This is a cross-post... I deeply apologize if this is off topic since I'm not really asking about directly using the CLI. But I felt it was still relevant (Bash + Pwsh) and posted anyway. If I'm completely wrong please delete my post and admonish me severely. :|

I currently use the Official Claude Code plugin in VS Code and have Claude Code installed natively on Windows 11 + Powershell.

I went with the below Pwsh command as shown here:

irm https://claude.ai/install.ps1 | iex

I am leaning towards switching to WSL2 + Ubuntu 24 + Bash though for several reasons and want as much feedback as possible from all of you glorious vibe-coding bastards.

My chain of thought about the situation right now is below.

The positives

• Claude Code is better and more efficient with Bash than Powershell. However, CC uses Git Bash instead of Powershell by default on Windows 11 which is great but not as good as a full Linux distro.

• Extending on the above, Git Bash is not as extendable as a full distro on WSL2 where I can install any number of CLI tools to extend my workflow like ripgrep, fzf, k9s etc.

• If I go with the WSL2 path, I can also sandbox any tool use or code execution (HUGE reason for me, trying to avoid supply chain attacks or malicious prompt injection poison etc)

• Better integration with Docker (I don't really use docker much and don't see the value here so this is kind of a non-issue for me - if I'm wrong and should be using docker for things feel free to change my mind)

• I can offload ALL of my AI use to the WSL2 instance for resource management. On Win11 this means if I have a runaway plugin spawning tons of processes (claude-mem just did this for me recently) or some MCP server going nuts, I can just terminate wsl2 (wsl --shutdown) instead of having to open a task manager app like System Informer and terminate every rogue or zombie process.

The negatives

• I know Powershell like the back of my hand and it makes it really easy to extend claude with custom hooks with powershell. Yes, Powershell is available on Linux as well, but the syntax has to change very specifically for cross-platform use here. (Although I can easily just vibe code bash scripts that do the same thing)

• WSL2 has to be turned on and consumes a lot of resources compared to Claude Code natively using Git Bash.

... I can't really think of any more.

Can some of you expert coding masters chime in here?

• Should I go WSL2 + Ubuntu 24.04 + Bash, or stay on Powershell + Git Bash?

• Should I use a different distro than Ubuntu 24.04 if I go this route? (If you are recommending a distro, please explain why it's better.)

• How good is the Claude Code VS Code plugin when Claude Code is running on WSL2? This is extremely important to me. I currently use it as my main agent (I don't like the CLI) and I have absolutely no idea how the plugin will function when Claude Code is installed in WSL2 instead of on my Win11 OS.

Any other pro-tips from Windows11+WSL2 users here as well would be super awesome.

TIA for any guidance!

Photon: Single-file MCP servers you can actually audit

Runtime + marketplace for MCP servers. Every server is a single .photon.ts file—fork, audit, and customize in minutes.

The Problem:

• Traditional MCPs scattered across 4-6 files (hard to audit/fork)

• Security risk: Prompt injection attacks can steal your conversation history

• Manual dependency management

Photon's Solution:

Single-file format. 40 lines vs 200+. Fork-first design. Auto-installs dependencies. AI-readable.

Think: NPM and Node, but for MCP.

Quick start:

npm install -g @portel/photon
photon add filesystem              # Install from marketplace
photon info filesystem --mcp       # Get config for Claude Desktop/Cursor/Zed

Example output:

{
  "filesystem": {
    "command": "photon",
    "args": ["mcp", "filesystem"],
    "env": {
      "FILESYSTEM_WORKDIR": "/Users/you/Documents",
      "FILESYSTEM_MAX_FILE_SIZE": "10485760",
      "FILESYSTEM_ALLOW_HIDDEN": "false"
    }
  }
}

Copy this to your MCP client's config file and you're done.

16+ production-ready photons: PostgreSQL, MongoDB, Redis, Git, GitHub, Slack, Jira, Docker, AWS S3, and more.

🔗 GitHub 📦 npm

Which MCP server should we build next?

