c0brac0bra

Composable components and automatic updates were not new to React though. I guess maybe the way it did it was better than previous frameworks, using the shadow DOM and stuff

Not sure how exactly React became so standard, but I'd assume it was a combination of the backing of Facebook and the frustration with performance and developer UX of Angular, which was most popular at that time.

In our case, we had started a large project with Vue because most of our small team was familiar with it, but our tech lead decided to pivot to React because we had so much trouble hiring for Vue development in our city.

"certain other distinct features" <- there's the rub.

I wouldn't bid without a discovery session and clearly defined scope and granular requirements.

perl 6 eat your heart out

I really like Decap, but I wish that the UX was better. With a bit more polish it would be very compelling.

This is similar: https://deepgram.com/agent

Yes. I think $0.05/min is a high multiple of what other agent-oriented realtime TTS products are charging.

And that's a shame

I would prefer "octopoden"

Not really. My city seems to be mostly inaccurate trolling.

You're not missing anything.

Yea they do E2E checks with playwright as well, among other things. A bunch of stuff could get cached from those checks I suppose, especially if it's user-written code.

Wanted to say thanks for apify/crawlee. I'm a long-time node.js user and your library has worked better than all the others I've tried.

I've wondered about this as well. Is there a way to have a small, efficient LLM model that can estimate general task complexity without actually running the full task workload?

Scoring complexity on a gradient would let you know you need to send a "Sure, one second let me look that up for you." instead of waiting for a long round trip.

Excellent; thanks for the info.

I've been developing with Deepgram for a while, and this is one of the coolest demos I've seen with it!

I am curious about total cost to run this thing, though. I assume that on top of whatever you're paying Cerebrium for GPU hosting you're also having to pay for Deepgram Enterprise in order to self-host it.

To get the latency reduction of several hundred milliseconds how much more would it be for "average" usage?

A lot of modern cloud deployments read from a secret management system or vault at deployment time, and the secrets are made accessible to the application through various indirect methods so they cannot be accessed later on (i.e. if someone were to gain access to a running Kubernetes container).

At no point does the application have access to the vault itself, and access to read the vault is guarded by IAM role permissions.

Considering switching some ancient Talend and Airflow processes over to this if I can get the time.

I haven't built too much UI at all, but with a little that I did it worked fine.

I recall back a few years reading some articles speaking about the sun entering in a Grand Solar Minimum cycle similar to the Maunder minimum, and that the result could be global cooling, etc.

Not sure if there's been additional research or conjecture since then.

After trying out a bunch of tools, I found Appsmith worked best for me but for some cases I had to move on to Windmill (which is not exactly low-code but can be)

I absolutely love Windmill. The ability to stitch scripts together into workflows is great.

Aura can stream responses and has a time-to-first-byte around 200-400ms

For $5 for 20 hours of audio you can try https://deepgram.com.

They give $200 of credit.

Keep in mind that Blackhole has to be licensed for commercial use

clickhouse-local had been astonishingly fast for operating on many GB of local CSVs.

I had a heck of a time running the server locally before I discovered the CLI.

There's examples of whisper.cpp that run in the browser. Pretty laggy but they work.

That's what exactly what I did a fairly basic distributed cron and it worked fine.

As a technical person who finds extreme friction with the sales side, it would be nice to find such a person.

I'd like to see Deepgram Aura on here.

You can check out PicoVoice Eagle (paid product): https://picovoice.ai/docs/eagle/

You pass N number of PCM frames through their trainer and once you reach a certain percentage you can extract an embedding you can save.

Then you can identify audio against the set of identified speakers and it will return percentage matches for each.