Anil Punjabi's Blog

I built this quick demo prototype (https://newcopilotapp.vercel.app/) for a VIN based Recall lookup tool for LucidMotors using the following tools:-

VS Code

Github CoPilot

Figma Designs

A lightweight PRD

STEPS:

> Installed VSCode and the GitHub CoPilot extension

> Selected a next.js application

> Included PRD & Figma designs and guided the design via prompts

> Tested demo on local machine

> Connected to Vercel and deployed to Vercel instance'

The URL provided above should work for many days but in case it fails here's a quick video.

At Abound, Fraud was a key track as we dealt with Fraud on a regular basis. The rules tend to become very comprehensive and every platform has it's own set of Fraud patterns. Here are some best practices that I've learned over the years that should be implemented as a baseline for any startup that is just now coming up to speed in terms of Fraud rules:

I'll start with the most critical sections and explain the rationale behind each rule. Due to length, I'll provide Part 1 first:

Detection Rules & Patterns:

Velocity Checks

Hourly/Daily Limits:

Max 10 transactions per hour WHY: Most legitimate users rarely exceed 10 transactions hourly. Higher volumes often indicate automated scripts or rapid testing of stolen cards.

Max 50 transactions per 24 hours WHY: Even active business accounts rarely exceed this threshold legitimately. Exceeding suggests potential money laundering or automated fraud.

Account Change Monitoring:

Login attempts (max 5 failed/hour) WHY: Legitimate users typically remember their credentials. Multiple failures indicate potential brute force attacks or credential stuffing.

Password changes (max 2/24hrs) WHY: Multiple password changes in short periods often indicate account takeover attempts or compromise of recovery methods.

Email/phone changes (max 1/7 days) WHY: Core identity verification details shouldn't change frequently. Rapid changes suggest identity manipulation or account hijacking.

Card addition/removal (max 3/month) WHY: Normal users have a stable set of payment methods. Rapid card cycling is a classic fraud pattern for testing stolen cards.

Cooling-off Periods:

30-minute lock after failed attempts WHY: Provides enough time to deter automated attacks while not overly inconveniencing legitimate users who may have forgotten credentials.

24-hour wait after suspicious activity WHY: Gives security teams time to investigate while preventing rapid exploitation of compromised accounts.

Device & Location Intelligence

Device Fingerprinting:

Browser/OS tracking WHY: Sudden changes in device signatures often indicate account takeover. Legitimate users tend to use consistent devices.

Screen resolution/plugins WHY: Helps identify automation tools and emulators commonly used in fraud attempts.

Language/timezone settings WHY: Mismatches between claimed location and device settings are strong fraud indicators.

Location Assessment:

VPN/proxy detection WHY: While some legitimate users use VPNs, sudden VPN usage for financial transactions is suspicious, especially from high-risk countries.

Tor exit node identification WHY: Tor usage for financial transactions is extremely high-risk and often indicates attempts to hide criminal activity.

IP reputation scoring WHY: IPs associated with previous fraud attempts are likely to be used again. Clean IPs with long histories are more trustworthy.

Travel Pattern Analysis:

Physical impossibility checks WHY: Transactions from different countries within impossible timeframes (e.g., NY to Tokyo in 2 hours) are clear fraud indicators.

Typical movement patterns WHY: Users typically have predictable geographic patterns. Sudden deviations without prior travel indicators (flight bookings, etc.) are suspicious.

Transaction Pattern Analysis

Amount Patterns:

Rolling averages monitoring WHY: Users typically have consistent spending patterns. Sudden large deviations indicate potential account compromise.

Standard deviation tracking WHY: Helps identify "bust-out" fraud where compromised accounts start with small transactions before making large fraudulent purchases.

Time-based analysis WHY: Most users have consistent transaction timing patterns (e.g., shopping during awake hours). Off-pattern timing suggests compromise.

Merchant Categories:

Risk scoring by merchant WHY: Certain merchant categories (e.g., virtual goods, cryptocurrency) have higher fraud rates and require additional scrutiny.

New merchant warnings WHY: First-time transactions with new merchant categories, especially high-risk ones, are common in fraud patterns.

At Abound, we dealt with insufficient funds (NSF) in ACH transactions quite often. It is a critical aspect of managing payment processes effectively. Below is a step-by-step guide that explain how to minimize risks related to ACH transactions with insufficient funds:

1. ACH Insufficient Funds Prevention Strategies

a. Pre-Transaction Risk Mitigation

Pre-Authorization and Customer Verification

Use customer verification tools to validate the account holder's identity and the status of the bank account before initiating the transaction.

Services like Plaid, Yodlee, or micro-deposits can confirm account validity and ownership. Plaid being the leader, although a bit expensive.

Real-Time Balance Check

Implement real-time bank account balance checks using tools that provide up-to-date financial data access - Plaid has a service that can be used as a Proxy.

For higher-risk or recurring payments, request updated balance confirmations before each payment attempt.

Creditworthiness and Risk Scoring

Use internal or third-party risk scoring models to determine the probability of insufficient funds.

Flag high-risk transactions for additional scrutiny or manual review. Determine hig-risk transactions by using some type of an AI/ML model which derives insights from user's typical transactions (as is reported on their bank account) and/or on your platform. Most Fraudsters will typically do smaller transactions on their platform before they attempt to do a bigger transaction on your platform.

Set Spending Limits for next & existing users

Implement spending-limits using a combination of user's history on your platform and other insights such as their bank history length. e.g. If a user just opened a brand new account on your platform and it so happens that they have created a brand new bank account as well, then the chances are high that they are trying to commit Fraud on your platform.

