Claude Sonnet 4.5

1. claude-code

   1 435 138,002 9.7 Python

   Claude Code is an agentic coding tool that lives in your terminal, understands your codebase, and helps you code faster by executing routine tasks, explaining complex code, and handling git workflows - all through natural language commands.

   

   It was something that was used for token efficiency. Most of the settings and use cases are quite poorly communicated but asking Claude to review the latest release changelog (https://github.com/anthropics/claude-code/blob/main/CHANGELO...)

   The quite useful tool is to use /opusplan along with /codex:rescue (https://github.com/openai/codex-plugin-cc) means you get quite a strongly reviewed plan using native claude + codex without having to implement of the mostly useless trust-me-bro plugins and other bs.

2. SaaSHub

   www.saashub.com featured

   SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives

   

3. Substrate

   2 3 871 8.6 TypeScript

   An Open-source Framework for Human Understanding, Meaning, and Progress. (by danielmiessler)

   

   Some napkin math -- total global labor compensation is about 50% of the GDP, which puts it in the USD 50 - 60 Trillion range: https://ourworldindata.org/grapher/labor-share-of-gdp

   This source claims that knowledge workers alone (probably because they are paid much more) account for 35 - 50 Trillion of that: https://github.com/danielmiessler/Substrate/blob/main/Data/K...

   If LLMs can boost their productivity even by an average of 5% (studies from ~2024 put it in the ~30% range depending on task) that is ~1.5 - 2.5T in value annually. Even if the AI industry can capture a fraction of that, that is a huuuge monetization opportunity.

   Note, at 5% productivity boost, humans are not just in the loop, they are the loop. AGI or large-scale replacement of humans is not even needed, but the financial opportunity is already immense, and it scales with how much human productivity can be improved (i.e. how much work can be offloaded to LLMs.)

   Now, I don't think AGI will happen soon (or has already happened, depending on how you define it) but I do think humans will be a much smaller part of the loop and large-scale job displacement will happen once companies figure out how to properly use AI.

   At this point, the financial upside for the AI industry is extremely high but will be limited by the social turmoil that will inevitably ensue (which we're already seeing brewing in the data center backlash.)

4. Open-BFME

   3 1 26 9.5 C++

   Discontinued Open Source, 1:1 binary swappable recreation of the Lord of the Rings, Battle for Middle-Earth [Moved to: https://github.com/Open-BFME/Open-BFME-1]

   

   Great timing. I just started using Claude Sonnet as a long term reverse engineering project[0] for a game I used to play as a kid. The cheaper tokens but sufficiently smart with hard verification makes it a perfect combo for the task

   [0] https://github.com/dginovker/BFME-Source-Code/

5. codex-plugin-cc

   4 8 28,917 7.5 JavaScript

   Use Codex from Claude Code to review code or delegate tasks.

   

   It was something that was used for token efficiency. Most of the settings and use cases are quite poorly communicated but asking Claude to review the latest release changelog (https://github.com/anthropics/claude-code/blob/main/CHANGELO...)

   The quite useful tool is to use /opusplan along with /codex:rescue (https://github.com/openai/codex-plugin-cc) means you get quite a strongly reviewed plan using native claude + codex without having to implement of the mostly useless trust-me-bro plugins and other bs.

6. claude-fails-to-follow-claude-md

   5 1 0 5.8 Dockerfile

   Reproduction for https://github.com/anthropics/claude-code/issues/13689

   

   For me claude-fable-5 failed to follow the instruction following test I'm making against various models https://github.com/marcindulak/claude-fails-to-follow-claude...

7. deccp

   6 1 90 - Python

   Evaling and unaligning Chinese LLM censorship

   

   https://github.com/AUGMXNT/deccp - one example for Qwen models. For GLM 5.2, abliteration/realignment works somewhat differently, but with Claude's help, you can finish the job.

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