#ML engineering best practices
387 related articles
·4 min
Heretic Uncensoring Technology Explained: Practical Applications on Jamba2 and Qwen3.5 Open-Source Models
Deep dive into Heretic uncensoring technology applied to Jamba2-Mini, Qwen3.5-9B, and 27B open-source models, exploring how refusal rates dropped from 97% to 4% and the safety debates involved.
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·3 min
Go Team Proposes Generic Collection Library 'container': A Deep Dive into the Standard Library Completion Plan
The Go team proposes new generic collection types under container/, including Set, ordered Map, queues, and more. A deep dive into the proposal and its design trade-offs.
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·5 min
Customer.io Summer Release Breakdown: Geofencing, Live Notifications, and Multi-Channel Engagement Get a Major Upgrade
Deep dive into Customer.io's major summer release: geofencing triggers, live notifications, flexible SMS providers, notification inbox, and WhatsApp management upgrades for unified multi-channel engagement.
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·2 min
NexaLibre Review: A One-Click Deployment and Hosting Platform for AI-Generated Code
NexaLibre is a one-click deployment platform for AI-generated code, featuring built-in HTTPS, auto backups, and custom domains. This review covers its features, positioning, and market potential.
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·3 min
Can Talking Like a Caveman Save 65% on Tokens? An In-Depth Analysis
Can caveman-style minimal prompts save 65% on Tokens? We analyze task quality, hidden cost transfers, and model robustness to reveal the right Token optimization strategies.
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·3 min
The Zero Error Rate Illusion in ML Models: How Data Scientists Can Manage Stakeholder Expectations
Why do stakeholders expect zero error rates from ML models? This article explores the cognitive gap between deterministic thinking and probabilistic reality, and provides practical strategies for data scientists to manage expectations.
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·3 min
The Zero Error Rate Illusion in ML Models: How Data Scientists Can Manage Stakeholder Expectations
Why do stakeholders expect zero error rates from ML models? This article explores the cognitive gap between deterministic thinking and probabilistic systems, and provides practical strategies for data scientists to manage expectations.
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·2 min
GitHub Copilot Stacked Sessions: A New Paradigm for Refactoring Legacy Code
Deep dive into GitHub Copilot's Stacked Sessions and Pull Requests for systematically refactoring legacy code, elevating AI-assisted programming to task-level collaboration.
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·2 min
LangChain or LangGraph? Choosing the Right Framework for Production-Grade AI Applications
In-depth analysis of core differences between LangChain and LangGraph, exploring why more teams are migrating to LangGraph for production AI apps, with framework selection guidance.
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·2 min
LangChain or LangGraph? Framework Choices for Production-Grade AI Applications
In-depth analysis of LangChain vs LangGraph differences, why teams are migrating to LangGraph for production AI apps, and framework selection guidance based on project complexity.
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·3 min
localskills.sh: A Unified Management Platform for Cross-Tool AI Skills and MCP – Full Review
localskills.sh is a team-level platform for managing AI Skills, Rules, and MCP servers across Cursor, Claude Code, and Windsurf with a single install command.
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·3 min
localskills.sh: A Unified Management Platform for Cross-Tool AI Skills and MCP — Review
localskills.sh is a team-level AI skill and MCP server management platform that unifies distribution and reuse of AI Skills and Rules across Cursor, Claude Code, Windsurf, and more with a single install command.
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·3 min
Long Documents Don't Work: Why AI Agents Can't Follow Your Rules
Analysis of why AI Agents can't reliably follow long policy documents, covering context dilution, rule conflicts, and soft constraint limitations, with more reliable governance architectures.
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·4 min
A Guide to Full-Stack Machine Learning Project Repository Structure: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and externalized configuration to help ML developers move from experimental code to production-grade engineering standards.
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·4 min
Full-Stack Machine Learning Project Repository Structure Guide: From Chaos to Convention
A detailed guide to organizing full-stack ML project repositories, covering directory structure design, data-code separation, and configuration externalization to help ML developers move from experimental code to production-grade engineering.
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·3 min
Anthropic Breaks HAWK-256: A Security Wake-Up Call for Post-Quantum Signature Schemes
Anthropic publishes a practical key-recovery attack on HAWK-256, exposing vulnerabilities in post-quantum signature schemes and implications for PQC standardization.
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·4 min
What It Feels Like When Production Actually Runs Like Localhost
Why does production never match local? This article analyzes root causes like config gaps and dependency drift, and explores how Docker, Twelve-Factor App, and IaC practices bridge the dev-prod divide.
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·3 min
Kimi K3 Hands-On Tutorial: Claude Code Sub-Agent Configuration & GPT-5.6 Comparison Test
Complete guide to configuring Kimi K3 as a Claude Code sub-agent, including API setup, YFlow workflow building, and head-to-head comparison with GPT-5.6 and Claude Fable 5.
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·5 min
Loop Engineering Explained: The New Programming Paradigm Where AI Prompts Itself
Loop Engineering is a paradigm shift in AI usage. Learn how to build automated loops where agents explore, execute, and verify tasks autonomously, with a hands-on e-commerce case study.
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·6 min
Harness Engineering: From Context Engineering to the Third Phase of AI Programming
Explore Harness Engineering: the next evolution beyond context engineering for AI programming. Learn how to build enterprise-grade Skill systems and deliver real projects with mid-tier models.
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