Digest #45: January 2026
January at Mad Devs is all about learning systems and how we work with them. From contrastive learning and transfer learning in AI, to practical writeups on OpenCode, spec-driven development, audits, and better prompts, this digest focuses on tools and ideas that make modern engineering smarter and safer.
Blog highlights: modern AI learning
This month's articles look at how AI learns from data and how teams can reuse powerful models instead of starting from scratch.
The Power of Contrastive Learning: From Theory to Real-World Applications
Contrastive learning moved from a niche research topic to one of the key ways modern models learn useful representations without heavy labeling. The article explains the core idea of teaching models to tell similar examples from different ones, then shows how this helps in real tasks like recommendation, retrieval, and anomaly detection.
➡️ You can also find a short LinkedIn recap with the main takeaways and why contrastive learning matters in practice.
Transfer Learning from Large Language Models (LLMs)
This guide explores how to use large language models as a foundation instead of training your own from scratch. It walks through pre-training, adapting models to specific tasks, and different transfer learning strategies, then looks at how teams can combine fine-tuning, prompting, tools, and system design to get value in real products.
➡️ We also shared this updated 2026 perspective on LinkedIn with a focus on the system-level approach.
Tech Journal: experiments for 2026 engineering
![[How to Generate 100 Landing Pages Overnight with OpenCode and Free AI Models]](https://images.prismic.io/superpupertest/aWEESwIvOtkhBOXd_generate-100-landing-pages-overnight-with-opencode.png?auto=format,compress&w=610&h=332)
A practical workflow for using OpenCode with free AI models to generate dozens of landing page variants overnight. The article shows how to set up OpenCode, run a prompt that produces many layout and copy options, and then turn the best results into a reusable "layout constitution" for future frontend work.
Spec-driven development works best when the full context of a project stays visible. This article introduces session traces as a way to track how specs evolve, how agents and humans interact with them, and how to keep a single source of truth even when tools and sessions change.
The Solo Sentinel write-up examines how a lightweight AI-assisted audit can serve as a last line of defense before deployment. It explains how to frame an AI auditor, what to check at the final stage, and how to combine human judgment with automated scanning without turning every audit into a huge project.
AI agents do not replace engineering fundamentals; they reward them. This guide shows how classic principles like clear requirements, decomposition, and validation translate into better prompts, more predictable agent behavior, and workflows where AI becomes a reliable extension of the team rather than a source of chaos.
Key concepts for this month
New tools bring new risks and responsibilities. This month's glossary focuses on compliance, data, security, and the everyday practices that keep systems reliable.
📖 Continuous compliance automation
Learn how continuous compliance automation embeds checks and evidence collection into daily workflows so that controls are monitored in real time instead of only during scheduled audits.
📖 Compliance audit
Understand how a compliance audit reviews processes, controls, and documentation to confirm that an organization meets regulatory, legal, or internal standards, and where gaps still remain.
📖 Data pipeline
See how a data pipeline moves information from sources to storage analytics, covering ingestion, transformation, and delivery so that teams can trust the data behind reports and models.
📖 Cryptography in cybersecurity
Discover how cryptography protects data in transit and at rest, using encryption, keys, and secure protocols to keep information unreadable for attackers and readable only for intended recipients.
📖 Debugging in programming
Learn how debugging helps developers find and fix the parts of a program that cause incorrect behavior, using tools, logs, and step-by-step reasoning to make software more stable.
Careers at Mad Devs
We continue to grow our AI and infrastructure capabilities and are looking for people who enjoy solving complex, real-world problems with code and data.