I spent two days at HumanX 2026 in San Francisco covering 27 sessions across AI infrastructure, enterprise adoption, agentic systems, security, creative, retail, and finance.

I heard about AI in marketing. AI in brand strategy. AI in customer service. AI in software development. AI in creative production. AI in financial infrastructure. AI in cybersecurity. AI in retail design. AI in financial crime. AI in compliance.

I did not hear a single session about the supply chain.

Not one.

For someone who came up through operations and supply chain, who has personally sat with demand forecasting spreadsheets that were wrong more often than right, navigated supplier disruptions without early warning, and made inventory decisions that were really just educated guesses dressed up in numbers. For an industry that sits at the core of the global economy, the silence was striking.

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What we mean when we say “supply chain.”

To be precise, this is not about last-mile logistics or route optimization—the areas that do get occasional attention. The Supply Chain word gets used loosely, usually.

This is about upstream decision-making: demand forecasting, supplier risk management, inventory positioning, and procurement intelligence.

Demand forecasting: predicting what you’ll need to sell or produce before you’ve sold or produced it.

Supplier risk management: identifying which suppliers are fragile before they fail you.

Inventory positioning: deciding how much of what to hold where, across a network that doesn’t sit still.

Procurement intelligence: understanding pricing patterns, lead time trends, and supplier behaviour across hundreds of relationships simultaneously.

These are decisions that get made every week in every company that makes or moves physical goods. They are made by people working with incomplete information, under time pressure, using tools that were not designed for the complexity of the problem. And they are decisions where being systematically better — not perfect, just better — compounds into enormous financial advantage over time.

Yet at a conference dedicated to AI’s transformation of business, they were absent. AI should be solving the industry’s problem right now.

—

What I did hear

Here is what I did hear, to be fair.

P&G’s CIO Seth Cohen spent time on supply chain automation — specifically, unattended manufacturing scaled across nine locations, and molecular discovery work that cut development timelines from years to months. For a company like P&G’s scale, those results are genuinely significant. But they are the results of a decade-long data infrastructure investment that most companies, including most large companies, have not made.

Walmart’s Daniel Danker gave one supply chain example: a remote Canadian store used an internal tool called Code Puppy to combine weather data and ferry schedules for inventory planning. It’s a good story — a frontline associate solving a local operations problem with a tool headquarters gave them. But it’s a point solution, not a framework, and it was mentioned in passing in a session primarily about AI democratization.

That was essentially it. Two examples, mentioned briefly, in sessions about something else. For an industry representing somewhere between $15 and $20 trillion in global economic activity, that’s a remarkably thin presence.

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Why the supply chain is the most AI-ready industry nobody is talking about

What makes this gap surprising is that the supply chain is, on paper, one of the most AI-ready industries in existence.

It runs on data. Sensor data, transaction data, demand signals, supplier data, logistics data, and financial data. The data infrastructure in large supply chains is often more mature than in the marketing and creative functions that dominated the HumanX agenda.

The problems are structured. Unlike brand strategy or creative judgment, supply chain decisions are historically modelled, mathematically defined, and directly measurable. A demand forecast is either accurate or it isn’t. A stockout either happened or it didn’t. An inventory position either matched demand, or it left cash sitting on a shelf or a gap in a customer’s order. The feedback loops are tight, and the outcomes are concrete.

The stakes are direct. Having spent time inside these decisions, I can tell you that even small improvements in forecast accuracy at a meaningful scale translate into real money — in working capital, in margin, in customer retention. A 2% improvement in forecast accuracy at a company running substantial inventory isn’t a rounding error. It’s a financial event.

So why wasn’t it on stage?

—

Three reasons for the gap

The first is audience composition. HumanX skews toward technology leaders, marketing executives, founders, and investors. Supply chain and operations leadership tends not to show up at general AI conferences. They attend industry-specific events — operational forums, ERP user conferences, sector trade shows. The gap in the room creates a gap in the agenda.

The second is vendor invisibility. The companies building AI for demand sensing, supplier risk, and logistics optimization are largely invisible outside their industry. They’re specialized, often enterprise-only, and they sell through procurement channels rather than developer communities. They don’t generate the kind of press coverage that gets sessions programmed at a conference like HumanX.