Manage your risk in such cases by either blocking the user (if your business permits) or set a lower spending limit for the user so that you can understand their behavior while managing your risk exposure.

b. ACH Transaction Handling

Authorization Holds or Pre-Notifications (Prenotes)

Send a prenotification (prenote) to the receiving bank to confirm that the account is valid and can accept funds.

This process takes a few business days but can help validate account readiness.

Dual Payment Authorization

Split high-value ACH payments into two smaller payments, only processing the second after the first is cleared to reduce exposure to NSF.

Clear Communication and Reminders

Notify customers of upcoming ACH debits with reminders before processing the transaction.

Allow for a cancellation window to prevent unnecessary processing, usually 30 minutes, sometimes more.

Hold Funds for 3-4 days

Since an NSF typically occurs within the first 96 hours, it might be a good idea to hold funds for a few extra days before customers can use it for their operations.

c. Post-Transaction Follow-Up

Failed Transaction Retry Strategies

Utilize ACH retry logic (re-presenting the ACH debit) based on NACHA rules. The first retry attempt can be made 2–3 business days after the initial failure.

Implement smart retry timing, avoiding weekends and holidays to improve success rates, or time it closer to their salary schedule.

Fee Recovery for NSF

If your terms allow, notify the customer about NSF fees and provide a grace period or payment plan to resolve the negative balance.

Alternative Payment Requests

If an ACH debit fails, request an alternative payment method like a credit card or initiate a manual transfer for critical payments.

2. Automation and Tools for NSF Prevention

Consider integrating specialized tools and platforms designed for ACH risk management:

Plaid Account verification, balance check

Stripe / PayPal ACH ACH processing with built-in risk controls

Early Warning Services NSF check and real-time alerts

Riskified / Kount Fraud prevention and risk analysis

3. Communication Best Practices

Your compliance team may or may not have advised you, but since you have real users in this pipeline and most of them are not trying to commit Fraud, it is a best practice to keep them informed every step of the way:

Terms and Conditions: Ensure customers are aware of your ACH debit policies, including potential NSF fees.

Proactive Notifications: Send reminders of upcoming debits 1–2 days before the scheduled transaction.

Startups have to prove themselves to the market with generating some revenue and a growth trajectory. It does not need to be 100X growth MoM, but it needs to show a positive growth trajectory. The general rule of thumb is to show a 1/4 revenue based on the burn rate.

So if you are burning $100K per month, then your revenue should track closer to $25K per month and show incrementality month-over-month. Again, this is top line revenue, not net revenue or profit.

Here are the some pricing model examples of popular startups.

Feature-Based Pricing

HubSpot: Basic ($45/mo), Starter ($800/mo), Professional ($3,200/mo)

Each tier adds more sophisticated marketing, sales, and CRM features

Higher tiers include advanced automation, reporting, and customization

Canva: Free, Pro ($12.99/mo), Enterprise (Custom)

Pro adds brand kits, premium templates, background remover

Enterprise includes advanced security and collaboration features

Calendly: Free, Essentials ($10/seat/mo), Professional ($15/seat/mo), Teams ($20/seat/mo)

Free tier offers basic scheduling with single calendar

Each tier progressively adds features like group events, workflows, and team management

Enterprise tier available with custom pricing for advanced security needs

Clear feature differentiation drives upgrades (e.g., automated workflows in Professional)

Usage Tier-Based Pricing

Amazon Web Services (AWS):

S3 Storage: $0.023 per GB for first 50TB, decreasing rates for higher usage

EC2 Compute: Pricing varies by instance type and usage hours

Mailchimp:

0-500 contacts: $11/mo

501-2,500 contacts: $20/mo

2,501-5,000 contacts: $35/mo

Scaled pricing based on email subscriber count

Per User Pricing

Slack:

Pro: $7.25 per user/month

Business+: $12.50 per user/month

Enterprise Grid: Custom pricing

Microsoft 365:

Business Basic: $6 per user/month

Business Standard: $12.50 per user/month

Business Premium: $22 per user/month

Value-Based Pricing

Salesforce:

Essentials ($25/user/month) for small teams

Enterprise ($165/user/month) for large organizations

Pricing reflects ROI potential for different business sizes

Adobe Creative Cloud:

Individual plans vs. Business plans

Photography-specific plans vs. All Apps access

Freemium

Zoom:

Free: 40-minute limit on group meetings

Pro ($149.90/year): Removes time limits, adds features

Spotify:

Free: Ad-supported, shuffle-only on mobile

Premium: Ad-free, offline listening, any-order playback

Flat Rate Pricing

Buffer:

Essential: $6/mo for all core features

Team: $12/mo for collaboration features

Basecamp:

$99/month flat fee regardless of users

All features included, unlimited projects

Free Trial Models

Adobe Creative Cloud:

7-day free trial, full feature access

Converts to paid subscription

Netflix:

30-day free trial (in select markets)

Full access to all content

Reduced Price Trial Offering

Shopify:

$1/month for first 3 months

Then standard pricing ($29, $79, or $299/month)

LinkedIn Premium:

First month at $0.99

Then regular pricing ($29.99-$59.99/month)

Pay As You Go

Google Cloud Platform:

Per-second billing for compute resources

No upfront commitments required

Twilio:

Pay per SMS sent/received

Pay per minute for voice calls

Monthly vs Annual Pricing

GitHub:

Team Monthly: $4/user/month

Team Annual: $3.33/user/month (17% savings)

Asana:

Premium Monthly: $13.49/user/month

Premium Annual: $10.99/user/month (18% savings)

How to decide what's best for me:

Consider a hybrid approach. e.g. Slack uses per-user pricing with feature tiers and annual discounts

Pricing structures often evolve with company growth e.g. Dropbox started with freemium, added business tiers and enterprise options

