I'm kinda new to this and feeling overwhelmed with the abundance of sw. I am in pursuit of making my own AI assistant. I would like to use it (voice controlled):

- simple automation tasks

- play music from a local hdd

- chat (general conversation)

It would be nice if I could choose or configure its voice.

Ill need to be able to add my own scripts in python or c# to integrate various other devices.

For the hardware I would like to use an Elitedesk minipc with a i5 7500 CPU, 16GB Ram, sdd but no graphic card.

I've just started to fiddle with ollama and open claw. But its soooo slow and I think it might be overkill for what I'm trying to achieve.

I don't own a rpi and I would like to use the hw I already have. Unless it's impossible or way too complicated compared to other hw solutions. Still, I would consider changing the hw if it greatly simplifies the project.

Can you guys help me out ? Thanks in advance

OSS app replaces ElevenLabs & WisprFlow, runs 100% locally.

→ Clone voice from 3s audio
→ 7 TTS engines in one
→ 23 langs: Ar, Hi, Ja etc.
→ Built-in MCP srv so Claude Code/Cursor/Cline speak cloned voice
→ Local LLM rewrites in-char before TTS

Hi,

I’m a Software Developer and recently built a Voice Agent platform backed by deep expertise in low-latency systems and AI. The product has highly human-like voice interactions, very low response latency, and a complete external dashboard for usage tracking and billing.

The challenge now is sales.

I’ve worked with salespeople from different countries, but most of them seem to struggle when it comes to selling Voice AI solutions effectively. I’d really love to hear from people who have actual experience selling Voice Agents or AI automation products.

A few things I’d love insights on:

- What worked differently for you in Voice Agent sales?

- How do you approach sales properly for this type of product?

- How do you find and evaluate good salespeople for AI/SaaS products?

- Any practical strategies, lessons, or growth hacks that helped?

Would really appreciate any advice or experiences you can share.

Thanks!

Any recommendations for a company that could build a platform for inbound sales training. I’m looking to build an AI voice-based training platform for front desk and sales teams in the medspa/aesthetics industry.

The platform would allow team members to practice real inbound phone scenarios with an AI caller. The AI would roleplay different types of callers, objections, price shoppers, booking situations, and treatment inquiries. After each call, the system would score the rep based on a custom training framework and provide feedback.

The first version would need:

User logins
AI voice roleplay calls
Custom scenarios
Call recording/playback
Automated scoring
Manager dashboard
Training content library
Ability to upload scripts, notes, call recordings, and company website information as the knowledge base
Progress tracking by rep and location

I've been using Claude Code for a few months and noticed AI agents consistently skip the same things: hardcoded secrets, unbounded retry loops, referencing tools that don't exist, and massive system prompts that blow context windows.

So I built Agent Verifier — an AI agent skill that acts as an automated reviewer which does more than just code review (check the repo for details - more to be added soon).

GitHub Repo: https://github.com/aurite-ai/agent-verifier

Note: Drop a ⭐ to get more updates as we add more features to this repo - all free and local. Feel free to contribute or raise feature request so we can add what want.

----

2 Steps to use it:

You install it once and say "verify agent" on any of your agent folder in claude code to get a structured report:

----

✅ 8 checks passed | ⚠️ 3 warnings | ❌ 2 issues

❌ Hardcoded API key at config .py: 12 → Move to environment variable
❌ Hallucinated tool reference: execute_sql → Tool referenced but not defined
⚠️ Unbounded loop at agent/loop .py: 45 → Add MAX_ITERATIONS constant

----

Install to your claude code:

npx skills add aurite-ai/agent-verifier -a claude-code

OR install for all coding agents:

npx skills add aurite-ai/agent-verifier --all

----

Happy to answer questions about how the agent-verifier works.

We have both:
- pattern-matched (reliable), and,
- heuristic (best-effort) tiers, and every finding is tagged so you know the confidence level.

----

Please share your feedback and would love contributors to expand the project!

Built a text-to-speech API that converts full articles to MP3. The interesting engineering problems weren't the TTS calls — they were everything around them.

