Small Language Models with Hugging Face transformers Library + smolLM3
Running a 70B model in production is expensive, and for many tasks, unnecessary. If you're building a focused pipeline, a well-trained 3B model will match or beat the 70B on your specific task at a fraction of the cost.
Learn how to use generative AI at work, build RAG and agentic apps, fine-tune models, work with the Hugging Face ecosystem, and prototype AI products with hands-on resources.
My Fall-Detection Model Scored 94%, and It Was Lying to Me
How a single evaluation choice inflated my results by 25 points, and what rebuilding honestly taught me about ML systems people might depend on
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I Built an AI Data Agent Which Can Query Data and Answer Business Questions. Here’s How.
A step-by-step guide to building a data agent and conversational interface that let business users to explore data in natural language without SQL
The post I Built an AI Data Agent Which Can Query Data and Answer Business Questions. Here’s How. appeared first on Towards Data Science.
I Built a Tool-Calling Agent in Python. Here’s How I Debugged It
A minimal loop with real API calls, validation, compact outputs, and trace evidence before adding an agent framework
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Top 5 Claude Skills for Writing (Ranked by GitHub Stars)
Search “best Claude Skills for writing” and you get lists padded with skills that write commit messages and internal status reports. Useful things. Not writing. This list only includes repositories that exist for writing. Every entry is a repository whose entire reason for being is writing or editin...
Is This Slop? Detecting AI-Generated Content Without a Model
Research-backed cues to detect LLM-generated text along with the mathematical intuition as to 'why'
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Building Document Structure with Loop Engineering: Recovering a PDF’s Outline from Body Typography for RAG
Enterprise Document Intelligence [Vol.1 #5octies] - Rules propose, LLM validates: six deterministic signals on span-level typography surface heading candidates, one bounded loop keeps the real ones, and the same toc_df drops back into the RAG pipeline
The post Building Document Structure with Loop E...
A practical guide to Bronze, Silver and Gold, with a working Python and DuckDB example
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This chapter is divided into eight parts; they are: • Metrics for LLM Inference • Measuring a Single Request • Warmup and Synchronization • Measuring GPU Work with CUDA Events • Measuring Memory Usage • Measuring Concurrent Requests • Multiple GPUs and Multiple Machines • Cost per Token The most com...
7 Approaches to Reduce Inference Latency in Your LLM Workflows
From quantization to speculative decoding, here are seven engineering strategies to ship faster, more responsive generative AI applications in production.
Prompt, Context, Loop: The Three Engineering Layers Every RAG System Is Built On
Enterprise Document Intelligence [Vol.1 #M2] - Every RAG system is built in three engineering layers stacked on one LLM call: prompt (the call itself), context (what fills the model’s window), loop (when the next call fires and when it stops). Knowing which layer you are standing on is half of build...
Read about MiniMax's own architecture, and see how it runs a real task against the actual API. Learn the pieces of the MiniMax story that weren't covered in the launch post.
This chapter is divided into nine parts; they are: • Reading Logits from a Model • Greedy Decoding • Temperature Sampling • Top-$k$ Sampling • Nucleus Sampling • Repetition Penalties • Beam Search • Stop Conditions • Structured Output Constraints The model returns a vector of logits for every positi...
How use Claude to craft an outstanding resume that lands offers
The post How Claude Help Me Build My $200k+ ML Resume appeared first on Towards Data Science.
I Replaced a 15-Minute Booking Process with a LangGraph AI Agent
A step-by-step guide to building, running, and monitoring a stateful customer support agent using Python, LangGraph, and Langfuse.
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Agentic Misalignment Explained: When AI Agents Go Rogue
Imagine hiring an AI assistant to handle important tasks, only to find that it quietly ignores your instructions because it believes it knows better. This is known as agentic misalignment, where an AI intentionally pursues its own objective instead of the one set by its operator. To understand how o...
Coding Agents Don’t Need Bigger Context Windows — They Need a Context Compiler
Most coding agents treat prompt construction like retrieval: gather more files, add more context, hope the model figures it out. But that approach breaks down fast. As context grows, irrelevant code competes for attention, and when the window fills, agents start compressing their own memory—often mi...