The third — and this is the one that matters most — is that the enterprise supply chain is genuinely hard to disrupt quickly. The data is siloed across ERP systems, warehouse management platforms, and procurement tools built over decades. Integration alone is a multi-year project. The risk tolerance for AI-driven decisions in the supply chain is low because the consequences of errors are operational and financial. You can course-correct a bad marketing campaign. You cannot easily recover from a production halt caused by a procurement decision that an AI made without the right context.

This is exactly why AI hasn’t disrupted the supply chain at the pace it’s disrupted other functions. And it’s exactly why, when it does, the value creation will be disproportionately large.

—

What the conference did tell me

The sessions I attended gave me frameworks that apply directly to this gap, even though none of them were about the supply chain.

Databricks CEO Ali Ghodsi said something that stayed with me: current models are sufficiently capable, but they fail in enterprises because they lack context. The bottleneck isn’t the AI. It’s the organizational and data infrastructure around it. He expects enterprise AI adoption to take five to ten years.

That timeline maps almost exactly to where supply chain AI is in its adoption curve. The context problem is especially acute here. A demand forecasting model trained on historical sales data without context about upcoming promotions, competitor pricing moves, macroeconomic shifts, or supplier lead time changes will produce outputs that experienced planners will correctly distrust — because they know what the model doesn’t know. The data exists. The integration and context layer doesn’t.

The building of a trustworthy agentic AI session reinforced this from a different angle. Panellists from Dataiku made a distinction that matters enormously for supply chain: back-office decision agents — the ones affecting clinical outcomes, credit decisions, or supply choices — require far stronger testing and explainability than personal productivity tools. A demand forecasting agent that informs procurement isn’t a chatbot. It needs to be auditable, explainable, and designed to fail gracefully. Most current AI deployments are not built to that standard.

—

What finance figured out that supply chain hasn’t yet

What I found most instructive at HumanX wasn’t what was said about supply chain. It was watching what happened to industries that got forced into AI governance before they were ready — and what that forced them to build.

The finance sessions were the clearest example. Multiple panels addressed AI compliance frameworks in regulated financial services — what one session called the “compliance flywheel.” The argument was that embedding compliance, risk, and governance early in AI product development actually accelerates innovation rather than slowing it. Shared semantic definitions, data lineage, and auditability become infrastructure that compounds over time. Organizations that treat compliance as an early-stage design constraint end up with more durable systems than those that bolt it on later.

The financial crime session added a sharper edge to this. Jonathan Levin of Chainalysis described how generative AI has dramatically lowered the barrier to entry for financial fraud — enabling impersonation, automation, and scale that wasn’t previously possible for lower-skill actors. The response from defenders has been to build proactive threat-hunting systems, intelligence-sharing networks, and AI agents that can process evidence and flag suspicious patterns faster than any human analyst.

I kept thinking about procurement fraud while sitting in those sessions. Supplier impersonation. Fake invoices. Bid manipulation. Ghost vendors. These are supply chain problems that have existed for decades and are about to get significantly harder to detect as AI makes fraudulent activity more convincing and more automated. The financial services industry is building defensive infrastructure right now because regulation and litigation forced the conversation. Supply chain hasn’t been forced there yet. But the same pressures — fraud escalation, operational failure, regulatory scrutiny, and eventually litigation — are coming.

The compliance frameworks being built in regulated finance are the template for what supply chain AI governance will eventually need to look like. The difference is in the timeline. Finance is building it now under pressure. Supply chain will build it later, under more pressure, starting from further behind.

—

The investment angle

The publicly traded companies most exposed to the supply chain AI wave are not the model providers. They are the enterprise software platforms that own the data: the ERPs, the warehouse management systems, the supply chain visibility platforms, the procurement analytics tools. Every one of those companies is currently navigating the same question: do they build AI natively into their platforms, partner with AI providers, or get disrupted from below by AI-native startups that don’t carry decades of integration debt?

That question is not answered. The window where it remains unanswered is the window where the investment opportunity is most interesting — both in the incumbents navigating the transition and in the new entrants who might make the integration question irrelevant.

I track this closely because I sit at the intersection of it. I understand the operational problem from having lived inside it. I understand the AI capability layer from building tools on top of it. Those two lenses together are what make the gap visible.

—

I came back from HumanX with a lot of material about where AI is moving and what serious operators and investors think. Most of it confirmed what I already believed about infrastructure, reliability, and the gap between demo performance and production reality.