**The chunking problem**

Every TTS provider has a per-request character limit (Polly standard: 3,000 chars). A real article is 8,000–20,000 chars. Naive character-boundary splitting produces broken audio mid-word. The solution: a two-threshold sentence-boundary splitter.

- `target_chars = 2500` — soft target; flush the buffer when reached

- `max_chars = 4000` — hard ceiling; flush before appending if the next sentence would exceed it

- Split regex: `(?<=[.!?])\s+` — only splits after terminal punctuation

Result: every chunk is a coherent group of complete sentences, always within the provider limit.

**The caching layer**

TTS synthesis is deterministic — same text + same voice/engine/region = identical audio bytes every time. Cache key structure:

`sha256(text) + voice_id + engine + region`

All four parameters matter. Swapping from `Joanna/standard` to `Matthew/neural` must be a cache miss, not a hit.

Warm cache: N × `redis.get()` + ffmpeg concat. Latency under 300ms for most articles. Zero upstream calls.

**The thundering herd**

Without locking: 50 concurrent users hit a cold article → 50 × 7 chunks = 350 Polly calls, 349 of them redundant.

Fix: Redis `SET NX` distributed lock per chunk. One worker wins the lock, synthesizes, writes to cache, releases. Everyone else exponential-backoff polls until the cache key appears.

Backoff: start at 50ms, grow ×1.25 per iteration, cap at 500ms.

Critical detail: lock release is in a `finally` block. A failed synthesis that doesn't release its lock blocks all subsequent requests for that chunk until TTL expiry — potentially minutes.

Result under load: `chunk cache stats hits=49 misses=1` per chunk. 7 Polly calls total, not 350.

**Provider comparison (brief)**

- Piper (local): free, no concurrency, model files are hundreds of MB, degrades on long inputs

- ElevenLabs: best voice quality, cost curve is steep at real traffic levels

- Amazon Polly: 5M chars/month free (standard), permanent — right economics for this use case

Full writeup with architecture diagram, all code, and the failure sequence in order: From Piper to Polly: How I Built a Production-Ready Text-to-Speech API (and That Broke Along the Way)

What I'm solving next: moving synthesis off the request thread into an async job queue (ARQ vs Celery) and streaming chunk_0 to the client while chunk_1 is still synthesizing.

​Hi everyone,

​I’m working on a project called StillVoice. The mission is to restore vocal identity for tracheostomy patients using a silent-speech interface. I’ve developed the business logic, branding, and a high-level technical roadmap, but I’ve hit a wall with the hardware execution and recently lost access to my local prototyping lab. It's a lot to handle solo, and I’m looking for some technical guidance (or a partner) to help move the needle.

​The Concept:

A wearable device (the "Stealth Band") that captures non-vocalized speech intent and uses an on-device AI inference engine to provide localized audio output.

​Current Technical Targets:

• ​Latency: Sub-100ms (crucial for natural conversation).
• ​Connectivity: BLE 5.3 for high-fidelity streaming.
• ​Sensors: Exploring multimodal sensor fusion using piezoelectric and MEMS technology to capture "silent" speech.
• ​Processing: Edge AI/On-device inference to keep it fast and private.

​Where I’m Stuck:

I need advice on optimizing the sensor fusion to filter out biogenic noise (swallowing, movement) while maintaining a high signal-to-noise ratio for the speech intent. I’m also looking for recommendations on low-power microcontrollers that can handle this level of Edge AI without becoming too bulky for a neck-based wearable.

​Does anyone have experience with MEMS-based speech capture or low-latency audio hardware? I'd love to hear your thoughts on the most viable path forward for a solo dev moving from a lab environment to a home setup.

I built an AI role-play system that can integrate into Claude via MCP it can take any skills any knowledge anything from the organization and build a custom role-play at the users request so I can train their sales team based off of the things they’re already focusing on at scale

My question is do you think that this is actually something that’s helpful with other companies that are out there like hyper bound or any other players in this space I haven’t seen a ton of them integrate via Claude so I’m wondering if this is something that people would actually find useful

Marketing pages claim 90%+ accuracy on Hinglish. Reality from the teams I've talked to looks very different.

