DEV Community: Harshal Patil

Standalone AI pilots struggle to scale because piping secure data to external LLMs introduces friction and security risks. True business value is unlocked when we embed AI natively within existing databases to prove capability through direct action.

Harshal Patil — Thu, 27 Aug 2026 15:29:10 +0000

Harshal Patil

Aug 27

How in 18 seconds, Oracle takes you from a server grid to a customer service dashboard via motion design - Explained by Advids

#video #youtube #customerservice #motiondesign

2 min read

How in 18 seconds, Oracle takes you from a server grid to a customer service dashboard via motion design - Explained by Advids

Harshal Patil — Thu, 27 Aug 2026 15:28:50 +0000

Most teams assume implementing enterprise AI requires extracting data into standalone, external reasoning engines.
The truth is, true business value is only unlocked when AI is embedded natively within the databases, applications, and workflows where the data already lives.

Why do standalone AI pilots often fail to scale?

Engineering teams spend months manually piping data from secure backend silos to external AI models.
Security postures are compromised when sensitive contracts and proprietary sales data leave the internal ecosystem.
Employees refuse to adopt new AI tools that force them to constantly context-switch away from their primary business applications.

What the video actually does

The video illustrates how Oracle eliminates these silos by embedding Oracle AI directly into Oracle Fusion and OCI. We see a visual transition from raw backend data—inventory, supply chain, and sales history—directly into front-end business actions. Without leaving the ecosystem, the system uses this amassed data to instantly generate a vendor service agreement, draft a social ad campaign, and build a detailed 17-week holiday advertising project plan.

The integration extends across the entire organizational chart to provide concrete utility. The platform seamlessly handles automated job requisitions for recruiters, formulates context-aware customer service replies regarding delayed shipments, and even executes physical compliance checks on a factory floor using OCI Vision to detect hardhats and safety gloves.

To make AI truly actionable at the enterprise level, it must be embedded where the work is already happening, transforming raw data into business outcomes without crossing external boundaries.

How is your architecture team handling the integration of LLMs with your secure internal databases and business applications?

Most teams mistake denser dashboards for deeper infrastructure visibility. The next evolution of network management requires systems that reason about operator intent, shifting from passive data display to actionable visual proof.

Harshal Patil — Thu, 27 Aug 2026 02:40:36 +0000

Harshal Patil for Advids

Aug 27

How Kentik establishes intelligent network control with motion design - Explained by Advids

#video #youtube #intelligentnetwork #motiondesign

2 min read

How Kentik establishes intelligent network control with motion design - Explained by Advids

Harshal Patil — Thu, 27 Aug 2026 02:39:57 +0000

Network teams are drowning in telemetry across hybrid clouds, making it increasingly difficult to answer simple questions about capacity and security quickly. Kentik AI Advisor solves this by transforming raw network data into actionable, intent-driven advice through a conversational interface.

Moving from data display to intent-driven reasoning

We think the biggest bottleneck in network management isn't a lack of data, but a lack of contextual reasoning.

Most teams assume they need more granular dashboards and alerts to understand their infrastructure.
The truth is, true network intelligence requires an AI layer that can reason about operator intent and recommend decisive action.

Why is hybrid network management so overwhelming?

Network engineers are forced to manually correlate data across traffic, cloud, and device interfaces to diagnose a single performance issue.
The constant pressure to balance cost, performance, and security leaves teams with limited resources, ultimately delaying critical strategic initiatives.
Traditional tools only display raw metrics and graphs, requiring deep domain expertise to translate anomalies into a concrete remediation plan.

What the AI Advisor actually does

The video demonstrates how Kentik shifts the workflow from manual correlation to natural language querying. When a user asks, "Where do I need to add capacity in my network?", the system doesn't just return a raw traffic graph. It analyzes 48 hours of telemetry across CDN interfaces and application traffic, automatically prioritizing the findings into high and medium-priority buckets based on urgency.

Instead of leaving the user to guess the next step, the AI generates specific action items, like upgrading particular CDN cache interfaces or planning capacity expansion for precise WAN links. It seamlessly handles security contexts as well, allowing users to ask about DDoS vulnerabilities and receiving an instant breakdown of exposed devices and active alerts, transforming complex analysis into simple, decisive actions.

To scale network operations effectively, monitoring tools must evolve past simply displaying data and start reasoning about the infrastructure to deliver precise, actionable advice.

How is your infrastructure team currently bridging the gap between raw telemetry and actionable capacity planning across hybrid environments?