The most valuable thing I came back with was silence.

Nobody talked about the supply chain. Not because it isn’t ready. Not because the problem isn’t large enough. Because the people who understand the problem and the people building the tools are not yet in the same room.

Finance got there first because it was forced. Supply chain will get there when it is forced to.

The question worth asking now — before the forcing event — is which companies are building the governance, the data infrastructure, and the AI capability to be ready when that moment arrives. Because the ones who are will look obvious in hindsight. They always do.

————

Jinlu Wang has a background in supply chain, ERP implementation, and enterprise operations. She now builds automated trading systems and web applications and covers AI infrastructure, fintech, and enterprise technology for Trade with Harp, a paid investment research and trading community.

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Ninety-eight per cent of job postings at Canada’s top universities now require DEI compliance

By Mark Milke

Since the first European university was founded in Bologna, Italy, in 1088, the goal of higher education was supposed to be a search for truth. But too many modern ivory towers in the West, including in Canada in recent decades, have been drawn into trendy anti-reality policies, activism and illiberalism.

A key example is the rise of so-called diversity, equity and inclusion (DEI) policies and bureaucracies at universities. Some institutions now select students and professors based on race, ethnicity or gender on the assumption that doing so will somehow make up for discrimination 100 years ago. Others attribute differing economic outcomes between groups almost entirely to systemic racism, ignoring other contributing factors.

Both assumptions are flawed, but they explain why DEI offices have sprung up like toxic weeds over the last 10 years across Canadian and American campuses.

These policies rest on the mistaken premise that group disparities can only be explained by discrimination. In reality, income and wealth in a liberal democracy such as Canada or the United States result from multiple factors: education levels, family dynamics, geography (people in rural areas, including reserves, earn less than those in major cities), the length of time an immigrant has lived in the country, and a variety of other influences.

When these factors are accounted for, more similarities than differences emerge between groups.

For example, there is an average income gap between Indigenous and non-Indigenous Canadians, but that is partly due to differences in education levels and geography. A greater proportion of Indigenous Canadians live in rural areas or on reserves, where employment opportunities and wages tend to be lower. When these factors are considered, the gap largely disappears. In 2020, Indigenous Canadians with a bachelor’s degree had a median income of $76,000, while non-Indigenous Canadians earned $77,500. Among those with graduate degrees, the trend reversed—Indigenous Canadians earned a median income of $85,000, slightly more than the $83,000 median for non-Indigenous Canadians.

The same holds true for women. The four highest-earning female groups in Canada, based on ancestry, are Korean, Chinese, South Asian and Filipino—reflecting their education levels and hours worked.

Yet despite these economic realities, DEI policies continue to promote a simplistic racism-explains-all ideology. This mindset has now deeply embedded itself in university hiring practices, where race-based selection criteria have become widespread.

A new Aristotle Foundation index analyzed 489 academic job advertisements at Canada’s 10 largest universities, examining their use of DEI criteria in hiring. Of those, 477 postings—98 per cent—used some form of DEI requirement or strategy to fill academic vacancies.

The data was categorized based on how DEI principles were applied, ranging from generic statements to explicit hiring preferences based on race, ethnicity or gender. This analysis allowed for the creation of a University Discrimination Index, offering a clearer picture of how deeply DEI influences hiring decisions.

The findings are striking. Every University of Toronto employment posting and 96 per cent of Dalhousie University’s job listings mentioned or implied that a candidate’s “contribution to DEI” was an asset. McGill University and the University of Saskatchewan required all applicants to complete a DEI survey. Nearly two-thirds of postings at the University of British Columbia and 55 per cent at the University of Manitoba required candidates to submit a DEI statement or essay.

Some might argue that requiring DEI statements does not necessarily mean a university will discriminate in hiring. But that is unlikely, given the vast amount of time, money and advocacy DEI proponents dedicate to promoting race-, ethnic- and gender-based hiring quotas.

Beyond the implicit message of who is likely to be hired, some universities are explicit. The University of New Brunswick barred white males from applying for a research chair position in “quantum sensors for space” in its physics department. At UBC, nearly one in five academic job postings explicitly restricted applications to individuals from a particular race, ethnicity or other immutable trait.

Even without addressing the lack of viewpoint diversity at Canada’s universities, the core problem with diversity, equity and inclusion policies is not the desire for a diverse workforce—diversity is beneficial because talent exists in all groups.

