Daian Chow 🍁

learning to build chips and sensors | 🇨🇦 BASc ECE and CS | MASc in Microsystems

[Jeremy @jeremyct]

My friend worked at a startup for 3 years.

Took a $20,000 pay cut for equity they promised would change his life.

Wore the hoodie. Believed the mission. Worked every weekend without being asked.

Company got acqui-hired last year.

Founders walked away with $8,000,000 each.

Employees got 90 days notice and a LinkedIn recommendation.

The equity was worth $0.

He’s 31.

Starting over.

Startups sell you a dream because dreams are cheaper than salaries.

[Dan Rudolph @danrudolph]

@CalebChamberla6 Year 15 of bootstrapping

[Born to gamble @borntogambles]

He is 38 years old and lives in California. He built a weapon sight that runs on any phone and got paid 300,000 dollars for it.

You zoom in and tap once. The system immediately locks onto the target and tracks it automatically. It builds a real-time polygonal model of the visible world and uses motion analysis to keep the lock stable without constant manual input. The only thing you control is one slider.

Raise the sensitivity and the app becomes extremely strict. It filters out almost all noise and only follows clear deliberate movement. Lower the sensitivity and it starts picking up faint motion across large empty areas. This mode is especially effective when tracking small distant objects against the sky.

Most people set the slider wrong at first because they do not understand what the system is actually optimizing for. Once you match the setting to the environment the tracking becomes reliable enough to run without constant supervision.

This kind of capability used to require entire teams and specialized hardware. Now similar logic fits inside a regular phone app. The gap between what used to be restricted technology and what individuals can build is shrinking fast.

Picked up a lot of these kinds of systems and approaches inside @NeuroClub. The difference between people who just use apps and people who understand how they actually work keeps getting bigger.

He spent his entire life working in a completely unrelated field before he started building things like this. He only began a short time ago.

[DRONEFORGE @droneforge]

🚨 New Drone Simulator

[5.31.2026] Researchers release CrazyFlow - a GPU accelerated, differentiable simulator in JAX.

Benchmarked on a RTX4090:
> Parallel sim: ~700M steps/s at 1M worlds
> Max scale: 4.2M drones, 900M+ steps/s
> Gradients: 9M gradients/s through 10 sim steps
> Depth rendering: 350k frames/s at 64x64 over 1,024 envs

Read our 11 minute research note on the paper: https://t.co/OYXmRNQ6pt

[Object Zero @Object_Zero_]

Single Crystal CVD Diamond

Have no doubt, you are at the dawn of an industrial revolution. There is a string of breakthroughs happening throughout upstream industries that all compound.

Diamond manufacturing is now able to produce CPU size single crystals wafers.

Currently these are marketed as heat spreaders because they have thermal conductivity of 2,200 W/mK which means they move heat incredibly effectively.

However, that somewhat misses the wood for the trees…

Diamond has physical and electrical properties that exceed traditional silicon, making it uniquely suited for high demand applications.

Thermal Conductivity: Heat is the enemy of electronics. Diamond conducts heat better than almost any other known material, about 5 times better than copper and over 10 times better than silicon.

A diamond chip can act as its own heat sink.

Ultra Wide Bandgap: Diamond can handle massive amounts of voltage and operate at incredibly high temperatures without electrical breakdown.

This makes it perfect for high power applications like electric vehicle inverters, power grids, and aerospace technologies.

High Frequencies: Electrons move very quickly through diamond, allowing chips to operate at much higher frequencies, which is ideal for advanced telecommunications and radar.

Radiation Hardness: Diamond is incredibly resilient to radiation, making diamond based chips ideal for satellites, space exploration, and nuclear facilities.

To make a material act as a semiconductor, you have to "dope" it. To do this you inject impurities into the crystal lattice to create a positive (p-type) or negative (n-type) charge.

Diamond's atomic structure is so tightly packed that forcing other elements into it is hard. While p-type doping (with boron) has been figured out, reliable n-type doping (with phosphorus) remains a massive hurdle.

