Liam Lang

A new week is a new opportunity to show up fully. For your team. For your health. For life.

@fares__2001 bro stop i can't this is too real ��

BEG YOU 😭💔 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 I STILL NEED MILK 😭 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 I STILL NEED MILK 😭 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 I STILL NEED MILK 😭 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 I STILL NEED MILK 😭 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 🇵🇸 12€ 12€ 12€ 12€ chuffed.org/project/suppor…

SHIV BHAGAT NANDI KI ADBHUT DARSHAN🚩 SHIV 🔱 NANDI N A N D I B H A G A T B H A G A T N A N D I B H A G A T SHIV 🔱 NANDI N A N D I B H A G A T B H A G A T N A N D I B H A G A T SHIV 🔱 NANDI N A N D I B H A G A T B H A G A T N A N D I B H A G A T SHIV KO PRATHAM BHAGAT NANDI🙌 @grok

MY CHILD IS DYING😭 ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ we’ll remind you ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ that ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ I desperately need milk for my baby🍼💔 chuffed.org/project/suppor… …

Happy Father's Day. 💚 To every dad making life a little better, every single day.

OM NAMAH SHIVAY 🔱 H A R H A R M A H A D E V OM NAMAH SHIVAY 🔱 H A R H A R M A H A D E V OM NAMAH SHIVAY 🔱 H A R H A R M A H A D E V OM NAMAH SHIVAY 🔱 H A R H A R M A H A D E V OM NAMAH SHIVAY 🔱 H A R H A R M A H A D E V @grok JAI SHREE MAHAKAL 🌹 OM NAMAH SHIVAY 🔱

Diagnostics. Research. Monitoring. Records. The instruments of medicine have always existed in pieces. The next step isn't creating more tools. It's bringing them together into a connected health infrastructure.

@LifeNetwork_AI While some safety data returns via pharmacovigilance, real-world efficacy insights almost never shape new discovery programs.

@mohkhi9 @gazafunds @Blaze_Knight95 @singingsox @patfahy23 @KermitHigby @decrescentc8n6 @NaijMizuho @seanquincy1 sending love and support, hope you get the help you need for bella ❤️

@Radha88139 @grok May Tripurari’s blessings always stay with you ��✨

TRIPURARI KI KRIPA DRISHTI BANAE RAKHE 🚩 O M J A Y T R I P U R A R I O M J A Y T R I P U R A R I O M J A Y T R I P U R A R I O M J A Y T R I P U R A R I O M J A Y T R I P U R A R I O M J A Y T R I P U R A R I TRIPURARI KI KRIPA DRISHTI BANI RAHE 🚩🙏. @grok

I Incase u forgot ︎ ︎ ︎ ︎ ︎ ︎ ︎ we’ll remind you ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ that ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ ︎ We are human beings, not just numbers. 💔 NEVER STOP TALKING ABOUT US. WE DON’T SLEEP. chuffed.org/project/suppor…

Giao dịch 2 chiều. Khớp lệnh nhanh, chênh lệch thấp, sàn được quản lý uy tín.

@Tickmill Love the flexibility and low spreads. Reliable moves fast.

A drug spends years in trials, then decades in the real world. The real world generates far more evidence about how it actually performs than any trial. That evidence rarely goes anywhere. Which patients respond. What side effects emerge. How it behaves across populations the trial never tested. The most valuable evidence in the entire lifecycle — and it almost never flows back into what gets discovered or trialed next. Every new program starts from the same incomplete picture as the last. AI can generate more candidates than ever. But a pipeline that can’t learn from its own history won’t produce a different outcome. The missing infrastructure isn’t discovery. It’s the loop back.

@LifeNetwork_AI Exactly Faster tools in a broken sequential system still leave us waiting The real leap will come from rethinking the entire parallel, integrated structure of drug development

Those who knew must go to prison for a long time

In the last 30 years, computing power has increased by a factor of a trillion. Yet developing a new therapy remains one of the slowest, most expensive, and most failure-prone processes in modern science. This is the paradox worth understanding. AI has accelerated target identification. Gene sequencing has collapsed from years to days. Computational modeling can simulate molecular interactions at a scale that was unimaginable a decade ago. Yet the pipeline moves at the same pace. The reason is that technology has made individual stages of the process faster. What it has not changed is the structure of the process itself. Drug development is still sequential. Each stage waits for the one before it. Each program still assembles its own evidence infrastructure from scratch. The handoff between controlled validation and real-world performance still happens after approval. Better tools applied to a fragmented, sequential process produce better science at the same speed. What compresses the timeline is not faster tools inside the existing structure. It is a different structure entirely.

@LifeNetwork_AI More candidates alone do not guarantee better outcomes. AI should also improve candidate quality to reduce attrition rates during the validation phase.

@DefnPak SubhanAllah this hits deep ��

Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah Allah ♥️ I LOVE ALLAH♥️