Building My Own Personal AI Assistant: A Chronicle, Part 2
Building a personal AI assistant is rarely a single, monolithic effort. In this piece, I walk through my latest addition: a task breaker module that decomposes complex goals into structured, actionable steps — and why that single component changed how I think about AI-driven productivity.
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memweave: Zero-Infra AI Agent Memory with Markdown and SQLite — No Vector Database Required
The problem with agent memory today
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Introduction to Deep Evidential Regression for Uncertainty Quantification
Machine learning models can be confident even when they shouldn't be. This article introduces Deep Evidential Regression (DER), a method that lets neural networks rapidly express what they don't know.
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Mastering Deep Agents: Context Engineering that Actually Works
Deep Agents can plan, use tools, manage state, and handle long multi-step tasks. But their real performance depends on context engineering. Poor instructions, messy memory, or too much raw input quickly degrade results, while clean, structured context makes agents more reliable, cheaper, and easier ...
5 Practical Tips for Transforming Your Batch Data Pipeline into Real-Time: Upcoming Webinar
Bringing your batch pipeline to real-time requires careful consideration. This post brings you five practical tips to make the most of your modernization efforts. Join us for an upcoming webinar to learn even more.
The post 5 Practical Tips for Transforming Your Batch Data Pipeline into Real-Time: U...
From OpenStreetMap to Power BI: Visualizing Wild Swimming Locations
How to turn OpenStreetMap data into an interactive map of wild swimming spots using Overpass API and Power BI.
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RAG Isn’t Enough — I Built the Missing Context Layer That Makes LLM Systems Work
Most RAG tutorials focus on retrieval or prompting. The real problem starts when context grows. This article shows a full context engineering system built in pure Python that controls memory, compression, re-ranking, and token budgets — so LLMs stay stable under real constraints.
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Data Modeling for Analytics Engineers: The Complete Primer
The best data models make it hard to ask bad questions and easy to answer good ones.
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We have come far in our series of Excel 101, exploring various functions and formulas of the service and how best to use them in real-world scenarios. For those who are new to this, make sure to check out the complete list of Excel functions that we have covered so far in the links shared […]
The po...
MiniMax M2.7 Goes Open-Weight to Let You Run Agents Locally
Following in the footsteps of the recently released Gemma 4, MiniMax has now made its latest model, MiniMax M2.7, completely open-weight. In simple terms, developers can now download the model, run it on their own systems, and start building with it. This is in contrast with the model being a comple...
Your Model Isn’t Done: Understanding and Fixing Model Drift
How production models fail over time, and how to catch and fix it before it breaks trust.
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By compiling a simple program directly into transformer weights.
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Why storing and retrieving data isn’t enough to build reliable AI memory systems
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A deep-dive and practical guide to cross-encoders, advanced techniques, and why your retrieval pipeline deserves a second pass.
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Introduction to Reinforcement Learning Agents with the Unity Game Engine
A step-by-step interactive guide to one of the most vexing areas of machine learning.
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Understanding BERTopic: From Raw Text to Interpretable Topics
Topic modeling uncovers hidden themes in large document collections. Traditional methods like Latent Dirichlet Allocation rely on word frequency and treat text as bags of words, often missing deeper context and meaning. BERTopic takes a different route, combining transformer embeddings, clustering, ...
From Karpathy’s LLM Wiki to Graphify: AI Memory Layers are Here
Most AI workflows follow the same loop: you upload files, ask a question, get an answer, and then everything resets. Nothing sticks. For large codebases or research collections, this becomes inefficient fast. Even when you revisit the same material, the model rereads it from scratch instead of build...
When Things Get Weird with Custom Calendars in Tabular Models
Since September 2025, we have had Calendar-based Time Intelligence in Power BI and Fabric Tabular models. While this feature offers great possibilities, we must be aware of its pitfalls. Here are some of them.
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AI can feel like a maze sometimes. Everywhere you look, people on social media and in meetings are throwing around terms like LLMs, agents, and hallucinations as if it’s all obvious. But for most people, it just feels confusing. The good news is, AI isn’t nearly as complicated as it sounds once you...
A long-form article featuring over 100 visualizations, covering a range of topics from how to build linear regression model, measure the quality and how to improve the model
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