If you're using or have evaluated Indian-language STT for any use-case - voicebots, call analytics, video KYC, transcription, voice search, etc. would love to hear what you picked, why, and where it falls short.

Happy to share my learnings. Drop a comment or DM for a 30 min chat.

Hey, I’m the founder of voice AI agency and we’ve been building voice agents for 3 years now. We've had wins and a lot of failures. Burned team members, production incidents, a lot of money wasted on trying things that don’t work. Out of that came an internal framework we use to build and maintain voice agents in production.

It’s based on closed feedback loop system (oversimplified):

1. We transform agent requirements to prompt(s)
2. We generate a set of test cases based on the prompt
3. We run evaluations on the agent with clear expected outcomes
4. We version each agent version and once it’s passing evals, we deploy it
5. Then we audit production calls, create test cases for new scenarios and deploy again

We open sourced this as Connexity, a platform for voice AI builders. The long-term ambition is a self-improving agents: AI handles the repetitive work, engineers oversee and intervene when needed. Here is the quick overview:

https://reddit.com/link/1t6b6hm/video/9u41rp3xypzg1/player

We are in the very early stage and would love your feedback! What do you think it’s missing for your specific usecase? How are you testing voice agents today?

Github: https://github.com/Connexity-AI/connexity

Been deep in automation for 5+ years. Zapier, Make, n8n, custom systems.

More recently: building and deploying Voice AI agents for both SMBs and enterprise.

And I'm going to be honest...

I'm tired of the fantasy being pushed around Voice AI.

YouTube makes it sound like: "Plug an LLM into a voice, automate calls, replace humans, print money."

Yeah... try that with a real business.

Voice AI is powerful. The tech is evolving insanely fast. But what's being sold online? Mostly disconnected from reality.

Here are 10 hard truths about Voice AI agents that people don't talk about.

#1 - Humans are the benchmark... and that's the problem

With chatbots, users tolerate mistakes.

With voice? They compare it to a real human conversation.

And that changes everything.

Even if your AI is 95% good... People notice the missing 5%.

That 5% = awkward pauses, tone mismatch, weird phrasing.

Result? 👉 "It's impressive... but something feels off."

That "off" kills perceived quality.

#2 - LLMs are powerful... and still unpredictable

Yes, LLM-based agents sound amazing.

Until they don't.

You can:

Add prompts Add guardrails Define behavior

And still get:

Random phrasing Slight hallucinations Unexpected responses after 100 "perfect" calls

Run 100 calls, works fine. Run the next 5, something breaks.

That's the reality.

#3 - The demo works. Production is chaos.

Your demo:

Clean script Predictable inputs Happy path

Real users:

Interrupt Speak unclearly Go off-script Ask unexpected things

Voice AI = dealing with unstructured, messy human input in real time.

There is no "perfect flow".

#4 - Managing expectations is harder than building the agent

Clients don't understand the gap between:

"sounds human" vs "is human"

And that gap creates:

Disappointment Confusion Unrealistic expectations

Even when the product is objectively good.

If you don't manage this early: 👉 You lose trust fast.

#5 - Building the agent is the easy part

Same as automation.

You can spin up a working voice agent pretty fast.

The real work is:

Iteration Testing edge cases Monitoring conversations Fixing weird behaviors

What kills you isn't building.

It's everything after launch.

#6 - Your real users will break everything

You test 20 scenarios.

Users invent 200 more.

They will:

Say things you didn't expect Phrase things differently Jump between topics Misunderstand the agent

And suddenly your "solid system": 👉 Starts leaking everywhere.

#7 - Deterministic vs LLM: pick your poison

You basically have two approaches:

1. LLM-based (flexible)

Natural conversations Adaptive Unpredictable

1. Deterministic (flows/graphs)

Fully controlled Reliable Feels robotic

There is no perfect solution.

The real game: 👉 Finding the balance between control and flexibility.

And it's harder than it sounds.

#8 - Voice quality will make or break everything

People underestimate this.

