🐍 Python | 🤖 AI/ML | Software Engineer | Practical roadmaps • Hands-on tutorials | Freelance & collabs open | DM 📩
Pune, India
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This weeks goals
- 4x long form articles
- Post short content daily
- 10k steps 5x a day
- 0 order-ins
Lets see how many I can hit....
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Definitely not a Manchester United fan
🚨 #BREAKING: Virginia man charged with duct taping his wife to a chair so he could watch football upstairs “in peace”
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If you are running PHP this is something you should play attention to.
News: PHP will ship an urgent security release on 21 October at 7:30pm IST for two critical flaws, both rated CVSS 9.2. Word of the bugs leaked early, and watchTowr says they are real remote code execution flaws. PHP runs WordPress, Drupal and most shared hosting. List your PHP servers now and patch the moment the fix lands.
php.net/archive/2026.php
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Hiring Post 📫
#HIRING Senior Software Engineer - Platform and Architecture for a Incident Management Tool - 7+ years XP (4+ in .NET/C#) - reply or DM with Resume
The Role
We are an enterprise incident management and alerting platform (will disclose name once you DM). When our customers have an outage, we are the system that has to work. That means reliability, speed and correctness are not nice-to-haves; they are the product.
We are hiring a Senior Software Engineer who thinks like an architect and ships like an operator. You understand why a feature exists, how it fits the system, and what breaks when it scales. Then you build it, test it and close it out. This is not a people-management role and not a pure ticket-taker role. You own outcomes from design through production.
Location: Chennai, India.
Reports to: Chief Technology Officer (CTO).
What you'll do
Translate use cases into system design. Take a customer problem or product requirement, break it down, and propose the data model, API contract and processing flow before a line of code is written.
Own features end to end. Design, build, test, deploy and support. Push work to a real close, not 90% done.
Build across our full platform. Make sound calls on where logic belongs: Azure SQL stored procedures, the core .NET application, or Azure Functions.
Design for scale and reliability. Alert ingestion, routing and notification paths run under load and under pressure. You understand throughput, concurrency, idempotency, retries and failure modes.
Raise the bar on testing. Write strong unit, integration and regression tests. Define test cases from the use case, not just the code path.
Be the go-to technical partner for Product. Sit in design discussions, challenge assumptions, flag constraints early, and give honest estimates.
Lead technical design with the team. Run DB and API design reviews, document decisions, and help other engineers move faster.
Use AI as a force multiplier. Use AI coding and analysis tools daily to understand legacy code, generate tests, prototype and ship faster.
Our stack
Our platform powers mission-critical alerting for enterprises worldwide. You will ship to it from week one.
Application: .NET / C#, core application plus services
Serverless: Azure Functions for event-driven and async processing
Data: Azure SQL Database, with core business logic in stored procedures
Cloud: Microsoft Azure
Interfaces: REST APIs and inbound/outbound integrations with monitoring, ITSM and collaboration tools
You know how to get the most from this stack: when stored procedures are the right tool, how to design Azure Functions for scale, and how to evolve the core application while the business keeps moving fast.
What you bring
> 7+ years building and shipping production software, with 4+ years in C# / .NET.
> Deep SQL Server or Azure SQL experience: schema design, query tuning, indexing, and writing and refactoring stored procedures.
> Hands-on experience with Azure, especially Azure Functions, queues or Service Bus, and App Services.
> Proven system design skills. You can whiteboard a data flow, defend your trade-offs, and spot the bottleneck before it ships.
> Strong API design: clean contracts, versioning, auth, error handling and backward compatibility.
> A testing mindset. You write test cases from requirements and treat untested code as unfinished.
> Comfort in a mature, established codebase. You can read unfamiliar code fast, understand the business logic, and extend it safely.
> Product sense. You ask "what problem does this solve" and "who uses it" before "how do I build it."
> Bias for speed with judgment. You know when to move fast and when a shortcut will cost us later.
