k9294

5시간 전discuss

[flagged]

I'm a huge fan of fountain pens, it's a very satisfying experience to write with a nice pen on a proper paper.

I use platinum 3776, it's quite expensive one, but it's just amazing. I originally bought it for sketches, but ended up using it for journaling.

Ended up buying one to my wife, she said I'm crazy to spend so much on a pen (200$) but few weeks after the gift she said that it's amazing and she enjoys it a lot.

k9294

8일 전discuss

I'm founder of ottex.ai, I use stt pretty much all the time when work with AI and quite often for communications to draft emails and chat messages.

I started ottex half a year ago after I tested gemini 2.5 flash native audio support. I was blown away by the quality of transcripts and decided to built an app to use it myself.

Currently the default model in the app is Gemini 3 flash, but you can connect to 9 providers and God knows how many models to play with.

I would suggest you to try this models for ai prompting:

- Gemini 3 / 3.5 flash - Soniox rtt v5 - Mistral transcribe v2 - assembly 3.5 pro

k9294

19일 전discuss

Meh... Waiting for OpenAI models without this 5 more days subscription bullshit.

It looks like Anthropic baiting people into Max subscriptions before turning the model off. No thank you.

k9294

지난달discuss

What about request caching? If you swap to a cheaper model mid execution it might cost more that to make multiple requests to the already cached provider?

k9294

3개월 전discuss

+1 for Go! it's my go-to language for any new project at the moment. It's simple, idiomatic, has no awaits, fast compile times, static typing, and it is very opinionated, which helps a lot because agents "subconsciously" follow these standards. Comparing it to TS, it's like day and night; a TS codebase rots at the speed of light...

I also created a guardrails library (inspired by Java's ArchUnit) to prevent code rot - https://github.com/ksanderer/goarch. It helps enforce code standards, decouple the codebase, prevent cross-module imports and crashes builds with concise error messages for agents to fix problems early, very nice experience

k9294

3개월 전discuss

Working on https://ottex.ai - voice ai for busy professionals.

Think wisprflow + granola with 30+ top STT models under single login and pay as you go billing model with 25% markup over API.

k9294

3개월 전discuss

What is yours agentic development experience with elixir? I used to like elixir a lot during a pre agentic era, but with coding agents it feels like the language isn't the best choice - slow compile time, weak type system (at least it was a year ago, I know there is work on that front), small ecosystem...

k9294

3개월 전discuss

Small advice - make one repo “main” and link to it from the website instead of an organisation.

I wanted to star the project to track the progress but it feels a bit weird.. Which repo shall I track? Server? Cli? Sounds like a misc repos.

k9294

3개월 전discuss

That's really cool!

One thing I'm confused with is how to create a shared resources like e.g. a redis server and connect to it from other vms? It looks now quite cumbersome to setup tailscale or connect via ssh between VMS. Also what about egress? My guess is that all traffic billed at 0.07$ per GB. It looks like this cloud is made to run statefull agents and personal isolated projects and distributed systems or horizontal scaling isn't a good fit for it?

Also I'm curious why not railway like billing per resource utilization pricing model? It’s very convenient and I would argue is made for agents era.

I did setup for my friends and family a railway project that spawns a vm with disk (statefull service) via a tg bot and runs an openclaw like agent - it costs me something like 2$ to run 9 vms like this.

k9294

3개월 전discuss

[dead]

k9294

4개월 전discuss

Nope, but I will think about this, thank you for the idea. Maybe it's time to start a technical blog for ottex

k9294

4개월 전discuss

There is no cold starts at all. It’s running non-stop.

Bunny bills per resource utilization (not provisioned) and since we run backend on Go it consumes like 0.01 CPU and 15mb RAM per idle container and costs pennies.

k9294

4개월 전discuss

We at ottex.ai use bunny.net to deploy globally an openrouter like speach-to-text API (5 continents, 26 locations, idle cost 3$).

Highly recommend their Edge Containers product, super simple and has nice primitives to deploy globally for a low latency workloads.

