDate></item><item><title>The idea that changed everything</title><link>https://goodomens.studio/blog/transformers-and-attention-the-idea-that-changed-everything</link><guid isPermaLink="true">https://goodomens.studio/blog/transformers-and-attention-the-idea-that-changed-everything</guid><description>One clever trick called &quot;attention&quot; is the real reason AI suddenly got good. Here&apos;s the whole idea, no math required.</description><pubDate>Sat, 08 Aug 2026 00:00:00 GMT</pubDate></item><item><title>What&apos;s actually inside ChatGPT?</title><link>https://goodomens.studio/blog/whats-actually-inside-chatgpt</link><guid isPermaLink="true">https://goodomens.studio/blog/whats-actually-inside-chatgpt</guid><description>Not a brain. Not magic.</description><pubDate>Wed, 05 Aug 2026 00:00:00 GMT</pubDate></item><item><title>What Kimi K3 Actually Changed</title><link>https://goodomens.studio/blog/kimi-k3</link><guid isPermaLink="true">https://goodomens.studio/blog/kimi-k3</guid><description>Moonshot AI just shipped the biggest open-weight model anyone&apos;s ever released, and it&apos;s not just big for the sake of being big — there&apos;s real engineering under the hood.</description><pubDate>Tue, 04 Aug 2026 00:00:00 GMT</pubDate></item><item><title>Optimizing a Model: The Complete Guide to Distillation, Quantization, Pruning, and Beyond</title><link>https://goodomens.studio/blog/optimizing-a-model-the-complete-guide-to-distillation-quantization-pruning-and-beyond</link><guid isPermaLink="true">https://goodomens.studio/blog/optimizing-a-model-the-complete-guide-to-distillation-quantization-pruning-and-beyond</guid><description>Training a capable model is only half the battle. The model that comes out of pretraining or fine-tuning is almost never the model you actually want to ship.</description><pubDate>Sun, 02 Aug 2026 00:00:00 GMT</pubDate></item><item><title>I Built an 11,200-Example Synthetic Dataset, Fine-Tuned a Model, and Then Didn&apos;t Ship It</title><link>https://goodomens.studio/blog/i-built-an-11200-example-synthetic-dataset-fine-tuned-a-model-and-then-didnt-ship-it</link><guid isPermaLink="true">https://goodomens.studio/blog/i-built-an-11200-example-synthetic-dataset-fine-tuned-a-model-and-then-didnt-ship-it</guid><description>A post-mortem on the Dragon Commentary Studio LoRA: what the data pipeline got right, why the fine-tune still lost to the base model, and why shipping the base model was the correct engineering call.</description><pubDate>Sun, 26 Jul 2026 00:00:00 GMT</pubDate></item><item><title>Keeping a Vision Pipeline Honest: Three Hallucination Guards I Built for a Video-Captioning Agent</title><link>https://goodomens.studio/blog/keeping-a-vision-pipeline-honest-three-hallucination-guards-i-built-for-a-video-captioning-agent</link><guid isPermaLink="true">https://goodomens.studio/blog/keeping-a-vision-pipeline-honest-three-hallucination-guards-i-built-for-a-video-captioning-agent</guid><description>How Dragon Commentary Studio separates perception from schema, filters OCR by consensus, and gates every caption through a critic loop.</description><pubDate>Sun, 19 Jul 2026 00:00:00 GMT</pubDate></item><item><title>I Trained a Board-Game AI From Zero Using Self-Play (and Broke It Several Times First)</title><link>https://goodomens.studio/blog/i-trained-a-board-game-ai-from-zero-using-self-play-and-broke-it-several-times-first</link><guid isPermaLink="true">https://goodomens.studio/blog/i-trained-a-board-game-ai-from-zero-using-self-play-and-broke-it-several-times-first</guid><description>How Caro5&apos;s bot went from a frozen laptop and a useless first model to a generate → train → arena → promote loop running on Modal.