> Clear written and verbal communication with engineers, product and customers.
Nice to have
> Experience with incident management, alerting, monitoring or on-call platforms.
> Background in high-throughput, event-driven or real-time messaging systems (SMS, voice, push, email delivery).
> Experience evolving architecture at scale, including service design and data-tier optimization.
> Familiarity with integrations across ITSM, observability and collaboration tools.
> Experience with LLM APIs, agents or MCP in a production product.
> Enterprise SaaS background with multi-tenant architecture and security or compliance requirements.
How we work with AI
We strongly encourage AI in this role. We expect you to use it every day, and to use it well.
> Use AI coding assistants to read and explain existing code and stored procedures, draft implementations, and generate test cases.
> Prototype designs and spikes in hours, not weeks.
> Review AI output with an engineer's eye. You own what ships, regardless of who or what wrote it.
> Share what works so the whole team gets faster.
In the interview, we will ask you to show us how you use AI on a real problem.
What success looks like
By 30 days, You have
Mapped the core alert processing path across the monolith, Functions and stored procedures. Shipped your first fix or small feature to production.
By 60 days, You have
Led design for a mid-sized feature, including data model, API and test plan. Shipped it with strong coverage.
By 90 days, You have
Own a product area end to end. Product brings you problems, not tickets. You have improved at least one scaling or reliability constraint in the stack.
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You can spend months learning a new technology and still struggle to use it when a real problem appears.
You watch tutorials, complete courses, save articles, and understand the examples. Everything makes sense while someone else is explaining it.
Then you open a blank editor and realize you don't know where to start.
That's because understanding something and being able to use it independently are two different skills.
To close that gap, build something without following a tutorial step by step. Debug the failures, make decisions without a predefined answer, and explain why you chose one approach over another.
You don't have to stop taking courses or reading documentation. You just need to pair learning with enough practice to make the knowledge your own.
Don't measure learning by how much you consume. Measure it by what you can now do without being shown how.
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How is there a football match happening in Paris ??
Indian X/Twitter told me Paris was doomed and worse than Somalia now
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If you are in PCMC, Shri Charbhuja Mandir Vishnu Dham (श्री चारभुजा मंदिर विष्णु धाम) is a good temple to visit.
Visit during evening hours to see the real beauty of this temple.
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🎉 New Series Production Debugging Playbook
Part 1/6 : Debugging High Latency
Your API is responding slowly. Before increasing CPU, adding database indexes, or scaling your service, find out where the time is actually going.
Here's how I'd approach this as a production engineer.
1. Start with latency percentiles
Average latency can hide serious problems. If most requests take 100 ms but a small percentage take 2 seconds, the average might still look acceptable while some users experience a painfully slow application.
• p50 (median): 50% of requests complete within this time. It represents the typical request.
• p95: 95% of requests complete within this time. The slowest 5% take longer.
• p99: 99% of requests complete within this time. The slowest 1% take longer.
Compare these percentiles over time and break them down by endpoint, region, service version, or customer segment where relevant.
If p50 stays stable but p99 spikes, investigate what makes a subset of requests slow. If all percentiles rise together, look for a broader bottleneck.
2. Trace a slow request
Metrics tell you that latency increased. Distributed traces help you understand where the time is spent.
Imagine a request passing through an API gateway, user service, product service, and database. A distributed trace breaks the request into spans, showing the duration and relationships between operations.
Look for the slowest spans, repeated downstream calls, retries, and time spent waiting for dependencies.
One important detail: don't simply add every span duration together. Parent spans often include the time spent in their child spans, and parallel operations can overlap. Use the trace timeline to understand the actual critical path.
3. Identify the real bottleneck
Once you know which component is slow, investigate the reason.
• Application: Check CPU saturation, thread or worker exhaustion, garbage collection, and slow external calls.
