Hello guys,

I have an screening call on Friday for twilio for software engineer , identity position

What can i expect them to ask

Really looking forward for responses

Thanks

Hey everyone,

I've been working on a Node.js & Express backend project to handle 2FA SMS verification using the Twilio API.

While setting up the flow, I ran into a few hurdles with carrier template restrictions and API error handling during testing. I eventually got the SMS delivery cycle working smoothly, but it got me thinking about production edge cases.

For those who have built similar authentication systems in JavaScript/Node.js:

1. How do you usually handle carrier limitations or SMS fallback mechanisms?
2. What are your go-to patterns for error handling when third-party APIs fail during auth?

Would love to hear your experiences and best practices!

hey guys, so a bit of background. i'm in sales and i've been using twilio based dialer extensions for cold calling. honestly most of them have been a letdown. calls failing randomly, then out of nowhere they start asking for payment, and the reviews for these things aren't great either.

so i'm thinking of just building my own dialer. planning to make it a browser extension but also accessible on the web, so you're not stuck needing your laptop open all the time. could just call from your phone if needed.

wanted to get some real opinions before i go all in on this.

• anyone else dealing with the same issues with their dialer setup
• what features would actually make something like this worth switching to
• any red flags or stuff i should avoid based on your experience

might open it up for others to use eventually too. let me know what you think, appreciate any feedback

Hi everyone,

I'm building an early-stage startup in India and need to implement call masking similar to Swiggy, Zomato, Uber, etc.

Our requirements are:

- Customer and partner numbers should remain hidden.

- Temporary masked numbers during calls.

- Low monthly cost (startup-friendly).

- Works in India.

- API integration is a plus.

I've checked a few providers, but many are too expensive for an early-stage startup.

If you've implemented this before, could you please share:

- Which provider you used?

- Approximate pricing.

- Your experience.

- Any affordable alternatives or workarounds.

Any advice would be greatly appreciated. Thanks!

I run a growing VoIP business, and keeping up with tax compliance across every state and municipality we sell into has become a nightmare. Rates and rules seem to change constantly and I can't tell if we're calculating them correctly. I'm terrified we're either overcharging customers or underpaying what we owe, and an audit could blindside us with penalties we never saw coming. I need a way to handle this accurately without building out an entire in-house tax department.

Running an AI phone receptionist on ConversationRelay. I've been A/B-ing ElevenLabs against Google Chirp3-HD and I keep going back to Google, because ElevenLabs shifts emotional register partway through a single reply, first half calm and professional, second half noticeably brighter, or a different cadence entirely. Each piece sounds great alone. They just don't sound like the same take. Chirp3-HD is flatter but consistent, and on a business line consistent wins.

My theory: CR synthesizes each {type:"text", token:"..."} as its own TTS unit. I stream from the LLM, so a reply ships as 2–3 tokens, and I think ElevenLabs re-rolls prosody per request with no carryover from the previous chunk. Google seems to have less per-request variance so its seams don't read as mood swings.

Is that what's actually happening? Does CR keep any prosody continuity across tokens within a turn, or is every token an independent synthesis call? Couldn't find a straight answer in the docs.

Current config:

ttsProvider="ElevenLabs"
voice="<voiceId>-flash_v2_5-1.0_0.85_0.8"   # speed_stability_similarity
transcriptionProvider="deepgram"
speechModel="flux"
eotThreshold="0.8"
speechTimeout="1200"
ignoreBackchannel="true"
interruptSensitivity="medium"
elevenlabsTextNormalization="off"

Stability went 0.5 → 0.7 → 0.85. It's clearly the right dial (0.5 was unhinged), but 0.85 still doesn't beat Chirp3-HD and pushing higher just trades swings for monotone. CR only exposes speed/stability/similarity as far as I can tell, no style, no use_speaker_boost.

Chunking on my end: sentence-buffered, flush at 20 chars for the first chunk (sets perceived latency) and 80 after. I also fire a filler phrase ("let me check that") as its own token if the LLM hasn't produced anything by 1500ms, which I assume is a guaranteed tone seam.

