u/HpartidaB — reddlx

I built a free transcription tool where your audio never leaves your browser (Whisper on WebGPU, no sign-up, no caps)

Every "free" transcription tool I tried had a catch: 30 trial minutes, a sign-up wall, watermarked exports — and all of them upload your audio to their servers. Otter's free plan lets you import 3 files. Not per month: ever.

So I built Gate32: Whisper running inside the browser via WebGPU. No account, no minute caps, no watermark. The privacy claim is verifiable — load the page, go offline once the model is cached, and it still transcribes, because there is no backend to upload anything to.

It handles audio and video (or records from your mic), lets you fix the text with synced playback, and exports TXT, Markdown, SRT, VTT and JSON. Spanish and English UI, dozens of languages. Open source.

https://gate32.autoritasai.com/en/

What I'd genuinely like feedback on: how long it takes on your machine. On my laptop 9 minutes of audio took 1:33 with WebGPU, but I have no idea how it behaves across different GPUs — and without WebGPU it falls back to WASM, which is much slower. Timings very welcome.

u/HpartidaB — 13 days ago

▲ 5 r/learnAIAgents

I built a stress testing tool for AI agents after realizing most demos don’t survive real users

Over the last few months I’ve been working on AI agents, especially conversational agents for sales/support flows.

One thing became obvious pretty quickly:

Most agents look great in a controlled demo.

But they start breaking when the user behaves like a real person.

Not maliciously. Just realistically.

They ask unclear questions.

They compare prices.

They get impatient.

They ask for things the agent shouldn’t promise.

They change context halfway through.

They try to force discounts.

They ask about refunds, guarantees or legal conditions.

They insult the bot.

They don’t answer properly.

And suddenly the “working agent” is not that solid anymore.

So I started building a tool called Arena.

The idea is simple: instead of manually testing an agent with a few happy-path conversations, Arena simulates different user profiles and stress-tests the agent before it reaches real users.

For example:

- hostile user

- indecisive buyer

- urgent buyer

- price comparer

- refund seeker

- sceptical user

- over-informed user

- silent user

After the test, it generates a score from 0 to 100 and flags issues like:

- hallucinated policies

- missed escalation

- over-explaining

- context drift

- bad objection handling

- weak behaviour under pressure

The more I build this, the more I think the next bottleneck won’t be “can we build agents?”

It will be:

Can we prove they behave well enough before putting them in front of users?

Curious how others are handling this.

If you’re building AI agents, how are you currently testing them before production?

Manual testing? Eval frameworks? Internal QA? Nothing yet?

reddit.com

u/HpartidaB — 3 months ago

▲ 6 r/AI_Agents