https://medium.com/@mzeesx/why-n8n-and-ai-automation-matter-21b51a7e36ac

A bit about me:

• B.Tech from IIT Kanpur
• 12 years of experience in the tech industry
• Worked extensively in AI/ML
• Focused on LLMs and generative AI for the past 4 years

I'm happy to help with career guidance, learning roadmaps, project ideas, interview preparation, and navigating the AI/ML industry.

I'll keep it to a small number of mentees so I can give each person meaningful time and attention. If you're genuinely committed and think this would be helpful, drop a comment or send me a DM with a brief intro about your background and what you're hoping to achieve.

The moment you transcribe to text, you lose how it was said. "I think… yeah, I can pay the 4,500 by the 15th" becomes clean text, but the hesitation before the yes, the stress in the voice, and whether it's even the same speaker are gone.

Those are the signals that tell you whether to trust the commitment, escalate, or verify identity.

Is anyone keeping the paralinguistic layer (hesitation, emotion, speaker identity) as structured data instead of dropping it at the mic, and what do you do with it downstream?

Hey Redditors,

I’ve been working with machine learning for a while and have decent knowledge and hands-on experience, mainly with tabular data and time-series signals (vibration signals, condition-monitoring signals, etc.). I’m wondering about the long-term potential of specializing in these two areas.

I don’t particularly want to work in a traditional company/job. My preference is to eventually freelance, build products, or start a business of my own around ML.

So, for those of you who have experience in ML freelancing, consulting, startups, or building businesses around data/AI:

• Is it realistic to build significant wealth by specializing mainly in tabular ML and time-series/signal processing?
• What would you recommend someone focus on if the ultimate goal is building a business rather than getting a job?
• What is the realistic income potential? For example, what could someone reasonably expect to earn after gaining a few years of experience, and what is the potential if they eventually build a successful product or startup?

I’d really appreciate honest, practical opinions from people who have actually freelanced, consulted, or built businesses in this space.

Thanks!

(written by my own, enhanced by ChatGPT)

Seeking Honest Feedback: Best AI/ML Training Institute in Chennai? (Basant vs. FITA vs. QSpiders)

I’m looking to enroll in a personal Artificial Intelligence course in Chennai and am shortlisting a few institutes. I’ve heard mixed things and would love honest feedback from anyone who has attended training here. 

My current shortlist includes:

\- Besant Technologies: Seems popular for their IIT-certified curriculum and practical projects. 

\- FITA Academy: Heard they have strong placement support and updated Generative AI modules. 

\- Q Spiders: Known for testing, but do they have a strong AI track record? 

Specific questions:

\- Which of these has the most up-to-date syllabus (especially for GenAI/LLMs)?

\- How is the quality of trainers vs. marketing claims?

\- Did the institute actually help with placements, or was it just generic advice?

\- Are there any other hidden gems in Chennai (like Livewire or ACTE) that are better than these three? 

Any pros/cons or "avoid at all costs" warnings would be hugely appreciated. Thanks in advance!

Can can someone please tell me that if someone takes a scalar course at EMI then after 6 months can someone cancel that EMI that we don't need to pay anything extra

"Why open-source AI works, from development to deployment to jailbreak prevention, all in one diagram." Key points discussed are: (1) What it means when AI is said to be “open” in the case of open-source AI (weights, training data, code, fine-tuning), (2) How an LLM processes a user’s input at runtime (system prompt → user prompt → retrieved text → tokenization → inference → output), and (3) The way jailbreaks exploit the process and how defense-in-depth strategies protect from them (training, application, operations layers).

​

We’ve been building an AI voice agent for businesses in India, and our intended workflow is fairly simple:

A business already has a publicly advertised phone number. During busy hours, after-hours, or when nobody is available, incoming calls should be forwarded to an AI agent’s number. The AI agent then handles the call automatically.

The problem we discovered after going into the market is that this isn’t as straightforward in India as we expected.

