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Claude AI Usage Limits: What Changed and How to Make Them Last

Thursday, 2026.03.26 J.D. H. Leave a comment

TL;DR: Claude’s usage limits feel tighter in March 2026 because they are. The short version: Anthropic has more demand than GPU capacity right now. Millions of new users arrived as word got around about how good Claude is and especially after the OpenAI Pentagon boycott, the off-peak 2x promotion ends March 28, and multiple Max subscribers report usage meters jumping from under 50% to 100% on single prompts. Below: what the limits actually are, why they’re worse right now, and

Claude Project Instructions: Examples & Templates (2026)

Thursday, 2026.03.26 J.D. H. Leave a comment

TL;DR: Claude project instructions tell Claude how to behave inside a specific project. Here’s a starter template you can paste right now: Customize the bracketed parts for your workflow. That template covers 80% of what most people need. The rest of this post gives you more examples and explains when project instructions are the right tool vs. profile preferences. If you’re looking for Claude’s account-wide custom instructions (the ones that apply to every conversation), that’s a different feature. I have

I Tested 13 Local LLMs on Tool Calling: March 2026

Thursday, 2026.03.19 J.D. H. Leave a comment

I built a deterministic eval harness and tested 13 local LLMs on tool calling (function calling) to find out which models work decently well for agentic tasks. The result that surprised me most: a 3.4 GB model scored higher than everything else I tested, including models five times its size. If you’re running a local AI stack with Open WebUI, LM Studio, or any OpenAI-compatible frontend, tool calling is one of the key features that enables agentic behavior. It lets

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How 25,000 Junk Folders Were Breaking My AI Doc Organizer (Garbage In, Garbage Out)

Thursday, 2026.03.05 J.D. H. Leave a comment

How 25,000 Junk Folders Were Silently Breaking My AI Document Organizer (Garbage In, Garbage Out) Thousands of (somewhat) zombie Quicken folders taken care of via compressing them into one 7z archive: Details: My AI File Organizer Was Fighting 25,000 Phantom Folders (And Losing 😜) For a while, my automated document filer was misbehaving. Scan an insurance card — it suggests filing it in a folder called Q-Final. Scan a bank statement — it wants to put it in Attach. The

Claude AI Custom Instructions: A Real Example That Actually Works

Monday, 2026.02.16 J.D. H. 2 comments

TL;DR: Here’s a custom Claude instruction that works well for most people: Why this works: Claude Opus 4.6 follows instructions more precisely than earlier models, so you don’t need to repeat yourself or pad it out. Anthropic’s own guidance says to explain the intent behind your rules, not just the rules themselves, which is why each line above says both what to do and why. Keep it short, be specific about what to stop doing, and iterate as you go.

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The Ultimate Claude Code Workstation

Friday, 2026.02.06 J.D. H. 2 comments

A Hardware Guide for People Who Actually Work With AI With regards to AI and Claude, here’s what most people are not writing about yet: the hardware side of working with Claude, and more importantly, the human side. A lot of AI blog and Reddit posts out there fall into one of two camps. Camp A is obsessing over local LLM rigs, how many NVIDIA H100s you can cram into a tower, how much VRAM you need to run a

Getting Z-Image-Turbo Working in RuinedFooocus (AI)

Saturday, 2026.01.10 J.D. H. Leave a comment

Getting Z-Image-Turbo Working in RuinedFooocus (The Settings That Actually Matter) If you’ve been following AI image generation tech, you’ve probably heard about Z-Image-Turbo: a 6 billion parameter model from Alibaba’s Tongyi MAI team that’s been making waves. It generates photorealistic images with impressive prompt adherence, runs on consumer GPUs with ~10-16GB VRAM, and does it all in just 8 steps. Pretty wild stuff! 🔥 I’ve been testing it in RuinedFooocus and wanted to share some tips because, spoiler alert, the

Topaz Video AI benchmarks 2023 (GPU list)

Friday, 2023.07.07 J.D. H. Leave a comment

Based on the various posted results, the performance in FPS for various GPUs when running Topaz Video AI depends on several factors, such as: Here are some examples of user-reported benchmark results for different GPUs and AI models: GPU Artemis 1X Artemis 2X Artemis 4X Proteus 1X Proteus 2X Proteus 4X Gaia 1X Gaia 2X Gaia 4X RTX 3080 Ti¹ 17.73 fps 8 fps 2.09 fps 13.09 fps 6.21 fps 1.99 fps 8.14 fps 5.32 fps 2.62 fps RTX 4080⁴