Matthias Gattermeier

Don't believe the BS that you can use Claude Code for free. Ollama recently made their API compatible with Claude Code. Many creators quickly jumped on the opportunity to farm engagement with the hook: "You can now use Claude Code for free!" My thought? Claude Code without Opus 4.5 is not Claude Code. Period. But this is exciting news. Not because I can use Claude Code for free, but because I see an opportunity to optimize costs by delegating easier tasks to local LLMs. The key question is: what tasks can local LLMs handle? I tested out 7 local LLMs. In this post, I will explain the BS and share my 1st experiment. -- Why the BS? Claude Code has been praised as one of the best AI tools by its users, not only for coding but for many other tasks. But the price feels steep to many. The $20/month plan is not enough for any serious work. You need to at least subscribe to Max 5x ($100/month). Many heavy users, including me, subscribe to Max 20x ($200/month). It’s a steal. But still, many were eager to try it but aren't ready to pay. Ollama's recent announcement means you can buy a Mac, a Strix Halo, or a GPU and use Claude Code for "free" with local LLMs. It’s appealing, as it is a one-time investment, and you can use the machine for other purposes. Creators are leveraging this opportunity to farm engagement. But the reality? Claude Code without Opus 4.5 is not the same Claude Code we praised. Local LLMs are far less intelligent. -- But for those who understand the difference, we see an opportunity to optimize costs by delegating some easier tasks to local LLMs. I'm interested in finding out what tasks local LLMs can handle. This is my typical flow when using Claude Code. This is for coding, but I have similar flows for marketing and content creation. 1. Research and planning 2. Create PRD and implementation plan 3. Break plan into bite-sized tasks 4. Implement + review with reflection pattern 5. Final review with agents 6. Final review and QA by me Based on my quick tests, we can forget about asking local LLMs to do research and planning. All of them failed at a simple instruction: "Visit https://learnparrot.ai/ and tell me about the website." So, I think the most viable use cases would only be (4) — implementation + review loops. While it looks like a very narrow use case, it is where we burn a lot of tokens. So I think it is worth a try. The main selection criteria for this will be instruction-following capability. One very common task is to refer to code samples or templates to code a new feature or page. This is a good test of instruction-following. So, I crafted my first test: - Used Opus to create HTML that I can screenshot as a LinkedIn carousel to display info for each model. - Turned one of the pages into a template and deleted the rest. - Asked each LLM to refer to the template to code its own page, given its specs. Swipe the carousel to see the results. Who would you hire? #LocalLLM #ClaudeCode #VibeCoding

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I turned 3 projects worth of AI agent lessons into a single npx command. My last post about building AI development teams got a lot of attention. The number one question: "How do I actually set this up?" The honest answer was painful — it took half a day just to configure Project C. Eight agent definitions, skill files, file ownership matrices, settings... all by hand. And if Claude Code's spec changed? Your carefully crafted prompts were already outdated. So I built create-agent-system. npx create-agent-system One command. Pick a preset. Get a working agent team. What it does: - 3 presets (solo-dev / small-team / full-team) — opinionated defaults, not blank canvases - 8 agent types with role isolation and file ownership - 8 skill packages (scoring, TDD, visual QA, code review, ADR writing...) - Intersection skill computation — agents only get skills relevant to their preset - /sync-spec skill that validates your setup against the latest official docs The key design decision: scaffold once, stay up to date forever. Most scaffolders generate boilerplate and walk away. create-agent-system bundles a sync-spec skill that checks your configuration against  the latest Claude Code documentation via Context7 MCP. Your agents evolve with the platform. The technical highlight I'm most proud of: intersection skill computation. Each agent has default skills. Each preset activates certain skills. The agent gets the intersection — not the union. Why? Because irrelevant skills pollute the agent's context. I learned this the hard way when a Backend agent started demanding screenshots for visual QA checks. 5 things I learned building this: 1. Good defaults are the best documentation 2. Presets are opinions — opinionless tools are unusable 3. Scaffolding without sync is half the job 4. More agents = more important to filter skills precisely 5. There's a right level of abstraction — 100-line Handlebars templates are not it It's open source: https://lnkd.in/gmHwBYfA Full writeup with design philosophy, preset comparison, and code examples: https://lnkd.in/g62cPXCX #ClaudeCode #AI #OpenSource #DeveloperExperience #AIEngineering

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