Calculating Gemini CLI Token Costs for Agentic Vibe Coding

While parallelizing AI workflows with background agents is a massive productivity booster, this “fire and forget” vibe coding introduces a new challenge: keeping track of your LLM API costs. If you want to quickly convert your terminal token usage into actual dollars, I highly recommend using this Gemini CLI Cost Calculator.

Using the Gemini CLI, you get a transparent summary of your token usage at the end of every session:

How I am prompting LLMs: Should you say Thank You? Please?

https://huggingface.co/blog/jdelavande/thank-you-energy is an interesting article.

What it doesn’t mention is the “exponential” cost of saying “Thank You” at the end of a long conversation… as each follow-up prompt must send the entire conversation, real world energy consumption is likely much higher than the “synthetic” Thank You on an empty context.

Personally I’m currently typically prompting LLMs like this:

1. I use “imperative” language (“do”, not “could you” nor “please”)
2. I frequently create new sessions, instead of never ending long conversations (/clear in Gemini CLI)
3. I don’t send any follow-up prompt when the task at hand is completed to my satisfaction
4. I on (pretty rare) occasions still can’t quite avoid an “oh wow, you’re awesome” 😀

Sending a “Thank You” to an LLM as the last prompt to end a conversation does not like a good idea energy wise.

From Prompt to Production: AI Vibe Coding Web Frontends by Chaining Google’s Stitch, AI Studio, and Antigravity

I recently sat down to finally try out hands-on for myself just exactly how easy it is in February 2026 to have an AI generate a well designed full-fledged working HTML/CSS/JS front-end UI.

The Design Phase: Stitch

Starting with Google’s Stitch, I iterated on a few high-level graphical design ideas.

This feels similar to what you would have done with your human graphical designer, using tools like Figma, back in the pre-AI era.

Gemini Fixed Audio Bug

I’m experimenting with using the Gemini Live API, and have (obviously) “vibe coded” (parts of) what I’m doing.

It worked surprisingly well right away, but there was this annoying audio bug. (Signal Processing is not my forte.)

Then I had an idea for something which I didn’t really think would actually work, but hey, try it anyway, right?

I recorded a short audio clip of the problem, and uploaded it to Gemini, asking it for help. And oh boy, is this impressive or what:

Vorburger.ch AI Git Memory aifiles

Like everyone else, I am increasingly using AI tools.

Today I finally got around to start setting up what will be the (public) “memory” of my personal future AI agents that will work for me.

Being a developer, I don’t want this to be hidden away in some proprietary black box, but instead want to be able to inspect, edit, and version control it.

I also don’t want it be specific to any one AI tool or provider, but instead be portable and reusable across all of them; whether that’s the (awesome!) Gemini CLI, Anthropic’s Claude Code, OpenAI’s, or my very own Enola.dev.

Google Agent Development Kit (ADK) for Java with Gemma on Ollama

Good news for Java developers! Google’s 🧰 Agent Development Kit (ADK), already popular in the 🐍 Python community, has officially arrived for ☕ Java.

This opens up exciting possibilities for millions of Java developers and enterprises looking to integrate powerful AI agent capabilities into their existing applications.

The initial version of the Java ADK directly supports the Gemini and Claude large language model (LLM) APIs already. In the Java ecosystem, LangChain4j is a popular framework (together with Spring AI) which already has many (20+ and more through the OpenAI API) integrations with other LLMs.

Jules.google

jules.google is my new best friend. It’s just so very awesome.

It just made a fix for ch.vorburger.exec issue #263 from my prompts; see PR #279 - it’s great.

Jules directly works on your project in an isolated new VM for each task, where it iterates until a solution passes the build. This is the next step of where we’re heading as an industry, and the generation beyond the current assistants that are inside your traditional IDE. With Jules, I didn’t manually edit files anymore at all; only chat. Here is the story of the aforementioned PR:

Running Ollama LLMs in an AMD Radeon RX 7600 XT on Fedora 40

Ollama makes it very easy to get up and running with large language models (LLM).

I recently got a shiny new AMD Radeon RX 7600 XT, and of course had to try out Ollama with that new graphics card on my Fedora Linux Workstation.

Because I’m generally not a big fan of running shell scripts with sudo from strangers, and usually prefer launching some 👹 daemons like this one in the foreground instead of having them be started by systemd, I installed and started it simply like this, at least for starters: