Hi everyone, I’ve spent the past year building a platform for neutral, data-driven public company research, and I’d love to share some of the technical choices and things I’ve learned along the way. By "neutral approach," I mean the platform avoids investment advice and focuses solely on aggregating financial data — there’s no room for “buy x and you make guaranteed profits” or similar claims.
The platform gets used by investors who try to find undervalued stocks. Today, I want to share a bit of the technical background and hope you like it — otherwise, I’m here to read your roast.
I am using Django as my web framework (hard learning curve, but worth it) with PostgreSQL as the relational heart of my project. Django-allauth is there to authenticate your account — I really recommend it. It’s somewhat tricky to set up, but once configured, it's flexible enough to support various auth providers (I kept things simple for now and have been happy with: Google + normal signup). Then you can easily integrate Mailjet or SendGrid into these authentication workflows (Mailjet is pretty good, I’d say!)
If you visit the platform, you may recognize a very simple — maybe clinical — design. That’s because I had to admit that I’m not a good designer, so I opted for a clean, minimal layout to avoid clutter.
My choice for the frontend was Jinja (within Django), Tailwind, and some vanilla CSS. For visualizations, I strongly recommend you work with ECharts, or if you have a project related to stocks — checkout tradingview‘s JS libary. There are indeed tons of other JS libraries I used over time, e.g., for PDF generation, but one other thing I need to give a shoutout to here is htmx — literally a game changer for me to communicate fast and easy between front- and backend.
For caching operations, I decided on Redis (very easy setup via Django).
To ensure that my stock data is current, I’ve implemented Celery along with Celery Beat. I am using Redis to as the message broker, but am eager to try out RabbitMQ for my next project — but this one does not require priority queues, and I had Redis on board already, so I stuck with it.
The datasets I work with are listed inside the FAQ. To sum it up real quick: I use a mix of open government data (easy to get, hard to organize at scale) and paid APIs (e.g., for the exchange-related information, which adds up very fast to $$$$).
Lately, I’ve been thinking about how to integrate LLMs in a way my users benefit from. So I’ve created a schema that allows LLMs to analyze earnings call transcripts. Currently, there are already 500 reports generated by DeepSeek R1 and GPT-4 (no sign-up required example — for Microsoft).
Apologies if some descriptions lack polish — this is the only thing I’ve made so far. I hope that I can write more precise/formal descriptions once I’ve finished my CS bachelor :)
Also: I know the platform isn’t fully responsive yet. Several mobile issues were reported — that’s what I’m currently working on.
This post is more focused on my tech stack/experience, but here are some major features I’ve built based on the journey above:
- Watchlists (List of stocks you may want to invest in)
- Portfolios (An extended version of watchlists, with performance metrics, historical data, and community feedback — you can rate shared portfolios, e.g., for their diversification grade)
- Company Screeners (Basically a criteria filter, to discover new ideas/investments — Example: “Show me all US stocks in the Tech or Energy sector with a dividend growth of 25%”)
- Company Report (A detailed analysis of a company divided into financials, earnings reports, catalysts, and Investor Relations)
- Workspaces (Take notes on SEC statements, read SEC documents in an optimized user interface)
- Some others are: Calendars, News articles, Company comparisons, People reports, Market reports
Thanks for reading it, hope it helps someone. Please let me know, if you have any questions or feedback :)
$2.9k/mo
Hey everyone!
We are very excited to announce that you can now install Huzzler on your mobile device and receive push notifications. We have opted to use a PWA instead of a native app as we plan on shipping as many features in the coming weeks (problem / solution directory, accountability, marketing guides..).
To install the app: Simply visit the Huzzler homepage on a mobile device. A popup will appear with instructions on how to install the app. Cheers and let me know if you have any feedback 😁
Thanks!
Scalability in microservices architecture isn’t just a trend—it’s a lifeline for modern software systems operating in unpredictable, high-demand environments. From streaming platforms handling millions of concurrent users to fintech apps responding to real-time transactions, scaling right means surviving and thriving.
As a software product engineering service provider, we’ve witnessed how startups and enterprises unlock growth with a scalable system architecture from day 1. It ensures performance under pressure, seamless deployment, and resilience against system-wide failures.