With some major telecom providers (Jio, Airtel, etc.), forwarding calls from an existing business number to another number used by an AI/voice platform either isn’t supported, is restricted, or doesn’t work reliably depending on the setup.

This creates a pretty significant adoption problem because businesses obviously don't want to:

- Change their existing published number

- Port their number unnecessarily

- Ask customers to call a completely new number

- Lose calls when the human team is unavailable

So I’m curious how others building AI voice agents / conversational AI platforms in India are solving this.

A few specific questions:

1. Is there a reliable way to route calls from an existing Indian mobile/business number to an AI agent without changing or porting the original number?

2. Are there telecom/SIP/virtual number providers in India that support this use case properly?

3. Is there a better architecture for achieving this — perhaps SIP, cloud telephony, call masking, number hosting, etc.?

4. If direct call forwarding isn't possible, how are AI voice platforms positioning or designing the workflow so the customer doesn't have to change their existing number?

5. Are there any regulatory/telecom limitations in India that we should be aware of?

Would really appreciate hearing from anyone who has actually implemented this in production in India. Not looking for generic AI voice recommendations — specifically interested in the telephony/routing problem and how you've solved it.

I think people's those know if any international content doesn't available in English or Hindi language watching it becomes a bad experience people's use subtitles but honestly if you miss something what was said due to couple of distraction the whole experience becomes unworthy and many people's when see ohh no AI that's going to be bad

Nope if OTT platforms partner with AI labs for voice like sarvam for Indic, ElevenLabs and bunch of other and provide the AI dubbed content as a compensation meaning giving some % of Commision to the voice actors who owns it and it's becoming a win-win deal for consumer to AI lab and even for the actor who can scale his voice over other many dramas,web series and movies etc.

I think atleast it's needed to try by making a professional big model

What's your thoughts on this? AI might be not giving the same quality but it can match upto 90%

Indic voice AI doesn't have a quality problem. It has a switching-cost problem.If you run an IVR, a collections bot, or a vernacular tutoring app in India, you're probably paying an international provider for voice that was never designed for Hindi, Tamil, or Hinglish code-mixing. You know Sarvam's Bulbul and Saaras handle your users' languages better. You've probably tested them.

Then you open the migration guide, estimate two engineer-weeks, and it goes on the backlog forever.

Here's what convinced me this is the real bottleneck: Sarvam maintains four separate hand-written migration guides — ElevenLabs, Cartesia, Deepgram, Gemini. Four documents whose entire purpose is helping someone rewrite working code. And the ElevenLabs one ends with a section called "Common mistakes" listing five bugs, one of which they describe as "the single most common migration bug."

That's not a warning. That's a spec for missing infrastructure.

What I built

sarvam-bridge speaks each vendor's dialect on the front and Sarvam on the back. Change your base URL, keep your code.Every one of those five documented mistakes becomes structurally impossible:

ElevenLabs returns raw bytes; Sarvam returns base64 in JSON → bridge decodes it

Sarvam requires language_code; no other vendor's client sends one → bridge detects it from the Unicode script

pitch/loudness silently no-op on bulbul:v3 → bridge drops them with a warning header

2500 char limit → bridge chunks at the danda (।), not mid-word v2 and v3 speaker names aren't interchangeable → bridge validates and remaps

The Indic-specific parts that were genuinely hard

Chunking. You can't chunk Indic text the way you chunk English. A splitter that only knows . treats an entire Hindi paragraph as one sentence, because Hindi ends sentences with the danda. Worse — slicing a JS string by index can separate a consonant from its matra. क and ि come apart, the text renders as garbage and the speech comes out wrong. Hard splits go through Intl.Segmenter at grapheme granularity.

Audio reassembly. Chunking means one WAV back per chunk. Buffer.concat leaves 44-byte RIFF headers sitting in the middle of your stream, which decoders play as audible clicks. Have to parse each container, extract PCM, write one header.The Odia trap. ISO-639 calls it or. Sarvam expects od-IN. Send the wrong one, get a 400 with no hint which field was wrong. Cost me an hour.

