Spring Builders: Orson Amiri

Tracking Social Media Performance On Google Search Console

Orson Amiri — Tue, 04 Aug 2026 10:37:23 +0000

Google Search Console is expanding beyond traditional websites by allowing brands and creators to track how their content on social platforms performs in Google Search. The new platform properties feature, now globally available, supports Instagram, TikTok, X, and YouTube. Users can monitor Google-driven clicks, impressions, search queries, click-through rates, and average position for each verified social account or channel, giving them a clearer picture of how audiences discover their content through search.

Setting up the feature requires users to add and verify each social media account as a separate property. Once connected, Search Console provides Performance, Insights, and Achievements reports that highlight traffic trends, leading content, search queries, and key milestones. Users can filter data by posts, dates, countries, devices, and search surfaces, helping them identify content that is gaining visibility or attracting interest over time.

The integration gives brands and creators a valuable way to connect search behavior with Social Media content. Query data can help creators refine captions, titles, hashtags, and future content strategies, while brands can compare performance across websites, Instagram, TikTok, X, and YouTube. However, keywords do not guarantee rankings, and the feature only measures Google-driven discovery rather than native engagement metrics such as likes, comments, shares, watch time, or follower growth.

Ultimately, Google Search Console's platform properties help close the measurement gap between search and social publishing. The feature provides useful insights into how social content appears in Google Search, but it does not replace native platform analytics, conversion tracking, or revenue attribution. Instead, it gives marketers and creators another set of data-driven clues to understand audience interests, identify high-performing content, and make smarter cross-channel decisions.

How To Switch From ChatGPT To Claude: Step By Step Guide

Orson Amiri — Fri, 31 Jul 2026 10:46:33 +0000

Switching from ChatGPT to Claude is less about moving an entire account and more about rebuilding your AI workspace. While Claude offers an official memory-import process that can help bring over useful preferences, work habits, and recurring context, it does not automatically transfer your ChatGPT conversations, Custom GPTs, uploaded files, or integrations. Before making the move, review the information you plan to import and remove anything outdated, inaccurate, confidential, or sensitive.

The transition starts by moving the context that matters most. Claude’s memory import feature can help bring over preferences and work-related details from ChatGPT, while custom instructions and reusable prompts can be copied manually. Account-wide preferences can be added to Claude’s instructions, while client-specific rules, publication guidelines, and workflow requirements are better placed inside individual Claude Projects. This helps keep your AI workspace organized and ensures that each project has the right instructions and reference material.

For users with extensive ChatGPT workflows, the process may require a little more rebuilding. You can export your ChatGPT data as a backup, manually identify important conversations and files, and recreate essential Custom GPT workflows as Claude Projects by transferring their instructions and knowledge files. For a detailed walkthrough, check out the ChatGPT To Claude step-by-step guide. Once recreated, test these Projects with real tasks and files to make sure they deliver the expected results.

Ultimately, moving from ChatGPT to Claude does not have to be an all-or-nothing decision. Claude may be a better fit for certain workflows involving Projects, memory, documents, and writing, while ChatGPT continues to offer capabilities such as Custom GPTs, image generation, data analysis, apps, and custom actions. The smartest approach is to compare both tools using your everyday tasks, assess factors such as accuracy, speed, output quality, and available features, and then choose a primary assistant while keeping the other available for tasks where it performs better.

Elon Musk Unveils New Payment Platform X Money: Everything You Need To Know

Orson Amiri — Thu, 30 Jul 2026 06:17:38 +0000

Elon Musk's long-promised vision of an "everything app" is taking a major step into financial services with X Money. Beginning its rollout on July 27, 2026, the service is currently available to select U.S. Premium and Premium+ subscribers aged 18 and above. It brings peer-to-peer payments, direct deposits, bill payments, checks, wire transfers, and a Visa debit card into the X platform, allowing users to manage everyday financial activities alongside social and creator interactions.

X Money is designed to function more like a fintech wallet or neobank experience than a traditional bank. Users can send money to other X users, receive paychecks through direct deposit, and access an interest-bearing account. Premium+ users can earn up to 6.00% APY, while eligible card purchases can provide 3% cashback. The X Money Card also supports Apple Wallet, has no foreign transaction fees, and offers ATM fee reimbursement under the stated terms.

Cross River Bank serves as the regulated banking partner, holding deposits and supporting the financial infrastructure, while Visa powers the debit card and payment network. Deposits receive standard FDIC insurance coverage, with a multi-bank sweep program potentially providing up to $10 million in aggregate pass-through coverage when applicable conditions are met. For businesses and consumers exploring broader Financial Management Solutions, X Money represents another example of financial services becoming increasingly integrated into digital platforms.

