OCOPD Lumen Anchor Protocol: Prompt Templates for Managing Long Gemini Sessions
A practical, evidence-aware guide to the Lumen Anchor Protocol, an original prompt-structuring framework for maintaining decisions, constraints, and open questions during long AI sessions. Includes reusable templates, checkpoint rules, controlled updates, and a baseline-versus-protocol test plan.
Read ArticleBuild a Reliable Webhook Consumer with Idempotency, Retries, and Signature Verification
Learn how to build a production-oriented webhook consumer that verifies signatures, handles duplicate deliveries, separates receipt from processing, retries transient failures, and records enough state to debug delivery problems.
Read ArticleFine-Tuning LLMs: When and How
A practical guide to deciding between prompting, RAG, and fine-tuning, preparing supervised datasets, running a controlled fine-tuning experiment, and evaluating whether the result is actually better.
Read ArticleDesigning an AI Agent Reasoning Loop: Prompts, Tools, State, and Stop Conditions
A practical prompt engineering guide for moving from a one-shot LLM response to a controlled agent loop that selects tools, observes results, updates state, and stops safely.
Read ArticleOpenHands Self-Hosting Guide: Run Autonomous AI Coding Agents Locally
A practical prompt engineering and deployment guide for self-hosting OpenHands 1.x with Docker sandboxes, scoped credentials, local repositories, and review-oriented agent instructions.
Read ArticleLoRA vs QLoRA vs Full Fine-Tuning: Cost, Quality, and When Each Makes Sense
A practical guide to choosing and implementing LoRA, QLoRA, or full model fine-tuning. Learn what each method changes, how VRAM and dataset quality drive the decision, and how to build a repeatable evaluation-first training workflow.
Read ArticleWhen an AI Agent Makes a Mistake in Production, Which Layer Should Stop It?
Build a layered AI agent safety architecture that turns model output into an untrusted proposal, then uses deterministic policy checks, scoped authorization, approval gates, idempotent execution, and audit records to prevent costly production actions.
Read ArticleAI Agent Approval Queue: Build vs Buy for Production Workflows
A practical evaluation of building, buying, or combining an AI-agent approval queue. It focuses on the production concerns hidden behind a simple pending-status table: immutable action versions, expiration, delegated authority, notification escalation, audit evidence, and safe execution after approval.
Read ArticleFireDucks vs pandas, Polars, cuDF, and Dask: When Pandas Acceleration Is Actually Worth It
FireDucks is a pandas-compatible, compiler-accelerated DataFrame library designed to speed up existing CPU-based pandas pipelines with a small import change. This comparison explains its lazy execution model, reported performance range, compatibility and observability trade-offs, and when Polars, cuDF, or Dask is a better fit.
Read ArticleWhat Are the Different Types of Fine-Tuning? A Practical Selection Guide for SFT, RFT, Distillation, and LoRA
A practitioner-focused guide to choosing among supervised fine-tuning, reinforcement fine-tuning with GRPO, distillation, and LoRA/QLoRA. Learn to decide based on the most important constraint: whether you can write a correct answer or only reliably judge one.
Read ArticlePractical Automated Feature Engineering in Python: Validating py-evoFE for Tabular ML
A practical workflow for evaluating and operationalizing py-evoFE-style evolutionary feature engineering without contaminating validation data, overfitting the search, or deploying opaque transformations that cannot be reproduced.
Read ArticleRunning a Local LLM Proxy: OpenAI-Compatible Gateways
A practical evaluation of OpenAI-compatible local LLM gateways, with a focus on LiteLLM-style proxies that let IDEs, agents, scripts, and internal tools reach local inference through one stable API endpoint.
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