u/EveningAd8851

I am unable to pay the rent of my flat .I am lagging ₹7000 of total Amount

I have skills , Technical knowledge and Problem Solving skills

-I can Build Website for your company or local Business

-I can build AI AGENTS or Chatbot that can reply customers based on your business information.

- I can design a CATALOGUE for your products or business

- I can provide a LEAD sheet of bussiness from Google map that don't have their website and their full details(200+ rows)

Please help me and give some work

I can generate daily lead lists of local businesses (from Google Maps) that don’t have a proper website.

These are high-potential prospects for anyone doing web design, SEO, or outreach.

Includes: • Business name

• Phone number

• Location & map link

• Website/social presence status

You can use this to directly reach out and pitch your services.

Custom niche + location available.

DM if you want a sample

I can generate daily lead lists of local businesses (from Google Maps) that don’t have a proper website.

These are high-potential prospects for anyone doing web design, SEO, or outreach.

Includes: • Business name

• Phone number

• Location & map link

• Website/social presence status

You can use this to directly reach out and pitch your services.

Custom niche + location available.

DM if you want a sample.

I’ve been experimenting with lead generation as a side hustle.

Recently, I created a dataset of ~400 restaurants across US cities and noticed something interesting:

• Many don’t have websites
• Some rely only on Facebook pages
• Contact details are easily available

This creates a simple opportunity:

→ Identify businesses with no website
→ Reach out via phone/social
→ Offer basic website services
→ Close small deals ($100–$500 range)

I structured the data to highlight:

• No website businesses
• Social-only presence
• Ready-to-contact info

Still testing this approach, but it looks promising for freelancers or anyone trying to get initial clients.

Curious if anyone here has tried something similar or has better outreach strategies?

I analyzed 400 local restaurants across multiple US cities here’s something interesting:

• A large percentage don’t have a proper website
• Many rely only on Facebook or outdated pages
• Several listings have incomplete or inconsistent contact info

From a marketing perspective, this feels like a huge missed opportunity:

• No owned traffic
• Poor conversion funnels
• Heavy dependency on third-party platforms

Curious how you’d approach this:

If you were targeting these businesses, would you:

1. Pitch websites first
2. Offer local SEO / Google optimization
3. Focus on social media conversion instead

Would love to hear how others here would tackle this.

Built a targeted lead list for people doing cold outreach / web design sales.

Dataset:
• 400 local restaurants (USA cities)
• Phone numbers, location, social links
• Highlighted businesses with NO website (high-conversion prospects)

This isn’t random data

Example use-case:
→ Find restaurants with no website
→ Call/message them
→ Offer a simple website
→ Close clients

I can also generate custom lead lists for any niche/location.

DM me if interested.

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I’m working with a system and facing a practical evaluation bottleneck.

Setup:

I have full observability: traces, spans, logs

I also have an evaluation engine (can benchmark specific components)

But I cannot run evaluation across the entire multi-agent system (too expensive / complex)

Problem: When something clearly fails (errors in traces), it's easy to isolate and evaluate.

But the real issue is silent inefficiency:

No explicit errors

But degraded performance (latency, poor outputs, unnecessary token usage, etc.)

The challenge is: 👉 How do I identify which part of the agent pipeline to send into the evaluation engine without brute-forcing everything?

What I’m trying to do:

Use traces/logs to detect potential inefficiency signals

Narrow down suspicious components (specific tools, prompts, sub-agents, chains)

Run targeted evaluation on those parts

Do root cause analysis and fix

What I’m missing:

Systematic ways to detect underperformance without explicit failures

Industry approaches for observability-driven evaluation in multi-agent systems

Proven heuristics / metrics to flag “evaluation-worthy” spans

Questions:

How do you detect silent degradation in LLM/agent systems?

What signals do you rely on from traces/logs beyond errors?

Do you use automated anomaly detection, baselines, or sampling strategies?

Any frameworks or patterns used in production (OpenTelemetry, Langfuse, etc.)?

Would really appreciate insights from people running LLM systems at scale.It would be a great help for me 🙏🏻🙏🏻🙏🏻

I’m working with a system and facing a practical evaluation bottleneck.

Setup:

I have full observability: traces, spans, logs

I also have an evaluation engine (can benchmark specific components)

But I cannot run evaluation across the entire multi-agent system (too expensive / complex)

Problem: When something clearly fails (errors in traces), it's easy to isolate and evaluate.

But the real issue is silent inefficiency:

No explicit errors

But degraded performance (latency, poor outputs, unnecessary token usage, etc.)

The challenge is: 👉 How do I identify which part of the agent pipeline to send into the evaluation engine without brute-forcing everything?

What I’m trying to do:

Use traces/logs to detect potential inefficiency signals

Narrow down suspicious components (specific tools, prompts, sub-agents, chains)

Run targeted evaluation on those parts

Do root cause analysis and fix

What I’m missing:

Systematic ways to detect underperformance without explicit failures

Industry approaches for observability-driven evaluation in multi-agent systems

Proven heuristics / metrics to flag “evaluation-worthy” spans

Questions:

How do you detect silent degradation in LLM/agent systems?

What signals do you rely on from traces/logs beyond errors?

Do you use automated anomaly detection, baselines, or sampling strategies?

Any frameworks or patterns used in production (OpenTelemetry, Langfuse, etc.)?

Would really appreciate insights from people running LLM systems at scale.It would be a great help for me 🙏🏻🙏🏻🙏🏻