Product Led Growth - It's everywhere

At Abound, very early as we were defining our core principles, we baked in Product Led Growth (PLG) as an essential component of our acquisition and retention strategy. If you are a startup, and if you cannot afford a sales team, you must utilize PLG for scaling efficiently and sustainably. Unlike traditional sales-led approaches, PLG puts the product experience at the center of acquisition, conversion, and retention efforts. Especially if you are a consumer facing startup (B2C or C2C or P2B2C)

How Abound utilizes Product Led Growth

We typically establish a core set of OKR goals on a quarterly basis and then we bubble up projects that match the specific OKR goals in mind. This often includes goals for Referral Customer Acquisition Cost (R-CAC), Average Revenue Per User(ARPU), Long Term Value (LTV), Retention percentage, Monthly recurring revenue (MRR)

Here are some of the techniques that we utilize currently:

Shared customer acquisition with Partners, such as Times of India, Willow

Referrals

Trial Subscriptions

Key PLG Techniques From Other Startups

The most successful PLG startups typically employ a combination of these proven techniques:

Freemium Model - Offering a free version lets users experience value immediately, as seen in Spotify's free tier with ads. This reduces adoption friction and builds a base of potential paying customers.

Time-Based Free Trial - Companies like Netflix use full-access trials to let users experience premium features, creating desire for continued access.

Feature-Limited Trial - Zoom's 40-minute meeting limit on free calls is a masterclass in creating natural upgrade motivation while delivering real value.

Referral Programs - Dropbox's space-for-referrals program, Airbnb's give $40, get $40 program, and Meshy.ai's social sharing rewards show how to turn users into advocates.

Self-Service Onboarding - Canva's template-based quick start demonstrates how to make users successful without human intervention.

In-Product Education - Figma's interactive tutorials guide users to proficiency, increasing engagement and retention.

Viral Features - Calendly's scheduling links naturally spread to non-users, creating organic growth loops.

Usage-Based Pricing - Companies like Snowflake and SendGrid align pricing with value, charging based on compute time or emails sent.

Product Analytics & Smart Expansion - GitHub and Asana use usage patterns to identify upgrade opportunities and suggest relevant features.

User Feedback Loops - Notion's feature request system keeps product development aligned with user needs.

Community-Driven Growth - Figma and Notion leverage user communities and template galleries to drive adoption.

API-First Features - Shopify's App Store and Slack's integration marketplace enable ecosystem-driven growth.

Getting Started with PLG

Start by identifying your product's core value and ensuring users can experience it quickly. Focus on removing friction from the user journey and creating natural expansion opportunities. Remember that PLG isn't about eliminating sales - it's about making the product do the heavy lifting in acquisition and conversion.

The Future of PLG

As users increasingly expect self-service experiences and instant value, PLG will become even more critical. Startups that master these techniques early will have a significant advantage in scaling efficiently and building sustainable growth engines.

Here's how you can avoid getting scammed on Memecoins. Memecoins have been popping off recently (again) and unfortunately many of the times the creators of those coins have been taking the money, also known as rug pulling and running away.

This won't prevent every possible way that someone could rug pull but it will help prevent you falling prey to obvious scams and I will actually go through examples of several tokens that already rug pulled and show you how if you had run their addresses through this tool you would have come away with red flags that hopefully would have prevented you from buying.

The tool is RugCheck.xyz -> it identifies red flags like honeypots and rug pulls to protect your investments. It can scan any smart contract across multiple chains.

Enter the smart contract address to scan any smart contract token or NFT . You may be wondering you know how do you find this token address and the most easy place to find it if it's a meme coin it's probably not even on coin gecko yet so you'll probably find the address on Dexscreener.com

Simply type in the address of the token that you want to find for example say you were looking for the address for AI Doge which was a meme coin that was popping off recently you would type it into Dexscreener and scroll down here on the right to see the token name and then click on this copy button to copy it. This has 84 out of 100 which is actually not bad, but it does say potential Honeypot because it contains external calls which cannot be analyzed by the scanner and this could be potentially dangerous. Please use caution and do a bit more research.

As a savvy investor you must:

Sentiment Analysis: Using HuggingFace, RoBERTa model and MindsDB

Here's a quick walkthrough on how you can setup up a complete sentiment analysis pipeline using MindsDB and the Twitter RoBERTa model from Hugging Face, running on an AWS EC2 instance. We'll analyze customer feedback from MindsDB's sample PostgreSQL database and come up with a prediction for the customer sentiment.

Steps

1. AWS Account with EC2 access

2. Docker Setup

3. Setting up MindsDB

4. Connecting to Postgres database

5. Configuring RoBERTa model & predicting sentiment for sample data using the model

Part 1: Setting Up the EC2 Instance

1.1 Launch EC2 Instance

Log into AWS Console and navigate to EC2

Click "Launch Instance"

Select Ubuntu Server

Choose t2.large (minimum recommended for ML workloads)

5. Configure storage to be 30GB or more

6. Create keypair

7. Create some network settings,

--- Allow HTTPS trafic from Internet, and

----Allow HTTP traffic from Internet

8. Configure security group by going to the Security tab and clicking on the security group link:-

These three settings should already be there:

1. Allow SSH (Port 22)

2. Allow HTTP (Port 80)

3. Allow HTTPS (Port 443)

Add one more setting for the MindsDB port number:-

--> Allow Custom TCP (Port 47334) for MindsDB

8. Launch instance, note down the public IP address and download your key pair to your local machine

1.2 Connect to Your Instance

chmod 400 your-key-pair.pem

ssh -i your-key-pair.pem ubuntu@your-instance-public-ip

Part 2: Installing Docker

2.1 Update System and Install Dependencies

sudo apt-get update

sudo apt-get upgrade -y

sudo apt-get install -y \

apt-transport-https \

ca-certificates \

curl \

software-properties-common

2.2 Install Docker

Installing Docker is quick, just follow the steps outlined here.