Enterprise AI fractures because IT governance fights data science flexibility. Real platforms don't bridge this gap with policy; they solve it by unifying the actual workspace in a single, observable lifecycle.

Harshal Patil — Wed, 26 Aug 2026 05:27:56 +0000

Harshal Patil

Aug 26

How Domino establishes a unified AI platform with animation - Explained by Advids

#video #youtube #aiplatform #explaineranination

2 min read

How Domino establishes a unified AI platform with animation - Explained by Advids

Harshal Patil — Wed, 26 Aug 2026 05:27:26 +0000

Enterprise AI and data science platforms often fracture between the people building the models and the people securing the infrastructure. Domino Data Lab solves this by functioning as an open enterprise AI platform, visually demonstrating how competing priorities can seamlessly coexist on a single central hub.

Unifying the Data Science and IT divide

We think the biggest hurdle in scaling enterprise AI isn't finding the right algorithms, but reconciling the historically conflicting needs of data scientists and IT administrators.

Most organizations assume that giving data scientists the flexibility they demand means sacrificing the governance IT requires.
The truth is, a properly structured MLOps platform provides self-serve agility for researchers while actively centralizing control, security, and visibility for IT.

Why do AI initiatives fracture in the enterprise?

Data science teams get bogged down waiting on IT to provision custom development environments, packages, or hardware tiers.
IT loses visibility and governance when frustrated researchers resort to shadow IT to use their preferred open-source tools.
Moving a model from a localized Jupyter notebook into a secure, production-ready environment requires massive code rewrites and context switching.

How the platform visualizes unified infrastructure

The video illustrates Domino's role as a central portal bridging this gap. On one side, it ingests the preferred tools of data scientists—languages, IDEs, packages, and compute resources. On the other, it securely outputs APIs, applications, and dashboards.

Instead of showing a rigid workflow, the interface highlights how the Workbench and MLOps layers unite the two personas. We see a data scientist easily selecting their preferred IDE (like Jupyter or RStudio) and launching a local hardware tier, representing total flexibility and productivity. Immediately after, the focus shifts to an IT administrator monitoring data drift and access thresholds in the exact same UI, proving that governance and security are baked directly into the development environment.

To scale enterprise AI successfully, the infrastructure must stop treating data science and IT as competing forces and start housing them within the same governed, flexible lifecycle.

How is your engineering organization currently managing the handover between localized data science sandboxes and governed IT production environments?

Backend query optimization cannot fix perceived AI latency on its own. True product trust is earned when UI motion visually proves incremental state changes rather than hiding behind static loading spinners.

Harshal Patil — Sun, 23 Aug 2026 17:36:41 +0000

Harshal Patil

Aug 23

The 3 motion design principles by Tableau that make AI queries feel instant - Explained by Advids

2 min read

The 3 motion design principles by Tableau that make AI queries feel instant - Explained by Advids

Harshal Patil — Sun, 23 Aug 2026 17:36:17 +0000

We often ignore how much heavy lifting the UI does to mask backend latency in modern applications. When integrating generative AI into data analytics, the gap between a user typing a natural language prompt and the database returning a complex visualization can easily kill momentum, but Tableau Next solves this through deliberate agentic design.

Masking AI latency through agentic motion

We think the perception of AI speed is dictated just as much by UI motion as it is by the actual backend compute time.

Most teams assume faster databases and optimized LLMs are the only ways to make AI tools feel instantaneous.
The truth is, deliberate UI staging and progressive rendering mask the processing gap, making the wait feel like continuous action.

Why do conversational analytics often feel sluggish?

Users are left staring at static loading spinners while the LLM parses natural language and fetches backend data.
Sudden, jarring layout shifts occur when complex data visualizations finally render all at once on the screen.
The rigid disconnect between a chat interface and the actual data workspace forces users to constantly context-switch.

How Tableau structures the interaction

The video demonstrates how Tableau Next, integrated with Agentforce, completely avoids the traditional "loading" trap. When a user queries "What's driving decreased inventory?", the interface maintains conversational anchoring. The chat bubble doesn't just sit there; it smoothly transitions into actionable data points, staging the delivery so the user feels continuous forward progress while the heavy lifting happens behind the scenes.

Next, progressive visualization takes over the experience. As the user asks to break the data down by region, the structural elements of the bar chart and metric cards appear fluidly before the final granular numbers populate, avoiding any jarring layout shock. Finally, the UI seamlessly slides from the rendered insight directly into a collaborative Slack workflow, keeping the user anchored in a single, fluid action.

