A comprehensive guide to fine-tuning open language models locally on a Mac with Apple Silicon using MLX, covering setup, dataset preparation, LoRA training, quantization, testing, and deployment—all without cloud GPUs or costs.
MLX is an Apple-designed open-source array framework optimized for Apple Silicon's unified memory, enabling efficient on-device fine-tuning.
The MLX LM toolkit provides a full pipeline from installation to training, testing, and serving with simple commands.
This article covers five concrete agentic workflows, one for each major stage of a data science pipeline, from automated EDA to feature engineering, with code patterns and real-world scenarios.
Data scientists spend about 45% of their time on data preparation, which can be automated with agents.
The article provides five agentic workflows including automated EDA and feature engineering, with tested code.
This tutorial covers three methods to create Google Sheets using Gemini: within the spreadsheet via built-in integration, through the Gemini web app with export, and by generating Google Apps Script for advanced automation. It also includes tips for better results.
Gemini is an AI integration in Google Sheets that lets you create, populate, and analyze spreadsheets using natural language.
Method 1: Use Gemini's side panel inside Google Sheets to generate tables, formulas, and analysis via prompts.
Take a practical look at multimodal, any-to-any systems for vision-language reasoning, speech interaction, document intelligence, real-time assistants, local deployment.
Omni models are shifting from multiple specialized models to unified architectures that handle multiple input types.
NVIDIA Nemotron 3 Nano Omni excels in enterprise workflows like video analysis and document intelligence.
Local coding models are now mature, running on consumer GPUs with privacy and efficiency. This article reviews the best seven models, covering general coding, multimodal, reasoning, and more.
Qwen3.6 27B MTP is the best all-round local coding model for reasoning and agentic workflows.
Gemma 4 31B IT QAT supports multimodal inputs like screenshots and UI issues.
This article breaks down each essential math discipline (statistics, linear algebra, calculus, discrete math), explains its role in data science, and maps out an efficient learning path. It emphasizes that mathematical intuition, not just coding, is the true differentiator in an AI-driven job market.
Data science algorithms are mathematical operations; understanding the math is more important than writing code.
Statistics and probability are the most frequently used math areas in daily data science work.
WebMCP is an open web standard that lets websites expose structured, callable tools directly to browser-based agents, replacing unreliable pixel clicking and DOM scraping. This article covers its core concepts, two APIs, authentication breakthrough, and a real-world travel booking example.
WebMCP is a browser-native agent protocol co-developed by Google and Microsoft, with a draft published in Feb 2026 and Chrome 149 shipping enabled. It reduces task errors by 67% and improves completion rates by 45%.
It offers two APIs: Declarative (for HTML forms) and Imperative (for JavaScript), allowing websites to explicitly define tools with typed inputs and outputs.
An in-depth review of ChatLLM by Abacus AI, covering supported AI models, AI agents, coding tools, integrations, pricing, usage limits, and how it compares to ChatGPT.
ChatLLM provides access to dozens of top AI models including GPT, Claude, Gemini, Grok, and more under one subscription.
The platform offers multi-model chat, document analysis, image/video generation, coding, automation, and team collaboration.
Agentic AI is not failing because the technology is bad. It is failing because of five specific misconceptions that teams carry into their first deployments and each one is correctable.
Autonomy should be structured with human checkpoints at irreversible actions
Production success rates plummet due to compound error via Lusser's Law
The author explains the motivation behind building a custom AI assistant instead of using existing tools, detailing the architecture, tech stack, and implementation process including LLM, LangChain, memory management, and tool integration.
Building a custom AI assistant provides better control, data privacy, and workflow customization.
The stack includes GPT-4o, LangChain for orchestration, SQLite-backed persistent memory, and tools like DuckDuckGo search.
This article introduces five fun projects built with OpenAI Codex, ranging from simple apps to full-stack clones, showcasing Codex's versatility and usefulness in software development.
Five projects cover simple app, mobile app, startup prototype, full-stack clone, and 2D game.
Each project emphasizes using Codex as a coding partner through iterative improvements.
This article demonstrates building time-series machine learning models in Python using sktime, covering data preprocessing, forecasting pipeline construction, model evaluation, and cross-validation. Through a complete case study of industrial HVAC sensor temperature forecasting, it showcases sktime's scikit-learn-style API and how to handle time-series-specific structures like seasonality and trend.
sktime offers a scikit-learn-like API designed for time-series forecasting, classification, regression, and clustering.
TransformedTargetForecaster chains transformations such as imputation, detrending, and deseasonalization with a forecaster.
Local models in 2026 are good enough. For the tasks Claude Code handles daily: code completion, refactoring, debugging, codebase explanation; a well-chosen quantized model running locally covers the vast majority of real use cases at zero per-token cost and with no rate limits.
Local models are now viable for Claude Code, reducing costs and avoiding API rate limits.
Ollama, LM Studio, and llama.cpp natively support the Anthropic Messages API.