# 1. download the script # #

curl -fsSL https://get.docker.com -o install-docker.sh

# # 2. verify the script's content

cat install-docker.sh

# # 3. run the script

sudo sh install-docker.sh

2.3 Configure Docker

sudo systemctl start docker sudo systemctl enable docker sudo usermod -aG docker ubuntu

Log out and log back in for group changes to take effect.

Part 3: Setting Up MindsDB

3.1 Pull and Run MindsDB

If you want to persist your models and configurations in the host machine, run these commands:

mkdir mdb_data

Note that we are calling the docker run command directly, as it will install the MindsDB instance and the particular engine from huggingface right away and run it as well on port 47334

docker run --name Anil_mindsdb_container -p 47334:47334 -p 47335:47335 -v $(pwd)/mdb_data:/root/mdb_storage mindsdb/mindsdb:huggingface

3.2 Connect to MindsDB

Now you can access the MindsDB editor by going to 

http://<ec2-public-ip-address>:47334 in your browser

Try both, (http and https) - sometimes if https is not configured you might be able to connect via http

Part 4: Connect to Sample PostgreSQL Database

MindsDB offers a sample PostgreSQL database in read only mode, so let's connect to it

CREATE DATABASE example_db

WITH ENGINE = "postgres",

PARAMETERS = {

"user": "demo_user",

"password": "demo_password",

"host": "samples.mindsdb.com",

"port": "5432",

"database": "demo",

"schema": "demo_data"

};

Part 5: Configuring Twitter RoBERTa Model and Sample Database

5.1 Create Sentiment Model

Create a new model called "sentiment_classifier" that is responsible for generating a field called "sentiment". We are going to use a model called "twitter-roberta-base-sentiment", which will be provided by the "huggingface" engine.

CREATE MODEL sentiment_classifier

PREDICT sentiment

USING

engine='huggingface',

model_name= 'cardiffnlp/twitter-roberta-base-sentiment',

task='text-classification',

input_column = 'comment',

labels=['negative','neutral','positive'];

You can confirm it by observing it in the left side tab under models.

5.2 Query Results

Test to see how does the sentiment classifier provide the data

SELECT * FROM sentiment_classifier

WHERE comment='It is really sucks to do NLP with MindsDB';

This is the output you should see.

Try changing the text with different content and see what happens.

#Now let's try with the sample database

SELECT input.comment, model.sentiment

FROM example_db.user_comments AS input

JOIN sentiment_classifier AS model;

You should see an output like this.

Conclusion

You now have a complete sentiment analysis pipeline that can automatically process customer feedback using the Twitter RoBERTa model. This setup provides valuable insights into customer sentiment and can be easily adapted for other text analysis tasks like support requests from tools like Zendesk, Intercom, etc.

Remember to:

Regularly monitor system performance

Keep all components updated

Review and adjust sentiment analysis results

Back up your data regularly

Monitor costs and resource usage

Now that Google Chrome offers ChatGPT as a search alternative to Google's search engine, I was inspired to give it a go as my primary search engine. Here's what I found.

The Simple Things Aren't So Simple

One of my first tests was something remarkably basic: checking the current time in Mumbai, India. With Google, this would have been instantaneous - type "time in Mumbai," get an answer in milliseconds. With ChatGPT, the experience was jarringly different. Not only did it take significantly longer to respond, but it also gave me a calculated time based on its understanding of time zones rather than real-time data. When I asked again an hour later, I got the same time - because ChatGPT isn't connected to live data.

The Corporate Research Conundrum

When researching companies, the limitations became even more apparent. I asked about a local startup, Auxia.io. While Google would have immediately shown me their website, social media profiles, and recent news, ChatGPT provided a generic summary that felt synthesized rather than specific. This pattern repeated with several other companies - lots of general information but no way to actually connect with them.

Well: ChatGPT, word of advice, sometimes I just want you to behave like Google.

The Good, The Bad, and The Context-Aware

What ChatGPT Does Better:

Complex Queries: When I asked "What's a good laptop for a graphic designer who also does some gaming and has a budget of $2000?", ChatGPT provided a thoughtful, detailed analysis. Google would have given me a mix of product listings and reviews to sort through.

Comparative Analysis: Asking about the differences between Google Pub/Sub vs Kafka got me a clear, structured explanation rather than having to piece together information from multiple sources.

Natural Language Understanding: I could ask questions conversationally and get relevant answers, unlike Google's keyword-focused approach.

Where Google Wins:

Real-Time Information: Weather, stock prices, sports scores - anything requiring current data is Google's domain

Verification: With Google, I can quickly cross-reference information across multiple sources

Direct Connections: Finding contact information, social media profiles, and actual websites is instantaneous, and of course image search

Local Information: Trying to find nearby restaurants or services was nearly impossible with ChatGPT

The Unexpected Challenges

Some limitations caught me by surprise:

Recipe Searches: While ChatGPT could provide recipes, I couldn't see photos of the finished dishes or read user reviews. I am sure this will arrive on a later date.

Product Research: Without access to current prices or real user reviews, making purchase decisions became more difficult.

News Updates: Asking about recent events led to outdated or incomplete information.

Real-World Impact on Productivity

The most noticeable impact was on task completion time. Simple queries that would take seconds on Google often turned into minute-long conversations with ChatGPT. While these conversations were sometimes more informative, they weren't always more productive.

