u/Hephaestite

Can’t believe how many people are sleeping on this one simple trick to reducing your token consumption. Simply write all your instructions and context onto an atomically flat crystalline surface with a 2kv scanning electron microscope.

My first time running a model on the 2019 Mac Pro and have to say it's performed amazingly. Zero tuning done at getting 55tk/s on the Q4 K XL quant and 70tk/s on the Q4 K XS quant. Thats about twice the speed of my RTX4080 with CPU offloading, and roughly 5-7x faster that the 2013 Mac Pro with dual D700's that is sat next to it. To say I'm impressed would be an understatement!

Finally acquired a 2019 Mac Pro to join my 2013 trash can. Unbelievably this still had the factory stickers on it and honestly looks like it’s never been used.

A month or so ago I started building a CLI tool essentially with the goal of replicating some of the functionality of a popular Rust based tool called RTK. The premise is fairly simple, the CLI tool sits between your AI coding agent and whatever bash tool its trying to use and intercepts the response and then minimises it to avoid unnecessary token consumption.

While RTK already existed, I wanted to do some extra stuff and figured it would be a fun thing to try and implement in C# with nativeAOT compilation, and additionally I didn't really want to learn rust (just vibecode it and just trust that Claude / Codex was doing it right).

Hypabolic/Hypa

I'm not at v0.1.1 and I've ended up going way beyond what I initially intended in terms of feature set. I've implemented a full MCP Server and Client for a start, so coding harnesses can talk to Hypa via MCP but additionally Hypa can act as the actual MCP client for anything you'd want to connect to.

The biggest advantage of this MCP proxy is that it keeps the MCP connections and their entire tool lists out of the agents context window. Shockingly just a few connections can eat up 15-20% of context (more if using local LLMs with smaller context windows).

I've also built a dedicated markdown mode for it, where it will index and parse repo markdown docs, giving the calling agent access to a table of contents and direct access to markdown sections, code blocks, etc.

My next job is to try and extend it's harness support. Currently its limited to just Claude and Codex, but I want to add VS Code, Pi, OpenCode, and maybe a few more.

As an example of what the calls and token savings look like:

● Bash(hypa dotnet build 2>&1 | tail -5)
  ⎿  ok (build succeeded)
     [hypa: 308→5 tok, -98%, reducer=dotnet-build]

● Bash(hypa dotnet test --filter "CodeIndex|MarkdownStructureProvider|CodeStructureProviderRegistry|CodeLanguageRegistry" 2>&1 | tail -20)
  ⎿  Test run for /home/matthew/development/atomic/Hypa/tests/Hypa.GoldenTests/bin/Debug/net10.0/Hypa.GoldenTests.dll (.NETCoreApp,Version=v10.0)
     Test run for /home/matthew/development/atomic/Hypa/tests/Hypa.IntegrationTests/bin/Debug/net10.0/Hypa.IntegrationTests.dll (.NETCoreApp,Version=v10.0)
     Test run for /home/matthew/development/atomic/Hypa/tests/Hypa.UnitTests/bin/Debug/net10.0/Hypa.UnitTests.dll (.NETCoreApp,Version=v10.0)
     Passed!  - Failed:     0, Passed:    10, Skipped:     0, Total:    10, Duration: 4 s - Hypa.UnitTests.dll (net10.0)
     [hypa: 683→185 tok, -73%, reducer=dotnet-test]

● Bash(hypa git diff HEAD~1 --name-only 2>/dev/null || git diff HEAD~1 --name-only)
  ⎿  src/Hypa.Cli/Commands/CodeCommand.cs
     src/Hypa.Runtime/Application/Services/CodeIndexService.cs
     src/Hypa.Sdk/CodeIntelligence/CodeIntelligenceModels.cs
     [hypa: 88→43 tok, -51%, reducer=git-diff]

  Searched for 3 patterns, read 1 file, listed 1 directory (ctrl+o to expand)

● Bash(hypa dotnet test tests/Hypa.UnitTests --no-build 2>&1 | tail -8)
  ⎿  Test run for /home/matthew/development/atomic/Hypa/tests/Hypa.UnitTests/bin/Debug/net10.0/Hypa.UnitTests.dll (.NETCoreApp,Version=v10.0)
     Passed!  - Failed:     0, Passed:  1299, Skipped:     0, Total:  1299, Duration: 29 s - Hypa.UnitTests.dll (net10.0)
     [hypa: 126→93 tok, -26%, reducer=dotnet-test]

Small gains individually but these really add up over the course of a session.

I'd love to get some other people using it and feedback on it. Hopefully some bug reports and suggestions on the tool parsers that people would like to see.

Full docs are available here Hypa — Open Source CLI Tool | Hypabolic

Hope to get some feedback from you all (even if you don't like it)

The “Trash Can” Mac Pro, once the most expensive machine you could buy from Apple, mine was just shy of £10,000 in 2016 — that’s £14k in today’s money.

Until recently mine was just running as a kubernetes single node development platform, it’s 64gb of ram and 24 logical cores made it perfect for that.

Its most powerful asset, a pair of D700 GPUs, essentially sat idle for years… that is until yesterday when I discovered that while its old southern islands based GPUs weren’t supported in ROCm, they were now supported under Vulkan — thanks to new drivers and a new Linux kernel.

That means it can run basically any model that llama cpp can throw at its 12gb of VRAM. Time to do some benchmarks, right?

Qwen 3.5 9B Q4 MTP — 11 t/s output at 70k context
Qwen 2.5 coder q4 — 22 t/s output at 70k context

Not exactly lightening fast but totally usable, especially for planning tasks where you can just set it and forget it.

The thing that’s really blown my mind though is that the planning output from qwen 3.5 is significantly, and it’s not even close, better than Claude Sonnet 4.6. It absolutely smashed planning on a complex csharp .net 10 app with nuget packages that sonnet struggled with, qwen just googled the docs.

Mind blown 🤯

What other ancient hardware are people running that’s still capable of doing real LLM work?