As WhatsApp has introduced its new feature of username, after a few days, it hit a roadblock in India. The Indian government has asked WhatsApp to pause the rollout of its upcoming username feature. This has raised concerns that it could make online scams, phishing, and identity impersonation easier.

What has changed?

WhatsApp introduced its new feature that allows users to connect using unique usernames instead of sharing their phone numbers. Meta has launched these features by understanding that it will be offering greater privacy. The feature is expected to roll out globally in phases later this year.

But Why Is India Government Concerned?

It is no secret that online scams are happening daily in India; therefore, the Indian government believes the feature could:

Increase in phishing and online fraud
Enable fake usernames that mimic trusted people or organizations
Make impersonation scams more convincing

India already has over 850 million WhatsApp users. The government is urging Meta to delay the launch until its security concerns are addressed.

WhatsApp's Response

Hence, though this feature is not live yet, Meta still says this feature will have several safeguards:

Reserved usernames for public figures, government bodies, and verified accounts
Detection systems for impersonation and suspicious activity
Limits on messaging unknown users
Extra context for first-time chats, such as account age, shared groups, and location

Now, What Comes In The Bigger Picture

India recorded over 2.81 million cybercrime cases in 2025, with nearly 75% linked to online fraud, making digital safety a growing national priority.

Do let us know your thoughts in the comments and share your opinions on safeguarding privacy with usernames. Also, do you think sharing phone numbers adds an extra layer of trust and security?

","tags","upvote_count",6,"comment_count","channel_slug","service-promotion","created_at","2026-07-07T06:46:10.564Z","updated_at","2026-10-08T12:14:08.094Z","image_url","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/cc8b858519e3f54c96aabc9fa7841dccf298bc9f10dfd8d989c3c92daeee5852.jpg","video_url","discussion_type","general","problem_status","problem_summary","expected_behavior","actual_behavior","what_i_tried","accepted_comment_id","reposted_from_id","repost_comment","language","en","variant_group_id","e706ebac-c24d-43b7-8f8b-b4d1767dd783","audience","public","prompt_text","poll_options","display_name","Manpreet Singh","username","avatar_url","https://oneshopaicommunityin.blob.core.windows.net/avatars/avatar/b1cd54c4eefe0625bc163d4740629ec2a03d4373da22d35a6f17edcd3a1658c4.jpg","author_role","author_public_page",true,"reactions",[165],"poll","category_id","category_slug","category_label","available_languages",[119],"repost_count","repost_original_video_url","author_flair",{"_148":149,"_150":151,"_152":153,"_154":155,"_156":157},"author",{"_126":127,"_129":130},"level",5,"level_title","Builder","level_progress",0.05,"league","platinum","top_badge",{"_158":159,"_23":160,"_161":162,"_163":164},"key","talent-scout","Talent Scout","tier","silver","category","contribution",{"_166":167,"_168":97,"_169":16,"_170":-5},"emoji","❤️","count","reacted","reactionId","017cfc61-9732-4336-b89c-6a036cc16da2","bfc19026-0016-477c-baec-f938a6c3111b","ChatGPT VS Human","

ChatGPT: \"AI is overhyped.\"

Human: Maybe. But what technology do you think we'll actually use every single day five years from now?

It doesn't have to be AI

Could be robotics, quantum computing, smart wearables, biotech, or something that's barely on people's radar today.

What's your prediction?

",[193,194,195,196,197],7,"2026-07-13T13:27:19.324Z","2026-10-08T12:14:08.208Z","deb25714-7f5c-47c8-92a4-9049ef8c7039","Himani Rawat","manihimani08gmailcom",[192],[119],{"_148":186,"_150":187,"_152":56,"_154":162,"_156":188},{"_126":180,"_129":-5},3,"Regular",{"_158":189,"_23":190,"_161":162,"_163":191},"early-bird","Early Bird","onboarding",{"_166":167,"_168":176,"_169":16,"_170":-5},"chatgpt","ai","robotics","technology","trending","aece0b1a-7333-427a-ba4c-938fecd1b98f","c4ca21c0-f8f2-44e6-80c7-f2a734012231","Imagine it's 2050!","

You wake up in a full-robotic set-up, your coffee is delivered by robot, and you use a technology that barely exists today, but by then, it's completely normal.

What is it? 🧐

AI agents?
Smart glasses?
Personal robots?
Something else?

Drop your predictions! It'll be fun to come back to this thread in a few years! 😎

",[219,194,220,197,221,222],8,"2026-07-13T13:36:01.043Z","2026-10-08T12:14:08.125Z","d8ae4834-aea0-42e7-b419-b0ca88611d8d","Chetna","chetnashukla",[218],[119],{"_148":213,"_150":214,"_152":215,"_154":216,"_156":217},{"_126":207,"_129":-5},2,"Member",0.5,"bronze",{"_158":189,"_23":190,"_161":162,"_163":191},{"_166":167,"_168":203,"_169":16,"_170":-5},"robotic","futuretrend","tech","2026","19cb0636-9643-4e72-8a0a-19907be719cb","55f6bce7-a178-4237-9c84-dea23984c174","What's the hardest part of freelancing pricing or clients?","

Genuinely curious about this one. When I started freelancing, pricing was my biggest confusion. Charge too little and it feels like undervaluing yourself, charge too much and the client disappears.

After some experience though, I realized the real problem was never pricing it was working with the wrong clients. With the right client, price is rarely the issue.

Curious what this community thinks:

What do you struggle with the most pricing, client communication, or deadlines?
Any lesson from your first freelance client that you still follow today?
When a payment gets delayed, do you follow up persistently or just let it go?

Not looking for generic tips here genuinely want to know how people actually deal with this stuff in real life.

","whats-trending","2026-07-22T07:36:39.330Z","2026-10-08T12:14:08.008Z","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/8e1f1b49e1d23b320e2a985cee60e0cc31ed6615877dd65fa73bc174b08a9514.jpg","992f46c9-b003-4d6d-ba5a-a7e04757463e","priya","priya12",[239],[119],{"_148":213,"_150":214,"_152":215,"_154":216,"_156":238},{"_126":232,"_129":-5},{"_158":189,"_23":190,"_161":162,"_163":191},{"_166":167,"_168":97,"_169":16,"_170":-5},"4b674d41-91de-4e19-bd9c-9307d12bad76","cace0fd7-1ec6-4aff-af20-7a51ab515134","AI Isn’t Replacing Marketers - It’s Making Them More Efficient","

Digital marketing involves endless tasks: creating content, analyzing data, managing campaigns, and understanding customers.