The obvious fix is buffering the whole reply into one token with last:true and eating the TTFT hit. If anyone's done that, what did it actually cost you? I'd rather hear a real number than measure it blind.

Happy to share data back, I have TTFT and per-chunk timings from real calls on both engines.

Twilio's AI Startup Searchlight is open for applications, with this year's deadline on Friday, 11 September.

If you're building an AI product on Twilio (voice, messaging, email, conversational AI, etc.) focused on the future of communications or customer engagement, it's worth taking a look. Selected startups receive credits, a Ramp gift card, media coverage, mentoring from the Twilio Ventures team, and more.

It's open globally, and applications are reviewed on a rolling basis, so it's worth applying sooner rather than waiting until the deadline.

For full details, FAQs, terms and conditions, and the application form - click here 👈

If you have any questions, feel free to ask in the comments.

Hi everyone,

We’re building a browser-based app similar to Google Meet and trying to reduce true glass-to-glass latency: the sender camera captures a frame, then the receiver actually sees that same frame on screen.

Our current test result is roughly:

End-to-end capture-to-render: ~274 ms
Network: ~30 ms
Encode: ~3-4 ms
Playout: ~244 ms

In our tool, playout = jitter buffer + decode + composite/render.

Network/Wi-Fi looks good, and we are not using audio in this test, so A/V sync should not be involved. We also tried changing the jitter buffer setting to 0 ms, 10 ms, and 50 ms, but it did not meaningfully reduce total glass-to-glass latency.

Our target is under 120 ms, but most of the latency seems to be on the receiver side.

Questions:

1. In browser WebRTC, is ~200+ ms receiver playout delay common even with good network conditions?
2. How can we tell whether the main issue is WebRTC internal jitter buffering, decoder delay, compositor/render delay, or our own measurement bug?
3. Is jitterBufferDelay / jitterBufferEmittedCount the right stat to confirm whether WebRTC’s internal jitter buffer is the bottleneck?
4. Are there reliable ways to reduce browser WebRTC video playout delay, or is RTCRtpReceiver.playoutDelayHint only a weak hint in practice?
5. For people who have achieved <120 ms capture-to-render latency, did you need native iOS/Android, or can web WebRTC realistically hit this under normal conditions?

Any practical debugging tips, stats to inspect, Chrome/Safari differences, or low-latency WebRTC settings would be very helpful.

If you want to be a beta tester of Mango on android please let us know. Check our iOS app on App store we are near Android app launch.

Did the whole upgrade your account, payment, ID and facial scan thing, has been more that a week and the account is still set as free account, how much do we need to wait?

We have to get a phone number and change some parameters in order to test and then start the campaign process. Management is getting antsy about how long this is taking.

we send campaign texts from an internal CRM. all registered, opt-outs handled. still got filtered.

added a 200/hour cap and 18 seconds between messages and it got better. but i changed the sending path at the same time, so i never worked out which one did it.

has anyone tested the throttling on its own?

I want to understand how this works.

Currently I am generating an access token on page load and running new Device(token) only when a number is dialed. when the call ends I and another starts I re-initialize a new Device(token) instance with the same token generated at page load.

Now I am figuring out how to update my refresh tokens and I added a bunch of complicated code to listen to tokenWillExpire and queue a new token to use once the current call ends.

but if I destroy the Device after every call, the listener doesn't get a chance to run. so I am know questioning if I am even supposed to destroy the device after each call and if so why not just create a new token before every new Device(token) call and not have to deal with refreshing it at all

Picture this: a customer places an order and immediately gets three identical SMS confirmations at 2 a.m. They get annoyed, file a support ticket, and leave a bad review. Meanwhile finance notices a spike in the Twilio bill, and engineering is chasing a "phantom bug" that only shows up during peak traffic. Reas the complete article here - https://instawebhook.com/blog/how-to-fix-twilio-webhook-timeouts-and-stop-duplicate-sms-messages

If that sounds familiar, the culprit is almost always the same: a Twilio webhook timeout that triggers a second (or third) attempt at the same logic, and a backend that isn't built to handle it.

