Show HN: SendFiles.online – Make a file into a URL quickly https://ift.tt/3jONWQM

Show HN: SendFiles.online – Make a file into a URL quickly https://ift.tt/37kdgZN February 16, 2021 at 06:36AM

Show HN: Jam, an Open Source Clubhouse (w/ WebRTC) https://ift.tt/3dixZky

Show HN: Jam, an Open Source Clubhouse (w/ WebRTC) https://jam.systems February 14, 2021 at 04:02PM

Show HN: Hubstore – Showcase your Python desktop applications https://ift.tt/2ZlpvRC

Show HN: Hubstore – Showcase your Python desktop applications https://ift.tt/2MjHxkl February 16, 2021 at 03:32AM

Launch HN: Noya (YC W21) – Direct air capture of CO2 using cooling towers https://ift.tt/37jqZjH

Launch HN: Noya (YC W21) – Direct air capture of CO2 using cooling towers Hello HN! I'm Josh, one of the co-founders of Noya ( https://noyalabs.com ). Noya is designing a cheaper process to capture CO2 directly from the atmosphere. We do this by retrofitting industrial cooling towers owned and operated by other companies to perform carbon capture. We then sell the captured CO2 to companies that need it, and pay a piece of the proceeds to the companies that own the cooling towers. As the wildfires in California became worse and worse, my co-founder (and roommate at the time) Daniel and I became increasingly concerned that we weren't doing enough to be a part of the solution. The more that climate catastrophes became the norm, the more we became obsessed with one seemingly-simple question: If climate change is caused by having too much CO2 in the sky... can't we just reverse it by yanking CO2 out of the sky? Humans have known how to scrub CO2 out of gas mixtures for almost a century [1]; but, we haven't been able to widely apply this type of tech to scrubbing CO2 from the air because of its high cost. For example, one popular direct air capture project is estimated to capture 1M tons of CO2/year [2], but has an estimated equipment cost of $700M and all-in costs of ~$1.1B [3]. The single largest component of this cost is in the piece of equipment called the air contactor — the big wall of fans you see in the image linked above — which clocks in at $212M by itself. Yet fundamentally, all that air contactors do is put air into contact with something that captures CO2, whether it's an aqueous capture solution or some sort of solid sorbent. These costs felt astronomical to Daniel and I, so we set out with the singular focus to reduce the costs of carbon capture by reducing the costs of the air contactor. But no matter how we thought about it, we couldn’t get around the fact that to capture meaningful amounts of CO2, you need to move massive amounts of air since CO2 is very dilute in the atmosphere (0.04% by volume). Looking at the existing solutions, we began to understand why it makes sense to build something equally massive: so you can go after economies of scale. As Daniel and I were feeling stuck late one night, he got a call from his dad. They started talking about the refrigeration facility Daniel’s dad runs in Venezuela (where Daniel's from), and they started talking about the cooling towers at the facility. Cooling towers move air and water into contact with each other to provide cooling to industrial processes (descriptive video: https://www.youtube.com/watch?v=pXaK8_F8dn0 ). As Daniel listened to his dad, Daniel realized that if we could just add the blend of CO2-absorbing chemicals we had been developing into the water his dad’s cooling tower used, we could use it as an air contactor and achieve CO2 capture at the same time the cooling tower was cooling its processes. This eliminates the need to build millions of dollars worth of dedicated equipment to pluck CO2 from the sky. Our cooling-tower-based carbon capture process works as follows: we add our chemical carbon capture blend into a cooling tower's water, we connect the tower to some pieces of downstream processing equipment to regenerate the captured CO2, and then we pressurize the CO2 into cylinders for sale as "reclaimed CO2" to companies that need it. All of this is installed onto a cooling tower that another company already owns and operates. In exchange for letting us install this process on their towers, we will cover the cost of installation, and the companies will get a piece of the revenue generated through the sale of their CO2. We’re well on our way towards making this process a reality. We’ve partnered with a local farm to install our process in their cooling towers, and we've just produced CO2 using our industrial-scale prototype. We're excited for the opportunity to reverse climate change and ensure we have a future on this planet that is good. Please let us know what questions, concerns, or feedback you have about what we're building - I’ll be here all day! [1]: https://ift.tt/3u1lL5J [2]: https://ift.tt/2Zm55HY... [3]: https://ift.tt/3rVR3ZT... February 15, 2021 at 10:23PM

Show HN: ConstexprJS: A static site generator without a DSL https://ift.tt/2ZpDgOV

Show HN: ConstexprJS: A static site generator without a DSL https://ift.tt/3bea8Qy February 15, 2021 at 09:34PM

Show HN: We Broke Topcoder Ratings (incl. link to Elo-MMR repo) https://ift.tt/2LRlIIr

Show HN: We Broke Topcoder Ratings (incl. link to Elo-MMR repo) https://ift.tt/3qnYE2V February 15, 2021 at 09:35PM

Launch HN: Pyroscope (YC W21) – Continuous profiling software https://ift.tt/2Zhzo2L

Launch HN: Pyroscope (YC W21) – Continuous profiling software Hi HN! Dmitry and Ryan here. We're building Pyroscope ( https://pyroscope.io/ ) — an open source continuous profiling platform ( https://ift.tt/2L1jeqC ). We started working on it a few months ago. I did a lot of profiling at my last job and I always thought that profiling tools provide a ton of value in terms of reducing latency and cutting cloud costs, but are very hard to use. With most of them you have to profile your programs locally on your machine. If you can profile in production, you often have to be very lucky to catch the issues happening live, you can't just go back in time with these tools. So I thought, why not just run some profiler 24/7 in production environment? I talked about this to my friend Ryan and we started working. One of the big concerns we heard from people early on was that profilers typically slow down your code, sometimes to the point that it's not suitable for production use at all. We solved this issue by using sampling profilers — those work by looking at the stacktrace X number of times per second instead of hooking into method calls and that makes profiling much less taxing on the CPU. The next big issue that came up was storage — if you simply get a bunch of profiles, gzip them and then store them on disk they will consume a lot of space very quickly, so much that it will become impractical and too expensive to do so. We spent a lot of energy trying to come up with a way of storing the data that would be efficient and fast to query. In the end we came up with a system that uses segment trees [1] for fast reads (basically each read becomes log(n)), and tries [2] for storing the symbols (same trick that's used to encode symbols in Mach-O file format for example). This is at least 10 times more efficient than just gzipping profiles. After we did all of this we ran some back of the envelope calculations and the results were really good — with this approach you can profile thousands of apps with 100Hz frequency and 10 second granularity for 1 year and it will only cost you about 1% of your existing cloud costs (CPU + RAM + Disk). E.g if you currently run 100 c5.large machines we estimate that you'll need just one more c5.large to store all that profiling data. Currently we have support for Go, Python and Ruby and the setup is usually just a few lines of code. We plan to release eBPF, Node and Java integrations soon. We also have a live demo with 1 year of profiling data collected from an example Python app https://ift.tt/3ajTu2z... And that's where we are right now. Our long term plan is to keep the core of the project open source, and provide the community with paid services like hosting and support. The hosted version is in the works and we aim to do a public release in about a month or so. Give it a try: https://ift.tt/2L1jeqC . We look forward to receiving your feedback on our work so far. Even better, we would love to hear about the ways people currently use profilers and how we can make the whole experience less frustrating and ultimately help everyone make their code faster and cut their cloud costs. [1] https://ift.tt/1OF8Adn [2] https://ift.tt/1ezUKqz February 15, 2021 at 09:30PM