alokv28

Labels for production nodes (e.g. "1nm") have diverged from specific physical distances (e.g. gate width) for years now.

ironic title is ironic

The node label (e.g. "5 nm") no longer represents a physical dimension and hasn't for some time -- it's simply a continuation of a naming convention. In fact, while we used to scale down transistors by simply reducing the gate width (where the naming convention originated) we now take far more elaborate steps to do so.

Congrats on the launch! Can you say more about how you drive your costs down?

They never claimed to be doing Raman spectroscopy.

It's up now: https://www.youtube.com/watch?v=a5Hlc1suTss

Yup, such a blatant copycat attempt. All the way down to the angle the photos are taken from. Good thing for Boosted that you can't just copy a well engineered drivetrain.

That relation ties together entropy, a previously macroscopic quantity, and discrete, countable energy states, first introduced via quantum mechanics.

I'd replace it with S = k log(Omega), which links thermodynamics to quantum mechanics.

Same boat here! Any sense of how long the queue is?

The color change of gold and CdSe particles with size are due to two different effects.

CdSe is a semiconductor, and reducing the CdSe nanoparticle size increases its band gap through a process called quantum confinement. For smaller particles, it requires a photon to have higher energy (i.e. smaller wavelength) to be absorbed.

Gold particles derive their color from the scattering mechanism mentioned in the parent comment.

Not that I disagree with what you wrote, I just want to point out that silicon dioxide is probably the most important reason silicon has reached such ubiquity in electronics. No other material has a native oxide as good as SiO2, even though the semiconductors themselves have superior properties than silicon.

I know very little about what's available in Ruby, but I would have used the Pandas library to accomplish this task in python. Their in-memory data structure, a DataFrame, is more than capable of handling those operations.

I'm trying to figure out where something like this fits into the python data ecosystem.

For datasets that fit in memory, Pandas seems like the best bet. Good I/O functions (JSON, CSV), easy slicing (numpy array-like syntax), and some sql-like operations (groupby, join).

For large datasets, you'd need a proper db.

So is Dataset then useful for datasets that cannot fit in memory but aren't too large?

That was hard to read...

"The manufacturing process for most solar panels involves manufacturing a block of sapphire or other crystalline silicon and then slicing a .2mm-thick sheet off of it with a wafering saw."

Painful

Just want to point out that Stanford does have its fair share of "MIT startups".

Here's a small subset:

* Alveo Energy

* Amprius

* Blue River Technology

* C3Nano

* Momentum Machines

* QuantumScape

* Solum

Although if the OP's simple model had a category for startups simultaneously validating customer demand and driving technological advantages, most of these companies would be there.

StraightUp http://readstraightup.com

Circa app

Twitter

Internet archive link to the demo/screencast: https://web.archive.org/web/20070407145348/http://www.getdro...

I still prefer Keynote to JS presentation tools since playing with layout is easier manually than with css (for me).

But one killer feature Keynote lacks is slide inheritance. I love using build-outs and I wish I could edit a parent slide and have the changes propagate to its children. This feature would be much easier to build out with a JS presentation framework.

I've found that Foursquare has a higher signal-to-noise than Yelp for restaurant reviews/tips.

Most energy entrepreneurs I know started out leveraging government programs including SBIR/STTR, ARPA-E and DOE grants, and national lab facilities.

Thanks for answering!

Can I export/download my data?

Any more info available on the "M7 Motion Coprocessor" chip? That's the most interesting addition IMO.

On OSX I have Meta bound to the Apple key.

What also helped is a tip borrowed from Steve Yegge [1] to bind C-x C-m to M-x (also C-c C-m, in case you miss the x). This avoids my most common use of Meta, allowing me to use the more comfortable Control (bound to Caps Lock).

[1] https://sites.google.com/site/steveyegge2/effective-emacs

I'm confused. Is there an announcement or update of some sort associated with this project? It's unclear from the article.

Not everything Intellectual Ventures does relates to patent trolling.

Intellectual Ventures also has an applied science laboratory that's even spun out a startup, Kymeta [1], in which Bill Gates invested.

Kymeta is a very cool company; they're developing revolutionary satellite transceivers using metamaterials [2].

[1] http://www.kymetacorp.com/ [2] http://en.wikipedia.org/wiki/Metamaterials

Nice to see!

I choose Mathematica over python for a lot of scientific/numerical computing due to how "lisp-y" Mathematica is.

I'll be keeping a keen eye on Julia as well!

Thanks for the response, Hunter! Looking forward to seeing where y'all invest.

Nice read, thanks for reflecting on the process!

Did you try anything creative with the fund terms or was it pretty standard VC fund structuring? I've heard that there's often LP pushback against changing standard terms, even if the changed terms might better align LP and VC incentives. I'm also curious if LPs view investment in a micro-fund the same as they would a larger, traditional VC fund.