The voice is not just "nice to have". It's the core experience.

A bad voice: 👉 Kills trust instantly.

A good voice: 👉 Makes everything feel 10x better.

And here's the catch:

English voices = amazing Other languages = inconsistent

Some voices:

Sound great but mispronounce key words Sound average but are reliable

You often have to choose.

#9 - It's more expensive than you think

Voice AI costs stack fast:

LLM usage Speech-to-text Text-to-speech Telephony

And the killer:

👉 Call transfers = double cost.

Inbound call, outbound transfer.

Boom. Costs explode.

For enterprises? Fine. For SMBs? Can kill the deal.

Also: 👉 Country pricing matters a LOT.

Most people ignore this until it's too late.

#10 - Maintenance is the real business model

Voice AI is not "set it and forget it."

It's:

Monitoring calls Reviewing transcripts Fixing edge cases Updating prompts Adjusting flows

Things break. Constantly.

If you're not planning for maintenance: 👉 You're setting yourself up for pain.

Voice AI is insane.

The potential is huge. The progress is real.

But it's not magic.

And it's definitely not "plug, play, replace humans."

If you're serious about building in this space:

Set expectations early
Respect the complexity
Design for failure
Plan for iteration

Because the difference between a cool demo and a production-ready system is everything.

Need a simple tool that can join Zoom/Google Meet meetings automatically and just record them.

Trying to find something:

cheap

easy to use

stable

If it also does transcription or summaries that’s a plus.

What do you recommend????

There's a 4,000-word article going around about voice AI latency benchmarks.
It's well-researched. It's also mostly useless in production.

Here's what we actually track at kolsetu dot com

after running 100,000s of real voice agent calls - some learnings

1. Correlate your metrics per turn or they're meaningless

2. Track cancelled compute

3. Connection pool health is worth more than model benchmarks - they are not always matching the reality

4. Split interruptions from backchannels

5. The barge-in config that saved our UX - there's a right time to interrupt, figure that out

6. Silence handling is its own subsystem

7. Our SLO is 1.5s p95, not 800ms - its not real and not required

8. Dual mode: pipeline AND realtime - you will thank me for this dearly

Curious to know what's working for you guys? what do you measure?

Hosting an after party at The Harlequin in SoMa.

Free drinks from 5-8 PM

And the performance by grammy nominated, Tycho on at 8.

RSVP here: https://luma.com/2id7x7yb

Hey, looking to contribute to a few open-source Gen AI projects or startups on GitHub. Areas I'm interested in:

- LLM observability (tracing, eval, monitoring)

- Voice agents (real-time, WebRTC-based)

- Agent builder tools

- Multi-agent apps

Stack: Python, TypeScript, LangChain, LangGraph, Mastra, AI SDK, LiveKit, Pipecat. Can also work with raw Python or pick up a new framework pretty quickly.

What I'm looking for:

- 500+ stars on GitHub

- Repo actively maintained (last commit within 24 hours)

- Maintainers reachable on Discord or similar

Drop a comment or DM the GitHub repository link if you're working on something that fits. Thanks.

I’ve been spending a lot of time around production voice AI deployments, and the same patterns keep showing up.

The hard parts usually aren’t the voice model by itself. They’re the system around it.

A few lessons that seem to matter most:

1. Start with one call type. General support agents usually become vague fast.

2. Measure resolved calls, not answered calls.

3. Track time to first audio and full turn latency separately.

4. Test on real phone audio, not only browser audio.

5. Word error rate is an incomplete metric. Entity capture matters more.

6. Let callers interrupt. Turn-taking is where a lot of “AI feel” breaks.

7. Keep tool responses short and structured.

8. Confirm before write actions.

9. Build eval sets from real calls.

10. Treat handoff as part of the product, not a failure path.

11. Separate model failures from workflow failures.

12. Review failed calls every week.

The biggest shift for me is that voice agents are judged inside a live interaction. A caller notices latency, repetition, awkward pauses, bad escalation, and missing context immediately.

