iso1337

The problem with these silk biotech companies will be scale up and competing with existing cheap and abundant fibers.

Silkworm silk already has issues with water and such clothing has to be professionally cleaned. Many silks have the property of supercontraction and will shrink by 50% upon exposure to water.

Furthermore, these silk companies claim they can produce stuff as strong as natural spider silk, but that’s a very tall hurdle to jump. They stole VC interest by claiming that the fiber properties are engineerable, but very little academic research has been done in that space.

So you have to wonder what is the market for expensive fibers that don’t perform well in the context of clothing that gets wet.

Already you see companies like Bolt Threads pivoting towards cosmetics, because why not? The technical challenge is much lower and the profit per gram should be much higher.

I'd recommend the following book:

How Economics Shapes Science https://www.amazon.com/Economics-Shapes-Science-Paula-Stepha...

It looks into key challenges of the scientific labor market.

I’ll give your analogy a try.

Google could do that because the basic components were so cheap due to consumer imaging.

On the lab side, there’s no similar downward pressure for making more accurate automated pipettes cheaper - I fully believe it’s possible, but the market isn’t there. Also, with imaging books, you can get away with a lot since ML/cheap labor/etc can be thrown at the distortion correction problem. With lab tests, you can’t really accept that same amount of error (ok, you can iff you understand the distortion and can process more samples to compensate).

I do agree that most companies just want a service that works. The issue is that there’s no standardization. Depending on the assay you do, you will need wildly different setups and sensor systems. When you optimize whatever you are doing, often you have to re-tune your entire assay to bring it line with the new dynamic range of your output.

If that weren’t the case, companies like Emerald and Transcriptic would have succeeded years ago.

Selling things like DNA or cell lines have been the closest to a standard, scalable product. Otherwise, it’s just replacing armies of people at a CRO (Contract Research Org) with a custom project for automation and assaying (Zymergen’s model).

Not to mention other cars have a lot of shiny surfaces too.

It would use far less water than crops.

Maybe they were referring to the i3 REx, which also has an ICE.

Also i3s use a special, skinny tire that is really expensive

Charging a subscription for CarPlay is unacceptable too

What did you replace flying with?

Are you referring to the one that hit Puerto Rico? Cynically I think that's because many people don't consider that truly part of America. (Note that's not my personal views, but just trying to make sense of why there was such a lackluster response).

My original comment was really trying to get at the human factors involved as to why we don't care. We just seem to be bad at responding to trends but better at responding to single events. Just like how the US generally don't care about the 40k+ USians killed every year in traffic accidents, but the US responded strongly to the singular event that was 9/11.

Edit: care about on an emotional, popular media level. Deaths per vehicle mile traveled have been going down over time in the US, due to increased safety regulations imposed on the manufacturers.

I think that there needs to be an equivalent of a climate 9/11 event. Maybe Miami getting wiped off the map by a category 9 hurricane (and call me a pessimist but the loss of a city in a developing country to climate change just won't register in the minds of the developed world).

We as a species are very emotionally driven and slow trends and statistics just aren't as compelling as a big event. I don't know how bad the environment will have to get for that to happen though.

Otherwise, the people creating the most carbon emissions will just view this as an inconvenience and crank their AC higher. The numbers involved in the reporting (2C, 3C etc) just sound small and insignificant when in reality of course that's just an average.

Source please.

https://en.m.wikipedia.org/wiki/Life-cycle_greenhouse-gas_em...

Solar is listed at 4x more and wind seems to be on par with nuclear. Both solar and wind are far better than the hydrocarbon alternatives.

I used to be very pro nuclear but I think nuclear has a lot of hidden costs - very long ramp up time, long and expensive cleanups.

Those older texts were written in Classical Chinese, so there's still a barrier in terms of context and education to fully understanding them.

https://en.wikipedia.org/wiki/Classical_Chinese

I filled in the form and was immediately met with a login form. No thanks and also made a mental note to never visit this site again.

There’s zero reason I should need to login to see these tips.

I like these commute options that make it easier to get around but starting at $2/ride seems a bit high. If I were to use this twice daily for 200 days a year, I'd be looking at $800/year! I'd rather buy a bike in that case, and just use the bikeshare for one offs.

I've never used a GoBike, but are those prices real & what are the typical usage patterns?

Edit: I see, there are monthly and yearly plans that make it much more competitive with ownership. Great!

While this fee is outrageous, I think EV specific fees are inevitable as gas tax revenue dwindles.

The fairest would be use-based but most people seem fundamentally opposed to tracking through tags or mileage (some people are okay with it when it results in lower insurance rates though). Trucks have the most impact on the roads so it may be possible to impose mandatory mileage or tracking via tags.

So instead I think we will end up with a flat tax per vehicle.

Edit: fairest as in people who use the most pay the most. However, exclusionary housing policies end up shifting that burden in a way that results in fairly regressive policies. Just look at the Bay Area which happily passed high bridge tolls but is much slower to build housing on the peninsula.

Able to? Yes.

Willing to? Not as much.

Just consider the following: 1) it’s been so long since people have first hand witness of the horrors of smallpox, polio, etc. So long that anti-vax is alive and well in affluent, educated enclaves. 2) antibiotics are not profitable. Achaogen tried but the business case just wasn’t there. https://www.bloomberg.com/news/articles/2019-05-03/antibioti...

So I agree with your last statement that we at not totally helpless, but I do think (as with global climate change or deferred maintenance on levees) there has to be an immediate and pressing need for people to put their money and actions in the right place.

What are your plans for when the carpool sticker expires in 2022/23?

Yeah, that and the incentives are not there for programmers. They are often treated as the support, not given proper training and managed by people who has no idea how to develop software. This is slowly changing but is still bad.