And as 2025 brings faster digital transformation, knowing how to scale smartly isn’t just beneficial—it’s vital.
Why Scalability in Microservices Architecture Is a Game-Changer
Picture this: your product’s user base doubles overnight. Traffic spikes. Transactions shoot up. What happens?
If you're relying on a traditional monolithic architecture, the entire system is under stress. But with microservices, you’re only scaling what needs to be scaled!
That’s the real power of understanding database scalability in microservices architecture. You’re not just improving technical performance, you’re gaining business agility!
Here’s what that looks like for you in practice:
- Targeted Scaling: If your search service is flooded with requests, scale that single microservice without touching the rest!
- Fail-Safe Systems: A failure in your payment gateway won’t crash the whole platform—it’s isolated.
- Faster Deployments: Teams can work on individual services independently and release updates without bottlenecks.
📊 Statistics to Know:
According to a 2024 Statista report, 87% of companies embracing microservices list scalability as the #1 reason for adoption—even ahead of speed or modularity. Clearly, modern tech teams know that growth means being ready.
Scalability in microservices architecture ensures you’re ready—not just for today’s demand but for tomorrow’s expansion.
But here’s the catch: achieving that kind of flexibility doesn’t happen by chance!
You need the right systems, tools, and practices in place to make scalability effortless. That’s where staying updated with current trends becomes your competitive edge!
Core Principles that Drive Scalability in Microservices Architecture
Understanding the core fundamentals helps in leveraging the best practices for scalable system architecture. So, before you jump into trends, it's essential to understand the principles that enable true scalability.
Without these foundations, even the most hyped system scalability tools and patterns won’t get you far in digital business!
1. Service Independence
It's essential for each microservice to operate in isolation. Decoupling allows you to scale, deploy, and debug individual services without impacting the whole system.
2. Elastic Infrastructure
Your system must incorporate efficient flexibility with demand. Auto-scaling and container orchestration (like Kubernetes) are vital to support traffic surges without overprovisioning.
3. Smart Data Handling
Scaling isn’t just compute—it’s efficient and smart data processing. Partitioning, replication, and eventual consistency ensure your data layer doesn’t become the bottleneck.
4. Observability First
Monitoring, logging, and tracing must be built in within every system to be highly scalable. Without visibility, scaling becomes reactive instead of strategic.
5. Built-in Resilience
Your services must fail gracefully, if its is destined to. Circuit breakers, retries, and redundancy aren’t extras—they’re essentials at scale.
These principles aren’t optional—they’re the baseline for every modern system architecture. Now you’re ready to explore the trends transforming how teams scale microservices in 2025!
Top Trends for Scalability in Microservices Architecture in 2025
As microservices continue to evolve, the focus on scalability has shifted from simply adding more instances to adopting intelligent, predictive, and autonomous scaling strategies. In 2025, the game is no longer about being cloud-native—it’s about scaling smartly!
Here are the trends that are redefining how you should approach scalability in microservices architecture.
🔹 1. Event-Driven Architecture—The New Default
Synchronous APIs once ruled microservices communication. Today, they’re a bottleneck. Event-driven systems using Kafka, NATS, or RabbitMQ are now essential for high-performance scaling.
With asynchronous communication:
- Services don’t wait on each other, reducing latency.
- You unlock horizontal scalability without database contention.
- Failures become less contagious due to loose coupling.
By 2025, over 65% of cloud-native applications are expected to use event-driven approaches to handle extreme user loads efficiently. If you want to decouple scaling from system-wide dependencies, this is no longer optional—it’s foundational.
🔹 2. Service Mesh for Observability, Security, & Traffic Control
Managing service-to-service communication becomes complex during system scaling. That’s where service mesh solutions like Istio, Linkerd, and Consul step in.
They enable:
- Fine-grained traffic control (A/B testing, canary releases)
- Built-in security through mTLS
- Zero-instrumentation observability
A service mesh is more than just a networking tool. It acts like the operating system of your microservices, ensuring visibility, governance, and security as you scale your system. According to CNCF's 2024 report, Istio adoption increased by 80% year-over-year among enterprises with 50+ microservices in production.
🔹 3. Kubernetes Goes Fully Autonomous with KEDA & VPA
Though Kubernetes is the gold standard for orchestrating containers, managing its scaling configurations manually can be a tedious job. That’s where KEDA (Kubernetes Event-Driven Autoscaling) and VPA (Vertical Pod Autoscaler) are stepping in.