This article cuts through the 90,000 options to the seven models worth your time in 2026, including FLUX.1 Schnell, FLUX.1 Dev, FLUX.1 Kontext Dev, Stable Diffusion 3.5 Large, FLUX.2 Dev, Playground v2.5, and Kolors, with details on licenses, best uses, and trade-offs.
FLUX.1 Schnell is the only flagship model under Apache 2.0, enabling commercial use with fast generation.
FLUX.1 Kontext Dev excels at text-guided image editing while maintaining identity across iterations.
Explore the best Python web development repositories for building APIs, full-stack web apps, dashboards, machine learning demos, internal tools, and interactive Python-based user interfaces.
Python is widely used for web development beyond scripting and data science.
This article reviews 10 GitHub repositories covering frameworks for APIs, full-stack apps, dashboards, ML demos, and more.
A recent study reveals that delegating tasks to LLMs can silently corrupt documents. The DELEGATE-52 benchmark tested 19 models and found that even top models corrupt 25% of content after 20 interactions. Causes include compounding errors, deletion by weak models vs. hallucination by strong ones, context overload, and domain unfamiliarity. Agentic AI tools offer little remediation.
Delegating tasks to LLMs can lead to gradual document corruption, with top models corrupting 25% after 20 interactions and weaker models up to 50%.
Errors compound over time; weak models delete content while strong models hallucinate plausible but false information.
This guide covers the complete picture: what skills are technically, how to plan and design them, the exact file structure and naming rules, how to write instructions that Claude follows reliably, a complete working skill built from scratch, how to test and distribute, and what to do when things go wrong.
Claude Skills are folders with a SKILL.md file that use a three-level progressive disclosure system to minimize token usage.
Plan skills by identifying use cases, multi-step workflows, tools needed, and domain knowledge before writing any files.
This article covers five essential Python concepts that every AI engineer must know: tensors and autograd, the __call__ method, serialization (Pickle vs ONNX), abstract base classes, and environment configuration. Each concept is illustrated with clunky vs production-level code examples to help you build scalable, secure, and robust systems.
Tensors and Autograd: Leverage PyTorch's automatic differentiation to compute gradients without manual derivation.
The __call__ Method: Make Python class instances callable, enabling hooks and clean syntax in PyTorch models.
This article explores three post-hoc calibration methods—temperature scaling, Platt scaling, and isotonic regression—for improving the alignment between confidence and accuracy in large language models. It covers measurement metrics like ECE, challenges specific to LLMs, the impact of RLHF, and practical guidance for choosing the right method.
Temperature scaling works well for base models, but adaptive temperature scaling (ATS) is recommended for RLHF-tuned models.
Platt scaling is data-efficient and suitable for small calibration sets, but may degrade performance for strong models.
This article explains how AI agents are transforming data science workflows, automating routine tasks, and requiring new skills such as system design, tool integration, and agent observability. It covers frameworks like LangGraph, AutoGen, and smolagents, the shift from procedural to evaluative work, and emerging roles.
The agentic era is here: AI agents autonomously plan, execute multi-step tasks, and evaluate results, redefining data science.
Data scientists need new skills: system design, prompt engineering, tool design, agent observability, and multi-agent architecture.
This article breaks down 7 key steps to help you analyze and forecast time series data with Python, from understanding unique properties to deploying and monitoring models.
Time series data has unique properties like temporal dependence, stationarity, and seasonality that require special handling.
Master pandas time-aware data structures: DatetimeIndex, PeriodIndex, resampling, and rolling operations.
This article introduces five foundational papers on LLMs: Transformer architecture, GPT-3's in-context learning, scaling laws, RLHF instruction tuning, and retrieval-augmented generation (RAG), offering a systematic understanding of how modern LLMs work.
Attention Is All You Need introduces the Transformer with self-attention, the backbone of LLMs.
Language Models Are Few-Shot Learners shows GPT-3 performs tasks via in-context learning.
Explore 10 top open-source GitHub repositories for modern databases, analytics, SQL, caching, monitoring, replication, PostgreSQL, SQLite, and AI agent memory.
ClickHouse and DuckDB specialize in large-scale real-time analytics and embedded analytical SQL.
Supabase and Redis accelerate full-stack development and provide high-speed caching.
A step-by-step guide to generating a year's worth of daily temperature readings that mimic real seasonal patterns, using Mimesis, pandas, and NumPy for IoT sensor simulation.
Generate device metadata (ID, location, firmware) using Mimesis
Model seasonal temperature variation with a sine wave
This article dives deep into Ollama's configuration engine, covering how to fine-tune local language model parameters using the Modelfile, optimize hardware performance with server environment variables, and format prompt flows with Go template syntax.
The Ollama Modelfile is a declarative configuration file that defines model behavior, including base model, system instructions, and parameters.
Sampling parameters (temperature, Top-K, Top-P, Min-P) control the creativity and determinism of the model's outputs.