Depth-aware light injection in TypeGPU
I got a 448x448 monocular depth model down to ~8 ms on my M4 Pro across ~250 dispatches, which is fast enough to use in realtime :D
Since the inference is written directly in TypeGPU, I can just feed the depth buffer straight into the lighting pass. It never has to leave the GPU or go through any extra synchronization/interop step
Inference, lighting and draw all go through the same command encoder.
MISSION SUCCESS | ZhuQue-3 Y2 Reusable Launch Vehicle Achieved Full Success in Orbital Insertion and First-Stage Recovery
On August 19, 2026, at 07:35 (UTC+8), the ZhuQue-3 (ZQ-3) Y2 reusable launch vehicle lifted off from the Dongfeng Commercial Space Innovation Pilot Zone. Approximately 137 seconds after lift-off, the first and second stages separated. The second stage continued its flight and successfully delivered the Honghu 03 satellite, independently developed by Hongqing Technology, into its designated orbit. At approximately 07:41, the first stage performed a successful soft touchdown at the LandSpace Landing Site#1 in Minqin County, Gansu Province, following the planned trajectory — marking the full success of the flight test mission.
Apakah kamu orang tipe seperti ini?
-Suka menyendiri
-Tidak punya banyak teman, kalaupun ada hanya 1–2 orang
-Sering merasa tidak enakan dengan orang lain
-Lebih suka di kamar daripada keluar tanpa tujuan
-Bicara kadang tidak lancar, tapi lebih lancar saat menulis
-Suka self-talk dan obrolan yang dalam (deep conversation)
-Lebih nyaman chat daripada telepon
-Menyukai tempat alam yang jauh dari keramaian
-Tidak suka ikut campur urusan orang lain
-Pernah atau sering mengalami kegagalan dalam percintaan
-Cenderung memendam perasaan sendiri
-Tidak terlalu mengikuti fashion atau gaya hidup populer
-Tidak mudah FOMO atau ikut-ikutan tren barang viral
-Tidak suka basa-basi
Yuk saling connect kalau kita satu frekuensi.
There's a physicist at Stanford named Safi Bahcall who modeled this exact principle and the math is wild.
He calls it "phase transitions in human networks." When you're stationary, your probability of a lucky event is limited to your existing surface area: the people you already know, the places you already go, the ideas you've already been exposed to. Your opportunity window is fixed.
When you move, your collision rate with new nodes in a network increases nonlinearly. Double your movement (new conversations, new cities, new projects) and your probability of a serendipitous encounter doesn't double. It roughly quadruples. Because each new node connects you to their entire network, not just to them.
Richard Wiseman ran a 10-year study at the University of Hertfordshire tracking self-described "lucky" and "unlucky" people. The single biggest differentiator wasn't IQ, education, or family money. Lucky people scored significantly higher on one trait: openness to experience. They talked to strangers more, varied their routines more, and said yes to invitations at nearly twice the rate.
The "unlucky" group followed the same routes, ate at the same restaurants, and talked to the same 5 people. Their networks were closed loops. No new inputs, no new collisions.
Luck isn't random. Luck is surface area. And surface area is a function of movement.
The lobster emoji is doing more work than most people realize. Lobsters grow by shedding their shell when it gets too tight. The growth requires a period of total vulnerability. No protection, no armor, soft body exposed to the ocean.
That's the cost of movement nobody posts about. You have to be uncomfortable first. The new shell only hardens after you've already moved.