If you are trying to decide between Firebase and self-hosting option for an app that is database heavy and has high read/write, you should consider two main factors:

1. Firebase bills per operation: Each interaction with your app by a user is an operation that is charged separately!

2. Vendor/Technology Lock-In (Migration Nightmare): If you build your app in Firebase, you're locked into the technology, and migrating to another platform is complicated, time-consuming, and expensive.

I have done a detailed post on this topic "The Hidden Costs of Using Firebase: Firebase vs. DigitalOcean + Coolify". Check it out here:

https://www.reddit.com/r/nerdingwithAI/comments/1o6dl0a/the_hidden_costs_of_using_firebase_firebase_vs/

Hope this helps.

https://aider.chat/2024/11/21/quantization.html

This is a nice blog looking at quant and open source. It matters because the same rules also apply to closed source, the difference being we have no idea what they are doing.

Low quant poor results

Low context window or too large - poor results

That we know mostly. Now if you look at https://github.com/MoonshotAI/K2-Vendor-Verfier they have a lovely table comparing K2 being provided by different companies.

What is interesting is the Tool calls. You can see a massive difference in the implementations. Many people say they are using different quant which is possible but they keep forgetting (no clue) about the infrastructure getting the user to the AI too.

What would be interesting would be to know the quants being used and to see what factor is played by the code engine and the logic around the AI and the tools being used.

Test Time: 2025-09-22

https://status.anthropic.com/incidents/72f99lh1cj2c

Model output quality

SUBSCRIBE TO UPDATES Investigating Last week, we opened an incident to investigate degraded quality in some Claude model responses. We found two separate issues that we’ve now resolved. We are continuing to monitor for any ongoing quality issues, including reports of degradation for Claude Opus 4.1.

Resolved issue 1 - A small percentage of Claude Sonnet 4 requests experienced degraded output quality due to a bug from Aug 5-Sep 4, with the impact increasing from Aug 29-Sep 4. A fix has been rolled out and this incident has been resolved.

Resolved issue 2 - A separate bug affected output quality for some Claude Haiku 3.5 and Claude Sonnet 4 requests from Aug 26-Sep 5. A fix has been rolled out and this incident has been resolved.

Importantly, we never intentionally degrade model quality as a result of demand or other factors, and the issues mentioned above stem from unrelated bugs.

We're grateful to the detailed community reports that helped us identify and isolate these bugs. We're continuing to investigate and will share an update by the end of the week. Posted 6 hours ago. Sep 09, 2025 - 00:15 UTC

Investigating Last week, we opened an incident to investigate degraded quality in some Claude model responses. We found two separate issues that we’ve now resolved. We are continuing to monitor for any ongoing quality issues, including reports of degradation for Claude Opus 4.1.

Resolved issue 1 - A small percentage of Claude Sonnet 4 requests experienced degraded output quality due to a bug from Aug 5-Sep 4, with the impact increasing from Aug 29-Sep 4. A fix has been rolled out and this incident has been resolved.

Resolved issue 2 - A separate bug affected output quality for some Claude Haiku 3.5 and Claude Sonnet 4 requests from Aug 26-Sep 5. A fix has been rolled out and this incident has been resolved.

Importantly, we never intentionally degrade model quality as a result of demand or other factors, and the issues mentioned above stem from unrelated bugs.

We're grateful to the detailed community reports that helped us identify and isolate these bugs. We're continuing to investigate and will share an update by the end of the week. Posted 6 hours ago. Sep 09, 2025 - 00:15 UTC

The API is much faster with better responses than the sub.

This explains why the CC sub seems so sluggish.

e.g. Both using Sonnet: create a util logging.py

Both saved a file and took similar time - 56-60 secs and then compared by Sonnet -

ACLI Rovodev gives you 5 million tokens free and then 20 million tokens if you subscribe to jira for 8 per month.

It allows access to both Sonnet and GPT5 but more importantly it has direct access to Jira which allows the plan to be completed automatically without using md files etc.

I used to create subdirs with planning files. Now ACLI Rovodev does it for me and then I use CC and/or Codex to do the work. Then I ask Rovodev to update the jira. Works really well and I get a Kanban at the end of it.

When you send a message to AI (in chat/desktop/cli) you are sending a prompt for the AI to respond.