The problem with racial, ethnic and gender hiring quotas is threefold. First, they treat individuals based on group identity rather than individual merit. Second, they assume that identity largely explains success or failure. And third, they undermine merit as a hiring principle.

The result is an anti-individual, anti-meritocratic and illiberal hiring approach at many of Canada’s largest universities.

–

Mark Milke is the president of the Aristotle Foundation for Public Policy, which recently released DEI and academic hiring in public universities: An index of university discrimination in Canada.

© Troy Media

The views, opinions and positions expressed by all Troy Media columnists and contributors are the author’s alone. They do not inherently or expressly reflect the views, opinions and/or positions of Troy Media, TV Punjab and Ubiq Broadcasting Corp.

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ਇਹ ਵਿਗਿਆਨੀਆਂ ਦਾ ਕਹਿਣਾ ਹੈ

ਵਿਗਿਆਨੀਆਂ ਦਾ ਕਹਿਣਾ ਹੈ ਕਿ ਜੇ ਵਿਟਾਮਿਨ ਡੀ ਨਿਯਮਤ ਭੋਜਨ ਵਿੱਚ ਵਰਤਿਆ ਜਾਂਦਾ ਹੈ, ਤਾਂ ਇਹ ਕੋਰੋਨਾ ਤੋਂ ਬਚਾਉਣ ਵਿੱਚ ਸਾਡੀ ਮਦਦ ਕਰ ਸਕਦਾ ਹੈ. ਵਿਗਿਆਨੀਆਂ ਦਾ ਕਹਿਣਾ ਹੈ ਕਿ ਜਿਨ੍ਹਾਂ ਲੋਕਾਂ ਵਿੱਚ ਵਿਟਾਮਿਨ ਡੀ ਦੀ ਕਮੀ ਹੁੰਦੀ ਹੈ ਉਨ੍ਹਾਂ ਨੂੰ ਵੀ ਕੋਰੋਨਾ ਵਾਇਰਸ ਨਾਲ ਸੰਕਰਮਿਤ ਹੋਣ ਦਾ ਵਧੇਰੇ ਖਤਰਾ ਹੁੰਦਾ ਹੈ.

ਵਿਟਾਮਿਨ ਡੀ ਕੀ ਹੈ

ਵਿਟਾਮਿਨ ਡੀ ਇੱਕ ਚਰਬੀ-ਘੁਲਣਸ਼ੀਲ ਵਿਟਾਮਿਨ ਹੈ ਜੋ ਤੁਹਾਡਾ ਸਰੀਰ ਤੰਦਰੁਸਤ ਰਹਿਣ ਲਈ ਸੋਖਦਾ ਹੈ ਅਤੇ ਸਟੋਰ ਕਰਦਾ ਹੈ. ਇਹ ਸਰੀਰਕ ਕਾਰਜਾਂ ਜਿਵੇਂ ਕਿ ਇਮਿਉਨਿਟੀ ਨੂੰ ਵਧਾਉਣਾ, ਦੰਦਾਂ, ਮਾਸਪੇਸ਼ੀਆਂ ਅਤੇ ਹੱਡੀਆਂ ਨੂੰ ਮਜ਼ਬੂਤ ​​ਕਰਨਾ, ਮਾਨਸਿਕ ਸਿਹਤ ਤੋਂ ਲੈ ਕੇ ਦਿਲ ਦੀ ਅਸਫਲਤਾ, ਸ਼ੂਗਰ ਅਤੇ ਇਹ ਵੀ ਕਰ ਸਕਦਾ ਹੈ. ਸਾਨੂੰ ਕੈਂਸਰ ਵਰਗੀਆਂ ਗੰਭੀਰ ਬਿਮਾਰੀਆਂ ਤੋਂ ਦੂਰ ਰੱਖੋ.

