Debug example for Smallest AI Pulse STT WebSocket streaming with proper latency control.

1. High latency (10+ seconds) — Fixed by throttling audio to real-time pace
2. No restored/full transcript — Fixed by enabling full_transcript=true
3. Poor Hindi transcription — Fixed by using correct language code (hi or multi)

I don't know what's wrong with me today. I woke up feeling so heavy, like there's this weight on my chest that won't go away. Work has been so stressful lately, and I feel like I'm falling behind on everything. I just want to crawl back into bed and disappear for a while. I don't even know who to talk to about this.

• Model: kimi-k2-thinking (Moonshot Kimi-K2 Thinking)
• Serving backend: vLLM
• Serve command (summary):
  • tensor-parallel-size=8
  • distributed-executor-backend=mp

Every decoder transformer layer (like in Llama) has self-attention.

For each token being processed, the model computes 3 vectors:

RPS (Requests per Second): how many requests the system completed per second. RPS = successful_requests / benchmark_duration_sec

TPS (Tokens per Second): ambiguous term, can be more explicit:

• Output Token Throughput (tok/s) = total_output_tokens / duration

• Total Token Throughput (tok/s) = (input_tokens + output_tokens) / duration

• Per-request TPS (your sequential runs) = tokens_returned / request_latency

Your Neovim setup now includes:

• LSP Support: Pyright for type checking + Ruff for linting/formatting
• Auto-formatting: Automatic code formatting with Ruff on save
• Debugging: Full debugging support with nvim-dap

Default keyboard shortcuts for Ghostty terminal emulator. Platform-specific differences are noted where applicable.

If you haven’t already:

git clone https://github.com/zsh-users/zsh-autosuggestions ${ZSH_CUSTOM:-~/.oh-my-zsh/custom}/plugins/zsh-autosuggestions

Think in layers of memory, each with different lifetime & size:

1. Working buffer (short-term)
   The last K messages (e.g., 10–30) from the current chat. Fast, no processing.

2. Running summary (compressed short-term)

Here’s a step-by-step guide to deploy a Python backend to Google App Engine (GAE) using the Standard Environment — suitable for most web apps (Flask, FastAPI, etc.).

✅ Prerequisites • Python 3.7 – 3.10 (GAE Standard supports specific versions) • GCP project created • gcloud CLI installed and authenticated (gcloud init)

📁 1. Project Structure (example for Flask)

my-agent-app/