🐍 Python | 🤖 AI/ML | Software Engineer | Practical roadmaps • Hands-on tutorials | Freelance & collabs open | DM 📩
Pune, India
Joined February 2019
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Building AI applications is one thing. Making sure they behave as expected under adversarial conditions is another.
As LLMs and AI agents become part of real-world workflows, testing them goes beyond checking whether they produce the right answer. You also need to evaluate how they respond to malicious inputs, how agents execute actions, and where the underlying infrastructure might introduce risks.
That's what makes AIBeat worth exploring.
The toolkit brings together three components:
🔹 PromptBeat — for testing prompts and LLM behavior against adversarial inputs.
🔹 AgentBeat — for testing AI agents and investigating their behavior during execution.
🔹 RouterBeat — for evaluating the security of AI routing and relay infrastructure.
Each targets a different layer of the AI stack, helping developers think more systematically about security and reliability.
If you're building LLM-powered applications or AI agents, AIBeat is worth a look.
Explore it: aibeat.ai/
@Tophant001
🤝 Paid partnership
Saying "70% of the web was at risk last week due to PHP" sounded completely unbelievable if you only knew about frontend tech stacks from Twitter or Instagram.
The social media dev bubble talked about Next.js and React all day, but PHP still quietly ran the actual internet.
The reality check was massive.
Production Debugging Playbook, Part 4: Debugging Database Bottlenecks
When an application slows down, the database often gets blamed first.
But high database latency doesn't automatically mean you need a bigger database. The real bottleneck could be an inefficient query, lock contention, connection pool exhaustion, disk I/O, or resource saturation.
Here's how to investigate.
1. Spot the symptoms
Look at query latency (especially p95), active connections, slow query counts, database CPU, and disk I/O.
Correlate these signals with application latency, traffic spikes, and recent deployments to establish when the problem started.
2. Identify the bottleneck
Check whether the database is struggling with query execution, waiting on locks, running out of connections, or hitting resource limits.
Also distinguish between time spent executing a query and time spent waiting for a connection or another transaction.
3. Gather evidence
For PostgreSQL, useful tools include:
• "pg_stat_statements" to identify expensive queries.
• "pg_stat_activity" to inspect active queries and waiting sessions.
• "pg_locks" to investigate lock contention.
• "EXPLAIN (ANALYZE, BUFFERS)" to examine query execution and buffer activity. Use caution with "ANALYZE", since it actually executes the query.
4. Find the root cause
• Slow queries: Missing indexes, inefficient joins, full table scans, or poor query plans.
• Locks and blocking: Long-running transactions or concurrent updates.
• Connection exhaustion: Misconfigured pools, idle connections, or leaked connections.
• High disk I/O: Heavy reads/writes, large scans, or storage bottlenecks.
• Resource saturation: CPU, memory, or storage capacity limits.
5. Apply the right fix
Optimize queries and indexes, shorten transaction duration, resolve blocking, tune connection pools, or improve storage performance.
Scale the database only when evidence shows that capacity is the limiting factor. Adding connections or replicas without addressing the actual bottleneck can make things worse.
6. Verify recovery
Compare query latency, connection usage, CPU, I/O, and application error rates against the pre-incident baseline. Confirm the improvement persists under representative traffic.
The debugging sequence:
Observe → Isolate → Investigate → Diagnose → Fix → Verify
The examples in this visual use PostgreSQL to demonstrate the process. The underlying approach applies to other databases, including MySQL, SQL Server, and Oracle, although their diagnostic tools and commands differ.
Don't guess at database bottlenecks. Find the evidence, fix the cause, and measure the result.
Next: Debugging Network & Dependency Failures.
Production Debugging Playbook, Part 3: Debugging CPU, Memory & Resource Saturation
High CPU usage doesn't always mean you need more replicas.
High memory usage doesn't always mean you have a memory leak.
The goal is to identify what's saturated, understand why, and apply the right fix.
1. Spot the symptoms
Look at CPU and memory usage, pod restarts, OOMKilled events, request latency, and error rates.
Check whether the problem affects one pod, an entire service, or the Kubernetes node.
2. Identify the affected component
Use metrics to find the overloaded pod or node.
If one worker pod is at 95% CPU while other pods remain healthy, investigate that workload before scaling the entire cluster.