Example timeline for finding a restaurant's opening hours:

Google: ~5 seconds

ChatGPT: Unable to provide current information

The Future of Search?

After 1 week, I've concluded that ChatGPT isn't ready to replace traditional search engines. However, it excels as a complementary tool for:

Complex research questions

Programming queries

Educational explanations

Writing assistance

Problem-solving discussions

Helping write this blog

Conclusion

I switched back my search engine back to Google again

For now, I've settled into using both: Google for quick facts, current information, and finding specific resources, and ChatGPT for more complex analyses and discussions that benefit from its contextual understanding and ability to synthesize information.

The Wild World of Memecoins: Why 2025 Is Going to Be Insane 🚀

Let's talk about something crazy – memecoins are absolutely crushing it right now, and 2025 is shaping up to be even wilder. Look, I get it. When Dogecoin first hit the scene, everyone thought it was just a joke. But here's the thing: what started as a laugh has turned into a full-blown cultural phenomenon that's changing how we think about crypto.

And you want to know the best part? This isn't just about making money (though let's be honest, that's pretty nice too). It's about being part of something bigger – a community that gets your jokes, shares your memes, and maybe, just maybe, rides a silly dog-themed coin to the moon together.

Breaking Down the Memecoin Game 🎮

Alright, let me break this down in a way that actually makes sense. Our crypto guru Murad Mahmudov split this whole thing into tiers, and it's pretty spot-on:

The Big Dogs (Over $2B)

These are the heavy hitters, the ones your non-crypto friends might actually know:

Dogecoin (the OG)

Shiba Inu (the challenger)

Pepe (yes, that Pepe)

Floki

Bonk

The Up-and-Comers ($100M - $2B)

Keep your eyes on these ones – they're making serious moves:

Whiffs

Popcats

MOG

Giga

SPX 6900

The Wildcards (Below $100M)

This is where things get interesting – higher risk, but potentially way higher rewards.

The Baby Memes (Below $1M)

Super risky, but hey, everyone loves a good underdog story, right?

What's Hot Right Now 🔥

The memecoin world is wild, but here are some projects that are catching everyone's attention:

Gaming & Pop Culture Vibes:

Harry Potter tokens (because who doesn't want wizard money?)

Sonic (gotta go fast!)

MOG (for the gamers)

Retardo

Cultural Hits:

Obama

American Coin

Apu (Frog Coin)

The Tried & True:

Shiba Inu (still going strong)

Giga

SPX 6900 (Mission Coin)

Popcat

Community Success Story: BONK's Blueprint 🎯

Let's look at how BONK absolutely nailed the community game. This Solana-based memecoin exploded not just because of hype, but because they did community right:

What Made BONK Different 🌟

1. Community-First Approach

Daily community calls on Discord

Regular Twitter Spaces with devs

Transparent updates about development

Community voting on major decisions

Real Utility Development

NFT integrations

Gaming partnerships

DeFi applications

Merchant adoption program

Active Community Management

24/7 moderator presence

Multi-language support

Regular community events

Rewards for active members

You can check out their community in action:

Discord: discord.gg/bonk

Twitter: @bonk_inu

Website: bonkcoin.com

Keys to Their Success

Consistent engagement (not just during price pumps)

Clear roadmap shared with community

Regular AMAs and updates

Strong meme game (seriously, check their Twitter)

The Secret Sauce: Community is Everything

Here's the real talk – memecoins live and die by their communities. It's not about fancy technology or complicated whitepapers. It's about people coming together, sharing laughs, and believing in something together. The strongest projects? They're the ones where people actually hang out and have fun, not just talk about price all day.

Finding the Next Big Thing 🔍

Want to know where to spot these gems before they blow up? Here's the inside scoop:

DexTools

Real-time trading data

See what's newly listed

Check out what people are talking about

DexScreener

Cross-chain hunting

Price charts that make sense

Find the early gems

CoinGecko & CoinMarketCap

Check what's trending

New listings

Community vibes

Where the Action Is

Discord servers that are actually active

Telegram groups where stuff's happening

Reddit communities with real discussions

Launch Platforms

PinkSale

DxSale

UniCrypt

Where new projects are born

Pro Tip: You don't need to be first – you just need to be smart. Look for:

Real people having real conversations

Teams that actually talk to their community

Natural growth (if it looks too good to be true, it probably is)

Clear rules about tokens and locks

Memes that actually make you laugh

The Bottom Line

Here's the deal – memecoins aren't going anywhere. If anything, they're getting stronger. As we head into 2025, the winners will be the ones that keep it real, build something fun, and actually care about their communities.

Remember though – this is still crypto. Don't throw in more than you can afford to lose, and maybe don't remortgage your house to buy dog money (seriously, don't do that).

In the world of blockchain gaming, Axie Infinity emerged as a pioneering force, a virtual ecosystem where users could "play to earn" (P2E) through collecting, breeding, and battling NFT creatures known as Axies. Developed by Sky Mavis and powered by the Ethereum-based Ronin sidechain, Axie Infinity made headlines with its promise of a sustainable economy where players could earn real income in the form of cryptocurrencies like AXS (Axie Infinity Shards) and SLP (Smooth Love Potion) tokens. However, despite its initial success, the game faced a steep decline, revealing inherent structural weaknesses that many likened to a Ponzi scheme. Let's take a closer look at the rise and fall of Axie Infinity, its gaming experience, and what went wrong.

The Rise: A Groundbreaking P2E Game

Axie Infinity’s meteoric rise began in the early 2020s, with millions flocking to the game, particularly during the pandemic, when the promise of income through gaming attracted thousands of new users. The mechanics were simple: players collected NFT-based Axies, each with unique traits and abilities, to use in PvP battles. In addition to battles, Axies could be bred to create new ones, which added a layer of complexity and strategic depth to the game. This NFT-based system fueled its initial hype, with some players even able to quit their jobs and earn a living by trading or breeding Axies.