However, the service also raises questions about privacy, regulatory oversight, variable interest rates and rewards, and the risks of tying financial access to a social media account. X's privacy practices and account policies will be important considerations for users, particularly those who may rely on the service for significant financial activity. While X Money strengthens Musk's ambition to turn X into a comprehensive digital ecosystem, its long-term success will ultimately depend on whether users are willing to trust a social platform with their money.

HR In The AI Era Must Stay Deeply Human Ft. Arppna Mehra

Orson Amiri — Wed, 29 Jul 2026 10:11:09 +0000

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge speaks with Arppna Mehra, Vice President Human Resources at Honeywell, about how HR leadership is transforming in an era defined by AI, business disruption, shifting workplace cultures, workforce agility, and evolving employee expectations. Drawing on her three-decade career, Arppna reflects on how HR has progressed from personnel management to human resources and, increasingly, to people experience. Her leadership philosophy was shaped by an early decision to move beyond traditional HR processes and understand the business firsthand by engaging with line managers, sales, manufacturing, operations, supply chain teams, and customers.

A key theme of the conversation is the shift from control-led HR to trust-led leadership. Arppna explains that while HR was once heavily focused on compliance, policies, processes, and predictable execution, modern organizations need HR leaders who enable people and businesses to adapt. She also discusses the growing influence of AI in HR, emphasizing that technology should augment human intelligence rather than replace it. While AI can support administrative tasks, data analysis, attrition prediction, skill mapping, and productivity, human judgment remains critical in areas such as hiring, career development, empathy, culture, and employee relationships.

In the latest TechDogs Discover Dialogues episode, Arppna also explores the challenges of building future-ready workforces and leading through uncertainty. She stresses that employees value honesty and transparency over false confidence, while effective leadership today requires contextual agility and empathy to navigate diverse teams, distributed workforces, technical complexity, and broader economic uncertainty. She also highlights listening as an essential leadership habit, noting that people do not always need immediate solutions—they often need to feel heard, supported, and understood.

Arppna's perspective reinforces the idea that the future of HR is not about replacing human connection with technology but using technology responsibly while keeping people at the center. She advocates for a more personalized employee experience built on trust, psychological safety, transparency, humility, mutual respect, and meaningful human interaction. With more than three decades of experience spanning HR leadership, business transformation, mergers and acquisitions, organizational culture, change management, and workforce strategy, Arppna continues to champion a business-first, human-centered approach that balances AI-driven progress with empathy and authentic leadership.

Is AI Draining Our Water Supply? The Hidden Cost Of Data Centers

Orson Amiri — Tue, 28 Jul 2026 11:13:27 +0000

Artificial Intelligence may seem weightless and instantaneous, but every prompt depends on a vast physical infrastructure of processors, data centers, cooling systems, electricity grids, and semiconductor factories. These systems consume significant resources, including water. While AI is not single-handedly draining water supplies worldwide, the rapid expansion of AI infrastructure is raising concerns about its impact on water-stressed regions. The extent of this impact depends on factors such as climate, location, cooling technology, energy sources, and whether facilities use freshwater, reclaimed water, or closed-loop systems.

Water is required at several stages of the AI ecosystem. Data centers may use evaporative cooling to manage the heat generated by high-density computing equipment, while electricity generation and chip manufacturing add indirect water demands. Estimates of AI's water consumption vary widely because they depend on the model, workload, hardware, location, cooling system, and energy mix. This also makes viral claims about the amount of water used by a single AI prompt difficult to generalize. The environmental footprint of AI extends well beyond the water consumed inside a data center.

The bigger concern is where AI infrastructure is being built. Although data centers represent a relatively small share of overall water consumption at a national level, large facilities can place considerable pressure on local communities, especially in drought-prone or water-stressed regions. Data Centers can also require substantial land, electricity, transmission infrastructure, and cooling resources, creating broader environmental and social trade-offs. The issue is therefore less about every individual prompt and more about the cumulative impact of rapidly expanding AI infrastructure in locations where water resources are already under pressure.

Reducing AI's environmental footprint will require more responsible infrastructure decisions. Reclaimed and recycled water, closed-loop cooling, direct-to-chip and immersion cooling, efficient hardware, cleaner energy, careful site selection, transparent reporting, and stronger community oversight can all help reduce resource consumption. AI can also deliver environmental benefits through applications such as climate modeling, water management, energy optimization, and leak detection. Ultimately, AI may be digital, but its infrastructure has a very real physical footprint, and managing that footprint responsibly will be essential to sustainable AI growth.