ਵਿਟਾਮਿਨ ਡੀ ਦੀ ਸਪਲਾਈ ਕਿਵੇਂ ਕਰੀਏ

ਹੈਲਥਲਾਈਨ ਦੇ ਅਨੁਸਾਰ, ਜਦੋਂ ਅਸੀਂ ਧੁੱਪ ਦੇ ਸੰਪਰਕ ਵਿੱਚ ਆਉਂਦੇ ਹਾਂ, ਸਰੀਰ ਆਪਣੇ ਆਪ ਵਿਟਾਮਿਨ ਡੀ ਬਣਾਉਣਾ ਸ਼ੁਰੂ ਕਰ ਦਿੰਦਾ ਹੈ. ਇਹੀ ਕਾਰਨ ਹੈ ਕਿ ਇਸ ਵਿਟਾਮਿਨ ਨੂੰ ਸਨਸ਼ਾਈਨ ਵਿਟਾਮਿਨ ਵੀ ਕਿਹਾ ਜਾਂਦਾ ਹੈ. ਹਾਲਾਂਕਿ, ਕੁਝ ਭੋਜਨ ਅਜਿਹੇ ਹਨ ਜਿਨ੍ਹਾਂ ਵਿੱਚ ਵਿਟਾਮਿਨ ਡੀ ਮੌਜੂਦ ਹੁੰਦਾ ਹੈ. ਉਦਾਹਰਣ ਦੇ ਲਈ, ਸੈਲਮਨ, ਜਿਗਰ ਦਾ ਤੇਲ, ਟੁਨਾ, ਅੰਡੇ ਦੀ ਜ਼ਰਦੀ, ਮਸ਼ਰੂਮਜ਼, ਗਾਂ ਦਾ ਦੁੱਧ, ਸੋਇਆਬੀਨ ਦਾ ਦੁੱਧ, ਸੰਤਰੇ ਦਾ ਜੂਸ, ਓਟਮੀਲ ਆਦਿ ਦਾ ਸੇਵਨ ਕਰਕੇ ਸਰੀਰ ਵਿੱਚ ਵਿਟਾਮਿਨ ਡੀ ਦੀ ਸਪਲਾਈ ਕੀਤੀ ਜਾ ਸਕਦੀ ਹੈ. ਇਸ ਤੋਂ ਇਲਾਵਾ, ਤੁਸੀਂ ਵਿਟਾਮਿਨ ਡੀ ਸਪਲੀਮੈਂਟਸ ਆਦਿ ਦਾ ਸੇਵਨ ਵੀ ਕਰ ਸਕਦੇ ਹੋ.

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ਪੰਜੀਰੀ ਦੇ ਲੱਡੂ ਬਣਾਉਣ ਲਈ ਸਮੱਗਰੀ:

1 ਕੱਪ ਆਟਾ

1 ਕੱਪ ਖੰਡ ਦਾ ਰਸ

1 ਚਮਚ ਇਲਾਇਚੀ ਪਾਉਡਰ

1 ਛੋਟਾ ਕਟੋਰਾ ਬਦਾਮ (ਬਾਰੀਕ ਕੱਟੇ ਹੋਏ)

1 ਛੋਟਾ ਕਟੋਰਾ ਚਿਰੋਂਜੀ (ਬਾਰੀਕ ਕੱਟਿਆ ਹੋਇਆ)

1 ਛੋਟਾ ਕਟੋਰਾ ਕਾਜੂ

1 ਵੱਡਾ ਕਟੋਰਾ ਘਿਓ

ਪੰਜੀਰੀ ਦੇ ਲੱਡੂ ਬਣਾਉਣ ਦੀ ਵਿਧੀ:

ਸਭ ਤੋਂ ਪਹਿਲਾਂ, ਗਰਮ ਕਰਨ ਲਈ ਮੱਧਮ ਅੱਗ ‘ਤੇ ਇੱਕ ਪੈਨ ਵਿੱਚ ਘਿਓ ਰੱਖੋ.

ਘਿਓ ਗਰਮ ਹੋਣ ‘ਤੇ ਆਟਾ ਪਾਓ ਅਤੇ ਇਸ ਨੂੰ ਕੜਛੀ ਨਾਲ ਹਿਲਾਉਂਦੇ ਹੋਏ ਭੁੰਨੋ.

– ਜਿਵੇਂ ਹੀ ਆਟੇ ਵਿੱਚੋਂ ਹਲਕੀ ਜਿਹੀ ਬਦਬੂ ਆਉਣੀ ਸ਼ੁਰੂ ਹੋ ਜਾਵੇ, ਤਾਂ ਸਮਝ ਲਵੋ ਕਿ ਆਟਾ ਭੁੰਨਣਾ ਸ਼ੁਰੂ ਹੋ ਗਿਆ ਹੈ.