It’s already used: look up Polymerase chain reaction.

Copying isn’t as interesting as de novo synthesis, being able to specify an arbitrary sequence and have it synthesized.

The issue is also we don't know what to write. I'm being a bit extreme here, but not being able to do rational design limits things to mostly 1) build a lot of variants, using some educated guessing, 2) test those variants, 3) plug those results into some model and predict what to build next.

So the value of each individual variant is relatively low, since you don't know if it will work or not. Plus a lot of other investments have to be made into the toolchain (developing assays for the thing you actually care about is not easy).

In your plateaus, the possibility space explodes with each step function. We are already at the first rung of the ladder of building many 50bp variants. Generally, you'd probably want to be able to conduct an experimental cycle with 1000s of variants. At the upper end, you'd want to pay $5000-10000 for each cycle. There is demand for making protein-length sequences, but the prices the market is willing to pay is probably closer to $10/gene (fully customizable 2kbp) rather than the current price.

Another thing is that not all sequences are created equally. Some sequences will just not synthesize well (maybe there's secondary structure), so to limit costs one ought to limit the number of retries per sequence.

Maybe in the distant future.

There are really three steps: writing, storage, reading.

Writing: There are dreams of using biology to write DNA (currently we use chemical synthesis). See the San Diego startup Molecular Assemblies.

That could miniaturize a big part of it and not require your body to host bottles of nasty chemicals. Maybe you could find clever ways of siphoning the chemical building blocks and ATP energy from the host.

Storage and retrieval: maybe just have bioinert containers implanted. You’d like to be able to reuse them so ideally there would be a robust flushing mechanism.

Reading: the Minion reader from Oxford Nanopore that this paper uses is about the size of a usb stick. The technology inside is a hybrid of electronics and protein. This technology will undoubtedly get better and smaller as sequencing has a a lot of R and D money going into it.

There are already xenobases being developed, eg Romesburg. Those add extra bases beyond the 4-5 seen in nature.

Related is querying: You can synthesize another pair of Oligos to selectively amplify part of the DNA that you wrote and only sequence that. Look up PCR if you are interested.

From the paper:

“Our system’s write-to-read latency is approximately 21 h. The majority of this time is taken by synthesis, viz., approximately 305 s per base, or 8.4 h to synthesize a 99-mer payload and 12 h to cleave and deprotect the oligonucleotides at room temperature. After synthesis, preparation takes an additional 30 min, and nanopore reading and online decoding take 6 min.”

Also the amount of data that can be written is very small in general for oligosynthesis. The most high throughout methods are microarrays by Agilent and others (not used in this paper). You can buy about 32 megabits for $6000 (http://www.customarrayinc.com/oligos_main.htm). The actual cost of synthesis is maybe $2000 as a guess.

So currently DNA storage would be good for small datasets that you would like to store for a long time. Physical density is useless most of the time if you can’t efficiently generate a lot of data to begin with.

We would need several orders of magnitude increase in the write capacity, which is slowly being worked on. Typically people would like to compare synthesis costs with Moore’s law, using Carlson curves ( http://www.synthesis.cc/synthesis/2016/03/on_dna_and_transis... ).

However there hasn’t been as much progress in synthesis as in sequencing. Why? My theory is that Theres not a very big market for DNA synthesis, so the big investments needed haven’t really been there. Maybe storage on dna could be that market, but it would need to show quick and easy wins (eg stepping stones of practicality like what early integrated circuits had).

It’s a shame that all 15,000 photos aren’t digitized and available online to be viewed by all. It would be such a humanizing view of China’s past.

This has the much less interesting name of Three-North Shelter Forest Program on wikipedia.

https://en.wikipedia.org/wiki/Three-North_Shelter_Forest_Pro...

It's hard to assess the progress as most sources are from Xinhua, but China has been doing this since the 70s.

Looks like they forgot to proof read their website too.

"If you are want to consume carbon-neutral hydrogen, please reach us at sales@charmindustrial.com"

I wish them the best of luck, but I've seen the first wave of biofuels fail and pivot to selling cosmetics or go under (Amyris, Solazyme, LS9). Sure, this is different, but the economics haven't really changed. It's damn hard to compete against something you can pump out of the ground and has no price tag on its externalities.

Personally, I think more effort put into lobbying and activism is better spent. I know we have the technology to make carbon-neutral fuels. I sincerely hope that we transition to using something like EVs for all ground-based transport and biofuels for applications like jet travel that need the energy density.

However, it's the market and the economics that don't work out. And they don't work in a way that prevents you from having easy stepping stones to scaling up. There's basically no-one who will buy this commodity at small scale for a much higher price. You have to succeed completely or fail.

You can even look at startups using conventional approaches and cheap feedstock (Siluria with methane). 10 years and still working on their tech. These big commodity markets have huge players who have huge competitive advantages.

Looks like Miscanthus grass. Although shouldn't it not matter? Why not partner with a corn grower and use corn stalk or chopped corn cobs? Or gasify cardboard.

This is great and makes sense as fleet operators care the most about total cost of ownership.

I wonder if they factored in th Nissan Leaf bartery’s propensity to slowly die after a few years due to lack of active thermal management. Perhaps it doesn’t matter as much in NYC.

Another win is slightly increased air quality. If they could get most of the cars in the city to be PHEV or BEV then air quality will improve even further.

IMHO data eng is too niche and new for that kind of content. But I would love to see if there is anything out there like that.

Is the goal here to get through system design interviews or something like that? You can check out pramp.com if so.

If it’s for learning, then reading some of the original Google papers behind a lot of the big data technologies has been very rewarding for me. You could try reimplementing the paxos algorithm for example.