</description><pubDate>Sun, 12 Jul 2026 00:00:00 GMT</pubDate></item><item><title>RLHF and Beyond: How Reinforcement Learning Taught Language Models to Behave</title><link>https://goodomens.studio/blog/rlhf-and-beyond-how-reinforcement-learning-taught-language-models-to-behave</link><guid isPermaLink="true">https://goodomens.studio/blog/rlhf-and-beyond-how-reinforcement-learning-taught-language-models-to-behave</guid><description>A base language model fresh out of pretraining is a strange creature. It has read a large fraction of the internet and can continue any text with uncanny fluency — but it isn&apos;t trying to help you.</description><pubDate>Sun, 05 Jul 2026 00:00:00 GMT</pubDate></item><item><title>Policy Gradients and PPO: Learning Behavior Directly</title><link>https://goodomens.studio/blog/policy-gradients-and-ppo-learning-behavior-directly</link><guid isPermaLink="true">https://goodomens.studio/blog/policy-gradients-and-ppo-learning-behavior-directly</guid><description>DQN taught me one way to act intelligently: learn the value of every action, then pick the best one. But there&apos;s a second lineage in reinforcement learning with the opposite philosophy — skip the values, and optimize the behavior itself.</description><pubDate>Sun, 28 Jun 2026 00:00:00 GMT</pubDate></item><item><title>DQN: The Moment Reinforcement Learning Met Deep Learning</title><link>https://goodomens.studio/blog/dqn-the-moment-reinforcement-learning-met-deep-learning</link><guid isPermaLink="true">https://goodomens.studio/blog/dqn-the-moment-reinforcement-learning-met-deep-learning</guid><description>In 2013, a small London startup called DeepMind posted a paper showing a single algorithm learning to play Atari games — from raw pixels, with no game-specific knowledge, using only the score as feedback.</description><pubDate>Sun, 21 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Reinforcement Learning from Zero: Agents, Rewards, and the Loop That Learns from Consequences</title><link>https://goodomens.studio/blog/reinforcement-learning-from-zero-agents-rewards-and-the-loop-that-learns-from-consequences</link><guid isPermaLink="true">https://goodomens.studio/blog/reinforcement-learning-from-zero-agents-rewards-and-the-loop-that-learns-from-consequences</guid><description>Supervised learning felt intuitive to me from day one: here&apos;s the input, here&apos;s the right answer, minimize the difference.</description><pubDate>Sun, 14 Jun 2026 00:00:00 GMT</pubDate></item><item><title>Below Four Bits: QAT, BitNet, and the Race Toward One-Bit Intelligence</title><link>https://goodomens.studio/blog/below-four-bits-qat-bitnet-and-the-race-toward-one-bit-intelligence</link><guid isPermaLink="true">https://goodomens.studio/blog/below-four-bits-qat-bitnet-and-the-race-toward-one-bit-intelligence</guid><description>Everything in this series so far has been post-training quantization: take a finished model, compress it, hope the damage is small.</description><pubDate>Sun, 07 Jun 2026 00:00:00 GMT</pubDate></item><item><title>GGUF and the Art of Choosing a Quant: A Field Guide to Local Models</title><link>https://goodomens.studio/blog/gguf-and-the-art-of-choosing-a-quant-a-field-guide-to-local-models</link><guid isPermaLink="true">https://goodomens.studio/blog/gguf-and-the-art-of-choosing-a-quant-a-field-guide-to-local-models</guid><description>Open any popular model&apos;s page on Hugging Face and you&apos;ll find the GGUF listings: Q4KM, Q5KS, Q6K, Q80, IQ2XS — a wall of cryptic suffixes, each a different point on a size-quality curve, downloaded millions of times by people running models on gaming PCs an…</description><pubDate>Sun, 31 May 2026 00:00:00 GMT</pubDate></item><item><title>The Outlier Problem: The Discovery That Shaped Modern Quantization</title><link>https://goodomens.studio/blog/the-outlier-problem-the-discovery-that-shaped-modern-quantization</link><guid isPermaLink="true">https://goodomens.studio/blog/the-outlier-problem-the-discovery-that-shaped-modern-quantization</guid><description>Here&apos;s a puzzle that stumped the field around 2022. Quantization recipes that worked beautifully on small language models — clean INT8, minimal quality loss — fell off a cliff on big ones.