Uncategorized – Pazsit's Dev Blog

How a Model Eval Turned out Agent Contract Refinement

Small Models Are Prompt Linters: How a Model Eval Turned out as an Agent Contract Refinement Exercise

I ran a five-model comparison to find out which model could drive my planner agent of my Forge agentic pipeline. I got back a code review of my prompt.

Building an Agentic Dev Pipeline — From Ad-Hoc Prompting to a Repeatable Protocol

My /forge pipeline starts with a planner agent. It interviews me, sends researchers into the codebase, and writes a plan.md that the coder agent implements without re-exploring anything. It had run on a frontier model since I built it, and I wanted to know whether a local model could take that seat. The planner is the most expensive stage, and it runs first, so every token it burns is spent before I know the task was worth doing.

So: a one-shot simple prompt, 4 models adn the pipeline. A frontier reference (Sonnet 5) plus three open-weight models I can host (qwen3.5-122b-a10b, qwen3.6-35b-a3b, qwen3.6-27b), producing plans, scored on contract compliance, grounding, hazard coverage, executability and consistency.

I got the ranking I wanted. I also got something I hadn’t gone looking for: a defect list for my own agent definition.

My Always-On Dev Environment on a Raspberry Pi 5

There’s a particular kind of friction every developer knows: you sit down at a different machine, your laptop instead of your desktop, or your phone while waiting for a train. Then suddenly your project feels far away. The repo isn’t cloned. The Node version is wrong. The database is empty. The ten minutes you had to jot down an idea evaporate into setup.

I wanted to get rid of that friction entirely. The result is a small, always-on development server running on a Raspberry Pi 5 in the corner of my room, and a setup where every device I own: desktop, laptop, Android phone. They’re just a different window into the same live project.

This article is the story of how that environment is built, why it’s shaped the way it is, and where its limits are.

Optimizing Relative Read Frequency Queries in SQL

My goal was just to create, test and use a relatively simple SQL query. Some complexity comes from aggregate functions and table self joins only.
I had 2 tables, article and view, where I store view count. I wanted to calculate a relative measure of read frequency, instead of absolute view counts. So recently published content is more comparable to older material.
To normalize this, we can calculate a views-per-day ratio and then compare all articles against the highest ratio one.

In this article, we’ll walk through:

Showcasing the schema and the goal to achieve.
Building the query that calculates relative read frequency.
Investigating indexing strategies based on execution plans.

Agile Challenges in Scale

Agile

Agile is about co-located smaller highly effective teams, which can focus and provide values in fast pace, driven by ownership and team spirit.
In Agile practices, team ceremonies (such as standups, sprint reviews, and retrospectives) are esential to improve communication, alignment, and efficiency. However, when Agile teams grow too large, these ceremonies can lose their effectiveness.
I like to highlight how large team sizes and poorly structured meetings can hinder productivity, communication, and decision-making, advocating for smaller team structures to preserve Agile’s core values.

Agile methodologies are built around small, cross-functional teams designed to work closely together, rapidly iterate, and adjust to changes. However, as teams grow in size, they often encounter communication bottlenecks and coordination challenges.

Hello fellow developers and everybody!

I starting this blog to explain codes, problems, solutions

Iterate on concepts, designs and cutting edge tech

and last but not least share my thoughts on IT software development field.