3. Gather evidence
Useful Kubernetes commands:
• "kubectl top pods -n prod"
• "kubectl describe pod -n prod"
• "kubectl logs -n prod --previous"
Check CPU throttling, memory limits, restart reasons, application logs, and resource requests. Compare the affected pod with healthy instances.
4. Find the root cause
• High CPU: inefficient code, expensive loops, increased traffic, or CPU throttling.
• High memory: memory leaks, large in-memory objects, or excessive caching.
• Thread/connection exhaustion: blocked workers, saturated connection pools, or slow dependencies.
• Node pressure: insufficient node capacity, resource limits, or noisy neighbours.
Remember: CPU usage measures consumption, while CPU throttling indicates the container is being restricted by its CPU limit. They are related, but not the same.
5. Apply a targeted fix
Optimize code, fix memory leaks, tune connection pools, or address slow dependencies.
Scale horizontally when additional replicas can distribute the workload. Adjust CPU or memory limits only after checking actual usage and capacity. Raising a memory limit may delay OOMKills, but it won't fix a leak.
6. Verify the result
After the fix, compare CPU, memory, throttling, restarts, latency, and error rates against the baseline.
Confirm the improvement persists under representative traffic and that the bottleneck hasn't shifted elsewhere.
The debugging sequence:
Observe → Isolate → Investigate → Diagnose → Fix → Verify
Don't scale blindly. Use evidence to distinguish an application bottleneck from a container limit or a node-capacity problem.
Next: Debugging Database Bottlenecks.
State of AI in corporate currently
The board wants an AI strategy. So leadership has AI write one. The team has AI turn it into 40 slides.
Then they book a meeting to figure out what the slides are saying. AI takes notes on the meeting.
Then the board asks for a one-pager summarizing the notes.
Corporate world have never been more aligned on absolutely nothing
Dario Amodei right now
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US & China both have people working regular IT jobs.
The differentiation is the builder mindset.
The post seems to confused Infosys employee mindert with a Blinkit founder mindset
Dear Indian IT companies....
If you want to build superior software like USA or China.... please pay salaries like them...
You can't pay 6 LPA & expect engineers to build products that are built by engineers who are getting crores of salary.
Start paying min 60 lpa - 1cr salaries....
In 1 year, we will overtake China
In 2 years, we will overtake USA
In 3 years, we will overtake Alien technology
You get what you pay for
10 resources to learn System Design better than most college courses.
Don't start by memorizing how Netflix, Uber or WhatsApp work.
First, understand the concepts that make these systems possible. Then study how real engineering teams apply them.
Here are 10 resources I'd recommend, in the order I'd learn them.
PART 1: MASTER THE FUNDAMENTALS
1. System Design: The Complete Course
Start here if you want a structured introduction to databases, scaling, load balancing, APIs and security.
🎥 System Design Course – APIs, Databases, Caching, CDNs, Load Balancing & Production Infra — freeCodeCamp
youtube.com/watch?v=C842vFY5…
2. Distributed Systems Fundamentals
Learn caching, replication, sharding, consistent hashing, message queues and the trade-offs behind distributed architectures.
📚 System Design Primer — GitHub
github.com/donnemartin/syste…
3. Databases and Distributed Data
Understand how real systems store, process and distribute data, and why consistency, reliability and scalability involve trade-offs.
📚 Designing Data-Intensive Applications — Martin Kleppmann
dataintensive.net/
4. How to Think Through a System Design Problem
Learn to translate requirements into architecture, estimate capacity, identify bottlenecks and justify your design decisions.
🎥 System Design Explained: APIs, Databases, Scaling, Load Balancing, Auth & Security — Hayk Simonyan
youtube.com/watch?v=oYxTTirK…
PART 2: LEARN FROM REAL-WORLD SYSTEMS
5. Netflix: Designing for Global Scale
How do you deliver video reliably to millions of viewers while managing latency, traffic and infrastructure costs?
🎥 Demystifying the Unusual Evolution of the Netflix API Architecture — ByteByteGo
youtube.com/watch?v=Uu32ggF-…
6. Uber: Real-Time Location and Matching
Explore geospatial indexing, real-time updates, dispatch systems and the challenge of matching riders with nearby drivers.