Theoretical ceilings

Band gap
Silicon wafer = 1.1 eV
Diamond CVD wafer = 5.5eV

Clock speed
Silicon wafer = 5-6 GHz clock wall
Diamond CVD wafer = 1-2 THz clock wall

Max Running Temp
Silicon wafer = 150°C
Diamond CVD wafer = 1,000°C

Whilst we etch silicon with photolithography and Extreme UV light, this doesn’t really work with chemically inert diamond.

Diamond CVD is currently etched with oxygen plasma etching, but this lacks the precision of EUV.

However, we can etch diamond to extreme precision with electron projection lithography. EPL was invented in the 90s by Bell Labs, IBM and Nikkon but abandoned as it was harder than EUV.

Electrons repel each other so the beams blurrs too readily.

What if we built a femto electron beam?
What if we built it to extreme such that it was a ‘single electron’ pulse?

What if we build a microscopic "bed of nails" containing millions of nanoscale tungsten or silicon tips (photocathodes). You shine a massive, highly complex femtosecond laser system across the entire array.

Every time the laser pulses, millions of tiny tips each fire a single, perfectly straight electron at the exact same time.

Turns out, research teams at likes of MIT and Stanford are currently experimenting with exactly this, laser driven nanotip electron emitters.

Pair that tool with Diamond CVD substrate tech and we approach the material limits of both semiconductors and nanotechnology.

Would require asynchronous logic to escape fatal clock skew and operate at full capability.

But I think I will live to see it.

[kache @yacineMTB]

it's really that simple

[Rafa Schwinger 🇻🇦 @Rafa_Schwinger]

Guys, I beg you to understand that the long term AI frontier is about statistics / information theory and physics. It is NOT about linear algebra.

Dense matrices are the best approach we have rn because of the TPU/GPU hardware and because the math is robust and general purpose.

However, it is inefficient.

And as we optimize towards more faithful intelligence representations sparse networks will dominate the intelligence / energy frontier.

The most important thing is not to be a super physicist information theorist, because only I can be so awesome after all, but to be able to think generally in these terms from first principles. You need to be able to think CONCEPTUALLY in statistics. You need to understand that these matrices are just encoding the necessary information to sample probability distributions.

The 21st century will be the century of information and statistics. Please develop intuition around these ideas.

[Alps @alpaysh]

i still think about this

[谁是藏镜人 @VedaAI00]

为了对付阳台上的鸽子，硬核开发者已经把「自动防空塔」开源了。

Reddit 用户 muxamilian 徒手搓了一个完全自主的 AI 驱鸽水枪系统。它的硬件极度廉价：一块 50 美元的 Orange Pi 5 开发板、一个 USB 摄像头、两个舵机和一把改装过的电动水枪。

但它背后的工程逻辑非常现代：
1/ **边缘端重计算**。没有依赖云端 API 导致的延迟，整个视觉识别完全在 Orange Pi 5 内置的 NPU 上本地跑通，确保了开火的实时性。
2/ **开放词汇目标检测**。系统运行的是 YOLO World v2 模型。这意味着它不是写死只能打鸽子，你只需要在代码里把 Prompt 改成“猫”或“马蜂”，它就能立刻切换打击目标。

当工业级的边缘 AI 被下放成 50 美元的开源玩具，物理世界的防御成本就被彻底击穿了。

我是「谁是藏镜人」，专注于挖掘 AI 与前沿硬件演进的底层逻辑。
关注 @VedaAI00，带你看清那些打破常规的工程突围。

[kache @yacineMTB]

people who have these simulator skills are going to be in high demand soon

[らじあん @rad1_study]

Peskin, Schroeder “An Introduction to Quantum Field Theory“ を読み切りました！！！
800ページもあって読むのに1年以上かかったけどこの本を通してすごく成長できたと思います
突然ゼミに入れてくれた素粒子論研の方々、その他色んな方々ありがとうございました
これからも頑張ります！！

[谁是藏镜人 @VedaAI00]

计算几何与 3D 打印的结合，正在让那些曾在教科书中被判「死刑」的机械结构起死回生。

山形大学展示了名为 **ABENICS** 的主动球关节机制。这不仅仅是一个万向节，它通过极度复杂的「球面齿轮啮合」（Spherical Gear Meshing），在单个旋转点上实现了完整的 3D 自由度（俯仰、翻滚、偏航）控制。