These tools monitor event sources (queues, databases, API calls) and adjust your workloads in real time, ensuring that compute and memory resources always align with demand. The concept of the best software for automated scalability management say that automation isn't just helpful—it’s becoming essential for lean DevOps teams.
🔹 4. Edge Computing Starts to Influence Microservices Design
As latency-sensitive applications (like real-time analytics, AR/VR, or video processing) become more common, we’re seeing a shift toward edge-deployable microservices!
Scaling at the edge reduces the load on central clusters and enables ultra-fast user experiences by processing closer to the source. By the end of 2025, nearly 40% of enterprise applications are expected to deploy at least part of their stack on edge nodes.
🔹 5. AI-Powered Scaling Decisions
AI-driven autoscaling based on the traditional metrics ensures a more predictive approach. Digital platforms are now learning from historical traffic metrics, usage patterns, error rates, and system load to:
- Predict spikes before they happen
- Allocate resources preemptively
- Reduce both downtime and cost
Think: Machine learning meets Kubernetes HPA—helping your system scale before users feel the lag. Great!
Modern Database Solutions for High-Traffic Microservices
Data is the bloodstream of your system/application. Every user interaction, transaction, or API response relies on consistent, fast, and reliable access to data. In a microservices environment, things get exponentially more complex as you scale, as each service may need its separate database or shared access to a data source.
This is why your choice of database—and how you architect it—is a non-negotiable pillar in the system scaling strategy. You're not just selecting a tool; you're committing to a system that must support distributed workloads, global availability, real-time access, and failure recovery!
Modern database systems must support:
- Elastic growth without manual intervention
- Multi-region deployment to reduce latency and serve global traffic
- High availability and automatic failover
- Consistency trade-offs depending on workload (CAP theorem realities)
- Support for eventual consistency, sharding, and replication in distributed environments
Now, let’s explore some of the top database solutions for handling high traffic—
MongoDB
- Schema-less, horizontally scalable, and ideal for rapid development with flexible data models.
- Built-in sharding and replication make it a go-to for user-centric platforms.
Cassandra
- Distributed by design, Cassandra is engineered for write-heavy applications.
- Its peer-to-peer architecture ensures zero downtime and linear scalability.
Redis (In-Memory Cache/DB)
- Blazing-fast key-value store used for caching, session management, and real-time analytics.
- Integrates well with primary databases to reduce latency.
CockroachDB
- A distributed SQL database that survives node failures with no manual intervention.
- Great for applications needing strong consistency and horizontal scale.
YugabyteDB
Compatible with PostgreSQL, it offers global distribution, automatic failover, and multi-region writes—ideal for SaaS products operating across continents.
PostgreSQL + Citus
Citus transforms PostgreSQL into a horizontally scalable, distributed database—helpful for handling large analytical workloads with SQL familiarity.
Amazon Aurora
- A managed, high-throughput version of MySQL and PostgreSQL with auto-scaling capabilities.
- Perfect for cloud-native microservices with relational needs.
Google Cloud Spanner
- Combines SQL semantics with global horizontal scaling.
- Offers strong consistency and uptime guarantees—ideal for mission-critical financial systems.
Vitess
Used by YouTube, Vitess runs MySQL underneath but enables sharding and horizontal scalability at a massive scale—well-suited for read-heavy architectures.
Bottomline
Scaling a modern digital product requires more than just technical upgrades—it demands architectural maturity. Scalability in microservices architecture is built on clear principles of—
- service independence,
- data resilience,
- automated infrastructure, and
- real-time observability.
Microservices empower teams to scale components independently, deploy faster, and maintain stability under pressure. The result—Faster time to market, better fault isolation, and infrastructure that adjusts dynamically with demand.
What truly validates this approach are the countless case studies on successful product scaling from tech companies that prioritized scalability as a core design goal. From global SaaS platforms to mobile-first startups, the trend is clear—organizations that invest early in scalable microservices foundations consistently outperform those who patch their systems later.
