A journey in the Gen AI world

In 2025, dozens of language models can now write, debug, or refactor code. But which ones are actually good — and which give the best value for money? Let's try to make a quick recap for your future programming session. Disclaimer: this is a moving market and each day can see tremendous changes so this is a picture in time and a methodology example for your own review  🧱 How We Calculate Token Costs The 80 / 20 blended formula Most IDE copilots send a large prompt (project context, previous messages, code files) and receive a short answer (patch, explanation). Field logs from Cline, GitHub Copilot, Cursor and Sourcegraph Cody show that roughly 80 % of tokens are input and 20 % are output . To give a single price you can reason about, we compute: Blended Cost ($/M) = 0.80 × Price_in  +  0.20 × Price_out Example : Claude 4 Opus costs $15 /M input and $75 /M output → 0.80×15 + 0.20×...

Natural language processing  has been floating around the tech world for yearsbut what about Natural Language programming?  What about programming whole application directly in Natural language? The tools are here, the agents are live, and people who’ve never written a line of code are already building automations, querying data, and triggering real-world actions … just by describing what they want. Before we get ahead of ourselves, let’s unpack what this term actually means—and why it’s already changing the way we create. Natural language programming isn’t one thing — it splits into two complementary approaches. Depending on the goal, you might want to generate code from a plain-English prompt, or trigger actions directly without code ever surfacing. These two paths are reshaping what “programming” even means. Text-to-code Tools like Codex, Copilot, Cline, or Windsurf translate prose into Python, JavaScript, and more. Text-to-action Autonomous agents interpret i...

Artificial intelligence has already impressed us with its ability to generate text, answer questions, translate, code, and summarize. But all of that… remained passive. Today, AI is taking a new leap. It moves into action. It no longer just talks to you. It can do: publish an article, send an email, schedule a meeting, even run an entire blog. And that changes everything. Real-world examples of AI in action When I talk about generative AI that acts, I am not talking about just producing text or images. I'm talking about executing concrete, autonomous, targeted tasks. Here are a few examples from the field: 1. Fully automated blogging (personal case) An AI agent can: Generate a blog post from an idea. Publish it on a platform like Blogger. Create a cover image or visual summary. Share it automatically on social media. Update a tracking sheet or project management board. This isn’t a dream. I already have it running, and I’ll share the how-t...