AI Dev Day India

31/07/2026

29/06/2026

ADDI 2026 Group Photo

01/06/2026

Thanks to Scrum.org and the team for providing your support to the event.

The 2026 edition of the ADDI is scheduled for Sunday, 28th June.

You can book your slot here - https://aidevdayindia.org/

Do not miss the chance to connect and network with the AI leaders.

25/05/2026

Palantir is quietly filtering out elite LeetCode developers.

Their hiring committees for forward-deployed roles have abandoned traditional algorithm assessments.

Candidates who prepare using standard FAANG study guides are systematically failing the onsite loops.

Instead of abstract puzzles, the evaluation hinges on a brutal, unstructured deployment simulation.

Most software engineers walk into these specific client-facing technical rounds completely blind to the grading matrix.

Understanding this unconventional architecture is the only way to bypass their hyper-competitive applicant filters.

The exact structural breakdown of their interview stages and levelling system is documented here ↓

https://agileleadershipdayindia.org/blogs/forward-deployed-ai-engineer-career-guide/palantir-fde-interview-process-levels.html

Operating without this intelligence leaves you preparing for an exam that Palantir no longer administers.

The internal rubric exposes a massive competency gap separating standard developers from forward-deployed engineering offers.

Elite candidates are already recalibrating their strategy around these highly specific technical and behavioral scenarios.

Review the precise evaluation criteria dictating today's hiring decisions before your initial screening call →

https://agileleadershipdayindia.org/blogs/forward-deployed-ai-engineer-career-guide/palantir-fde-interview-process-levels.html

23/05/2026

AI observability pricing models hide massive overage traps.

Engineering teams are blindly renewing contracts without realizing the structural ceiling built into their platforms.

The moment multi-agent architectures hit production traffic, those affordable per-seat licenses trigger a billing explosion.

Silent tool calls in generative environments are burning budgets before a single alert fires.

Standard performance monitors cannot calculate the non-deterministic cost of autonomous handoffs.

The market has fractured into three distinct ingestion models, but vendors expect you to choose the wrong one.

One platform severely penalizes loop iterations, while another offers a framework to drop ingestion costs to the floor.

Selecting the correct underlying backend is the single most critical infrastructure decision your team must make this quarter.

The complete 2026 cost and ingestion breakdown is here: https://aidevdayindia.org/blogs/ai-agent-observability-agentops-playbook/langsmith-vs-langfuse-vs-agentops-comparison.html

Relying on generic logging guarantees restrictive vendor lock-in and immediate compliance friction.

Your backend choice dictates whether you retain true data sovereignty or remain trapped inside proprietary markup tiers.

Evaluating these specific benchmarks is mandatory before finalizing any enterprise deployment.

Stop paying premium vendor penalties for basic orchestration.

Audit the actual math behind your current stack below ↓

https://aidevdayindia.org/blogs/ai-agent-observability-agentops-playbook/langsmith-vs-langfuse-vs-agentops-comparison.html

22/05/2026

Product leaders are funding the wrong AI architecture.

The choice between two deployment models dictates your infrastructure cost.

Agile teams are committing to a path without understanding the technical debt.

Leaders often assume custom-training a system is the superior option.

This single misconception inflates compute costs and delays product launches.

There is a critical distinction in how proprietary data must be handled.

Choosing incorrectly guarantees either severe hallucinations or financial drain.

The definitive technical blueprint for agile leaders is here: https://agileleadershipdayindia.org/blogs/ai-fundamentals-scrum-masters-product-owners/rag-vs-fine-tuning-ai-model.html

Scoping your next sprint requires this specific architectural context.

You must align business requirements with the correct retrieval method.

Those who grasp this distinction will ship cost-effective products.

Those who ignore it will rebuild their entire architecture next quarter.

Stop letting technical ambiguity ruin your product roadmap. ↓

Secure your enterprise deployment strategy right here: https://agileleadershipdayindia.org/blogs/ai-fundamentals-scrum-masters-product-owners/rag-vs-fine-tuning-ai-model.html

21/05/2026

Vibe coding is quietly breaking production systems.

Shipping features at record speed feels incredible right now.

Until an obscure AI-generated bug takes down your core application.

Engineering leaders are busy celebrating massive short-term productivity gains.

They are completely ignoring the catastrophic technical debt accumulating beneath the surface.

Because code written by "vibes" and autocomplete behaves very differently under actual user load.

There is a specific threshold where AI-assisted development stops being a superpower and turns into an immediate liability.

Most teams do not realize they have crossed this critical line until the incident response alert fires.

The complete breakdown of why vibe coding fails in production is here: ↓

https://productleadersdayindia.org/blogs/vibe-coding-production-disasters/is-vibe-coding-safe-for-production-code.html

This piece details the exact architectural patterns that cause these silent failures.

It exposes the hidden flaw in relying on language models for complex system design.

You will discover the specific indicators that your current codebase is already structurally compromised.

The underlying rules of software maintenance have permanently shifted.

Read it before approving your next major deployment - https://productleadersdayindia.org/blogs/vibe-coding-production-disasters/is-vibe-coding-safe-for-production-code.html

20/05/2026

Enterprise vibe coding causes silent production disasters.

Engineering teams are adopting AI assistants at record speed.

But the choice between Claude Code and Cursor Composer carries hidden architectural risks.

Developers are generating features faster than ever before.

Yet technical debt is compounding in ways traditional code reviews simply cannot catch.

What works for a rapid prototype severely breaks down at enterprise scale.

The difference between shipping reliable software and triggering a system failure comes down to your tooling strategy.

The complete architectural breakdown of Claude Code versus Cursor Composer for enterprise environments is here ↓

https://productleadersdayindia.org/blogs/vibe-coding-production-disasters/claude-code-vs-cursor-composer-enterprise.html

Making the wrong adoption choice now guarantees a massive refactoring effort next quarter.

Security flaws are slipping into codebases while leaders debate basic productivity metrics.

Review the critical limits of these tools before your next major release cycle.

17/05/2026

Your standard AI integrations are officially obsolete.

Engineering teams are wasting months building custom data connectors for their models.

There is a completely new architecture replacing legacy retrieval methods entirely.

Most developers are still hardcoding context windows and duct-taping databases to endpoints.

This approach guarantees data hallucinations and creates an entirely unmaintainable codebase.

Elite engineering teams have quietly migrated to a universal context protocol.

This standard allows models to autonomously request the exact secure data they need.

But building the server architecture to support this requires a highly specific routing mechanism.

If you get the underlying implementation wrong, your agentic workflows will fail silently.

The step-by-step technical guide to building a custom MCP server in Python is here ↓

https://aidevdayindia.org/blogs/mcp-model-context-protocol-server-guide/how-to-build-mcp-server-python-tutorial.html

This framework is rapidly becoming the universal baseline for enterprise AI infrastructure.

Continuing to rely on custom integration scripts will quickly render your products unscalable.

Every major foundational model is adapting to this exact standard right now.

Implement this architecture before your current AI tech stack becomes permanent legacy debt.