AI can help you do all of this faster and smarter.

Here’s where it can make a real difference:

✍️ Content Creation — Use ChatGPT to brainstorm ideas, create content outlines, write captions, and repurpose existing content.

📝 Copywriting — Tools like Jasper.ai can help create ad copy, headlines, emails, and other marketing content faster.

📊 Customer & Data Analysis — HubSpot’s AI features can help marketers understand customer interactions, automate workflows, and improve CRM efficiency.

🎯 Ad Optimization—AI can analyze campaign performance, identify patterns, and help marketers make better targeting and budget decisions.

🔍 Analytics & Insights — Google Analytics uses AI-powered insights to highlight trends and unusual changes in your data, helping you focus on what actually matters.

🎯 Personalization—AI can help tailor content, offers, and messaging based on customer behavior and preferences.

The goal isn’t to let AI do your entire job.

The goal is to let AI handle repetitive work so you can spend more time on strategy, creativity, and decision-making.

Start small. Pick one repetitive marketing task and introduce AI into your workflow.

The marketers who learn to work with AI will have a serious advantage.

",[263,194,264,265,266,13],"innovation-talks","2026-08-16T06:52:03.888Z","2026-10-08T12:14:08.431Z","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/02a36776bd2328e7063162a65489365ab1f70b6e5431a6a75a16d9e7755651c8.jpg","03234a12-2bed-4851-9d1b-63e1e8d5623f","Rohit Yadav","rohit_yadav_fcf7cc","https://oneshopaicommunityin.blob.core.windows.net/avatars/avatar/60d1df00ddb06f10fd172c601718b7e9b7e1fd6eec95fa68ede16fec71bda229.jpg",[262],[119],{"_148":149,"_150":151,"_152":257,"_154":258,"_156":259},{"_126":250,"_129":252},0.68,"gold",{"_158":260,"_23":261,"_161":258,"_163":164},"prolific-poster","Prolific Poster",{"_166":167,"_168":149,"_169":16,"_170":-5},"digitalmarketing","marketingai","contentmarketing","marketingstrategy","bf373681-3b0c-415e-99ed-398e9ac05a2a","d5948f84-8260-449b-bbc9-d5fc3df09605","AI Agents are getting smarter… but are we ready to trust them?","

A few years ago, AI mostly answered our questions.

Now, AI Agents can research, use tools, interact with systems and complete tasks on our behalf.

And honestly, this is where things get interesting.

The question is no longer:

Can AI do this?

It’s:

How much should we actually let AI do without us?

For me, the ideal future isn't AI replacing humans.

It’s AI handling execution while humans stay in control of important decisions.

Especially when agents are dealing with:

🔹 Business decisions

🔹 Customer data

🔹 Payments & finances

🔹 Code & production systems

🔹 Personal information

🔹 Critical workflows

So I want to know what everyone here thinks:

Would you trust an AI Agent to complete an important task completely on its own?

","2026-08-19T10:24:27.606Z","2026-10-08T12:14:08.425Z","dce4300f-0df4-46c0-83da-985066f9cfd2","Adarsh Singh","adarsh_singh_a811fa",[282],[119],{"_148":186,"_150":187,"_152":280,"_154":162,"_156":281},{"_126":274,"_129":-5},0.57,{"_158":189,"_23":190,"_161":162,"_163":191},{"_166":167,"_168":149,"_169":16,"_170":-5},"1ca6373f-45cd-42c2-80ce-dab276e15472","d6f2baa0-4728-4fdc-b4e0-87d1d5d9f457","Restaurant Hygiene Ratings: Bringing Food Safety to Food Delivery Apps","

When we order food online, we can see ratings, reviews, prices, delivery times, and photos. But there is one important question most platforms do not answer clearly:

How safe and hygienic is the kitchen preparing my food?

This is a growing consumer problem in India. A recent survey of more than 91,000 consumers found that only 32% rated restaurant hygiene and food-safety standards as good or excellent, while 57% said they had personally seen dirty kitchens, dining areas, or washrooms. Even more importantly, 74% wanted restaurant hygiene ratings to be publicly displayed on food-delivery apps.

The Problem

Food-delivery platforms are built around convenience, but consumers still have limited visibility into kitchen safety.

A customer may know that a restaurant has a 4.5-star food rating, but that does not necessarily tell them:

When the kitchen was last inspected
Whether it passed its inspection
What violations were found
Whether its hygiene certification is current
Whether the business has repeated safety issues

India already has an FSSAI Hygiene Rating Scheme, which rates eligible food businesses from 1 to 5 stars based on hygiene and food-safety conditions found during inspection. However, the scheme is voluntary.

The Solution: An AI-Powered Kitchen Safety Score

My idea is to create a Kitchen Safety Score directly inside food-delivery platforms.

Instead of relying only on customer reviews, the score could combine verified information such as:

FSSAI hygiene ratings
Inspection records
Food-safety violations
Certification status
Inspection recency
Verified consumer complaints
Repeat compliance issues

AI would organize and analyze these signals, while official inspections and verified records would remain the foundation of the score.

For example:

Kitchen Safety Score: 89/100

Last Inspection: 12 August 2026

Status: Verified

Risk Level: Low

Consumers could tap the score to understand exactly why a kitchen received it.

Why This Matters

This could create a new layer of trust and transparency in food delivery.

Consumers get better information. Restaurants can demonstrate compliance. Platforms can differentiate themselves through safety transparency.

Food ratings tell us whether people liked the meal. A kitchen safety score could help tell us whether we can trust where it was prepared.

Would you check a verified Kitchen Safety Score before ordering food?

",[307,308,309],"idea-exchange","2026-08-25T11:36:02.237Z","2026-10-08T12:14:08.353Z","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/81818bffeae966119af0c7f74fae87f72f975bedb32dd6be9b50a6d712bcc22a.jpg","4d12a1cc-2ee7-4533-b47c-c3a129c8b387","Adarsh kumar","adarshkumar",[304,305],[119],{"_148":97,"_150":299,"_152":300,"_154":155,"_156":301},{"_126":293,"_129":-5},"Connector",0.49,{"_158":302,"_23":303,"_161":155,"_163":164},"community-star","Community Star",{"_166":167,"_168":97,"_169":16,"_170":-5},{"_166":306,"_168":52,"_169":16,"_170":-5},"👏","artificialintelligence","food-delivery-apps","ai-solutions","6fb25e1b-e7a5-4f9d-bce1-2259f74e6fa6","24af010c-9f48-4e75-b5b6-2788cc00e1d5","AI Search vs Google Search: How Search Is Changing in 2026","

Search is changing quickly. For years, people used Google by typing keywords, checking search results, and visiting websites to find answers. Now, AI-powered search is changing this process.