</description><pubDate>Sun, 24 May 2026 00:00:00 GMT</pubDate></item><item><title>Quantization From the Bits Up: How 4-Bit Models Actually Work</title><link>https://goodomens.studio/blog/quantization-from-the-bits-up-how-4-bit-models-actually-work</link><guid isPermaLink="true">https://goodomens.studio/blog/quantization-from-the-bits-up-how-4-bit-models-actually-work</guid><description>Every series I&apos;ve written so far has bumped into quantization from the side — QLoRA compressing frozen bases, the optimization guide&apos;s memory math, GGUF files on Hugging Face with cryptic suffixes.</description><pubDate>Sun, 17 May 2026 00:00:00 GMT</pubDate></item><item><title>Life After Training: Merging LoRAs, Stacking Them, and Serving a Hundred Fine-Tunes on One GPU</title><link>https://goodomens.studio/blog/life-after-training-merging-loras-stacking-them-and-serving-a-hundred-fine-tunes-on-one-gpu</link><guid isPermaLink="true">https://goodomens.studio/blog/life-after-training-merging-loras-stacking-them-and-serving-a-hundred-fine-tunes-on-one-gpu</guid><description>The first three articles in this series were about making an adapter. This one is about what makes adapters a genuinely different kind of artifact from a fine-tuned model: what you can do with them afterward.</description><pubDate>Sun, 10 May 2026 00:00:00 GMT</pubDate></item><item><title>The LoRA Knobs: Rank, Alpha, Targets, and the Settings Everyone Copies Without Understanding</title><link>https://goodomens.studio/blog/the-lora-knobs-rank-alpha-targets-and-the-settings-everyone-copies-without-understanding</link><guid isPermaLink="true">https://goodomens.studio/blog/the-lora-knobs-rank-alpha-targets-and-the-settings-everyone-copies-without-understanding</guid><description>Every LoRA config file has the same handful of lines — r, alpha, dropout, targetmodules, learning rate — and almost everyone (me included, at first) fills them in by copying a config that worked for someone else.</description><pubDate>Sun, 03 May 2026 00:00:00 GMT</pubDate></item><item><title>QLoRA: The Paper That Put Large-Model Fine-Tuning on a Desk</title><link>https://goodomens.studio/blog/qlora-the-paper-that-put-large-model-fine-tuning-on-a-desk</link><guid isPermaLink="true">https://goodomens.studio/blog/qlora-the-paper-that-put-large-model-fine-tuning-on-a-desk</guid><description>LoRA solved half the memory problem: with adapters, the trainable state — gradients and optimizer moments — collapses to almost nothing.</description><pubDate>Sun, 26 Apr 2026 00:00:00 GMT</pubDate></item><item><title>LoRA: Why Fine-Tuning a Billion-Parameter Model Only Takes a Few Million Parameters</title><link>https://goodomens.studio/blog/lora-why-fine-tuning-a-billion-parameter-model-only-takes-a-few-million-parameters</link><guid isPermaLink="true">https://goodomens.studio/blog/lora-why-fine-tuning-a-billion-parameter-model-only-takes-a-few-million-parameters</guid><description>Full fine-tuning of a large language model is brutally expensive — not because of the forward pass, but because training multiplies memory.</description><pubDate>Sun, 19 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Building a Fine-Tuning Dataset: The Practical Craft Nobody Writes Papers About</title><link>https://goodomens.studio/blog/building-a-fine-tuning-dataset-the-practical-craft-nobody-writes-papers-about</link><guid isPermaLink="true">https://goodomens.studio/blog/building-a-fine-tuning-dataset-the-practical-craft-nobody-writes-papers-about</guid><description>The first three articles in this series were about the grand stuff — trillion-token corpora, scaling economics, synthetic generation.