Category: Experimenting

It is unsurprising to see organizations scrambling to control AI costs. I am spoiled by subsidized tokens. On Claude I select Opus 4.8, set thinking to MAX, and run my prompt … because I can. On Claude Max or GPT 5.5 Pro, the limits are so generous you feel like by not doing it, you are wasting quota. 10x FTW.

In the current token economy I do not see a rationale to downgrade my model for a prompt to save tokens. Default and go. Times are changing. AI companies are pushing organizations into usage based billing. GitHub copilot changed its policy last month. And people were pissed. Makes sense. You started at $50-100 a month with subsidized usage. Subsidy is gone, and your bill is $2000-$5000.

Will AI companies push everyone to usage based billing? Unlikely but possible that compute is less generous to the point that it’s noticeable. Similar to what we get with Claude Pro vs Max. To save on cost, will people want to invest in local LLMs? Microsoft is reportedly looking into Deepseek to reduce token costs. I think what’s next is a few things.

1. We get better with token optimization. Select a GPT 4, when 5.5 isn’t required. Start pinching pennies on our prompts and use the dials when appropriate.
2. Local models for appropriate tasks. While local models that code need beefy systems, professional office work can be serviceable with models < 16B parameters. My experience is that context switching models is not intuitive.
3. Maybe this is solved by a system like OpenRouter where all models are accessible and can be programmatically chosen depending on the task. Because to expect me to hit a drop-down select every time I prompt or mid-context, is just not happening.
4. Frontier models keep getting better to the point that older models become more affordable and “fine” for everyday coding. The problem here is that compute is limited. If we look at token costs for Claude, over the past year, the cost per token has not decreased for older Sonnet or Opus versions – but between models there is a notable cost difference. But compute is compute, the cost premium is on the model and effort, so affordability is gained via product choice, not a compute matter.
5. Local models for everything. Unlikely with the current token economics of memory. Increasing unified memory from 16 GB to 48 GB on a MBP puts a $2K laptop at $4K. This is the bare minimum needed to run a 32B parameter model. And you will likely still swap. 64 GB MBP forces you into Ultra territory at $5K. The trade-off is real for businesses where a $5K laptop may save under usage based billing scenarios.

In the consumer world, frontier AI companies hungry for fresh data are fighting for users. The cost subsidies will continue. There is talk of a price war now that models are mostly commodities. Google kicked it off by making Gemini AI Pro half the cost while giving a lot of Google Premium Perks. It made me take a hard look at my setup. But the Claude harness is too good right now.

If I was forced into usage based billing, I would have to look at my setup and make hard decisions for how to optimize. I would likely start by switching Frontier companies. I often need to go over to GPT or Gemini when I hit my usage on Claude Max. And.. for most of what I do it is fairly interchangeable because they’ve all copied Claude’s harness which improves user choice.

Google just dropped Antigravity 2.0 and ripped out the VSCode fork. I had been using AG since its early preview because it had extremely generous free Gemini caps within the IDE, all with a VSCode interface. With 2.0, it seems, they just decided to be Claude Code with a Gemini wrapper.

So 2.0 = Claude Code. It has scheduled tasks, connectors, MCP, projects and a primarily chat interface. Guess Google decided Anthropic won and with Codex doing the same, they couldn’t fall behind. They put a CLI behind it, and added projects, worktrees everything in Claude Desktop. And moved the IDE into another product.

It was inevitable because I think Anthropic proved, this interface is the right interface for most people. VSCode was never going to be the UX that my wife uses. Claude Desktop while having its own confusing UX issues, is a understandable means to connect with, and interact with an LLM. So here we are. Microsoft is probably next with Copilot. It’s about time. Copilot is a hot mess. So much so that even Microsoft employees do not want to dogfood it.

If you are a Claude Code regular you know that using it in terminal is the way to go. What seems intimidating becomes second nature over the desktop app. But unlike the desktop app which organizes your session views nicely, Code has always been at the mercy of your tmux client and terminal sessions. Super easy to start scrambling for screen real estate with all your terminal windows and tabs.

This week, Claude released Agent View for Claude Code. By hitting the left arrow on your keyboard in a Code session, you can get into an overview screen of your Claude agents. From here, you can spawn agents to do simultaneous work. Your agents can start working on features, planning enhancements, building tests, or bug fixing – all at once. Without opening another Claude Code terminal window. It leverages this by spawning multiple git worktrees on the current working folder so that agents do not interfere with each other.

