As a seasoned entrepreneur with over 2 and half decade of tech experience in the realm of design thinking and consulting, I've dedicated the last half-decade to spearheading digital transformation initiatives. I serve as the CEO and Founder of Qodequay Technologies Pvt. Ltd., where our mission is to blend groundbreaking technologies, such as AI, AR/VR/MR, Cloud, and Blockchain, with innovative design solutions. Our products cater to global enterprises, spanning continents from the USA to Australia, Europe to India.

With a vast 20+ years of expertise in guiding product creation for international clientele, my approach is deeply rooted in human-centered design. I'm proud to hold a credential in Design Kit: The Course for Human-Centered Design from NovoEd. My experience extends to leading diverse teams in digital content creation, marketing, communication, and the delivery of multimedia solutions tailored to clients' needs.

One of my most cherished ventures involves developing products for rural India, harnessing the power of data science to uplift and better the lives of the underserved communities. As a mentor, I've had the privilege to guide emerging designers from premier universities, enlightening them about the intricacies of design implementation and processes. I am not just an entrepreneur but a visionary, constantly pushing boundaries to revolutionize the digital landscape.

Shashikant Kalsha

AI influencers aren’t just a novelty anymore. They're everywhere now, and it's getting tough to spot who’s real and who isn’t.

Some people see them as a cost-saving hack. They’re always available, never asking for higher fees, and don’t complain about deadlines. Brands are jumping in because these digital personalities never go off-script, never age, and never end up in scandals.

But is that really enough to change the entire influencer game?

Let's look at both sides.

On one hand, AI influencers can churn out content on demand, reply instantly, and nail brand messaging every single time. No worrying about schedules. No drama. The creative options? Pretty much endless, since digital personalities can be whatever brands want.

On the other hand, there’s still no real substitute for genuine human connection. People follow influencers because they relate to them, trust them, or just like their quirks. Can an AI character actually build that loyalty? Or is it just a smart trick people will see through eventually?

There’s another big question too: Who’s responsible when an AI influencer messes up? If the AI says something insensitive or spreads something inaccurate, who takes the blame?

It’s fascinating to see these two worlds bump into each other. The efficiency and flexibility of AI, facing off against the messy, unpredictable reality of humans.

Maybe both can have a spot in the future. Brands could use AI for consistency and scale, but lean on real influencers for authenticity and deeper relationships.

For now, I’m curious. Would you trust a recommendation from an AI influencer as much as one from a real person?

Where do you think the future of influence is heading?

CEO, MD & Founder | Future-Tech Enthusiast and Author

Open article: Why Choosing the Right UI/UX Design Company Defines 80% of Your Product’s Success
 by Design Studio - UI/UX Design Agency • 1 min read

Why Choosing the Right UI/UX Design Company Defines 80% of Your Product’s Success

Lead Backend Engineer - Senior Backend Developer

Mohamad Shahkhajeh

Reading Query Plans Like a Surgeon: EXPLAIN / EXPLAIN ANALYZE for MySQL & Postgres 🔍

Query performance stops being a mystery when you treat plans like medical charts. Here’s the compact playbook I use in production:

1) Hunt the right queries
PG: pg_stat_statements. MySQL: Performance Schema / pt-query-digest. Pick by total time or p95, not vibes.

2) Use actual plans
PG: EXPLAIN ANALYZE (with buffers). MySQL 8+: EXPLAIN ANALYZE TREE. Estimates lie; actuals tell the truth.

3) Kill row-estimate drift
Refresh stats. In PG, raise per-column stats and add extended stats for correlated columns. In MySQL 8+, add histograms. Good cardinality = good plans.

4) Fix join order + hidden sorts
Filter early, join late. Make predicates sargable. Add an index that matches filter + order to avoid filesorts/disk sorts.

5) Coerce with structure, not hacks
Prefer better stats, sargable rewrites, and precise indexes over permanent hints.

6) Validate with diffs
Compare before/after plans: total time down, fewer rows into expensive nodes, fewer disk sorts. Don’t rely on one lucky timing run.

7) Protect p95 + cache
Test on a replica or during low traffic. Keep changes session-scoped. Warm only what you need.