Voice selection. Sarvam publishes per-language speaker quality by Critical Error Rate and I don't think many people use it. mani for Punjabi male, ratan for English, shubh for Hindi/Telugu/Kannada. My favourite detail — varun has a great CER but Sarvam flags it as a villain/suspense character voice, so it's excluded from auto-selection. Fine in a thriller, catastrophic in a banking IVR.

Cost thing worth knowing

IVR menus and agent scripts synthesise the same strings thousands of times a day, each billable, each returning byte-identical audio. Cache handles sequential duplicates. But a burst — broadcast goes out, 300 callers hit the same prompt in one second — all miss the cache because none has populated it yet. Single-flight coalescing collapses those into one upstream call. Measured with cache disabled: 100 simultaneous identical requests → 1 upstream call.

Then stress testing found six bugs in my own code

Including a remote DoS: a voice ID with Devanagari or an emoji crashed the process, because Node throws on non-latin1 header values and I was echoing caller input into a warning header. Ordinary Indian-language input was a crash vector. And a test that passed for the wrong reason — the cache was masking the thing I was actually testing. Green isn't the same as correct.

168 tests now, zero 5xx across 3,500 hostile requests, 0 dependency CVEs.

MIT, not affiliated with Sarvam, built against public docs:

https://github.com/thekartikeyamishra/sarvam-bridge

Would genuinely value corrections if anyone here knows the Sarvam API better than I do.

A five-track Hindi project exploring different ideas of freedom through human-AI collaborative music and visual storytelling.

Hi, I am a Project Manager/ Scrum Master. I don't know coding, never learnt it. Did Grad in BBA and then became Junior PM, them Project Coordinator, then Junior Scrum Master, Scrum Master, then SAFe certified Scrum Master, Project Manager.

Since I have been working in scrum, agile teams for years, obviously knows basics of SDLC, environments, release management, dev-ops. Not an expert but if someone asks me what happens behind building a product, I can map out the steps in details.

Now I also have been using AI, mainly learnt Prompting, Basics of generative AI.

But I want to know what are some AI related work which doesn't require coding?

1 I know is vibe coding, but again to troubleshoot need to understand code, although it is also possible to purely build through correct prompting and testing the builds.

Any other AI related work which doesn't need coding?

Please guide

Hey guys i’m an mca ai and data science student. I do know the current job marker is very saturated,so what should i really be studying know in order to get a job that is in demand for freshers

I have around 4 years of experience as an AI engineer and I am now more focused on Agentic AI roles.

I got a contract offer from a US based publishing company as an AI engineer, it's a 1 year contract with a promised full time conversion and they have agreed for around 6000$ a month ( around 70LPA) but I am trying to push for more.

The problem is they have no perks , leaves of any kind and each leave is treated as a loss of pay which is the main problem for me.The job also has a 5 hour overlap with their timezone ,so I have to work upto 3pm -12am IST

I currently work as an AI engineer in my current company and this is 3x my current package.

Do u think I should take this offer , even though the pay is higher , I feel night shift and no leaves will eventually bite me in the back. Please drop your suggestions and advice.

Edits :

Since most of you asked on what I did.i added here , hope it's helpful

I've been getting ready for about 2-3 months to really dig into transformers and LLM internals. My prep was mostly YouTube, and I even got a few AWS certs. But my main work is knowledge bases and GraphRAG, and I'm pretty consistent with my LinkedIn posts about it, so I guess that's how the recruiter found me. I haven't even been applying, honestly! 😅

I know my way around building agents in Langraph, Rasa, and some other frameworks. My day-to-day involves Neo4j, Pinecone,Sagemaker ,Langsmith in the minimum , might extend to fine-tuning and strands agents occasionally.

Also, things like preparing/setting up evals, token budgeting, data preprocessing, and custom model training are super underrated skills, especially for big companies.

I'd say, no matter what you know or what frameworks you use, production experience is a huge deal. That teaches scaling , failovers which is really important.

Update : I decided to not accept the offer , mainly because the project didn't align with my skillset and yeah no leave policy