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teds read --tag ai-engineering
AI Engineering
Practical posts on model selection, prototypes, evaluation, AI product decisions, and the path from experiment to useful system.
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Why Your AI Demo Feels Magical but Useless
How to diagnose the gap between impressive AI demos and reliable AI products by looking at failure modes, workflows, evaluation, and trust.
BlogWhy Open Weight Models Matter for Small Businesses
Open weight models give small businesses capable AI without per-token pricing, vendor lock-in, or sending sensitive data to a third party. Deploying them well is a different problem.
BlogWhy DevRel Is Different for Research and ML Tools
Developer Relations changes when the audience is researchers, PhD scientists, machine learning engineers, and data scientists because adoption depends on evidence, reproducibility, workflow fit, and technical trust.
BlogWhen Your AI Product Needs DevRel and When It Doesn't
A decision guide for AI founders deciding whether they need DevRel now, technical content first, developer experience repair, or a clearer product strategy.
BlogWhat AI Builders Can Learn from Painters
A practical essay on why models are colors, not masterpieces, and why AI products need composition, constraints, technique, and taste.
BlogTeaching PaliGemma 2 to Spot Construction-Site Hazards
Fine-tune PaliGemma 2 with Transformers 5, QLoRA, and a construction safety dataset so a vision-language model can return object labels and bounding boxes for job-site hazards.
BlogStop Asking Can AI Do This
A practical decision guide for replacing capability-first AI thinking with better questions about value, risk, evaluation, and workflow fit.
BlogEAGLE-3 Speculative Decoding in vLLM for Qwen3-30B-A3B-Instruct-2507
A current guide to EAGLE-3 speculative decoding with vLLM, using Qwen3-30B-A3B-Instruct-2507, a matching Red Hat AI speculator, and a tokenizer sanity check that works in Transformers 5.12.1.
BlogPreference-Aligning Vision Models: DPO Fine-Tuning Qwen3.5-4B in Transformers 5.12.1
A modern rewrite of an older SmolVLM notebook: align Qwen3.5-4B with Direct Preference Optimization (DPO), LoRA adapters, and current Transformers + TRL APIs.
BlogMultimodal AI Changes the Shape of Software
How text, image, audio, video, and document models change product design by letting users ask, point, upload, inspect, and correct.
BlogModern Visual Question Answering Fine-Tuning: Qwen3-VL-4B with QLoRA in Transformers 5.12.1
A current replacement for an older PaliGemma notebook: fine-tune Qwen3-VL-4B on a small VQAv2 split with QLoRA, modern Transformers APIs, and a cleaner multimodal training loop.
BlogModerating Memes with Qwen2.5-VL: Zero-Shot Hateful Content Detection in Transformers 5.12.1
A cleaned-up multimodal moderation walkthrough that uses Qwen2.5-VL with Transformers 5.12.1, the Hateful Memes validation split, and a reproducible zero-shot evaluation script.
BlogLet AutoScheme Pick the GGUF: Mixed Quantization with AutoRound and Qwen3.6
A clean, current tutorial for exporting mixed-quantized GGUF files from Qwen3.6 with AutoRound, AutoScheme, llama.cpp, and Transformers 5.12.1.
BlogHuman Preference Tuning for Small VLMs: SmolVLM2 + DPO in Transformers 5.12.1
A modern guide to preference-tuning SmolVLM2 with Direct Preference Optimization, TRL, PEFT LoRA adapters, and the current Transformers image-text API.
BlogHow to Know If Generative AI Fits Your Problem
A practical decision tree for choosing between prompting, RAG, fine-tuning, vision-language models, traditional ML, rules, or no AI.
BlogHow to Evaluate an AI Agent (Before It Ships)
Agent evaluation is not LLM evaluation. A practical three-layer framework — component, trajectory, outcome — for evaluating AI agents before they reach production, plus a maturity model to figure out where your team is and what to build next.
BlogHow to Evaluate a Technical Writer for AI Content
A practical scorecard for hiring or contracting AI technical writers who can write accurate tutorials, demos, explainers, and content developers trust.
BlogThe Hidden Work Behind Just Add AI
A reality check on the operational work behind AI features: data, evaluation, reliability, UX, safety, compliance, monitoring, and trust.
BlogGenerative vs Discriminative AI, Explained for Builders
A practical explanation of when to use generative models, discriminative models, retrieval, ranking, rules, or hybrid systems.
BlogThe Generative AI Stack, Explained Without Vendor Fog
A plain-English map of the generative AI stack: models, data, retrieval, orchestration, evaluation, deployment, UX, and security.
BlogGenerative AI Is Not the Product
Why model capability is not product value, and how AI teams can move from impressive demos to useful workflows people trust.
BlogThe Future of AI Belongs to Teams That Can Explain It
Why AI teams need to build, break, and explain their systems so users, developers, buyers, and communities can trust and adopt them.
BlogFrom Prompts to Practice: Instruction Tuning Qwen3 with Transformers 5
A modern walkthrough for instruction tuning a small Qwen3 instruct model with Hugging Face Transformers, TRL, PEFT, QLoRA, and chat templates.