FAQs
1. What is scalability in microservices architecture?
Scalability in microservices architecture refers to the ability of individual services within a system to scale independently based on workload. This allows you to optimize resource usage, reduce downtime, and ensure responsiveness during high-traffic conditions. It enables your application to adapt dynamically to user demand without overburdening the entire system.
2. Why are databases critical in scalable architectures?
A scalable system is only as strong as its data layer. If your services scale but your database can't handle distributed loads, your entire application can face performance bottlenecks. Scalable databases offer features like replication, sharding, caching, and automated failover to maintain performance under pressure.
3. What are the best practices for automated scalability?
Automated scalability involves using tools like Kubernetes HPA, KEDA, and VPA to auto-adjust resources based on real-time metrics. Best practices also include decoupling services, setting scaling thresholds, and implementing observability tools like Prometheus and Grafana. We just disclosed them all in the blog above!
4. Are there real-world case studies on successful product scaling?
Yes, many leading companies have adopted microservices and achieved remarkable scalability. For instance, Netflix, Amazon, and Uber are known for leveraging microservices to scale specific features independently. At Acquaint Softtech, we’ve also delivered tailored solutions backed by case studies on successful product scaling for startups and enterprises alike. Get in touch with our software expert to know more!
Source :
I'm reading this one myself and it's been useful so far. It's a free short book so if you don't have experience with prompting this should help you out. I found this book while browsing X posts.
It is shared via google drive, you can get it here -> https://drive.google.com/file/d/1AbaBYbEa_EbPelsT40-vj64L-2IwUJHy/view?usp=sharing
I cannot use Stripe in my country but I needed some payments infra, I found Dodo Payments to be a good solution right now. Hopefully it stays that way.
Good DX and it's available in countries where Stripe isn't.
I've come across this website that gives you a decent amount of marketing blocks to use. Pair this with some good sales copy and you should be good to go.
I looked at starter kit made by WebDevCody and saw this rate limiter implementation.
Very easy to use and does the job, suitable for when you have only one instance. You would need something like Redis to scale this up for multiple instances.
You can check it out here -> https://github.com/webdevcody/wdc-saas-starter-kit/blob/main/src/lib/limiter.ts
OpenRouter.AI is an LLM and multimodal model inference management service.
You issue an API key once.
Then you choose which model you want to use.
Set your prompt.
Ready to go.
You can also use it for free (up to 1k requests / day)
Here's a short demo.
Hello guys. We talk about tech stacks all the time, always using the latest shiny new javascript framework. But I'm wondering. What tech stack are you actually the most productive in? Can be different from what you use now, but what can you code the fastest with?
$2.9k/mo
Hey everyone, this is just a kindly reminder that you get $10 in advertising credits per friend you refer to Huzzler. At the time of writing, it costs $26 in credits to advertise your product on Huzzler and generate about ~2000 impressions.
How to refer a friend? Simply copy your referral link and send it to a friend, share it on X,..
Advertising credits can be spent here
Have an amazing day everyone!
Hi there,
I need some recommendations and guidance. I'm working on a personal project that I want to turn into a SAAS. I have no experience at building a SAAS app and I want to understand the steps to do so. Right now the backend is built in Python and I was thinking of Flask for the front end. Where should I start to take it to the next level? Thank in advance
Don't pay for Cursor or Windsurf.
Why?
Because Cline + OpenRouter.AI can you give the exact same experience for FREE (up to 1k requests/day)
I made a YT demo
new to this community. not sure if this is okay to post here but I wanted to share how quickly developing MVPs for my clients using Windsurf.
I used to lose days setting up auth, DB, styling, API routes, now I make about $4K/month just shipping MVPs fast. The secret is that I use windsurf with next.js to create a fullstack app fast. next.js is the only stack that works really well with windsurf. it can create:
- Pre-configured auth (Clerk/Supabase)
- Prisma + full database setup
- API routes ready to go
- Tailwind + shadcn/ui components
- ESLint, Prettier, Husky, commit hooks out of the box
What else do you need? I haven't had a single thing a client asked that windsurf couldn't do. I tweak a few config files (windsurf.json, .env), generate the project, and start building real features Most MVPs are live in 5-7 days. Clients love the speed.
For me, AI is not bad, it's good. It's fast made dev fun (and profitable) again. Let me know if you have any questions technical or other and ill do my best to answer them.