To make AI tools feel truly conversational, the interface must choreograph the wait time, turning loading states into fluid, progressive data reveals.

How does your engineering team handle the UX gap between an AI text prompt and a complex data visualization?

Traditional code scanners cannot inspect multidimensional AI tensor layers, creating a massive blind spot for malicious model injections. Security requires active visual verification at the tensor level, not just passive trust in text claims.

Harshal Patil — Tue, 18 Aug 2026 05:09:31 +0000

How HiddenLayer establishes a new standard for AI trust via 3D animation | Explained by Advids - DEV Community

Artificial intelligence has rapidly evolved into a top cybersecurity risk, turning open-source model...

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How HiddenLayer establishes a new standard for AI trust via 3D animation | Explained by Advids

Harshal Patil — Tue, 18 Aug 2026 05:08:33 +0000

Artificial intelligence has rapidly evolved into a top cybersecurity risk, turning open-source model repositories into prime targets for cybercriminals. Malicious actors are actively bypassing conventional security measures to distribute models embedded with ransomware and data-breach triggers.

Treating AI models as executable attack surfaces

We think the industry's rush to adopt generative AI has left a critical blind spot in the software supply chain.

Many teams assume traditional code scanning is enough to protect their infrastructure.
The truth is, AI model layers and tensors require their own native security posture to detect embedded malicious code.

Why are open-source repositories so vulnerable?

Development teams lack the proper tooling and education to inspect complex neural network components before deployment.
Conventional security tools cannot parse AI model layers, allowing malicious actors to easily slip past traditional defenses.
Deploying a compromised model leads directly to severe supply chain attacks, ransomware infiltration, and massive data breaches.

What the Model Scanner actually does

HiddenLayer addresses this blind spot with a patented approach that directly scans AI model layers, components, and tensors. The video demonstrates this workflow in action, showing the Model Scanner swiftly identifying tampering, embedded Windows executables, and malicious code hidden within an open-source model.

Microsoft actively relies on this technology to curate the open-source models available in the Azure AI model catalog. For every model scan, the interface displays an attestation card verifying the model is free from vulnerabilities, allowing developers to bring external assets into their ecosystem with absolute confidence.

To build a truly empowered AI development community, security must be integrated at the foundational tensor level, not just the application layer.

How is your engineering team currently validating the integrity of the open-source AI models you bring into your production environments?

Physical supply chains stop when print spoolers fail. If your system architecture merely claims connectivity instead of proving real-time queue transitions, you risk operational blind spots. Here is why output UX needs proof-first design.

Harshal Patil — Sun, 16 Aug 2026 13:51:05 +0000

Harshal Patil for Advids

Aug 16

How Vasion establishes a connected document workflow with motion design - Explained by Advids

#video #workflow #motiondesign #explainervideo

2 min read

How Vasion establishes a connected document workflow with motion design - Explained by Advids

Harshal Patil — Sun, 16 Aug 2026 13:46:40 +0000

When physical print stops, entire supply chains grind to a halt. We've noticed that scaling physical operations requires shifting print management from a localized IT headache to a highly available, cloud-native infrastructure.

Print isn't an IT problem—it's a critical operational dependency

Assumption: Output management is just about keeping local office printers online and stocked.
Truth: Output management is a mission-critical workflow that connects digital systems of record directly to physical operations.

Why Legacy Print Infrastructure Fails at Scale

Localized print servers create vulnerable single points of failure right on the warehouse floor.
Scaling from a single invoice to 10,000 daily shipping labels overwhelms isolated, legacy hardware.
Disconnected output workflows break the critical chain between your ERP system and the physical products moving out the door.

What the Video Actually Does

Vasion visualizes this operational dependency immediately, showing a highly stylized pharmaceutical assembly line where pill bottles physically stop moving the second a print job fails. A small digital factory is literally shown with the text "When print stops, operations stop," driving home the real-world stakes of document output.

The narrative then shifts to the solution, showing a warehouse worker packing boxes as the system smoothly scales from "An invoice every minute" to "over 10,000 labels a day." By highlighting the internal gears representing "built-in redundancies," the video proves that a cloud-native output solution integrates seamlessly into ERP, CRM, and EHR systems to keep operations running without manual intervention.

When you move output management to the cloud, you stop managing print servers and start guaranteeing operational continuity.

How does your infrastructure team architect fail-safes for physical output when your digital systems scale faster than your warehouse hardware?