Key tip: Start small. Fix one query properly end-to-end (evidence → plan → structural fix → plan diff → canary). Repeat.

📢 Full article on Medium: https://lnkd.in/dDqsTxx8

📢 Daily Dev Tips on Telegram: https://lnkd.in/dJVDj7iQ

#MySQL #PostgreSQL #QueryOptimization #DatabasePerformance #DevTips

🧭 Beating Replication Lag: Read-Your-Writes Without Melting the Primary 🚀

If a user saves a profile and can’t see it immediately, you didn’t ship a feature—you shipped distrust. In replica-heavy stacks, replication lag is normal; broken read-your-writes is optional. Here’s the playbook I use in production:

1) Set Freshness SLOs 📊
Make “freshness” visible: e.g., P99 read-your-writes ≤ 250 ms, replica staleness ≤ 2 s for 99.9%. If you can’t see it, you can’t protect it.

2) Lag-Aware Routing (Sticky TTL) 🩹
After a write, stick the user’s reads to primary for ~2 s. A simple hot-key per user solves 80% of UX surprises.

3) Write Fences (LSN/GTID) 🛡️
Record the commit position at write time, and only read from replicas that have replayed at least that position. Monotonic per-user reads without long primary pinning.

4) Fast Fallback on Staleness ⛑️
If a replica lags or fails the fence check, route to primary immediately. Keep a hard cap on stickiness to control cost.

5) Scope the guarantee 🔒
We’re not making the system globally linearizable—just guaranteeing I see my own writes. Team feeds and leaderboards can stay eventually consistent.

6) CDC for Reconciliation 🔁
Even with fences, races happen. Use CDC to heal divergences (e.g., last-writer-wins for simple docs, CRDTs for counters).

7) Dashboards that Matter 📈
Replica lag (time + LSN/GTID), fence satisfaction rate, fallback rate, and P95/P99 fence wait. Alert off a small canary cohort.

8) Chaos-Test Lag 🧪
Inject 2–5 s lag on one replica and watch your sticky TTL + fences + fallback keep SLOs green.

9) Cost Controls 💸
Admission control for primary reads and degraded non-critical content during incidents.

TL;DR: Start with a short sticky TTL, add fences, wire fallback, measure freshness, then layer CDC. Ship trust, not just features.

#PHP #PostgreSQL #MySQL #BackendEngineering #DistributedSystems

Taming Lock Contention & Deadlocks in Production 🧵⚙️

If your app “feels slow” only under real traffic, you likely don’t have a CPU problem—you have a lock contention problem. MVCC helps readers, but writers still block writers. Here’s a compact playbook you can apply today:

1) See the blocking tree 🌳
PG: join pg_stat_activity + pg_locks (via pg_blocking_pids());
MySQL 8+: performance_schema.data_locks + data_lock_waits + process list.
Goal: name the root blocker in minutes, not hours.

2) Kill deadlocks by ordering 🔁
Most deadlocks come from inconsistent lock order. Always lock rows in a deterministic order (e.g., ORDER BY id) before update.

3) Keep transactions tiny ✂️
Do non-locking reads & API calls outside the transaction. Inside: lock exactly what you need, update, commit.

4) Pick the right lock mode 🧠
Workers & queues → SKIP LOCKED to keep moving.
Authoritative changes → NOWAIT + retry (fail fast, don’t stall).

5) Eliminate hot rows 🔥
Ledger (append deltas), bucket sharding (N buckets per counter), or optimistic versioning (UPDATE … WHERE id=? AND version=?).

6) Batch & order 📦
Process work in chunks (200–1000) with deterministic ordering. Fewer round trips, fewer cycles.

7) Retry without stampeding 🐃
Bounded exponential backoff with jitter. Make the unit of work idempotent so retries are safe.

8) Guard with timeouts ⏱️
PG: lock_timeout, sane deadlock_timeout.
MySQL: innodb_lock_wait_timeout. Quick failures > silent stalls.

9) Production checklist ✅
No accidental FOR UPDATE in read paths.
Hot columns isolated; indexes match hot predicates.
Worker leases with expiry; SKIP LOCKED for concurrency.
Stress tests that intentionally create conflicts.