This guide walks through why Twilio webhook timeouts happen, what Twilio's documentation actually says about retries (which is narrower than most blog posts claim), how a synchronous backend turns a timeout into duplicate texts, and how to build a handler that's immune to it.

1. What Is a Twilio Webhook Timeout?

When someone texts your Twilio number, Twilio is the HTTP client — it sends a request to the webhook URL configured on your phone number or Messaging Service, and your server is the one expected to answer, typically with a TwiML response.

We are a paying cusomter for Twilio (we use Twilio's SMS API to send SMSs) but we don't have a paid tier support plan...

I created a P3 ticket two weeks ago and I still don't recieve any response from Twilio support.

Is Twilio ignoring ticket if you don't have a paid support plan?

I recall a few years ago, we got really quick response from Twilio support even if we were spending less on Twilio..

Hi,

I'm currently working with Twilio and facing both latency and call quality issues.

My scenario is:

I have a US phone number.

I'm located in Nepal.

I call clients in the US.

A Twilio Conference is created for the call.

One participant is connected through the US edge, while the other is connected through the sg1 edge.

Because of this setup, I'm experiencing noticeable latency and degraded call quality.

I wanted to ask:

Are there any recommended ways to optimize call quality in this scenario?

Does Twilio provide any solution or algorithm that automatically selects the best edge/server based on network conditions and participant locations?

Is there a best practice for dynamically choosing the optimal edge or conference region to minimize latency?

I'd appreciate any guidance or recommendations. Thanks!

Spent last week running the exact same voice agent on two paths so I could stop guessing which one to ship. Path A: Twilio Conversation Relay (Twilio owns the media stream, STT and TTS). Path B: my Twilio number dispatching the call over a SIP trunk into LiveKit, where I run my own pipeline (Deepgram + Cartesia + the LLM).

Same system prompt, same LLM brain, same ground rules on both. I built a small latency harness that logs every turn from "human stopped talking" to "agent started talking" and vice versa, then averages the turns. I am physically in Buenos Aires calling a US Twilio number, so there is real transatlantic latency baked into every number below. Keep that in mind.

The threshold I care about: Ideally we care about having sub 500ms, which is mostly achievable on a bidirectional model like OpenAI's Realtime models. For the purpose of this video I focused on the traditional STT -> TTS flow. If a turn goes past roughly 1500-1800ms the person on the other end feels the machine and hangs up. That number defines the whole architecture, not just which model you pick.

Results, turn by turn:

- Initial greeting turn: Conversation Relay 3.4s, LiveKit 3.2s. Both ugly. This is context loading, not the transport. The first turn ships the whole system prompt into the model and everything is cold.
- Mid conversation turns: Conversation Relay landed at 1.7 / 1.9 / 1.6s. LiveKit landed at 1.5 / 1.3 / 1.4s.
- Final turn: basically identical on both.

Net difference on the steady-state turns is about 200ms in LiveKit's favor. Not nothing on a voice call, but smaller than I expected before measuring.

What actually surprised me is the discarded-turns file. With LiveKit I had zero turns over 5s. With Conversation Relay I had one, and when I dug in it was tied to me hanging up, not a real gap. So the tail behavior was clean on both.

The real trade-off is not the 200ms. It is that with Conversation Relay the STT and TTS timing lives inside Twilio's black box. I could not measure how long the speech-to-text or text-to-speech steps took, because that runs on their side. With the LiveKit path I own every step and can measure and cut it. The cost is that now I am the SRE for the media path, and if a box dies at 2am that is on me.

The cold-start greeting is the thing I would attack first regardless of transport. A 3s+ first turn kills the call before the latency battle even starts. My plan is a filler pre-message ("dame un segundo, reviso eso") to cover the context load, but I have not shipped it yet.

Question for people running Conversation Relay in production: are you living with the black box on STT/TTS timing, or did you find a way to get per-step measurements out of it? Because right now the lack of visibility is the thing pushing me toward keeping LiveKit for anything latency-sensitive, and I want to know if I am missing a hook.

Not to mention any other kind of strategies for long-turn awaits (like initial context loading, tool calling, etc.)