What Is Google Search?

Google Search mainly helps users find relevant webpages.

For example, when someone searches “best AI tools for students,” Google shows:

Websites
Videos
Images
News
Other search results

Users then visit different websites to find the information they need.

What Is AI Search?

AI Search understands questions in a more conversational way and can provide a direct answer.

Instead of searching “best AI tools,” a user can ask:

“What are the best free AI tools for students, and what can I use them for?”

AI can understand the question, combine information, and provide a structured response.

Examples include:

Google AI Overviews
Google AI Mode
ChatGPT Search
Perplexity
Microsoft Copilot

AI Search vs. Google Search

The biggest difference is how information is delivered.

Google Search:

Shows a list of webpages
Users click websites for information
Rankings and clicks are important

AI Search:

Provides a summarized answer
Can understand longer questions
Can combine information from different sources
Sources and citations become important

Google is also adding AI features directly into Search, so traditional search and AI search are increasingly becoming connected.

What Does This Mean for SEO?

AI search does not mean SEO is dead.

It means SEO is changing.

Businesses should focus on:

Creating useful content
Answering specific questions
Using original research and examples
Building website authority
Making content easy to understand
Tracking brand mentions and AI citations

Google also says many traditional SEO practices remain relevant for AI search experiences.

Final Takeaway

The future of search is moving from “find me a website” to “help me understand and solve my problem.”

Businesses should not focus only on ranking #1.

They should create content that is useful, trustworthy, original, and easy for both people and AI systems to understand.

",[197,329,330],"trending-news","2026-08-25T12:12:03.216Z","2026-10-08T12:14:08.408Z","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/b7387d5bdb2677e30164b068b34b7a550bc8799e3167d58b824d82154e0d7015.jpg","58863970-8940-4772-b5d1-b0545883f15c","Rohan Sharma","rohan_sharma_0604cf",[328],[119],{"_148":186,"_150":187,"_152":326,"_154":162,"_156":327},{"_126":320,"_129":-5},0.37,{"_158":189,"_23":190,"_161":162,"_163":191},{"_166":167,"_168":97,"_169":16,"_170":-5},"ai-search-vs-google-sear","seo-trends-2026","76f4a99c-3993-42d3-90de-3c7ab9a7f2f4","NVIDIA Is Buying Hugging Face — What Does It Mean for AI?","

If you follow AI development, you’ve probably wondered what NVIDIA acquiring Hugging Face really means for the field. Both are major players, but what impact will this have on AI tools, research, and users?

Why NVIDIA Wants Hugging Face

NVIDIA has long been a hardware leader in AI, especially with its GPUs powering complex machine learning models. Hugging Face, on the other hand, is a hub for open-source AI models and tools, particularly in natural language processing (NLP).

By acquiring Hugging Face, NVIDIA aims to combine its hardware expertise with Hugging Face’s software and community ecosystem. This integration could accelerate AI research and make advanced models more accessible for developers and businesses.

What This Means for AI Development

One key effect will likely be tighter integration of Hugging Face’s tools with NVIDIA’s hardware. For example, optimized versions of popular models like transformers may run more efficiently on NVIDIA GPUs, reducing training time and costs.

Additionally, NVIDIA can leverage Hugging Face’s model hub and datasets to improve its AI platforms, such as the NVIDIA AI Enterprise suite. This could enable faster experimentation and deployment of models in various industries.

Impact on the Open-Source AI Community

Hugging Face has built a large open-source community around sharing models and datasets. NVIDIA’s acquisition raises questions about how this openness will be maintained.

So far, NVIDIA has committed to keeping Hugging Face’s libraries open source. However, corporate ownership may influence project priorities, potentially focusing more on enterprise applications that align with NVIDIA’s business goals.

Concrete Example: Faster NLP Model Deployment

Consider a startup building a chatbot using Hugging Face’s transformer models. Before the acquisition, they might have faced challenges optimizing performance on their own hardware.

Post-acquisition, NVIDIA could offer integrated solutions where the startup can deploy Hugging Face models directly on NVIDIA’s AI cloud platforms or GPUs with built-in optimization. This reduces complexity and speeds up product development.

What to Watch Going Forward

Keep an eye on how NVIDIA balances commercial goals with Hugging Face’s open ecosystem. The acquisition could lead to more powerful AI tools but might also shift the community’s focus toward NVIDIA’s strategic interests.

For AI practitioners, this means potentially easier access to cutting-edge models and faster deployment options, but also a need to monitor how open collaboration evolves.

In summary, NVIDIA buying Hugging Face is more than a corporate deal — it’s a move that could reshape AI development by merging top hardware with a vibrant AI software community. The real impact will depend on how both companies navigate integration while supporting innovation and openness.

",[347,348,349,350,351],"2026-09-04T08:11:41.036Z","2026-10-08T12:14:08.178Z","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/4d45e3d0366bf59f4dec10c178b95e8acc9464a03ddcbf6bfa16092d8e915e24.jpg","e0cc573a-8aff-461e-b5d5-7865d8a8428d",[344,345],[119],{"_148":149,"_150":151,"_152":257,"_154":258,"_156":343},{"_126":250,"_129":252},{"_158":260,"_23":261,"_161":258,"_163":164},{"_166":167,"_168":97,"_169":16,"_170":-5},{"_166":346,"_168":52,"_169":16,"_170":-5},"💡","nvidia","hugging-face","artificial-intelligence","machine-learning","ai-acquisition","2473c7fb-b0df-4d99-a1c6-6c51d9462523","294881cd-2b5f-47ab-baf6-8879251b4f13","AI Layoffs Slowed in August — Should We Be Less Worried?","

AI-related layoffs slowed in August, and at first, that sounds like good news.

According to Challenger, Gray & Christmas, U.S. employers announced 3,462 job cuts attributed to AI in August. That ended a five-month streak in which AI was the leading cited reason for monthly job cuts.

But there's another number worth looking at: more than 116,000 announced job cuts had been attributed to AI during the year through August.