• Database: Separate query execution time from connection acquisition waits. Inspect slow queries, execution plans, locks, and connection pool utilization.
• Network or dependencies: Check DNS resolution, connection establishment, timeouts, retries, and downstream service latency.
• Caching: Inspect cache hit rates, misses, eviction patterns, and cache response times.
Don't assume that a slow database span means the SQL query itself is slow. The application might be waiting for a connection, a lock, or a response across the network.
Use logs for request-specific context, metrics to understand system behavior, and traces to connect the stages. Correlate them using timestamps, trace IDs, and relevant deployment events.
4. Validate your hypothesis before changing anything
Suppose p99 latency jumps from 300 ms to 2.4 seconds after a deployment, but the error rate remains stable.
A trace shows that the request spends most of its extra time waiting for a database connection. Pool metrics confirm that all connections are occupied, while query execution time remains normal.
Now you have evidence for connection pool exhaustion, not a slow SQL query.
Investigate why the pool is exhausted, apply a targeted fix, and compare the same signals before and after. Verify that latency recovers without increasing database load or introducing new errors.
The principle to remember: Observe the symptom. Locate the delay. Test the hypothesis. Verify the fix.
Senior engineers don't debug by trying every possible solution. They use evidence to eliminate possibilities until the root cause becomes clear.
Next in the series: Debugging 5xx Errors & Failed Requests.
Last night, a swarm of Claude Opus 5.5 agents working for 14 hours and burning 387 million tokens were able to fully reverse engineer and decompile Kubernetes, including the scheduler, API server, and controller manager.
Full source code available here: github. com/kubernetes/kubernetes
Grab it before they take it down.
Made with AI
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Mumba/Navi Mumbai folks help out a bro
Mumbai Twitter, I need your help 🙏
Looking for a 3 or 4 BHK on rent in/around Navi Mumbai, Thane, Seawoods, Kharghar, or Chembur.
Ideally in a modern gated society with good kids’ play areas and a temple. If you live in such a society, please check your society groups and DM me if anything is available.
Brokers, please excuse.
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Mcdonalds jobs be really competitive man
Last night, a swarm of Claude Opus 5.5 agents working for 14 hours and burning 387 million tokens were able to fully reverse engineer and decompile Kubernetes, including the scheduler, API server, and controller manager.
Full source code available here: github. com/kubernetes/kubernetes
Grab it before they take it down.
Made with AI
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Last night, a swarm of Claude Opus 5.5 agents working for 14 hours and burning 387 million tokens were able to fully reverse engineer and decompile Kubernetes, including the scheduler, API server, and controller manager.
Full source code available here: github. com/kubernetes/kubernetes
Grab it before they take it down.
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Office these days is just pausing an urgent task to handle a more urgent task which I paused yesterday to handle another urgent task
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Distributed Systems Fundamentals, Part 3/3.
What happens when one failing service starts bringing down the entire system?
Reliable systems don't just handle failures. They contain them.
In this final part, we cover three resilience patterns every backend engineer should understand:
• Circuit Breakers: Stop calling an unhealthy dependency and give it time to recover.
• Bulkheads: Isolate resources so one failing component doesn't exhaust everything.
• Graceful Degradation: Preserve core functionality when non-critical dependencies fail.
The goal isn't to eliminate every failure. It's to prevent one failure from becoming a system-wide outage.
Save this series for your next system design interview or production incident.
Distributed Systems Fundamentals, Part 2/3.
What happens when the same payment request reaches your service three times? Or when messages arrive in the wrong order?
These are common problems in distributed systems, and handling them correctly is essential for building reliable applications.
In this visual, we cover:
• Idempotency: Make repeated requests safe.
• Delivery guarantees: Understand at-most-once, at-least-once, and exactly-once processing.
• Message ordering: Handle events that arrive out of sequence.
The goal isn't to assume that duplicates and failures won't happen. It's to design systems that handle them safely.
Made with AI
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