AI

16 posts
AI writes bad code, but what if that’s the good news?

AI writes bad code, but what if that’s the good news?

Should engineers fear messy AI code or embrace it as a tool for fast product validation? This story reveals why your cleanest code might not be your most valuable contribution. Why founders will use AI generated code more and more for MVPs and prototyping and why engineers should embrace this.

Pricing strategies in the era of AI: why hourly billing no longer works

Pricing strategies in the era of AI: why hourly billing no longer works

AI tools are changing how agencies work and how they should bill. Fewer hours, faster results, and ballooning token costs are reshaping agency economics. We dive into what comes next, and why value pricing might be the way forward.

Due diligence for AI startups: what actually matters

Due diligence for AI startups: what actually matters

Founders and investors due diligence: how to dig beneath the “AI-powered” facade and verify that their systems won’t break in secret.

The enshittification of the internet: are you building value or friction?

The enshittification of the internet: are you building value or friction?

AI agents shouldn’t become CAPTCHA solvers: discover how monetisation-driven UX patterns have turned the web into a hostile environment and what honest design can do to reverse the trend.

Three cursors walk into a codebase

Three cursors walk into a codebase

A cautionary tale about interns, AI tools, and outsourcing delivering 80% of a project—leaving internal teams with the clean-up. Learn why shortcuts often come with hidden costs.

Vibe coding and the junior developer dilemma

Vibe coding and the junior developer dilemma

AI tools are transforming how we code, but they're not replacing the experience needed to build real software. Let’s talk about what vibe coding gets right—and what it gets very wrong.

AI overdose: When developers stop thinking and start prompting

AI overdose: When developers stop thinking and start prompting

AI tools are reshaping how junior engineers approach problems, often replacing simple solutions with overly complex ones. Here’s why foundational thinking still matters. A real-life case of AI over-engineering gone wrong highlights why understanding problem domains still beats prompting.

Tech stack decisions for AI startups: what you need to know

Tech stack decisions for AI startups: what you need to know

Selecting the right tech stack is critical for AI startups. Python is essential for data science, but the backend, frontend, and infrastructure choices determine scalability and efficiency. Explore the best tech stack combinations, hosting tools, and ETL solutions to future-proof your AI startup.

Exploring LLM Options: Proprietary vs. Open Source

Exploring LLM Options: Proprietary vs. Open Source

AI can be challenging. We break down three AI integration tiers—proprietary models, open-source solutions, and custom-built systems—to help you choose the right approach. From quick MVPs to scalable solutions, discover how to leverage AI effectively for your product.

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