So, should we be less worried about AI taking jobs?

I wouldn't jump to that conclusion yet.

The reason is simple: AI doesn't always replace an entire job. Sometimes it replaces just a few tasks.

A marketer might use AI to research competitors, create first drafts, or analyze data. A developer might use it for routine coding. A customer support team might let AI answer common questions while humans handle complicated cases.

The job remains, but the job changes.

And that could be a much bigger story than layoffs alone.

Companies don't necessarily need to fire people to reduce the amount of human work they need. They might simply hire fewer people, expect existing teams to produce more, or redesign certain roles around AI tools.

At the same time, AI is creating demand for new skills. Companies need people who understand AI implementation, automation, data, security, and how to evaluate AI-generated work.

So August's numbers are worth watching, but one month isn't enough to prove that AI-related job disruption is slowing permanently.

For workers, I think there's a more useful question than “Will AI take my job?”

Ask yourself:

“Which parts of my job could AI already do?”

Then learn how to use AI for those tasks and focus your energy on the things that still require judgment, creativity, communication, and experience.

The job market is changing.

The safest strategy probably isn't trying to compete with AI at everything.

It's learning how to work with it before everyone else does.

",[373,374,375,307,376,196],"2026-09-04T10:12:29.042Z","2026-10-08T12:14:08.282Z","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/2312a2803dd9a8068f7823359c827822e6885c3fcfdb3bfb41cd6edf4c17eff5.jpg","21f0247c-fbc9-43f9-a0ff-c77cb481de9a","Mohit Yadav","mohit_yadav009","https://oneshopaicommunityin.blob.core.windows.net/avatars/avatar/5ab73b4ea1c390df89cfe8b75e2b695afadffe6ab977434b1805e6f564ab7f48.jpg",[372],[119],{"_148":186,"_150":187,"_152":368,"_154":162,"_156":369},{"_126":361,"_129":363},0.97,{"_158":370,"_23":371,"_161":162,"_163":164},"conversationalist","Conversationalist",{"_166":167,"_168":149,"_169":16,"_170":-5},"ailayoffs","aijobs","futureofwork","career","256d52d4-4796-4c60-99af-b35e04be816e","ff6c2c97-0d1c-4a62-bafd-9412ddc01954","AI Security Is Becoming a Career Path of Its Own","

For years, cybersecurity and artificial intelligence were treated as two separate areas of technology.

That is changing quickly.

AI is now being integrated into software, business applications, customer service systems, coding tools, and internal company workflows. As these systems become more capable, securing them is becoming a problem of its own.

Consider an AI agent that can access company documents, use APIs, update databases, or perform tasks automatically. If something goes wrong, the risk is much greater than an incorrect chatbot response.

This creates new security questions.

Can an attacker manipulate the model?

Can sensitive information leak through an AI application?

What happens if an AI agent receives a malicious instruction?

Can someone use the system's permissions to perform an unauthorized action?

These are no longer purely theoretical questions. Companies deploying AI have to think about them as part of their security strategy.

Interestingly, AI is also becoming part of the solution.

Security teams can use AI to analyze large amounts of code, identify potential vulnerabilities, investigate suspicious activity, and speed up repetitive security tasks. Cybersecurity-focused AI systems are already being developed to assist researchers with vulnerability discovery and analysis.

But there is an obvious problem.

The same capabilities that help security professionals could potentially help attackers. If AI makes vulnerability research faster for defenders, it could also make certain attacks easier to automate or scale.

That is why the intersection of AI and cybersecurity is becoming so interesting from a career perspective.

Instead of choosing between the two fields, professionals can develop skills across both.

Some emerging areas include:

AI security engineering
AI red teaming
AI application security
Model security
Agent security
AI-assisted vulnerability research

For someone starting today, there is no need to master everything at once.

A good foundation in programming, networking, operating systems, and cybersecurity can be combined with an understanding of machine learning, large language models, and AI applications.

The important part is learning how these systems work—not simply learning how to use an AI tool.

AI security is still a developing field, so the exact job titles and responsibilities will continue to change. But the underlying need is unlikely to disappear.

The more organizations depend on AI, the more they will need people who understand how to make those systems secure.

For students and early-career professionals, that creates an interesting opportunity.

The future may not be about choosing between AI and cybersecurity. It may be about becoming skilled enough to understand both.

","2026-09-04T11:31:29.815Z","2026-10-08T12:14:08.336Z","cec6c142-949d-43cf-9e45-a37eeea4098c","Jyotsana Chandoliya","jyotsana123","https://oneshopaicommunityin.blob.core.windows.net/avatars/avatar/84a1fc8e37703784353cebec3723c76ad055cdaa01ee0d417f0e7138ec14fc38.jpg",[395],[119],{"_148":391,"_150":392,"_152":393,"_154":162,"_156":394},{"_126":384,"_129":386},4,"Contributor",0.98,{"_158":260,"_23":261,"_161":258,"_163":164},{"_166":167,"_168":149,"_169":16,"_170":-5},"9f551101-1d0a-4dbe-8c2d-cc18fc8713b4","What I Wish I Knew Before Choosing AI Marketing Tools","

Marketers face a flood of AI tools. Picking the right one is a real challenge. You want results, not just hype. Start by listing what your marketing needs most: content creation, customer insights, automation, or ad optimization. Then test tools that match those needs. Here’s a hands-on look at three top AI tool types and how to use them.

Content Creation Tools: Write, Edit, and Generate Ideas

Content drives marketing. AI tools here save time and spark ideas. Popular picks include Jasper, Copy.ai, and Writesonic. They generate blog posts, emails, product descriptions, and social media updates fast.

Step 1: Sign up for a trial or free tier. Most offer limited words per month. Step 2: Input a clear prompt. For example, \"Write a 300-word email promoting our new product launch.\" Step 3: Review the output. Expect drafts, not final copy. You’ll need to edit for tone and accuracy.

Costs range from $15 to $50 monthly. The bigger plans offer more words and features like SEO suggestions. Jasper has a strong community and templates. Copy.ai is simpler but effective for quick tasks. Writesonic adds image generation in some plans.

Use these tools if you produce lots of content and want to speed up brainstorming. Don’t expect perfect, publish-ready text without editing. These tools best fit teams or solo marketers who can refine AI drafts.

Customer Insights and Analytics Tools: Understand Your Audience

AI excels at data crunching. Tools like Crayon, HubSpot’s AI features, and PaveAI turn raw data into actionable insights. They analyze website traffic, social media, and customer behavior to guide marketing strategy.