This turns “mystery slowdowns” into observable, fixable mechanics. Your database can scale and stay calm—when you design for contention like it’s a first-class requirement.

Question: What’s the most stubborn hotspot you’ve wrestled with (counters, inventory, payment states)—and which technique finally broke the logjam? 🤔

#PHP #Laravel #DatabaseEngineering #PostgreSQL #MySQL

Indexes That Pay Rent: Beyond “Just Add an Index”

If an index isn’t paying for itself in saved latency and cache hits, it’s taxing every write for nothing. Here’s a practical, production-tested playbook for covering, composite, partial, functional, and bloom indexes—plus when to skip them:

1) Start with rent, not religion
 Measure the read latency saved vs. the write cost added. Keep only what your real workload needs.

2) Covering scans for your hottest endpoints
 When the index contains all predicate + select columns, the engine skips table lookups. Fast path for high-QPS reads.

3) Composite = left-prefix reality
 Order columns by your most selective leading filters. (tenant_id, created_at) helps tenant + date ranges; it won’t magically optimize created_at alone.

4) Surgical covering (Postgres INCLUDE)
 Add non-key columns to enable index-only reads without changing sort order. Cover just enough to avoid bloat.

5) Partial indexes for hot slices (Postgres)
 Index only the rows you actually query (e.g., status='open', deleted_at IS NULL). Tiny index, tiny cache footprint.

6) Functional/expression indexes
 Index what you compare (e.g., lower(email)). Stop paying for function-on-column full scans.

7) Bloom indexes for many-equals filters (Postgres extension)
 Compact prefilter when queries AND several equality predicates across many columns. Not for ranges or sorts.

8) Write-heavy tables: be ruthless
 Minimize secondary indexes on events/outbox/logs. Align PK with ingestion order; send analytics to OLAP/materialized views.

9) Maintenance that actually moves the needle
 Online/concurrent builds, sensible fillfactor, occasional reindex when bloat grows. Stable pages = predictable I/O.

10) Keep a tight loop with EXPLAIN
 Design indexes to match query shapes (filters + sorts). Delete redundant/overlapping ones that don’t map to real predicates.
Takeaway: design indexes to match queries, not columns. Cover surgically, go partial where traffic is hot, use functional where predicates compute, try bloom when equality combos explode—and keep only what pays rent.



#DatabaseEngineering #PostgreSQL #MySQL #PHP #BackendTips

Online Schema Changes at TB Scale—Without Downtime 🧬⚙️

Big tables don’t forgive reckless DDL. Here’s the playbook I use to evolve schemas safely in production:

1) Expand → Migrate → Contract
Add the new shape in parallel, backfill + mirror writes until converged, then retire the old. No cliffs—ramps only.

2) Partition by access pattern
Range for time-based pruning, hash for hotspots, composite when you need locality and spread. Design attach/detach and target backfills per partition.

3) Make index changes online
Use engine-native online/concurrent builds; if risky, ghost-copy so writers never block.

4) Backfill with a throttle, not a gas pedal
Batch, measure replica lag/CPU/SLOs, auto-pause on breach, resume when healthy. Checkpoint by ranges.

5) Mirror writes, then prove it
Dual-write (or outbox → worker) and continuously validate with rolling checksums, dual-read samples, and query equivalence.

6) Route reads first, then writes
Ramp 1% → 10% → 50% → 100% reads to the new table; flip writers only after reads are boring and correct.

7) Guardrails that self-protect
Hard stops for replica lag, error rate, lock waits. Jobs pause themselves and alert.

8) Minimal runbook
Create partitioned v2 → start ghost backfill → mirror writes → validate → ramp reads → flip writes → freeze mirrors → final checksum → archive → drop off-peak.

Why it works: you separate table shape from traffic, control migration with feedback loops, and treat correctness as a first-class outcome—not hope.

Question: What’s scariest in your stack—replication lag, lock contention, or proving correctness—and how are you handling it today?

#BackendEngineering #MySQL #PostgreSQL #Scalability #SRE

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