Good Architecture Deletes the Signals Your Agent Depends On
Every boundary you draw removes a signal your tooling was relying on. That is a structure problem, not a search problem.
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AI Slop Is in Your Training Dataset Now. I Tested Three Ways to Spot It.
My AI detectors flagged many genuine reviews, and filtering them made the sentiment model less accurate.
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Inside a transformer, token index is a coordinate. Paragraph structure is what turns it into a metric.
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10 Things I’m Learning Beyond AI to Become More Technologically Fluent
Part 1: Understanding the technologies shaping our future
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Autoregressive rollout and uncertainty propagation. Second in a series on probabilistic forecasting for physical signals.
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RAG Isn't an Agent — I Built the Layer Between Retrieval and Action
RAG retrieves. Agents act. I built both separately, connected them explicitly, and ran the same nine tasks through all three systems.
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Beyond RAGs: Building Actually Truthful AI Harnesses
Retrieval is not evidence. How to build AI that proves its own claims.
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When the Correct Answer Is Nothing, What Does Your Pipeline Return?
The reliability mechanisms we add to LLM pipelines are often the ones that make them confidently wrong.
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I Trained a Tiny Network to Compress Data. It Drew a Pentagon.
Reproducing Anthropic's "Toy Models of Superposition" from scratch in NumPy, with hand-derived gradients and no borrowed numbers.
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How GRPO Trains Small Language Models with Verifiable Rewards
The mechanics behind local reasoning experiments with Unsloth and why the reward function matters as much as the model.
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Learn how to effectively code up an internal tool using Claude code or Codex
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Exploring TypeSafe's Jev and comparing it with OpenAI on intent classification
The post A New Kind of Model for AI Decision-Making? appeared first on Towards Data Science.
GPT-6 Astra Just Hit OpenAI's Highest Cybersecurity Risk Level
Why this matters less for what Astra can do and more for what every other model hasn't been tested for
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Your AI Assistant Wrote the Code. Who Checked the Defaults?
Five scikit-learn defaults that deserve a closer look before your next model reaches production
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PyTorch and TensorFlow tensor broadcasting: how silent shape errors cause difficult-to-debug machine learning bugs
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How Many Labeled Examples Does a Text Classifier Actually Need? I Measured It.
Before reaching for an LLM API on every classification problem, it's worth knowing what a decades-old baseline can already do with the labeled data you have — and exactly how much more data buys you.
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First in a series on probabilistic forecasting for physical signals. Next: what happens when you roll the forecast forward more than one step.
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When to Use One Model and When to Use a Team of Agents
When Codex is the right shape for the problem, when Claude Code is, and how I split 5 specialist agents between them on dense AI capacity work.
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Graph Engineering for AI Agents: From Prompts and Loops to Workflows
A viral debate over loops versus graphs points to a bigger shift in how we build AI systems. Here’s what graph engineering actually means, how it differs from prompt, context, and loop engineering, and why it matters.
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From Static to Dynamic Skills: A Different Model for Agent Knowledge
Why the skill-inflation panic is aimed at the wrong thing, and what it costs to make agent knowledge a build artifact instead of a file.
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Your Model Isn't Done Until Someone Else Can Call It
Building a FastAPI endpoint for churn prediction, and everything that broke between "it runs" and "it's live
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A practitioner's guide to estimating what an opt-in AI feature actually did, when nobody randomized it.
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One Capital Letter Was Silently Breaking My AI Support Bot, and It Wasn't in the New Model
A real Weave project that regression-tests three OpenAI models against the exact reply format your app depends on.
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