Step 1: Connect your data sources. This might be Google Analytics, Facebook Ads, or CRM platforms. Step 2: Set goals like improving conversion rates or identifying trending topics. Step 3: Review AI-generated reports and recommendations. These often include charts, heatmaps, and suggested actions.

Prices vary widely. HubSpot bundles AI with its marketing suite, starting around $45 monthly. Crayon focuses on competitive intelligence and can be pricier. PaveAI charges based on data volume.

Pick these tools if you want to base decisions on data, not guesswork. They suit marketers who track campaigns closely and adjust based on metrics. The learning curve can be steep if you’re new to analytics.

Automation Tools: Save Time on Repetitive Tasks

AI automation tools handle emails, social posts, and ad campaigns. Examples include Mailchimp’s AI features, Zapier with AI integrations, and Adext AI for ad optimization.

Step 1: Choose tasks to automate, like scheduling emails or ads. Step 2: Set up workflows or rules. For example, send a welcome email when someone subscribes. Step 3: Monitor results and tweak rules.

Mailchimp offers a free tier with basic AI features. Zapier pricing depends on task volume. Adext AI charges based on ad spend managed.

Automation suits marketers with repetitive tasks or large campaigns. It reduces manual work and errors. However, initial setup takes time, and you must monitor to avoid mistakes.

How to Pick the Best AI Tools for Marketers

Your choice depends on your goals, budget, and skills. If content is your bottleneck, start with content creation tools. If you need data-driven decisions, invest in analytics AI. For efficiency, automation tools pay off.

Try free trials before paying. Test how each tool fits your workflow. Look for tools that integrate with what you already use. Avoid stacking many tools that overlap.

Small teams or solo marketers benefit from all-in-one platforms like HubSpot or Mailchimp. Larger teams may prefer specialized tools combined. Always plan for editing and human oversight. AI tools assist but don’t replace marketers.

Example: A small e-commerce store used Writesonic to generate product descriptions, saving hours weekly. They paired it with Mailchimp automation to send personalized emails. This combo boosted sales by 15% in two months.

Remember, the best AI tools for marketers solve your real problems, not just the shiny ones. Focus on what moves your metrics. Use AI to cut grunt work, sharpen messages, and understand customers better. That’s how you get ahead.

",[],"2026-09-05T12:45:13.592Z","2026-09-19T12:13:02.694Z","3a4f6009-ea53-476b-8ce6-59951188094a",[408],[119],{"_148":391,"_150":392,"_152":393,"_154":162,"_156":407},{"_126":384,"_129":386},{"_158":260,"_23":261,"_161":258,"_163":164},{"_166":167,"_168":176,"_169":16,"_170":-5},"f392532e-49c6-404e-9470-2814c3970324","c90a2858-6df2-4858-a644-a63e2beb3043","Why Young Kids Lost AI for Now in NYC Schools","

I remember the moment when my neighbor’s kid came home from school, frustrated because the shiny new AI helper on their tablet had suddenly disappeared. It wasn’t broken; rather, New York City puts a temporary ban on AI for younger students, and that decision rippled through classrooms and homes alike. People were confused — some thought the city was outright rejecting technology, while others worried about missing out on a learning revolution. The truth sits somewhere in between, tangled with concerns about safety, fairness, and how kids actually learn.

Not just banning AI because it’s ‘dangerous’

Many assumed the city’s move was about fear, like the machines were about to take over or kids would fall into some digital trap. But the reality is more grounded. The ban came because educators and officials want to pause and understand AI’s real effects on young minds before letting it run wild in classrooms. They worry about misinformation, privacy, and how AI might change the way kids think, not just what they can do. It’s not a knee-jerk reaction but a cautious step to protect students while figuring out best practices.

AI isn’t a magic tutor yet

There’s a popular idea that AI tools instantly make students smarter or help teachers do their jobs better. That’s tempting, but it misses the point that AI is still a blunt instrument in many ways. The city’s temporary halt reflects doubts about how effective these tools really are for younger learners who are still grasping basics like reading and math. Kids need human guidance, encouragement, and correction that AI can’t reliably provide yet. Rushing into AI use without that support risks confusion and frustration rather than progress.

Privacy concerns hit home harder with kids

One big part of why New York City puts a temporary ban on AI for younger students involves data privacy. Unlike older students or adults, younger kids can’t fully understand what they’re sharing or how that data might be used. Schools gather tons of info on students already, and adding AI means feeding third-party systems with even more details. Parents and officials worry about where that data goes, who sees it, and how secure it is. This pause allows time to set clearer rules so kids’ personal information isn’t floating around unchecked.

Fairness and access aren’t solved by AI

Some thought AI could be the great equalizer in education, giving every child a custom tutor regardless of background. But the city’s move highlights how AI can instead deepen existing divides. Not all schools have the same tech resources or infrastructure, and AI tools often perform better with certain languages or cultural contexts. Without careful planning, young students from less privileged neighborhoods might get left behind or receive poorer AI experiences. The pause is partly about making sure AI doesn't widen gaps it’s supposed to close.

Teachers need time, training, and say

The voices of teachers matter here. New York City puts a temporary ban on AI for younger students also because many educators aren’t ready or comfortable using these tools yet. AI changes how lessons might be planned, how feedback is given, and what counts as learning. Teachers want to make sure AI helps them rather than replaces or complicates their work. The ban gives schools a chance to train staff properly and hear their concerns before full rollout, avoiding rushed or poorly supported adoption.

One clear thing to remember is that this isn’t about rejecting progress or technology. It’s about making sure new tools are introduced carefully, thoughtfully, and with kids’ best interests at heart. When New York City puts a temporary ban on AI for younger students, it’s buying time to get the details right — how to protect privacy, support teachers, and actually help kids learn better. That patience means the future of AI in classrooms could be smarter, safer, and fairer, not just faster or flashier.