</description><pubDate>Sun, 12 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Synthetic Data: Training Models on Text That Models Wrote</title><link>https://goodomens.studio/blog/synthetic-data-training-models-on-text-that-models-wrote</link><guid isPermaLink="true">https://goodomens.studio/blog/synthetic-data-training-models-on-text-that-models-wrote</guid><description>There&apos;s an idea that would have sounded like a joke a few years ago: take a language model, have it write its own training data, and train the next model on it.</description><pubDate>Sun, 05 Apr 2026 00:00:00 GMT</pubDate></item><item><title>Quality vs. Quantity: The Data Question That Keeps Changing Its Answer</title><link>https://goodomens.studio/blog/quality-vs-quantity-the-data-question-that-keeps-changing-its-answer</link><guid isPermaLink="true">https://goodomens.studio/blog/quality-vs-quantity-the-data-question-that-keeps-changing-its-answer</guid><description>Every training run begins with a budget question: you have finite compute and finite data — what maximizes capability?</description><pubDate>Sun, 29 Mar 2026 00:00:00 GMT</pubDate></item><item><title>From Raw Web to Training Corpus: The Unglamorous Pipeline Behind Every LLM</title><link>https://goodomens.studio/blog/from-raw-web-to-training-corpus-the-unglamorous-pipeline-behind-every-llm</link><guid isPermaLink="true">https://goodomens.studio/blog/from-raw-web-to-training-corpus-the-unglamorous-pipeline-behind-every-llm</guid><description>When I started learning ML, I assumed the hard part was the model — architectures, optimizers, all the math I&apos;ve written about so far.</description><pubDate>Sun, 22 Mar 2026 00:00:00 GMT</pubDate></item><item><title>Embeddings: How Meaning Becomes Geometry</title><link>https://goodomens.studio/blog/embeddings-how-meaning-becomes-geometry</link><guid isPermaLink="true">https://goodomens.studio/blog/embeddings-how-meaning-becomes-geometry</guid><description>Of all the ideas in machine learning, this is the one that genuinely changed how I see the field: neural networks turn meaning into geometry.</description><pubDate>Sun, 15 Mar 2026 00:00:00 GMT</pubDate></item><item><title>Scaling Laws: The Physics of Machine Learning</title><link>https://goodomens.studio/blog/scaling-laws-the-physics-of-machine-learning</link><guid isPermaLink="true">https://goodomens.studio/blog/scaling-laws-the-physics-of-machine-learning</guid><description>Most of machine learning research is messy and empirical — try something, see if the benchmark goes up. Then, in 2020, a team at OpenAI published a result with a completely different character.</description><pubDate>Sun, 08 Mar 2026 00:00:00 GMT</pubDate></item><item><title>The Transformer, Explained the Way I Wish Someone Had Explained It to Me</title><link>https://goodomens.studio/blog/the-transformer-explained-the-way-i-wish-someone-had-explained-it-to-me</link><guid isPermaLink="true">https://goodomens.studio/blog/the-transformer-explained-the-way-i-wish-someone-had-explained-it-to-me</guid><description>In 2017, a paper with the almost arrogant title &quot;Attention Is All You Need&quot; replaced decades of accumulated architecture research with one mechanism.</description><pubDate>Sun, 01 Mar 2026 00:00:00 GMT</pubDate></item><item><title>How Models Actually Learn: Gradient Descent and Backpropagation from First Principles</title><link>https://goodomens.studio/blog/how-models-actually-learn-gradient-descent-and-backpropagation-from-first-principles</link><guid isPermaLink="true">https://goodomens.studio/blog/how-models-actually-learn-gradient-descent-and-backpropagation-from-first-principles</guid><description>Every impressive thing a neural network does — writing code, recognizing faces, translating languages — comes down to one shockingly simple loop repeated billions of times: make a guess, measure how wrong it was, nudge every parameter a tiny bit in the dire…</description><pubDate>Sun, 22 Feb 2026 00:00:00 GMT</pubDate></item></channel></rss>