A practitioner's argument that meeting summarizers fail in the same way regressions fail when you skip the part where you ask what the data can support.
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From tokenisation to evaluation : how modern language models actually work in practice
The post The Must-Know Topics for an LLM Engineer appeared first on Towards Data Science.
RAG Is Blind to Time — I Built a Temporal Layer to Fix It in Production
Three weeks into testing, a learner told me my AI tutor gave her the wrong answer.
Not obviously wrong — just outdated enough to mislead.
That was the moment I realized something most RAG systems quietly ignore: they have no sense of time. My system retrieved the most similar document, not the most ...
Agent Memory Patterns in Cognitive Science and AI Systems
Memory shapes how humans think and how AI agents act. Without it, an agent only responds to the current input; with it, it can keep context, recall past actions, and reuse useful knowledge. AI memory spans short-term, episodic, semantic, and long-term memory, each with different design trade-offs ar...
The AI Agent Security Surface: What Gets Exposed When You Add Tools and Memory
Standard prompt attacks are merely the beginning. A structured framework to map and mitigate the backend attack vectors of agentic workflows.
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When Customers Churn at Renewal: Was It the Price or the Project?
A practitioner's guide to causal attribution when two churn drivers arrive at once.
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Unified Agentic Memory Across Harnesses Using Hooks
How hook implementation gives Claude Code, Codex, and Cursor persistent memory via Neo4j, without locking you into any one of them.
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10 AI Agents Every AI Engineer Must Build (with GitHub Samples)
If you’re an aspiring AI engineer looking to sharpen your skills, building AI agents is one of the most effective ways to get hands-on experience. AI agents represent practical applications of AI across domains, from personal assistants and recommendation systems to financial traders. Here are 10 AI...
Feature Engineering with LLMs: Techniques & Python Examples
Feature engineering is the foundation of strong machine learning systems, but the traditional process is often manual, time-consuming, and dependent on domain expertise. While effective, it can miss deeper signals hidden in unstructured data such as text, logs, and user interactions. Large Language ...
How Major Reasoning Models Converge to the Same “Brain” as They Model Reality Increasingly Better
Because there's only one reality to model!
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I Rewrote a Real Data Workflow in Polars. Pandas Didn’t Stand a Chance.
From 61 seconds to 0.20 seconds — and the mental model shift I didn't expect
The post I Rewrote a Real Data Workflow in Polars. Pandas Didn’t Stand a Chance. appeared first on Towards Data Science.
When the Uncertainty Is Bigger Than the Shock: Scenario Modelling for English Local Elections
A scenario analysis case study on calibrated uncertainty, historical error, and why some models are most useful when they refuse to forecast.
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Anthropic’s 10 AI Agents are Redefining Finance Work
The headline may sound extreme here. Of course, Claude is not replacing CFOs tomorrow morning. But with the debut of Claude’s new Financial Services Solution by Anthropic, it has clearly moved to a new direction in the world of finance, one where AI does way more than crunch numbers or explain stuff...
Timer-XL: A Long-Context Foundation Model for Time-Series Forecasting
Exploring the inner workings of a decoder-only Transformer foundation model
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Building a RAG system just got much easier. Google’s File Search tool for the Gemini API now handles the heavy lifting of connecting LLMs to your data. Chunking, embedding, indexing are all managed for you. And with the latest update, it’s gone multimodal. You can now search through both text and im...
Discrete Time-To-Event Modeling – Predicting When Something Will Happen
Part 1: The basics — discretization of time, censoring and the life table
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