u/NowHaraya

I didn't do some huge reset or anything. I just stopped keeping my phone next to the bed.
I still use my computer during the day and waste time online. Bedtime has been less messy though. I used to keep my phone there for alarms, rain sounds, checking the weather, and it always turned into something else.
Now I set the alarm earlier and keep the phone farther away. If I want white noise, I use the Sleenova mask I already had instead of opening an app.
I still do not fall asleep right away, but it is one less reason to pick up my phone.

booking a dermatologist appointment because i’m tired of guessing.

hair fall got worse after moving to UAE. scalp gets oily/itchy sometimes, ends are dry, and wash days look scary. i’ve tried being normal about it but i’m officially annoyed now.

before the appointment, i’m making a list:

vitamin D ferritin/iron B12 thyroid dandruff/seb derm check is it shedding or breakage is my front hairline traction from tight buns whether Be Bodywise hair growth serum is okay to continue whether i need actual medical treatment or just routine correction

also taking photos of my part line and collecting notes on when shedding is worse.

anything else i should ask? i don’t want to go and forget half the things.

agent + langgraph + ~7 tools. added comprehensive eval to CI as a blocking gate. p99 build time jumped from 6min to 24min. judge calls dominate (~200 scenarios × 2 samples).

engineers are batching changes to avoid the gate. defeats CD entirely.

tried:

1. parallelize judge calls (5x speedup, 429 risk)
2. semantic caching on unchanged scenarios (~60% hit rate, cache invalidation pain)
3. lighter eval on PR, heavy eval nightly
4. async eval post-deploy with canary rollback

leaning toward 4 but worried about action-taking agent shipping briefly-broken state.

how are people structuring this?

For batch transcription, Whisper / faster-whisper / whisper.cpp still feel like the default starting point.

But I’m trying to separate two use cases:

1.Batch transcription
Upload audio → wait → transcript
For this, Whisper is still great. Especially if privacy/local matters.

2.Realtime voice app / voice agent
User speaks → partial transcript → LLM starts reasoning → agent responds
Here the requirements feel very different.

The problems I keep seeing:

- chunking delay
- VAD / endpointing hacks
- no native diarization
- timestamps need extra work
- mixed-language audio gets messy
- GPU cost if you want scale
- hard to get low p95 latency
- local setup becomes infra work

Hosted tools I’m seeing people test: Deepgram, AssemblyAI, Speechmatics, Soniox, Gladia, OpenAI realtime/transcribe, and now Smallest AI Pulse for realtime STT.

I’m not trying to dunk on Whisper. It’s still the baseline.

But for a live voice agent or realtime captioning product, when do you personally stop self-hosting and move to a streaming STT API?

Is the line latency? concurrency? diarization? maintenance? cost?