The game also became a unique social phenomenon. Axie Infinity wasn’t just about gaming; it was about creating a new form of income for players worldwide, especially in developing nations where the game's earnings had a meaningful financial impact. The demand for Axies and SLP drove up token prices, making breeding and battles profitable. In 2021, Axie Infinity’s success reached its peak, amassing a vibrant player base and generating billions in revenue.

The Game Experience

At its best, the game experience in Axie Infinity was both engaging and strategic. Players had to carefully select their Axies’ traits, skills, and classes, creating balanced teams to outperform opponents. The game modes included the Classic arena and the newer "Origins," which introduced additional elements like revenge cards, potential points, and buffs. The strategic aspect, combined with the possibility of real-world earnings, made Axie Infinity feel thrilling and rewarding.

However, new players found the entry cost increasingly prohibitive as demand surged. To fully participate, users needed to purchase Axies, which eventually became costly, creating a barrier for newcomers and forcing players to rely on "scholarships," in which NFT owners rented out Axies to players in exchange for a cut of their earnings. This arrangement kept the cycle going but also revealed a dependency on new players, foreshadowing a potential downfall.

The Fall: Cracks in the Foundation

Despite the initial hype, Axie Infinity’s economic model was unsustainable. Its structure resembled a Ponzi scheme, where returns for early adopters depended on the influx of new users willing to buy in. As growth slowed, Axie’s economy faltered. The decline in demand caused a steep drop in the value of both AXS and SLP tokens. Moreover, a massive hack in March 2022 resulted in the loss of $625 million worth of cryptocurrency, damaging player trust and accelerating the game's downturn.

The dependency on constant player growth was unsustainable without diversified revenue or utility beyond attracting new users. The core issue was the lack of a sustainable in-game economy: once token prices fell, players lost the financial incentive to stay engaged. Without the influx of new users, there was little reason for the existing player base to hold onto or breed Axies, collapsing the in-game marketplace and sending token values plummeting.

What Could Have Been: Missed Potential

Axie Infinity’s downfall could have been mitigated with a more balanced economic design, less dependency on external investments, and a model that encouraged long-term player retention rather than quick earnings. If Axie Infinity had focused on enriching the gaming experience and adding non-monetary incentives, it might have sustained its player base even as the P2E craze waned. More balanced in-game utility for AXS and SLP, such as staking rewards or exclusive features tied to gameplay rather than speculative investment, could have made the game resilient against market fluctuations.

The idea of P2E itself is not flawed, but it requires a structure that emphasizes play over earning. Games like "The Sandbox" and "Decentraland" offer users immersive experiences with virtual land and asset ownership that provide additional in-game engagement, utility, and potential collaboration with brands and other users. These games don’t rely as heavily on player growth to sustain their economy, giving them a stronger foundation.

Learning from Axie Infinity

The story of Axie Infinity is a cautionary tale for future P2E games. A sustainable economy is vital to the longevity of any game, especially one with real-world earnings involved. P2E games should prioritize a rich gaming experience, ensuring that players remain engaged even when token prices fluctuate. Gods Unchained and Illuvium are examples of blockchain-based games building ecosystems that focus on gameplay first, with NFT integrations as a supplementary aspect rather than the sole motivator for player engagement.

At my current company, Abound, we have a constant need to explore and develop insights about the user sentiment and decipher (at scale) whether a piece of text reflects positive, negative, or neutral emotions.

From gauging customer satisfaction to doing root-cause-analysis, sentiment analysis is at the heart of providing quality support and developing key focus areas for everything that we do at Abound.

I'm guessing other smaller startups are in a similar boat as they start to receive a large number of customer requests for support

We evaluated 2 main approaches: Rules-Based Engines and AI-Based Techniques

1. Rules-Based Sentiment Analysis

How It Works A rules-based system uses predefined sets of linguistic rules, dictionaries, and logical conditions to classify sentiments. For example:

Words like "good," "excellent," or "amazing" are flagged as positive.

Words like "bad," "terrible," or "awful" are flagged as negative.

Modifiers like "very" or "not" adjust the sentiment score.

Rules engines rely on human expertise to curate these rules and are typically implemented with conditional logic (e.g., if-else statements).

Advantages

Interpretability: Every decision is traceable to a specific rule, making it easier to debug and refine.

Quick Setup: Useful for small datasets where building machine learning models isn't feasible.

Low Resource Requirement: No need for extensive computational power or training data.

Domain Specificity: Tailored rules can be extremely effective in narrow or specialized domains.

At Abound, we even ended up providing higher weightage to newer conversations so that if a user was having a poor experience earlier, and now has shifted to a more positive experience lately, that their conversation score is showing an improvement.

Challenges

Scalability: Expanding rules to cover complex scenarios becomes unwieldy.

Limited Scope: Misses nuances, such as sarcasm, idioms, or contextual sentiment shifts.

Rigidness: Cannot adapt to new language trends or word meanings without manual updates.

2. AI-Based Sentiment Analysis

How It Works AI-driven sentiment analysis leverages machine learning (ML) or deep learning (DL) models. These models are trained on large datasets, learning patterns in how words and phrases express sentiment. Popular techniques include:

Bag of Words (BoW) or TF-IDF: Basic ML models using word frequency as features.

Recurrent Neural Networks (RNNs) or Transformers: Advanced DL models like BERT, which understand context and semantics.

Advantages

Adaptability: Learns from data and adapts to different contexts without predefined rules.

Scalability: Handles large, diverse datasets with ease.