Hacky but useful — we just needed a UI for blog assets stored in Supabase (images, markdown, PDFs). Next.js + Tailwind. Auth, file upload, folder nav, publish button.
Nothing fancy. Just worked for us. Might work for you too.
npx create-supawald my-app
hey, this question is mainly for the founder of Huzzler!
first of all I want to say, it's really awesome!!
a little background about me: I am an uni student learning to build products.
so, I am really interested to know a detailed explanation of tech stack of huzzler. if you can, please go into whys of things as well.
what are you using for storing images/profiles pictures and all?
looking forward to hear from the founder! :)
For those using Cursor AI, what model(s) do you use? Is claude 3.5 still king? Heard great things about Gemini 2.5 pro as well
Shipping fast feels great—until the hackers show up.
Leo Jr. learned this the hard way. He’s a non-technical indie hacker who built and scaled an app publicly, attracting attention (and revenue) with spicy takes like "AI SaaS won’t work." But the attention didn’t stop at engagement. It made him a target.
Soon after, hackers began probing his app for security flaws. And they found plenty—API keys exposed in the codebase, easily bypassable paywalls, and more. The result? Half the internet was trying to break his app for fun.
This isn’t new. Pieter Levels, Marc Lou—other big names in the indie hacker world have dealt with DDoS attacks and vulnerability exploits after their products blew up. But Leo’s case stands out because he’s not a developer. He vibe-coded his way to success without fully understanding the security side of things.
What Indie Hackers Can Learn
As someone who's also building and shipping apps fast (and not immune to these risks), here are two key takeaways:
1. Hide Your API Keys
Publicly exposed API keys are an open invitation to hackers. Store them securely using environment variables instead of hardcoding them in your codebase. If you’re using Next.js, create a .env.local file and reference the keys like this:
NEXT_PUBLIC_API_KEY=your-key-here
Then access it in your code like this:
const apiKey = process.env.NEXT_PUBLIC_API_KEY;
Simple fix, big impact.
2. Stop Using CSS for Paywalls
CSS-based paywalls (display: none;) are laughably easy to bypass. Instead of relying on front-end styling, enforce the paywall logic on the backend. If that's too complex, a middle-ground solution is to obfuscate the content using Base64 encoding and set up DevTools protection to make it harder to bypass.
3. Securing Webhooks – The Overlooked Weak Spot
Webhooks are essential for automating tasks between apps—but they’re also an easy target for attackers if left unprotected. Here’s how to lock them down:
- Use a Signature and Timestamp – Your webhook’s receiving URL must be public, but you can secure the data using a signature, timestamp, and token to create a hashmap (a key-value store).
- Generate a HMAC Signature – Link the timestamp and token values, encode them using the HMAC algorithm with your ESP’s API key (in SHA256 mode), and compare the result with the signature.
- Reject Duplicate Tokens – Cache the token value locally and reject any request that tries to reuse the same token. This prevents replay attacks where hackers repeat or misdirect the webhook action.
Here’s a quick example in TypeScript for securing webhooks:
import crypto from 'crypto';
const verifyWebhook = (signature: string, timestamp: string, token: string, secret: string) => {
const data = `${timestamp}.${token}`;
const expectedSignature = crypto
.createHmac('sha256', secret)
.update(data)
.digest('hex');
return signature === expectedSignature;
};
Shipping Fast ≠ Ignoring Security
As an indie maker, I get it—speed matters. But security matters too. Leo’s experience is a reminder that even if you're not a developer, securing your app isn’t optional. Don’t let vibe coding turn into vibe hacking.
I’d love to hear from other makers—how are you balancing speed with security in your builds?
Let’s discuss in the comments.
$2.9k/mo
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I've been coding with Cursor AI for a while and it's awesome. But I still want to improve my workflow. Like when should you use agent vs chat mode, how to give it the right context?
$2.9k/mo
I feel like more people should be using DaisyUI. I've used it over 5 projects and its simply amazing. DaisyUI adds component class names to Tailwind CSS, providing you with all sorts of components such modals, cards, avatars, carousels, you name it. It's ridiculously easy to use and very powerful.
Take for example uncapped.ai. It's an AI wrapper with 30+ themes. I've implemented this in only 5 minutes with daisyUI. With any other library, this would take me ages.