🎥 System Design: Uber Ride-Sharing Platform — Mukul Raina
youtube.com/watch?v=2k2QBX9A…
7. WhatsApp: Messaging at Scale
How do you build a messaging platform that delivers messages reliably, supports concurrent connections and keeps conversations private?
📚 WhatsApp Engineering — Meta Engineering
engineering.fb.com/
8. Discord: Real-Time Communication
Explore the challenges behind real-time messaging, persistent connections, data storage and scaling communication across millions of users.
📚 Discord Engineering Blog
discord.com/blog
9. Dropbox: File Storage and Synchronization
Learn about file synchronization, metadata, storage systems and the engineering challenges of keeping files consistent across devices.
📚 Dropbox Tech Blog
dropbox.tech/
10. YouTube: Video Processing and Delivery
Understand the architecture behind uploading, processing, storing and delivering videos to users across the world.
📚 Google Cloud Architecture Center
cloud.google.com/architectur…
One final piece of advice:
Don't just watch these resources.
For every system, ask yourself:
• What are the functional and non-functional requirements?
• What breaks first when traffic grows?
• Why was this architecture chosen over the alternatives?
• What trade-offs did the engineers make?
• How would I design it differently today?
That's how you move from memorizing architecture diagrams to actually learning system design.
Save this list for your next learning session.
Everytime someone tells me coding is dead I show them supply chain attack examples
People are using it to expose CVEs but the maintainers or devs cannot patch or bring out new versions at the same speed because SWE isnt about just writing code.
The whole process around code generation is complex, and requires human coordination, judgements and expertise
Currently the AI tools are doing more harm than good to the world
We've never been more exposed to software supply chain attacks than we are today. And AI is making it worse.
Imagine you're building an AI app.
You install LangChain, pull in a bunch of Python packages, connect your AWS account, and deploy everything through GitHub Actions.
Everything works perfectly. But you probably haven't checked most of the packages you just installed.
You trust them because they're popular and widely used.
Now imagine one of them gets compromised.
That is exactly the kind of attack we've seen in 2026.
- In March, attackers compromised the Trivy security scanner used in LiteLLM's CI pipeline. They stole publishing credentials and pushed malicious versions of LiteLLM to PyPI.
The malicious code could steal SSH keys, cloud credentials, Kubernetes configs, and CI/CD secrets.
And it didn't stop there. One compromise helped attackers move further down the supply chain. A security tool intended to protect software became part of the attack.
- In May, attackers poisoned more than 170 npm packages and two PyPI packages. They even abused legitimate GitHub OIDC trusted publishing to distribute malicious releases.
- By June, a Shai-Hulud worm variant was targeting PyPI, stealing credentials and spreading further.
Now think about your own development environment.
- Your laptop has cloud credentials.
- Your .env file has openai api keys
- Your terminal has access to Kubernetes.
- Your GitHub account can deploy to production.
- Your AI coding agent can install packages to get its job done.
One compromised dependency could expose all of them, leading to loss of money and highly sensitive data theft.
Here are five things I do to reduce the risk.
>> I avoid installing brand-new releases and use a minimum release age where possible.
>> I pin dependencies and use lockfiles and hashes to make builds predictable.
>> I use short-lived credentials instead of keeping long-lived cloud keys on my machine.
>> I pin GitHub Actions to full commit SHAs instead of relying on tags that can move.
>> I restrict outbound network access from CI runners so compromised packages have fewer ways to send data out.
None of these practices makes you completely safe, but together they reduce the attack surface.
Especially when AI is writing, installing, and running more of it than ever.
One of my favourite quotes:
"I helped a stranger climb a mountain, and then I realized, I too was at the top."
As you transition from an individual contributor to a manager, your impact shifts from what you can accomplish yourself to what you enable others to accomplish.
That might mean helping someone develop confidence, removing a blocker, or giving a teammate the context they need to make better decisions independently.
Your success is no longer just about how good you are at solving problems. It's also about how much better the people around you become because they work with you.
Sometimes, helping others climb higher is how you reach the next level yourself.
Big big kudos to my buddy @techyoutbe for the launch of his latest product PostDeck.
As a content creator, this is an awesome utility which streamlines writing, style and planning.
Give it a try !!!
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