Nuanced Understanding: Captures sentiment in complex sentences, including sarcasm or implicit meanings.

Automation: Requires minimal human intervention once deployed.

Challenges

Data Dependency: Requires vast amounts of labeled data for training.

Computational Cost: Needs powerful hardware for training and inference.

Black Box Nature: Models are harder to interpret and debug compared to rules-based systems.

Bias Risk: Can inadvertently learn biases from the training data.

Rules Engine vs AI Techniques: When to Use What?

Why Rules-Based Systems Excel with Limited Data

Rules engines thrive in environments with:

Limited Data: Small datasets don’t provide enough examples for training AI models.

Specific Domains: Highly specialized vocabulary or contexts where human-defined rules outperform generalized models.

Resource Constraints: Organizations with limited budget or expertise

Transparent Decision-Making: When interpretability and traceability are critical, rules-based systems are superior.

Example Use Case: A smaller startup(such as ours), that is utilizing 1,000 customer reviews might use a rules engine to classify sentiments, leveraging a simple dictionary and modifiers.

Why AI Techniques Shine with Large Data

AI models excel when:

Large Data Sets Exist: Abundant labeled data enables training robust, nuanced models.

Varied Contexts: AI handles diverse language patterns and evolves with the data.

Scalability is Required: Processing millions of inputs across multiple languages is a breeze for AI-based solutions.

Subtle Sentiment Insights Are Needed: Advanced models can detect sarcasm, mixed emotions, or sentiment shifts within a text.

Example Use Case: A multinational corporation analyzing millions of tweets daily to gauge global brand sentiment would benefit from an AI-based approach.

Conclusion

The choice between a rules-based engine and AI-based techniques depends on the problem's scale, data availability, and resource constraints. Rules-based systems are like a scalpel—precise but limited in scope—while AI models are like a Swiss Army knife, capable of handling a wide range of tasks but requiring investment and preparation.

We were already talking to Zendesk and they have an AI based QA tool that can do a great job of providing insights to us, however the price was too high for us and we found that the models were often classifying conversations incorrectly as it lacked the specific domain specific insights.

RICE Framework for prioritization

At Meta & Amazon, my role was to build a product roadmap and justify the priority based on their feasability, ROI and long term potential. A basic framework that I often used was the RICE framework. It wasn't perfect because it didn't account for long term impact & competitor pressure, but it helped bubble up ideas with the highest potential in the short term.

Here's how it works.

The RICE framework evaluates ideas based on four criteria:

Reach: How many users will be affected by the feature in a given timeframe?

Impact: What is the effect on users if this feature is implemented?

Confidence: How certain are we about our estimates for reach and impact? Mention this as a range between 0% and 100%. This often leads to heated discussions and it is often the fun part in building a framework for the discussion.

Effort: How many person-months or development resources are required to build it?

Example 1: Implementing a Personalized Recommendation Feature

Meta's product team was considering a personalized recommendation feature for it's Marketplace product to improve user experience.

1. Reach: This feature would impact all logged-in Meta users, which could be millions daily. For estimation, say it reaches 50 million active users each month.

2. Impact: Personalized recommendations could significantly increase conversions and user satisfaction. The team estimates a high impact score (e.g., 4 out of 5), expecting a 10% increase in click-through rates on product suggestions.

3. Confidence: The confidence score depends on data from previous similar implementations (like algorithm tweaks). Suppose there's strong evidence, but some uncertainty about the exact impact, giving it an 80% confidence rating.

4. Effort: Developing and testing the recommendation system might take 12 person-months, involving data scientists, developers, and QA engineers.

RICE Score=(Reach×Impact×Confidence)/Effort

For this example: RICE Score=(50,000,000×4×0.8)/12 = 13,333,333

Example 2: Implementing an AR "Try-On" Feature

Meta was considering adding an Augmented Reality (AR) try-on feature for fashion and accessories, allowing users to virtually "wear" items before purchase.

1. Reach: This feature primarily targets fashion shoppers using compatible devices. If Meta's monthly active users in this category total 10 million, that's the estimated reach.

2. Impact: AR try-ons can enhance the shopping experience and reduce return rates. However, since not all users might use it, the impact is moderate. Estimate: 3 out of 5.

3. Confidence: AR technology is proven, but adoption rates vary, making the confidence lower than for recommendations. Assume a 70% confidence level.

4. Effort: Developing the AR feature involves complex software integration and 3D modeling, requiring significant effort. Estimated effort: 24 person-months.

RICE Score=(Reach×Impact×Confidence)/Effort

For this example:

RICE Score=(10,000,000×3×0.7)/24=875,000

Comparison:

Personalized Recommendations: 13,333,333

AR Try-Ons: 875,000

Decision:

While the AR feature offers an innovative experience, personalized recommendations yield a far higher RICE score, indicating a broader and more immediate impact. This type of comparison helps companies like Amazon & Meta prioritize features that provide the greatest return on investment and customer value​.

Why We Switched from LAMP to MERN: A Mobile-First Evolution

At Abound, we recently transitioned from the traditional LAMP stack to MERN, primarily driven by our mobile app needs. This decision has proven especially beneficial for our remittance services, where the ability to create custom React components has transformed our user experience.

Why MERN Won Us Over

The limitations of LAMP became apparent as our mobile user base grew. While LAMP served us well initially, our remittance app needed more flexibility and better performance. MERN offered exactly what we needed:

React Components for Better Mobile UX

React's component-based architecture has been a game-changer for our mobile app. We can now create reusable UI elements that load quickly and respond smoothly to user interactions. This is particularly noticeable in our remittance flows, where every second counts for our users.