",[],9,"2026-09-05T13:13:14.808Z","2026-09-19T12:12:51.962Z","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/57ca698072ebafa1bec0f19d88a74615f34daf53d89fa59854351957240ed803.jpg","b9b26274-026f-4101-ad56-20fcdd2ffc13","Tarak Sonkar","qwer","avatar:icon-zap",[428],[119],{"_148":186,"_150":187,"_152":426,"_154":162,"_156":427},{"_126":419,"_129":421},0.28,{"_158":159,"_23":160,"_161":162,"_163":164},{"_166":167,"_168":414,"_169":16,"_170":-5},"78892ec4-168b-41c6-b9d8-9c1049e9720c","Master These AI Skills To Stay Ahead in 2026","

Pick which AI skill to build next isn’t guesswork. Companies want specific abilities. Nail those, and you move from job seeker to must-have talent. This post breaks down 5 AI skills companies are looking for in 2026, what they do, and how to gain them.

1. Data Engineering for AI Pipelines

Symptom: You hear AI models fail or stall because data is messy, inconsistent, or slow to load. Cause: Companies need engineers who can build pipelines that feed clean, real-time data to AI systems.

Fix this skill by focusing on data engineering fundamentals with an AI slant.

Set up a data pipeline using Apache Airflow. Run airflow initdb to initialize, then airflow webserver to start the UI. It shows task status and logs.
Ingest data from multiple sources (CSV, databases, APIs). Write scripts in Python using pandas and SQLAlchemy to clean and transform data.
Build automated validation checks. Create Python functions that flag missing values or anomalies and trigger alerts.
Connect your pipeline to an AI model training script. Use scikit-learn or TensorFlow to consume the clean data.

What to expect: Your pipeline runs without errors, and models train faster with reliable data. If data errors persist, check each pipeline stage’s logs to locate the failure.

2. Machine Learning Model Deployment and Monitoring

Symptom: Models work in notebooks but fail in production or degrade over time. Cause: Lack of skills to deploy and monitor models in live environments.

Fix this by learning deployment tools and monitoring strategies.

Containerize a model with Docker. Write a Dockerfile that installs dependencies and exposes a prediction API using Flask or FastAPI.
Deploy the container to a cloud service like AWS ECS or Google Cloud Run. Use CLI commands like aws ecs create-cluster and gcloud run deploy.
Set up monitoring with Prometheus and Grafana. Configure metrics on request latency, error rates, and model confidence scores.
Implement automated retraining triggers. When accuracy drops below a threshold, retrain the model using fresh data and redeploy.

What to expect: Your model responds to real requests, and you track performance live. If latency spikes or errors rise, check logs and metrics dashboards to pinpoint issues.

3. Natural Language Processing (NLP) Specialization

Symptom: AI projects need language understanding but struggle with nuance, context, or multiple languages. Cause: Teams lack deep NLP skills beyond basic text classification.

Fix this by mastering advanced NLP techniques and tools.

Train transformer models using Hugging Face’s transformers library. Run pip install transformers, then fine-tune BERT or GPT on domain-specific text.
Build pipelines for tokenization, named entity recognition, sentiment analysis, and summarization. Use pretrained models as components.
Implement cross-lingual models. Use multilingual datasets and evaluate performance on languages your company targets.
Deploy chatbots or virtual assistants with context retention. Use tools like Rasa or Dialogflow for conversation management.

What to expect: Your models understand complex queries and respond appropriately. If responses seem off-topic, check training data quality and model hyperparameters.

4. AI Ethics and Responsible AI Practices

Symptom: AI systems show bias, unfairness, or violate privacy. Cause: Teams overlook ethical implications and responsible AI design.

Fix this skill by embedding ethics into your AI workflow.

Learn bias detection tools like IBM’s AI Fairness 360. Run their Python toolkit to analyze datasets and models for bias metrics.
Implement explainability with SHAP or LIME. Generate visual explanations for model decisions to audit fairness.
Enforce data privacy by applying differential privacy techniques or federated learning frameworks.
Develop documentation standards for AI models, including intended use, limitations, and risk assessments.

What to expect: Your AI outputs come with fairness reports and clear ethical guidelines. If bias persists, revisit data sampling and model training steps.

5. AI Automation and Integration Skills

Symptom: AI tools exist but don’t connect well with business workflows or user apps. Cause: Lack of automation and integration expertise.

Fix this by learning to embed AI into existing systems and automate repetitive tasks.

Create RESTful APIs for AI models using FastAPI or Flask. Test endpoints with curl or Postman.
Automate workflows using tools like Apache NiFi or Zapier. Connect AI APIs to CRM, ERP, or messaging platforms.
Write scripts to trigger AI tasks based on events, e.g., new customer data arrival or support ticket creation.
Use low-code platforms with AI connectors to prototype integrations quickly.

What to expect: AI features trigger automatically in workflows, reducing manual work. If automation fails, check API connectivity and event trigger logs.

Which 5 AI Skills Fit Your Next Move?

Data engineering powers AI models by feeding them clean inputs. If you like backend tasks and data flow, start here.

Model deployment and monitoring suit those who want to see AI in action in real environments and keep it reliable.

NLP specialization fits if language and communication fascinate you, and you want to build chatbots or translators.

Ethics is crucial if you want to ensure AI is fair, transparent, and respects privacy. This skill grows in demand as regulations tighten.

Automation and integration appeal if you like connecting tools and streamlining workflows to boost productivity.

Pick based on your current skills and career goals. Combine two or more for an edge — for example, data engineering plus deployment or NLP plus ethics.

Build your skill by doing projects, contributing to open source, or taking targeted courses. Track your progress with portfolios and certifications.

Mastering these 5 AI skills puts you where companies want you in 2026. Start today.

",[194,444],"career-opportunities","2026-09-06T10:42:53.902Z","2026-09-19T12:12:45.895Z","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/28e1b84f47b7dc889ae2f45d5ef0d2d89fe3073e50d1908cd9e2264ed0c3fd30.jpg","c1f84e81-4b65-4e44-8ae6-4e832e57b297",[443],[119],{"_148":213,"_150":214,"_152":215,"_154":216,"_156":442},{"_126":232,"_129":-5},{"_158":189,"_23":190,"_161":162,"_163":191},{"_166":167,"_168":203,"_169":16,"_170":-5},"careers","a9b09928-652e-4765-a3de-3e5174b93645","Marketers Need to Stop Confusing GEO and SEO in 2026","

Most marketers treat GEO and SEO like they’re the same thing. They’re not. This confusion costs time and effort—and worse, it buries results.

GEO isn’t just local SEO, it’s about geography tied to intent

People think GEO means local search optimization. Nope. Local SEO is one piece of GEO, but GEO covers more than that.

GEO targets users based on physical location or IP data, yes, but it also considers geographic behaviors, trends, and regional preferences that change how content or ads perform. For example, a campaign targeting “running shoes” in New York City might emphasize winter-proof gear, while in Miami it pushes lightweight, breathable models.