From Software Marketplaces To Agentic Commerce Ft. Andy Sen, CTO And Co-Founder At AppDirect

Orson Amiri — Fri, 24 Jul 2026 07:22:38 +0000

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge speaks with Andy Sen, CTO and Co-Founder at AppDirect, about the evolution of software commerce and the forces reshaping how businesses discover, purchase, manage, and scale technology. Drawing from his experience across IBM eBusiness, Walmart Labs, Salesforce, and AppDirect, Andy explores the journey from traditional enterprise software sales to SaaS subscriptions, digital marketplaces, and emerging AI-driven productivity.

Andy explains how SaaS transformed software buying by replacing large upfront investments, lengthy sales cycles, on-premise deployments, and complex upgrades with cloud-based subscriptions and continuous product improvements. He also highlights the rise of user-led adoption, where employees and teams increasingly discover and adopt software before formal enterprise procurement begins. As software stacks become more complex, marketplaces and ecosystems have become increasingly important, helping businesses access technology while managing subscriptions, spending, usage, governance, and security.

In this TechDogs Discover Dialogues conversation, Andy also shares his perspective on AI’s current impact. While he believes the industry is still waiting for transformative, category-defining AI applications, he sees clear value in individual and small-team productivity. He discusses how AI is enabling non-technical users to build tools while creating new governance challenges, and explains why scalable platforms must prioritize customer value and observability from the outset.

The discussion offers valuable insights into the future of software commerce, emphasizing that the industry is moving toward faster time to value, user-driven adoption, ecosystem-based marketplaces, and increasingly sophisticated SaaS management. Andy also stresses the importance of trusted advisors in helping businesses, particularly SMBs, select and package technology effectively. As AI-assisted and agentic commerce continues to emerge, organizations will need to balance innovation and productivity with strong visibility, governance, and customer-focused platform strategies.

YouTube's New Monetization Rules

Orson Amiri — Thu, 23 Jul 2026 12:35:46 +0000

YouTube is drawing a clearer distinction between responsible AI-assisted creativity and automated content farming with its updated monetization guidance. The July 2026 clarification does not ban AI-generated videos or introduce an entirely new monetization standard. Instead, it reinforces the existing requirement that monetized content should be original, authentic, and provide meaningful value to viewers. The focus is now firmly on the quality and originality of the final viewing experience rather than whether AI was used during production.

The updated policy highlights three broad categories of inauthentic content that could lose access to monetization. These include generic and repetitive videos built from templates with minimal creative variation, emotionally manipulative or shock-driven content such as exaggerated rescue stories, and AI personas that present themselves as human experts on sensitive subjects including health, finance, legal matters, and politics. Channels that repeatedly publish interchangeable videos or use deceptive techniques primarily to generate views may therefore face greater scrutiny under the revised rules.

At the same time, YouTube is not shutting the door on AI-assisted creation, recurring formats, faceless channels, reaction videos, or reused footage. Creators can continue using AI for scripts, visuals, characters, editing, and production support as long as their final work demonstrates genuine originality and creative value. Reaction videos and reused clips can also remain eligible when they add meaningful commentary, criticism, substantial editing, education, or a fresh narrative. The key question is whether creators are contributing something distinctive rather than simply producing content at scale.

For creators, the message is clear: quality matters more than volume. Channels built around repetitive templates, synthetic experts, copied tutorials, automated slideshows, or manipulative emotional hooks may need to rethink their strategies, while creators who bring research, storytelling, commentary, editing, and a unique perspective can continue to thrive. For advertisers and viewers, the tighter approach could help create a more trustworthy ecosystem by reducing the presence of low-value and deceptive content. Ultimately, YouTube is not removing AI from the creator toolkit—it is drawing a firmer line between using AI to enhance creativity and using it to mass-produce content with little originality or value.

AI Readiness Starts With Trusted Data Ft. Dave Shuman, Chief Data Officer At Precisely

Orson Amiri — Wed, 22 Jul 2026 12:29:14 +0000

In this episode of TechDogs’ Discover Dialogues, host Vikramsinh Ghatge speaks with Dave Shuman, Chief Data Officer at Precisely, about why enterprise AI readiness is fundamentally a data challenge. According to Dave, organizations need more than advanced AI models to succeed. Trusted data, consistent definitions, strong governance, data lineage, quality, and disciplined decision-making form the foundation for responsible and effective AI adoption. Drawing from his career across broadcasting, e-commerce, analytics, big data, IoT, smart cities, and enterprise data leadership, Dave emphasizes one recurring lesson: data creates value when it enables people to make better decisions faster.