Skeleton Loading for Better Perceived Performance

One of our favorite improvements has been implementing skeleton loading screens. Instead of showing loading spinners, users see placeholder UI elements that mirror the final layout. This creates a much smoother experience, especially on slower mobile connections.

Single Page Application Benefits

The SPA architecture has dramatically improved our mobile experience. Rather than full page reloads, our app updates only the necessary components, making it feel more like a native app. This is crucial for our users who often operate on varying network conditions.

Cross-Platform Development Efficiency

One of the most significant advantages we've discovered is how changes in one environment can seamlessly propagate across our entire ecosystem. When we update a React component for our web platform, the same improvements can be quickly adapted for our mobile apps. This has revolutionized our development workflow in several ways:

A single code change can enhance both web and mobile experiences

Bug fixes automatically benefit all platforms

New features can be launched simultaneously across web, iOS, and Android

Testing efforts are significantly reduced as core logic is shared

Development velocity has increased as teams can work on multiple platforms simultaneously

Real Results

The switch to MERN has delivered tangible benefits:

40% faster page load times on mobile devices

Smoother transaction flows in our remittance service

Better offline capabilities for users with unstable connections

Faster development cycles for new features

Reduced time-to-market for cross-platform features

More consistent user experience across all platforms

Looking Forward

Bitcoin frenzy and the ability to move funds at lightning speeds have put pressure on Central banks all around the world to step up their game and take a serious look at Digital Currency and the benefits it can offer to their economy.

China Pilot

Recent pilot experiments like the one in China called the Digital Yuan (e-CNY) is powered by a new payment processing network called Digital Currency Electronic Payment(DCEP). This initiative is often touted as the blueprint for how Central governments all around the world can develop & launch their own digital currencies for their constituents.

Key Goals

> China would like to eliminate the circulation of existing coins & notes by replacing them with digital equivalent

> Cross Border transfers: One of the key goals of a digital currency is to speed up and reduce the cost of cross border money transfers.

> Financial Inclusivity: Even though China leads the world in terms of users that utilize mobile payments for their day-to-day operations, there's still a large population (20% ~287MM people) in the country, the are unbanked. This user base does not have easy access to cash, and this new form of digital currency would greatly help this user segment by offering them an alternative to hard cash.

Source: Finbold

Rollout Plan

China's meticulous rollout plan shows how seriously they are treating this initiative.

1) China’s PBOC (People Bank of China) have been issuing this digital currency.

2) China has been running experiments over the last year by giving away 6.2 MM in e-CNY in the form of lotteries to Beijing residents. As part of this pilot, residents could spend these funds with select merchants.

3) China recently rolled out a mobile wallet app (on iOS & Android) so that users could carry their digital currency with them

4) Commercial banks and licensed non-bank payment institutions will be allowed to participate in the e-CNY payment system after they have proven that they meet the compliance requirements (including anti-money laundering and countering terrorist financing requirements) and regulatory requirements with regards to risk management

Several industry leaders are also jumping in to support the initiative:

> WeChat Messaging App will soon start accepting e-CNY.

> AliPay is also considering accepting e-CNY as a payment method

> JD.com (China's largest retailer) already accepts e-CNY as a form of payment

Outlook Ahead

> China's entry into the digital currency world without the use of Blockchain as the underlying framework shows how digital currencies and cryptocurrency can co-exist together

> The fact that e-CNY is backed by the Central Government gives everyone high confidence that the currency will not drop in value someday and this is greatly helping in increasing adoption

> As banks, merchants, retailers & other services (like utilities) start accepting e-CNY, the country is poised to make the transformation and move towards a truly digital economy & helping those who really need these capabilities

A BitCoin is a new type of digital currency that works just like regular currency except that it is anonymous and it is peer-to-peer.

As of Nov 19th, 2013:   1 BTC = $635.5 USD

Now, say you are buying flight tickets, the cost for the flight is $800. You may pay using your credit card, or via PayPal, or if you have some BitCoins you can pay using BitCoins. The cost would be about the same.

Where can I buy BitCoins?

You can buy BitCoins just like you buy a commodity, such as Gold. There are specific sites that allow you to trade is BitCoins. Here are a few notable ones:

CoinBase.com

Mt.Gox

Since BitCoins are traded worldwide you can find information about how to buy them in your country from this site:

http://howtobuybitcoins.info/

What are some web-sites that accept BitCoins?

BitCoins are quickly gaining market adoption and a few small players have started playing with it, but the larger eCommerce operators are still staying cautiously away from BitCoins at this time. 

1) WordPress

2) Gyft

3) Reddit

4) OkCupid

What is the benefit of using BitCoins?

BitCoins serve a major market need in terms of peer-to-peer payments. Say you pay for a service in BitCoins, the money simply transfers from one individual to the other. 

Are BitCoin earnings taxable?

Say, you mow your neighbor’s lawn, it doesn’t matter if he pays you $20 in cash, or $20 worth of BitCoins. (Or $20 worth of tomatoes for that matter)

In many jurisdictions, you are still legally required to report that as income. 

For argument sake, say you were paid in $20 worth of tomatoes, would you pay taxes on that? Probably not. 

That's the beauty of peer-to-peer BitCoin payments. 

Can BitCoins be used for money laundering?

In the month of March, 2013 the Treasury Department said Bitcoin exchanges that allow users to convert their virtual currency to dollars must register with the government and abide by anti-money-laundering regulations.

European regulators issued similar requirements in July 2013.

So, money laundering is no longer a possibility with BitCoins.

What is the difference between BitCoin and MintChip?

As of Nov 28th, 2013 - MintChip is an experiment that the Canadian government intends to run. It is similar concept as BitCoin but targeted specifically for the Canadian market and it is backed by the Canadian Federal Government.