This is not just SEO tweaking. GEO uses geo-targeting in ads, regional landing pages, even language and cultural cues. Ignoring this broader view means missing out on customizing messaging for geographic segments, which can drastically increase engagement.

SEO is about search intent and optimizing for the algorithm, not geography

SEO’s main job is to get your content visible in search engines. It focuses on keywords, site structure, backlinks, and user experience. Geography only matters when users add location modifiers (like “best pizza Chicago”) or when search engines infer local intent.

That means SEO is about understanding what people type, what questions they ask, and how to answer them better than competitors. Yes, local SEO blends in geographic signals, but general SEO is about content relevance and technical health.

Confusing SEO with GEO leads to wasting effort on geographic tweaks when the core problem is weak content or poor site speed. Fixing those SEO basics usually moves the needle faster.

GEO campaigns rely heavily on data and technology beyond traditional SEO

Marketers often treat GEO as a keyword game, but it’s more about data signals from devices, networks, and real-time user info.

Consider mobile apps using GPS data to push location-specific offers. That’s GEO in action. Or programmatic ads that change creatives based on city, weather, or even local events. SEO doesn’t handle that kind of real-time, location-aware personalization.

You need tools that analyze geographic data sources, integrate location APIs, and adapt content dynamically. Relying on SEO tools alone won’t unlock these GEO tactics.

SEO drives organic traffic, GEO often fuels paid and direct engagement

SEO is the backbone for organic search visibility. It builds credibility and long-term traffic by matching search queries with valuable content.

GEO tactics often show up in paid channels: localized PPC, geofencing ads, push notifications. They also work in direct engagement, like in-store experiences or localized email campaigns triggered by user location.

If you mix these up, you might underfund the paid GEO side thinking SEO alone will cover it. Or you might invest too heavily in paid GEO without building a strong organic SEO base first.

Ignoring GEO insights limits your SEO potential and vice versa

Some marketers think focusing on SEO alone is enough. They miss how geographic trends and user behavior shift over time and region. This gap can mean losing relevance in certain markets.

On the flip side, going all-in on GEO without strong SEO is like building on quicksand. Your content won’t rank well enough to catch the wider audience, limiting the impact of GEO-focused efforts.

The real win is blending the two thoughtfully: SEO lays the foundation for organic visibility, while GEO refines targeting and personalization based on location-specific data.

The one thing worth remembering

Don’t treat GEO and SEO as interchangeable. In 2026, marketers need to understand GEO as a location-based strategy that goes beyond SEO’s keyword and content focus. GEO is about real-world geography, user signals, and dynamic targeting. SEO is about search intent, content quality, and site performance.

If you mix them up, you waste effort and miss opportunities. Nail SEO basics first, then layer in GEO strategies with data and tech tailored to your target regions. That’s the path to smarter, more effective marketing next year.

",[13,459,460,461,462,463],"2026-09-06T11:27:52.262Z","2026-09-19T12:12:35.942Z","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/87d3a8b4051894a01d940a2e9ce682c65185dd32fa3e8fbccc9369fb1fca18c7.jpg","830faeb4-8545-417b-aed3-4938013cff67",[458],[119],{"_148":391,"_150":392,"_152":393,"_154":162,"_156":457},{"_126":384,"_129":386},{"_158":260,"_23":261,"_161":258,"_163":164},{"_166":167,"_168":203,"_169":16,"_170":-5},"geo","marketing","digital-marketing","local-seo","search-optimization","029fd2a9-915f-4413-8fe4-4798e0a12914","Why Short-Form Video Is Shaping Online Search Behavior","

Short-form video is becoming search content. It changes how users seek answers and brands reach audiences. Here is a numbered rundown of why this matters and what works.

1. User Preference for Quick Answers

Short videos provide fast, visual answers. They help when users want immediate solutions or demonstrations. They do not work well for deep research or complex explanations.

2. Mobile-First Consumption

Most short-form videos are viewed on mobile devices. This suits users on the go or multitasking. It does not suit desktop users needing detailed data or long reads.

3. Search Engines Index Video Snippets

Google and others now show short videos in search results. This boosts visibility for creators using video. It is less effective if the video lacks clear metadata or relevance.

4. Social Platforms as Search Engines

Apps like TikTok and Instagram serve as search tools. Users search topics via hashtags or keywords in video form. This fails when content is too vague or entertainment-only without info.

5. SEO Tactics Shift Toward Video

Optimizing short videos for search means using titles, descriptions, and transcripts. This helps videos rank and attract clicks. It does not help if the video quality is poor or content is unrelated.

6. Engagement and Retention Metrics Matter

Short videos with high watch time and interaction rank better. This works when content hooks viewers quickly. It fails if the video is boring or too long for the format.

7. Diverse Content Types Fit Short Video

How-tos, tips, product demos, and quick reviews suit short video search content. They fail for complex tutorials or detailed case studies.

8. Brand and Influencer Opportunities

Brands can use short videos to appear in search results and social feeds. Influencers gain reach by answering common questions quickly. This does not work if the message is too salesy or lacks authenticity.

Try starting with how-to or FAQ short videos. They match user intent and work well in search results. Avoid overly long or unfocused clips.

",[477,478,479,480,263],"2026-09-06T11:41:24.213Z","2026-09-19T12:12:31.353Z","77744b5a-435d-4260-a8ce-66be7a19cd8d",[476],[119],{"_148":186,"_150":187,"_152":368,"_154":162,"_156":475},{"_126":361,"_129":363},{"_158":370,"_23":371,"_161":162,"_163":164},{"_166":167,"_168":203,"_169":16,"_170":-5},"shortformvideo","searchcontent","videoseo","contentstrategy","1246e386-cfff-4217-bf62-8607d9907093","bc8f32a1-644b-4c27-9620-a346a6387460","Turns out prompting is the boring part","

I'd been doing the prompt thing. collecting them, saving them,

thinking that was the skill. then you sit in one of these and

realise the people actually building treat prompting as the boring

part. everything interesting is around it — what you feed the

model, catching when it's confidently wrong, making it run without

you babysitting it.

went back and looked at what they've run over the last year.

context engineering, rag, advanced rag, mcp and agents, langchain

and langgraph. that list is basically a syllabus if you're trying

to figure out what to learn next. I've been working through it in

that order.

haven't done their paid cohort so I can't speak to that. free sessions are open to anyone — thegenacademy.com

where are you in that list? and if you've built anything agentic that actually runs unsupervised, I want to hear what broke first.