If you’re in an existing Claude Code window, you can even background that that so that the session stays alive once you close terminal. Even crazier, is that you can have multiple terminal windows open and go from terminal (1), jump into agent view, to jump into the Claude session from terminal (2).

What manages this, is what Anthropic is calling the Supervisor process. It’s like a motherboard for your agents that remembers state. The whole thing is very freeing as where you once needed to have multiple terminal windows open to work on the same tasks, you now can have just one terminal and agent view.

But can I just say with a chuckle that, I feel terminal was not designed for being blown into a multi-windowed-multi-paned orchestration engine. It’s getting a bit out of hand. I often get lost in what exactly this window is showing me as I fall into a rabbit hole of terminals within terminals.

I have by my count lost my way many times staring at a black terminal window trying to recollect where in the matrix I am. Because I have:

• Claude Code running in every terminal window.
• cmux managing my terminal workspaces where some workspaces have multiple 2-up or 3-up panes.
• Claude agent view allowing me to spawn 3X more sessions in the background hidden from view.
• Claude agent view allowing me to move within different terminal windows without leaving my terminal window
• git branches where I have to remember what branch I had pulled and what working folder I’m in because every feature is usually on a new branch
• git worktrees, which the agent view will use because agent view has to use worktrees so as not to conflict in the working folder
• git worktree branches which… same as above

Insane.

There is a weird shift in how you go from a world where coding is a very structured and deterministic thing, to vibe coding. As a coder you might write a script. Every time you run that script the same thing happens. every. time.

When working with LLMs it is easy to forget that this thing is not what you think it is. It has structure like code, it has guardrails like code, it can even write structured code with guardrails! But run it enough times and you realize that it is not always doing the exact same thing.

And I find as a coder, it can be hard to change that way of thinking. I want to think of an LLM like a new abstraction layer over code that works the exact same way.

I have a family dashboard I created with Claude. It has three parts. Schedule, Homework and Meals. Every week on Sunday, it pulls each of our schedules, extracurriculars, extracts my kids calendar from their teachers handouts, overlays their important events for the week and generates a week view. I also work with my kids on their homework. I generate homework exercises as per the school curriculum and commit to memory progress. I ask Claude to write new homework into a set of daily one-pagers I can easily print out for the kids. Lastly, I get Claude to randomly select favourite recipes from my note repo, extract all the ingredients, put it back into my note repo so I have a grocery list every Monday. It works great. But it didn’t always.

At first, after a bunch of prompt testing I had a prompt that would run and generate what I thought was perfect. And next week, something would look off. It could be the schedule was in rows, rather than columns. I could be that their homework would have 5 questions instead of 15. It could be that the recipe cards I ask it to create no longer fit on one line.

My point to all of this is to say that as a coder, I was treating LLMs like code, but they’re kind of like people. Being non-deterministic, every time they do something they may do it slightly differently. That is unless you force some structure.

And so I learned, if you need something the same every single time, and often for output, that is what we want… you have to use a combination of prompting and coding knowledge to force the LLM into a structure. This could be by forcing it to deploy a script (which is deterministic) or having a template with built-in sentinels. Frankly these quirks feel odd but thinking about how we are coding using just our natural language alone, the trade-off seems fair.

Since Claude released routines, I’ve had a blast finding ways to automate my code. I have a laundry list of personal projects in my GitHub repository that I work on with Claude, daily. I regularly have 5+ Claude code sessions coding away on random thoughts and prototypes all at once. So when routines released, I wondered how different it would be over scheduled tasks for cowork. The beauty for me is in the tool calls.

I use Claude Cowork to run a daily financial market analysis and refine a daily thesis that’s ready in the morning. Scheduled runs prompting at a given time and day are a part of cowork that I love. Cronjob with more intelligence.

Claude routines can use my GitHub integration to run everything in the cloud without my machine being on. It pulls my project code, uses a managed instance to run everything and pushes it back to Git after it’s done. It operates like a coding partner for me at night.

Just like that, I now have a bunch of Claude Code routines that trigger nightly, review my project codebase for issues, propose enhancements. Every morning I end up with a list of bug fixes and proposed enhancements. I recently changed my routine to straight up pick an enhancement to work on so when I get up the feature is ready for PR. Claude documents the change, performs security and dependency reviews, and summarizes the change every night. My projects are slowly building themselves as I direct projects rather than code them.