Dave explains that organizations should begin their AI journey with the fundamentals that are often overlooked, including data catalogs, semantic layers, governance, and continuous data quality. He uses the OODA loop—observe, orient, decide, and act—to explain how enterprises can structure their data and AI strategies. Visibility into where data exists, how it is defined, who owns it, and whether it can be trusted is essential. Without this foundation, even the most sophisticated AI systems can produce confident but incorrect answers, creating risks that traditional software failures typically make easier to detect.

The conversation in Discover Dialogues also explores the importance of semantic layers, runtime governance, shadow AI, and measuring AI ROI through tangible business outcomes. Dave argues that governance must work in real time as AI-driven decisions are made, while ROI should be linked to measurable results such as risk avoidance, automation-driven labor savings, and revenue impact. He also highlights the difference between real-time data and reliable data, explaining that zero ETL can accelerate access to information but does not eliminate the need for transformation, cleansing, enrichment, or quality management.

For future data leaders, Dave offers three key principles: own the outcome rather than simply the output, translate technical concepts into language that business leaders can understand, and remain technically curious without allowing technical expertise to become the entirety of their professional identity. Ultimately, AI readiness starts with trusted, fit-for-purpose data. Organizations that combine strong data foundations with continuous quality, clear governance, business context, and outcome-focused leadership will be better positioned to turn AI investments into meaningful business value.

Apple One Price Hike In India: How Much More Will You Pay Now?

Orson Amiri — Wed, 22 Jul 2026 08:05:05 +0000

Apple’s subscription pricing in India is attracting attention after the company increased the cost of Apple Music across all three plans. The Student plan now costs ₹69 per month, up from ₹59, while the Individual plan has increased from ₹119 to ₹139 and the Family plan from ₹179 to ₹229. The latest changes mean subscribers will pay ₹10, ₹20, and ₹50 more each month, respectively.

The bigger question, however, is whether Apple One has also become more expensive. Reports claim that Apple One Individual, Family, and Premier plans now cost ₹195, ₹445, and ₹595 per month. However, these figures have not been fully confirmed by Apple. The company’s official India website currently lists the Individual plan at ₹195 and the Family plan at ₹365 per month, while the Premier tier is not displayed. Interestingly, Apple launched the Individual and Family plans in India at these same prices in 2020, making some of the reported price comparisons difficult to verify.

For now, subscribers should rely on the pricing shown on Apple’s official India pages rather than unconfirmed reports. At the current listed rates, the Apple One Individual plan saves users ₹217 per month compared with buying Apple TV+, Apple Music, Apple Arcade, and 50GB of iCloud+ separately. The Family plan offers ₹281 in monthly savings, based on the standalone prices of its included services.

The situation could change if Apple officially introduces the reported ₹445 Family or ₹595 Premier pricing in India. Until then, the confirmed price increases apply to Apple Music, while the reported Apple One hike remains uncertain. Existing and prospective subscribers should check Apple’s latest pricing pages before making decisions, especially if they are considering switching between individual subscriptions and an Apple One bundle.

7 Large Language Model (LLM) Trends To Watch

Orson Amiri — Tue, 21 Jul 2026 07:09:56 +0000

As we grow, learning becomes an essential part of our lives. From speaking and reading to solving problems and communicating effectively, we continuously build knowledge through education, experiences, and interactions. Large Language Models (LLMs) follow a somewhat similar journey. Trained on enormous volumes of text, books, websites, and conversations, these AI systems learn patterns in language and improve their ability to understand questions and generate useful responses. As developers continue refining them, LLMs are becoming more capable, accurate, and natural in the way they communicate.

Over the past few years, LLMs have evolved rapidly, powering applications ranging from customer service and content creation to software development and language translation. Advances in multimodal AI, fine-tuning techniques, and open-source models have made these systems more versatile and accessible. Models such as GPT, Gemini, Llama, Claude, and others are helping organizations explore new ways to integrate AI into their products and workflows, while making advanced capabilities available to a broader range of users and developers.

The growth of the Large Language Model ecosystem is expected to accelerate further in 2026. Businesses are increasing their investments in LLM technology, with many favoring paid and enterprise-grade solutions for greater reliability and performance. At the same time, hybrid AI strategies that combine proprietary and open-source models are gaining traction, while international providers are receiving greater acceptance. Enterprise use cases are also becoming clearer, with customer support and developer productivity emerging as two major areas of adoption.

