1,535 karma · joined January 31, 2020
Certain Ivies favour grade inflation yet their students do not suffer dilution of their degree value, this is not a privilege extended to many other institutions.
It is debatable whether they truely understand the financials of biotech. The grind of basic research will never go away. Many successful biotech company essentially acquihire researchers and work that is already 80% complete, the role of the company is to bring it to production. That itself will make up the bulk of the company's workload. Similarly, SpaceX had the benefit of leveraging an existing pool of talent and resources; you cannot build a heavy launch company in Zimbabwe. If they want to do both active basic research and at the same time trial therapies, then they would need an enormous amount of funding (on the software unicorn level). You will need scale on the same magnitude as Pharma giants like Pfizer, GlaxoSmithKline, Johnson and Johnson to be able to acquire companies, run trials, and discard ideas that do not work. In the current bull market, this is the perfect company to build with Coinbase's founder as the chief fundraiser.
On a side note, I strongly do not recommend trying out any of the biomedical implants without a good reason. You can approximate magnet implants to a reasonable extent with a bit of superglue and RFID chips can be placed in rings and jewellery. Risk of infection aside, accidental nerve damage is very difficult to fix with our current level of understanding.
https://news.ycombinator.com/item?id=27332700
For xenobots to be useful, the underlying cell behaviour needs to be better understood and controlled. Their research appears interesting if you are a computer scientist or physicist. But for practical applications and applied biotechnology what they are doing is an curious gimmick at best. If they can perfect their understanding, then they would have basically solved half the puzzle of organ engineering. But right now it is no different from black box engineering.
This is very similar to Turing's reaction-diffusion models on leopard spots — great for computer modeling and popular science magazines, not so immediately useful if your grandmother gets cancer.
I will admit it looks perfect to sell as a biotech startup to dumb SV VCs who don't do their due diligence when it comes to investing in applied science fields.
(On a side note, the state of biotechnology and life science knowledge on HN is utterly deplorable, repeating buzz words does not reality make.)
There is no Effective Julia style of guide. You either have to wade through infantile tutorials for those with minimal programming experience or several reference books worth of nitpicking on syntax. The actual methods themselves are not well documented and lack examples and usage guidelines.
The language and ecosystem do not feel like a project backed by commercial funding, it feels like one of those functional languages out of academia research where the structure and design of the language are more important than actual developer experience. There are many new projects but most are not actively maintained and updated. The language itself feels massive, with syntactic sugar and weird types everywhere. Trying to understand the implementations of other people's Julia code is frustrating, similar to reading a library written in pure C++ templates. Compared to Go/Rust/Dart, Julia feels overly convoluted. Julia literature is structured in a way that seems to heavily encourage you to take regular classes and lectures to learn and pick up the language. It is hard to feel productive from the get-go.