BlogFrom Prompt to Product
A practical lifecycle for turning a prompt experiment into a reliable AI workflow with evaluation, feedback, production constraints, and launch proof.
BlogFrom Prompt to Pixels: Generate Images with Qwen-Image, Diffusers, and Transformers
A current text-to-image tutorial using Qwen-Image, Hugging Face Diffusers, and the latest Transformers stack, with reproducible prompts, aspect ratios, seeds, and a fast FLUX.1-schnell fallback.
BlogFrom Model Selection to First Run
A practical workflow for choosing an initial AI model, running a baseline, inspecting outputs, and deciding what to test next.
BlogFrom Messy Labels to Production Boxes: Ultralytics YOLO26 + Transformers 5.12.1
A modern, reproducible object detection workflow that replaces legacy SuperGradients/YOLO-NAS setups with Ultralytics YOLO26 and current Transformers tooling.
BlogFrom Chat Window to Workflow: A Non-Technical Guide to AI Model APIs
A plain-language guide to AI model APIs for people who use chat windows but want to automate. Covers requests, responses, parameters, costs, and where to get hands-on practice.
BlogThe FRAME Method for Picking AI Projects
A practical framework for deciding which generative AI ideas are worth building before teams waste time on demos that never become products.
BlogThe First AI Experiment Should Be Small and Disposable
A practical TRIAL workflow for running early generative AI experiments that test assumptions instead of turning every demo into a product.
BlogTrain a Modern VLM on One GPU: Qwen3-VL with Unsloth and Transformers 5.12.1
A current vision-language fine-tuning guide that replaces an old Qwen2-VL notebook with a cleaner Qwen3-VL workflow, modern Transformers APIs, and a practical DocumentVQA example.
BlogFine-Tuning Qwen3.6-VL for Brain Tumor MRI Detection with Transformers 5
A practical, up-to-date tutorial for adapting Qwen3.6-VL to brain tumor MRI yes/no detection with Transformers 5, QLoRA, and reproducible multimodal training.
BlogFine-Tune ViTPose++ for Keypoint Detection with Transformers
A practical Transformers-native guide to fine-tuning ViTPose++ on COCO-style keypoints with generated heatmap targets, RT-DETR inference, and current pose-estimation caveats.
BlogFind What You Mean: Zero-Shot Visual Grounding with Qwen3-VL and Transformers
Use Qwen3-VL with the latest Transformers API to detect objects, ground natural-language phrases, and visualize bounding boxes from structured multimodal outputs.
BlogHow to Evaluate Generative AI When There Is No Single Right Answer
A practical guide to evaluating open-ended AI outputs with rubrics, comparative review, human feedback, automated checks, and regression sets.
BlogDETR Explained: Set Prediction Object Detection in Transformers 5.12.1
Learn DETR object detection with a modern Transformers 5.12.1 example, pinned requirements, and a clean explanation of set prediction, Hungarian matching, and object queries.
BlogDetect What You Can Name: PaliGemma 2 Object Detection with Transformers
A practical tutorial for using current PaliGemma 2 mix checkpoints with Transformers 5 to run prompt-driven object detection and draw bounding boxes from generated location tokens.
BlogCompress Qwen3.5 with AutoRound in Transformers 5.12.1
A current, practical guide to quantizing Qwen3.5 with AutoRound, using the modern Transformers 5.12.1 stack and a clean W4A16 workflow that scales from 0.8B to larger checkpoints.
BlogCoDeC Contamination Detection in Transformers 5.12.1 with Qwen3, Qwen2.5, and Gemma 3
A cleaned-up CoDeC walkthrough with a current Transformers 5.12.1 implementation, model-loading notes for Qwen3, Qwen2.5, and Gemma 3, and a practical scoring script.
BlogCaption by Consensus: Ranking Image Descriptions with SigLIP2 and Transformers
Use SigLIP2 with the latest Transformers APIs to score candidate image captions, choose the best description, and understand when a contrastive vision-language encoder is the right tool.
BlogYOLOv12 in Practice: A Real-Time Object Detection Guide
A practical guide to YOLOv12's attention-centric real-time detector, with a current install path, an inference example, and notes on the official detection, segmentation, and classification weights.
BlogThe AI Product Launch Checklist
A practical PROUD checklist for launching AI products with prototypes, production readiness, market fit, proof, feedback, and post-launch management.
BlogAI Coding for Internal Software Teams
Why non-tech companies with internal developers should train engineers to use AI coding tools with judgment instead of banning them or letting vibe coding run loose.
BlogAI Agents for Commercial Real Estate: From Deal Flow to Due Diligence
How autonomous AI agents — not chatbots — can compress the slowest parts of a commercial real estate practice: prospecting, underwriting, due diligence, zoning research, and reporting.
BlogThe Accidental AI Power User
Why the person using AI chat tools for Excel formulas, macros, reports, and internal workflows may be your company's next productivity multiplier.