However, LLMs still face challenges, including security and privacy concerns, domain-specific knowledge gaps, hallucinations, bias, and the computational demands of real-time processing. Despite these limitations, continued innovation is making LLMs smarter, more efficient, and easier to integrate into everyday tools and business operations. As these models continue to mature, understanding the key trends shaping their development will help organizations and individuals make better use of AI in 2026 and beyond.

The Rise Of AI Slop On Social Media

Orson Amiri — Mon, 20 Jul 2026 10:32:46 +0000

AI slop has evolved from an internet curiosity into a large-scale content phenomenon powered by generative AI and engagement-driven social media algorithms. Unlike thoughtfully created AI-assisted content, AI slop prioritizes quantity over creativity, producing endless variations of emotional, low-effort videos designed to maximize clicks, shares, and watch time. From tragic cat stories to talking fruits caught in dramatic love triangles, these synthetic narratives rely on familiar emotional triggers such as betrayal, danger, romance, and rescue to keep viewers scrolling. Their rapid production has transformed AI-generated entertainment into a repeatable business model rather than a creative experiment.

The growth of AI slop is closely tied to the accessibility of AI tools and the way recommendation systems reward attention. Modern text-to-video generators, AI voices, image creators, and editing platforms allow creators to produce content at unprecedented speed with minimal technical expertise. This has led to an explosion of repetitive, emotionally charged videos across social platforms. The trend extends beyond short-form video, with research from Pangram finding that 25.72% of long-form social posts in its dataset were entirely AI-generated, highlighting how synthetic content is increasingly shaping online conversations.

Major platforms including Meta, TikTok, Pinterest, YouTube, and LinkedIn are responding through AI labels, spam detection, reduced distribution of repetitive content, and user controls. While these measures aim to improve transparency and reduce AI-generated spam, no single moderation approach has proven sufficient. Experts increasingly advocate combining provenance metadata, watermarking, AI detection systems, behavioral analysis, disclosure requirements, and human review to better distinguish authentic content from mass-produced synthetic media.

Beyond entertainment, AI slop raises broader concerns about trust, originality, and the future of online content. As automated accounts flood platforms with emotionally manipulative videos, authentic creators face greater competition for attention, while users find it harder to distinguish genuine stories from fabricated ones. The growing volume of AI-generated media also places additional pressure on moderation systems and digital infrastructure. AI-generated content itself is not the problem; the challenge arises when speed, repetition, and engagement take precedence over creativity, authenticity, and meaningful human expression.

Building Trusted AI For The Future Of Work Ft. Naomi Lariviere, Chief Product Owner & VP At ADP

Orson Amiri — Fri, 17 Jul 2026 07:17:46 +0000

In this episode of TechDogs Discover Dialogues, host Vikramsinh Ghatge speaks with Naomi Lariviere, Chief Product Owner and Vice President at ADP, about how AI, data, trust, and human-centered product leadership are reshaping workforce technology. Drawing on more than two decades of experience spanning financial services and human capital management, Naomi explains that technology is most valuable when it helps people navigate meaningful workplace moments such as getting hired, receiving pay, building skills, or advancing their careers. She emphasizes that successful products are measured not by the number of features they offer, but by how effectively they remove friction and improve user confidence.

Naomi highlights how workforce platforms are evolving beyond traditional systems of record into intelligent systems that provide guidance and support. Rather than simply storing information or automating workflows, modern HCM platforms are expected to deliver timely answers, recommend next-best actions, and simplify complex decisions for employees, managers, and HR teams. She also stresses that AI should never be implemented simply because it is available. Instead, organizations should begin with real customer pain points and determine whether AI can meaningfully reduce effort, improve experiences, and solve problems more effectively.

When discussing Human Resources, Naomi underscores that trust must remain the foundation of AI adoption. Because HR and payroll systems influence pay, compliance, hiring, benefits, and other significant employment decisions, AI should always be deployed responsibly with appropriate safeguards and human oversight. She explains that while AI can streamline repetitive tasks and surface valuable insights, people should continue making decisions in high-risk scenarios where fairness, compliance, and individual livelihoods are involved.

Naomi concludes by sharing valuable leadership lessons for aspiring product professionals. She encourages leaders to replace the need for having all the answers with a mindset built on curiosity, empathy, and continuous learning. Transitioning from an individual contributor to a leader requires shifting the focus from personal achievement to enabling team success. Her broader message is that the future of workforce technology depends not only on smarter AI, but on building trusted, human-centered solutions that empower employees and organizations alike.