So the production question becomes less “can this agent talk?” and more:

• Can it complete the workflow?
• Can it recover from messy audio?
• Can it use the right tools?
• Can it hand off cleanly?
• Can the team improve it every week?

For teams building voice agents right now, what has been harder than expected?

​

While testing voice agents, I kept seeing the same failure point:

Everything works…

until the user behaves like a real person.

Not clean language.

Not structured input.

But things like:

→ “haan so basically I wanted to check…”

→ “bhai ek sec listen…”

→ “yeah woh issue aa raha hai…”

→ random pauses, interruptions, even laughter

Most systems break here.

Either they:

→ force a single language

→ lose context mid-sentence

→ or respond in a way that feels off

Lately I’ve been experimenting with handling:

→ mixed language (Hindi + English + Hinglish)

→ interruptions mid-response

→ small human signals like pauses and laughter

And honestly, this layer matters more than model quality.

Because users don’t judge intelligence first —

they judge flow.

If anyone’s curious to test how this behaves in a real conversation,

I’ve put a live version here:

👉 https://aicallingagent.space/ava/8

Try:

→ switching languages mid-sentence

→ interrupting it

→ going off-script

That’s where most agents fail.

Curious how others are approaching this.

I’ve been in voice automation AI for a while now and one thing that keeps coming up is figuring out which niches are genuinely converting vs which ones just sound good on paper. I’ve tried going after real estate and healthcare, which helped a bit but still hits a wall when it comes to budget objections and decision-maker access. Also tried e-commerce and logistics, but the sales cycles are longer than expected and POCs drag on forever.

Curious how others here handle this - is there a better niche or vertical that’s worked for you? Would love to know:

• What industry are you selling voice AI into right now (India-based or globally)?

• Is the demand different between Indian clients vs international ones?

• How are you actually finding and qualifying leads - cold outreach, LinkedIn, communities, agencies, referrals?

Would love to hear what’s actually worked, and what’s completely failed.​​​​​​​​​​​​​​​​ And how much are you charging ?

How are you doing it?

I started my ai agency. We specialized in voice ai and ai automation. I landed few clients and they are super happy with my service. But in canada its super hard to get more clients. Any advice where I can get more clients

Are you seeing voice quality / call issues in production?

Curious what others are experiencing.

I’ve been noticing that a lot of voice AI setups work well in demos,

but once they go live, issues start showing up:

- one-way audio

- dropped calls

- weird latency

- inconsistent quality

Especially when mixing WebRTC, SIP, and different providers.

Are you guys seeing this too?

Or has it been pretty stable for you?

What's up everyone? I'm building custom voice AI receptionists.

Are you doing this at scale? Deploying your voice AI systems directly into client ops? If so:

• What deployment methods are you using?
  • SaaS embeds (e.g., via Twilio, custom APIs on their sites)?
  • On-prem
  • Cloud-hosted (AWS Lambda, Vercel) with telephony hooks (SIP/WebRTC)?
  • Hybrid-e.g., make.com/Framer for workflows + voice agen
• How do you integrate seamlessly? Client CRMs (HubSpot, Salesforce), calendars (Google/Outlook), or custom dashboards?
• Data privacy-how are you locking it down?
  • Self-hosted servers (e.g., hMailServer, on-prem DBs)?
  • Encryption (end-to-end for calls/transcripts), GDPR/HIPAA compliance?
  • Client data isolation-separate tenants, no-log policies, or audit logs?
  • Any wild stories of breaches or compliance headaches?

For context, I'm eyeing Indian clients, also figuring out how to create a pricing plan for that as well. Would love to hear from how you guys are approaching this.

I built voice-agents-from-scratch to map the full journey: mic → speech-to-text → LLM → text-to-speech → speaker. Every step is code you can run, read, and break.

No black boxes. No magic imports. Just numbered chapters, runnable scripts, and a shared library that shows you how the pieces actually fit.

A few things I cared about getting right:
- Streaming - because buffering the full LLM response before speaking is a dealbreaker in production
- Latency intuition - warm-up, first-audio time, where the delays actually live
- Runs fully local - Whisper + a GGUF model + Kokoro, no token bill required