– ਹੁਣ ਸਾਰੇ ਸੁੱਕੇ ਮੇਵੇ ਇੱਕ ਇੱਕ ਕਰਕੇ ਮਿਲਾਓ ਅਤੇ ਲਗਾਤਾਰ ਹਿਲਾਉਂਦੇ ਰਹੋ.

– ਸੁੱਕੇ ਮੇਵਿਆਂ ਤੋਂ ਬਾਅਦ, ਖੰਡ ਪਾਓ ਅਤੇ ਹਿਲਾਉਂਦੇ ਹੋਏ ਇਸ ਨੂੰ ਆਟੇ ਦੇ ਨਾਲ ਚੰਗੀ ਤਰ੍ਹਾਂ ਮਿਲਾਓ.

– ਅੰਤ ਵਿੱਚ, ਇਲਾਇਚੀ ਪਾਉਡਰ ਪਾਓ ਅਤੇ ਮਿਕਸ ਕਰਦੇ ਸਮੇਂ ਅੱਗ ਬੰਦ ਕਰੋ.

–  ਪੰਜੀਰੀ ਨੂੰ ਥੋੜਾ ਠੰਡਾ ਹੋਣ ਦਿਓ … ਹਥੇਲੀਆਂ ਨੂੰ ਗਰੀਸ ਕਰੋ ਅਤੇ ਲੱਡੂ ਬੰਨ੍ਹੋ.

ਪੰਜੀਰੀ ਲੱਡੂ ਤਿਆਰ ਹੈ।

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1. ਡਾਰਕ ਸਰਕਲ ਘੱਟ ਕਰੋ

ਜਦੋਂ ਮੇਲੇਨਿਨ ਅੱਖਾਂ ਦੇ ਆਲੇ ਦੁਆਲੇ ਦੀ ਚਮੜੀ ਵਿੱਚ ਬਣਨਾ ਸ਼ੁਰੂ ਹੋ ਜਾਂਦਾ ਹੈ, ਤਾਂ ਇੱਥੋਂ ਦੀ ਚਮੜੀ ਕਾਲੇ ਹੋਣ ਲੱਗਦੀ ਹੈ ਅਤੇ ਇਸ ਲਈ ਇੱਥੇ ਕਾਲੇ ਘੇਰੇ ਆ ਜਾਂਦੇ ਹਨ. ਡਾਰਕ ਸਰਕਲ ਘੱਟ ਹੁੰਦੇ ਹਨ. ਅਜਿਹੀ ਸਥਿਤੀ ਵਿੱਚ, ਜਦੋਂ ਤੁਸੀਂ ਅੱਖਾਂ ਦੀ ਕਰੀਮ ਦੀ ਵਰਤੋਂ ਕਰਦੇ ਹੋ, ਇਹ ਮੇਲਾਨਿਨ ਦੇ ਉਤਪਾਦਨ ਨੂੰ ਘਟਾਉਂਦਾ ਹੈ.

2. ਚਮੜੀ ਨੂੰ ਹਾਈਡ੍ਰੇਟ ਕਰੋ

ਜਿਨ੍ਹਾਂ ਲੋਕਾਂ ਦੀ ਚਮੜੀ ਖੁਸ਼ਕ ਹੈ ਉਨ੍ਹਾਂ ਨੂੰ ਅੱਖਾਂ ਦੀ ਕਰੀਮ ਜ਼ਰੂਰ ਲਗਾਉਣੀ ਚਾਹੀਦੀ ਹੈ. ਇਹ ਚਮੜੀ ਨੂੰ ਹਾਈਡਰੇਟ ਰੱਖਦਾ ਹੈ ਤਾਂ ਜੋ ਬੁਢਾਪਾ ਅਤੇ ਝੁਰੜੀਆਂ ਜਲਦੀ ਨਾ ਆਉਣ.

3. ਚਮੜੀ ਨੂੰ ਕੱਸਣਾ

ਅੱਖਾਂ ਦੀ ਕਰੀਮ ਲਗਾਉਣ ਨਾਲ ਖੂਨ ਸੰਚਾਰ ਵਿੱਚ ਸੁਧਾਰ ਹੁੰਦਾ ਹੈ ਅਤੇ ਚਮੜੀ ਵਿੱਚ ਚਮਕ ਅਤੇ ਤਣਾਅ ਆਉਂਦਾ ਹੈ.