We connect all containers to one redis pubsub server to push important events like user billing overages, top-ups etc. Super simple, very fast, one config to manage all locations.

k9294

4개월 전discuss

Anecdotally, I’ve been seeing a lot of weird behavior from Opus when it decides, mid-execution, to switch to a different "simpler" solution, and that really pissed me off.

At one point, I carefully designed a spec document, forced Opus to reread it, create a plan with the planning tool that followed the spec, and use the task tool to track the implementation... AND AFTER OPUS READS THE FIRST FUCKING FILE, it says, "Oh, there are missing dependencies in project X. It’ll be hard to add them, so I’m going to throw away the whole plan and just do a simple fix..."

After that, I canceled my $200 Max plan, which I’d been subscribed to since June 2025, and decided to check out Codex

k9294

4개월 전discuss

Try ottex.ai - it has an OpenRouter like gateway with most STT models on the market (Gemini, OpenAI, Groq, Deepgram, Mistral, AssemblyAI, Soniox), so you can try them all and choose what works best for you.

My favorites are Gemini 3 Flash and Mistral Voxtral Transcribe 2. Gemini when I need special formatting and clean-up, and Voxtral when I need fast input (mostly when working with AI).

k9294

5개월 전discuss

It's actually a nice idea - an always-on micro AI agent with voice-to-text capabilities that listens and acts on your behalf.

Actually, I'm experimenting with this kind of stuff and trying to find a nice UX to make Ottex a voice command center - to trigger AI agents like Claude, open code to work on something, execute simple commands, etc.

k9294

5개월 전discuss

I've been experimenting with Gemini 3.1 Flash Lite and the quality is very good.

I haven't found official benchmarks yet, but you can find Gemini 3 Flash word error rate benchmarks here: https://artificialanalysis.ai/speech-to-text/models/gemini — they are close to SOTA.

I speak daily in both English and Russian and have been using Gemini 3 Flash as my main transcription model for a few months. I haven't seen any model that provides better overall quality in terms of understanding, custom dictionary support, instruction following, and formatting. It's the best STT model in my experience. Gemini 3 Flash has somewhat uncomfortable latency though, and Flash Lite is much better in this regard.

k9294

5개월 전discuss

You can test Gemini 3.1 Lite transcription capabilities in https://ottex.ai — the only dictation app supporting Gemini models with native audio input.

We benchmarked it for real-life voice-to-text use cases:

                <10s    10-30s   30s-1m    1-2m    2-3m
  Flash         2548     2732     3177     4583    5961
  Flash Lite    1390     1468     1772     2362    3499
  Faster by    1.83x    1.86x    1.79x   1.94x   1.70x

  (latency in ms, median over 5 runs per sample, non-streaming)

Key takeaways:

- 1.8x faster than Gemini 3 Flash on average

- ~1.4 sec transcription time for short to medium recordings

- ~$0.50/mo for heavy users (10h+ transcription)

- Close to SOTA audio understanding and formatting instruction following

- Multilingual: one model, 100+ languages

Gemini is slowly making $15/month voice apps obsolete.

k9294

5개월 전discuss

Gemini 3.1 Flash-Lite is our most cost-efficient Gemini model, optimized for low latency use cases for high-volume, cost-sensitive LLM traffic.

It provides a significant quality increase over Gemini 2.0 Flash-Lite and Flash-Lite models, matching Gemini 2.5 Flash performance across key capability areas:

Improved response quality: Aims to match 2.5 Flash performance and align with target Flash-Lite use cases.

Improved instruction following: Targeted improvements to serve as a reliable migration path for complex chatbot and instruction-heavy workflows.

Improved audio input: Improved audio-input quality for tasks like Automated Speech Recognition (ASR).

Expanded thinking support: You can control how much reasoning the model performs by choosing from minimal, low, medium, or high thinking levels. This feature lets you balance response quality and speed for your specific use case.

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Already available in Google AI Studio and OpenRouter

https://openrouter.ai/google/gemini-3.1-flash-lite-preview

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Gemini 3.1 Flash Lite Preview

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