Code Scientists

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At Code Scientists, we build software that makes the punchline less painful.

Have a great weekend, world. 🌎

Progress does not always start with a giant leap. Sometimes it starts with one clear next step, one better question, or one small improvement that makes the rest of the work feel possible. That is true in business, in software, and in just about every ambitious idea worth building. Momentum grows when the path gets clearer.

At Code Scientists, we help teams turn complicated ideas and messy workflows into practical software that supports real progress. Here is to building the week one smart step at a time.

Markus Pope
Chief Scientist
Code Scientists

Over the past year or so, I've been helping some colleagues at the University of Florida with AI-related research. A question came up this morning regarding AI-generated content. I thought my reply might be useful to some...

I think it’s a good idea for scholarly article content to be largely human-generated, and relegate the AI to ideation and grammar, even though the whole point of our work is to utilize generative AI in a process-oriented way to produce higher-quality work. There are a couple of reasons for this:

1️⃣ There is so much content being generated by LLMs that human-generated content is going to become more valuable.

2️⃣ Our work will likely become part of the corpora used to train these models at some point in the future.

Regarding point 2 above, models that are trained on model-generated content can degrade in quality over time and lead to model collapse (see https://www.nature.com/articles/s41586-024-07566-y). To me, it seems like this possibility imbues a sense of responsibility on the part of academia and research to limit AI’s roles in scholarly content generation.

Note that I called model collapse a possibility, whereas the researchers studying it call it an inevitability.

According to the research on the subject, it’s not good enough to preserve core ideas and embed them in largely AI-generated content. Model training is a statistical methodology, so rare ideas can get lost in the extent.

Every great product started as someone's wild idea in a notes app. Don't wait for the perfect moment to commit your vision. Just push it to the world and iterate from there. The most successful founders didn't have it all figured out; they simply refused to let their ideas stay in draft mode.

💡🧠 What's one idea you've been sitting on that's ready to deploy?👇

Happy ! 👾 Bringing it back to the basics today with a classic for all the database fans. Who’s "joining" us for the weekend? 🍻✨

True success isn't about having a perfect track record; it’s about the grit to keep showing up with the same level of fire, even when things don't go as planned. It's the "enthusiasm" that bridges the gap between where you are and where you want to be. If last week felt like a series of "bugs," use today to reboot. Keep that energy high and keep pushing forward.

The secret to actually moving the needle isn't found in a giant leap. It's found in small, manageable steps that make what feels impossible, begin to feel possible. Today, we’re just focusing on taking the first step. Whatever big project you've been putting off, or have felt apprehensive to start, break it down today. What is the one small thing you can do right now to get started?

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AI costs do not explode overnight. They creep up quietly.
What actually keeps AI spend under control 👇
• batching requests
• caching results
• routing to the simplest model that works
• setting real guardrails early
Practical design beats surprise invoices every time.

AI Cost Control That Actually Works Keep AI bills predictable. Route by difficulty, cache repeats, batch low urgency work, and set budgets with alerts. Cost control starts in design.

Think you need a data team to measure AI quality? Think again.
This guide breaks down how to:
• Define AI success in plain language
• Build “golden tests” that matter
• Watch real user signals
• Review impact weekly
Quality doesn’t need mystery. Just clear goals, and simple routines.

Evaluating AI Quality Without a Data Team You can measure AI quality without a data team. Set clear goals, use golden tests, track user signals, and review weekly. Simple loops create reliable results.