4. ਝੁਰੜੀਆਂ ਨਹੀਂ ਪੈਂਦੀਆਂ

ਜੇ ਤੁਹਾਡੀਆਂ ਅੱਖਾਂ ਦੇ ਹੇਠਾਂ ਦੀ ਚਮੜੀ ਢਿੱਲੀ ਹੋ ਰਹੀ ਹੈ ਜਾਂ ਝੁਰੜੀਆਂ ਆ ਰਹੀਆਂ ਹਨ ਤਾਂ ਇਸਦੀ ਵਰਤੋਂ ਲਾਭਦਾਇਕ ਹੈ. ਇਸ ‘ਚ ਐਂਟੀਆਕਸੀਡੈਂਟਸ ਅਤੇ ਵਿਟਾਮਿਨ ਹੁੰਦੇ ਹਨ, ਜੋ ਚਮੜੀ’ ਤੇ ਕੱਸਣ ਅਤੇ ਝੁਰੜੀਆਂ ਨੂੰ ਦੂਰ ਕਰਦੇ ਹਨ.

5. ਸੋਜ ਦੀ ਸਮੱਸਿਆ

ਜੇ ਅੱਖਾਂ ਵਿੱਚ ਸੋਜ ਦੀ ਸਮੱਸਿਆ ਹੈ ਅਤੇ ਅੱਖਾਂ ਦੇ ਹੇਠਾਂ ਬੈਗ ਬਣ ਰਹੇ ਹਨ, ਤਾਂ ਤੁਹਾਨੂੰ ਇਸਦੀ ਵਰਤੋਂ ਕਰਨੀ ਚਾਹੀਦੀ ਹੈ. ਇਹ ਬਿਹਤਰ ਹੈ ਕਿ ਤੁਸੀਂ ਕੁਦਰਤੀ ਤੱਤਾਂ ਨਾਲ ਤਿਆਰ ਕੀਤੀ ਆਈ ਕਰੀਮ ਦੀ ਵਰਤੋਂ ਕਰੋ.

ਅੱਖਾਂ ਦੀ ਕਰੀਮ ਕਿਵੇਂ ਲਗਾਈਏ

ਰਾਤ ਨੂੰ ਸੌਣ ਤੋਂ ਪਹਿਲਾਂ ਸਭ ਤੋਂ ਪਹਿਲਾਂ ਆਪਣੀ ਚਮੜੀ ਦੀ ਕਿਸਮ ਦੇ ਅਨੁਸਾਰ ਚਿਹਰੇ ਨੂੰ ਚੰਗੇ ਫੇਸ ਵਾਸ਼ ਨਾਲ ਸਾਫ਼ ਕਰੋ. ਪੂੰਝਣ ਤੋਂ ਬਾਅਦ, ਚਮੜੀ ਦੀ ਕਿਸਮ ਦੇ ਅਨੁਸਾਰ ਚਿਹਰੇ ਦੇ ਟੋਨਰ ਦੀ ਵਰਤੋਂ ਕਰੋ. ਤੁਸੀਂ ਗੁਲਾਬ ਜਲ ਦੀ ਵਰਤੋਂ ਵੀ ਕਰ ਸਕਦੇ ਹੋ. ਹੁਣ ਅੱਖਾਂ ਦੀ ਕਰੀਮ ਲਗਾਓ ਅਤੇ ਰਿੰਗ ਅਤੇ ਮੱਧ ਉਂਗਲ ਨਾਲ ਮਸਾਜ ਕਰੋ. ਸਵੇਰੇ ਠੰਡੇ ਪਾਣੀ ਨਾਲ ਚਿਹਰਾ ਧੋ ਲਓ।

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The post ਚਮੜੀ ਦੀ ਦੇਖਭਾਲ ਦੇ ਰੁਟੀਨ ਵਿੱਚ ਸ਼ਾਮਲ ਹੋਣਾ ਚਾਹੀਦਾ ਹੈ ‘ਆਈ ਕਰੀਮ’, ਬਹੁਤ ਸਾਰੀਆਂ ਮੁਸ਼ਕਲਾਂ ਦੂਰ ਹੋ ਜਾਣਗੀਆਂ appeared first on TV Punjab | English News Channel.