这里的工程价值极具颠覆性：
1/ **高度集成化**。传统的双足或类人机器人，一个肩膀通常需要 3 个电机和 3 组关节堆叠。ABENICS 将其压缩进了一个单一的球体，极大缩减了机甲的物理体积和重量。
2/ **高扭矩与零漂移**。不同于依赖摩擦力或线缆驱动的普通球头，ABENICS 使用的是刚性齿轮啮合，确保了在高速、重载下的动力传输效率和定位精度。
3/ **几何驱动的创新**。这种结构的齿轮齿形分布在球面上，传统的减材制造几乎无法加工。它是典型的「增材制造专用设计」（DfAM），只有 3D 打印才能释放它的潜力。

当机械结构不再受限于制造工艺，机器人的「肉身」灵活度将迎来非线性爆发。

我是「谁是藏镜人」，专注于挖掘 AI 与底层机械工程演进的深度闭环。
关注 @VedaAI00，带你看清从算法到硬核硬件的每一场进化。

[Lukas Ziegler @lukas_m_ziegler]

That's how parts get inspected! 🪡

The pace is ridiculous.

Imagine people still doing this instead of machines.

Renishaw system is one of the biggest shifts in coordinate-measuring machines (CMMs).

Instead of moving the entire machine to capture each point, REVO uses a 5-axis scanning head that collects data through rapid angular motion.

Combined with automated probe and stylus changes, the system can handle complex geometries and full surface scans that traditionally required slow, point-by-point probing.

~~

♻️ Join the weekly robotics newsletter, and never miss any news → https://t.co/GoA3ZuwWF9

[caio temer @canalCCore2]

Pouca gente sabe mas a Fuji não faz apenas câmeras e filmes. Ela também domina o mercado de máquinas para circuitos integrados. Esse modelo clássico CP-643 com 20 cabeças trabalha em um ritmo insano de 40 mil componentes por hora para criar as placas de circuito dos eletrônicos.

[Yann LeCun @ylecun]

Major difference in my mind:
- an engineer, given a problem, invents and tries multiple solutions and stops when the solution is good enough. The goal is product innovation and shipping.
- a scientist asks new questions, proposes various new solutions, compares them (sometimes with old ones), and writes about it. The methodology must be sound or else peers will sneer. The goal is scientific breakthroughs and technological progress.

Both can be called "researchers". Many people can do both: these are activities, not identities.

Importantly, most product innovations are built on scientific breakthroughs and technological innovations that happened 2, 5, 10, or 20 years earlier.

[Science News @SciencNews]

Tough peer-review process?

Your paper might end up being more highly cited. @Nature

[Semi Doped @semidoped]

Masterclass on IC Lithography

You can spend one hour and catch up on the entire arc of semi lithography.

We cover:
- Economics of modern lithography
- what is takes to build a leading edge fab
- how we evolved from DUV to EUV
- fun stories from history along the way
- where we are going with xLight and Substrate

Check it out!

Chapters:
(00:00) The 13F panic, and today's topic
(02:23) Why the real story is economics, not physics
(06:18) Austin in the clean room: graphene and bunny suits
(10:06) Rock's Law and the $20 billion fab
(18:08) DUV, the Sharpie, and a history of light
(24:58) Multi-patterning, explained with a football field
(34:45) How EUV makes 13.5nm light from tin droplets
(41:14) High NA, anamorphic optics, and the half-field tax
(46:45) The startups rethinking lithography: xLight and Substrate

@austinsemis @vikramskr

[Codetaur @codetaur]

every time I see people doing cool math visualization stuff with like, python generating svgs or pngs I just want to shake them and say "USE WEBGPU. MAKE THIS REALTIME. EXPLORE THE ENTIRE SPACE OF POSSILITIES AT WILL, PLEASE!"

[Nick McCleery @nick_mccleery]

Shilling my geometric constraint solver for simulating F1-style suspension systems again, having chatted with a former colleague about this earlier this week.

Has a lot in common with the inner workings of CAD system sketch solvers, if you ever wondered how those work!

[Tom Dörr @tom_doerr]

Simulates billions of fluid cells with real-time 8K raytracing

https://t.co/cqAWCXJoPD