",[506,507,508,509,194],12,"offer-skills","2026-09-13T16:10:45.029Z","2026-10-08T12:14:08.465Z","901b468f-64ca-4968-a252-52e808009687","Siddharth","siddharthrao","https://oneshopaicommunityin.blob.core.windows.net/avatars/avatar/ff8e6e9ca706630c4b0a1211a119a7a2980fc95a5831167c51ded617a6600bab.jpg",[503,504],"9aac1ef5-1058-4caa-9fdf-820d5281736e","post-a-need","Post a Need",[119],{"_148":186,"_150":187,"_152":501,"_154":162,"_156":502},{"_126":491,"_129":493},0.04,{"_158":189,"_23":190,"_161":162,"_163":191},{"_166":167,"_168":505,"_169":16,"_170":-5},{"_166":306,"_168":52,"_169":16,"_170":-5},11,"learningpath","aiagents","rag-","promptengineering","7d87a096-a109-40f0-b52d-7d8917e6bf5e","No client work yet? Build these three things over three weekends.","

You can't get hired without proof, and you can't get proof without being hired. It looks closed. It isn't.

Just don't build tutorial projects. A to-do app or a Netflix clone proves you can follow instructions, which nobody doubted. What a client wants to know is whether you can see a real problem and finish solving it.

Weekend 1 — automate something for someone you know.
\nA shop owner copying WhatsApp orders into a sheet. A tutor sending the same reminder thirty times. Watch them do it for twenty minutes, count the steps, then build it away.

You end up with a sentence like \"saved a 12-person agency six hours a week.\" That beats any skills list. Ask for two things when you're done: permission to write about it, and two quotable sentences.

Weekend 2 — build something you personally need.
\nA tracker, a calculator, one annoying thing in your own work. The thing you genuinely need is always more specific than anything you'd invent for a portfolio, and specific is what makes work look real.

Keep it to one screen. Ship it publicly. Working and ugly beats polished and hypothetical.

Weekend 3 — write up the first two.
\nNot a portfolio page. A post: what was broken, what you tried first, where it broke, what shipped, what you'd do differently.

This one matters most, because it's the only one that shows how you think. Anyone can end up with a working artifact. Almost nobody produces an honest account of their own mistakes and that's what tells a client you'll flag problems instead of hiding them.

Don't cut the \"what I'd do differently\" part. Everyone knows your first version had problems. Naming them is what makes you look like someone who can see their own work clearly.

Three weeks later you have a named beneficiary with a quote, a public working tool, and proof of your reasoning. That's a stronger position than a lot of people with two years of work they can't talk about.

\"But it's not real client work.\" The client doesn't care. They're buying a reduction in risk a thing they can click and a person who finishes. Whether money changed hands last time is a weak signal, and experienced buyers know it.

Pick the person you're helping this weekend. You probably thought of someone in the first paragraph.

Post it here when it's done and I'll give you real feedback, not the encouraging kind.

",[90,526,527,528,529],"how-collab-works","2026-09-14T12:13:15.893Z","2026-09-21T12:30:11.087Z","5d53d68e-cecb-4a1a-a8bc-dfcc33093347",[525],"87fbaed1-cebb-46e5-974b-0d90c78f0756","How Collab Works",[119],{"_148":186,"_150":187,"_152":501,"_154":162,"_156":524},{"_126":491,"_129":493},{"_158":189,"_23":190,"_161":162,"_163":191},{"_166":167,"_168":505,"_169":16,"_170":-5},"engagement","conversation","moderation","online","de178e1d-0556-49c0-a2c8-d7af447390e9","My First Freelance Projects Didn't Start With Big Clients","

When I was trying to get freelance work, I made the same mistake a lot of people make. I kept looking for the “perfect” project - something that would look impressive on my portfolio and pay well from the start.

But honestly, that approach just kept me waiting.

What helped more was taking on smaller projects where I could actually do the work, understand what the client needed, deal with deadlines, and learn how to communicate properly. Some of those projects weren't particularly exciting, but they gave me something more useful than another certificate or real experience I could show to the next person.

One small project became a portfolio example. That project helped me explain what I could actually do. Then the next conversation became easier because I wasn't saying, “I can do this.” I could actually show it.

I think that's the part people underestimate when starting freelancing. Your first project doesn't have to be your dream project. It just needs to give you something real to learn from and build on.

Sometimes the first project isn't about making a lot of money. It's about creating enough proof that the next opportunity becomes easier to get.

",[544,545,546,547,548,549],"freelance-gigs","2026-09-21T13:00:55.842Z","2026-09-29T11:17:33.807Z","2da4f31f-8374-45f1-8607-4390c2c27639",[543],[119],{"_148":186,"_150":187,"_152":501,"_154":162,"_156":542},{"_126":491,"_129":493},{"_158":189,"_23":190,"_161":162,"_163":191},{"_166":167,"_168":505,"_169":16,"_170":-5},"freelance","starting out","clients","small projects","career tips","freelance advice","8789d233-e502-40c5-ac43-92811398d8fe","428f63b6-1865-464f-84b3-11aa05d5277c","Cold Outreach Confusing? Here’s a Simple Funnel to Follow","

Ever sent someone a message and never received a reply?

You’re not alone. Outreach isn’t just about sending messages it’s about building a relationship, one step at a time.

Here’s a simple way to understand the outreach funnel:

1. Outreach — Send a personalised message.

2. Conversation — Start a genuine conversation.

3. Discovery — Understand their needs and problems.

4. Solution — Show how you can help.

5. Conversion — Build trust, deliver results, and turn the connection into an opportunity.

Remember: Don’t rush to sell. Focus on understanding first.

What’s harder for you getting a reply or turning a conversation into an opportunity?

#Sales #Outreach #BusinessDevelopment #StudentLearning #CareerGrowth

","2026-10-17T10:49:45.288Z","2026-10-17T11:39:07.061Z","https://oneshopaicommunityin.blob.core.windows.net/discussion-images/post_image/d89b1f3f5581fc5299f1763a92c1d9ba926ad9581e9e63b459212860f750824c.jpg","7cedd5c6-dd31-4b91-9429-88ccbd016d13","Hiresh 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Offer Your Skills

Offer Your Skills

Open Collaborations