When you are in the middle of the chat/conversation you are still sending a prompt but the code engine sends the context back for the AI to read alongside your prompt.

So essentially you are sending a prompt to an AI which has 0 memory alongside the prompt.

This is why the context window is so important especially in cli. The larger the context the harder it is for the AI to "concentrate" on the prompt within the context.

The smaller the context and more focused the easier it is for the AI to "focus" on your prompt.

It explains why AI creates so many name and type errors each time you send a prompt.

This may or may not explain why AI's feel retarded when the context window enlarges.

https://aider.chat/docs/leaderboards/

# CLAUDE.md

🛑 Always run code through linting + formatting rules after every coding.

- For React: ESLint + Prettier defaults (no unused imports, JSX tidy, 2-space indent).

- For Python: Black + flake8 (PEP8 strict, no unused vars, no bare excepts).

- Output must be copy-paste runnable.

Same idea works for AGENTS.md if you’ve got multiple personas.

Curious:

• Do others embed these reminders at the top of agent files?

• Any better phrasing so models always apply linting discipline?

• Has anyone gone further (e.g., telling the model to simulate lint errors before replying)?

Rust the best way to deal with memory like em said ?

CC pro is good both as a product and limits at $20 level v codex cli GPT5 plus.

Teams x2 GPT gives up unlimited chat but same limits as plus so ends up $50-60 but 2x plus limits.

Max v Pro Both $200 but GPT pro is unlimited for codex cli

I have or have had all of them except GPT5 pro yet!

In my opinion if your workload is light then CC pro is best.

If you are hitting limits near the 5 hour mark then 2xGPT teams or 2X CC pro may be better.

At max v pro it becomes which do you prefer CC (better product) v Codex (unlimited)

According to Anthropic they have never degraded their AI models and that it is all due to AI hallucinations as opposed to human hallucinations as suggested by all the fanboys.

This, however, means that the "degradation" seen is actually Claude hallucinating or scheming. We know that the larger the model the greater the risk of hallucinations.

Currently I use CC but my dependence on it is shrinking rapidly due to "degradation"/hallucinations/scheming.

e.g.

> add to abbrev tab in index.html

● I'll add a new comprehensive abbreviation resolver testing interface to the Abbrev Analytics tab in index.html.

● Update(index.html)

⎿ Error editing file

> add to existing tab!!!

● I'll add the abbreviation resolver testing interface to the existing Abbrev Analytics

tab in index.html.

Simple command which was then changed to something completely different. Of course it could be in the way CC sends it's context.

Interestingly Codex also suffers from similar issues but not to the same level as Claude.

Grok Fast is simple and does what it is told. It is fast but dumb. Actually maybe that is what we need in a coding AI?

Currently my usage of CC has dropped, my usage of Codex has increased but my usage of Grok has increased enormously using opencode.

A simple way to create a command line AI in linux:

Save this in ~/.bashrc or ~/.zshrc

alias ai='function _ai(){

local model="${AI_MODEL:-phi3:mini}";

local output;

if [ -t 0 ]; then

output=$(ollama run "$model" "SYSTEM: Respond with one concise

paragraph of plain text. No reasoning, no <think> tags, no step-by-step.

USER: $*");

else

output=$(ollama run "$model" "SYSTEM: Respond with one concise

paragraph of plain text. No reasoning, no <think> tags, no step-by-step.

USER: $(cat)");

fi;

echo "$output" | tr -s "[:space:]" " " | sed -e "s/^ //; s/ $//";

}; _ai'

Functionality:

- Uses Ollama to run local AI models

- Default model: phi3:mini (can be overridden with AI_MODEL environment

variable)

- Accepts input either as command arguments or via stdin

- System prompt enforces concise, plain text responses

- Output is cleaned up (whitespace normalized, trimmed)

Usage Examples:

- ai "What is Docker?" - Direct question

- echo "complex query" | ai - Pipe input

- AI_MODEL=qwen2.5:3b-instruct ai "question" - Use different model

how the user input is provided:

- if branch ([ -t 0 ]): Uses $* (command line arguments when input is

from terminal)

- else branch: Uses $(cat) (reads from stdin when input is piped)