Show HN: Scalable Time Series Modeling with open-source projects https://ift.tt/vsmFVi0

Show HN: Scalable Time Series Modeling with open-source projects https://ift.tt/oDAyIF9 October 6, 2022 at 09:57PM

Show HN: WebVM – x86 VM in WebAssembly, now with networking (via Tailscale) https://ift.tt/TquaoUD

Show HN: WebVM – x86 VM in WebAssembly, now with networking (via Tailscale) https://webvm.io October 6, 2022 at 09:24PM

Show HN: Coherence, the developer platform to end yak-shaving https://ift.tt/QaNu7ks

Show HN: Coherence, the developer platform to end yak-shaving Hey HN! I’m Zach, from Coherence (https://ift.tt/vzWtlbS). We’re building software that provides an integrated way to write, preview, and deploy code from one simple configuration. Check out our explainer video here: https://www.youtube.com/watch?v=V2YHVx8QsLg and a high level demo here: https://ift.tt/kf7MyZw We started Coherence because we found technology teams spent 30-40% of their time yak shaving and writing glue code, instead of building products our customers love. We don’t think every team should have to build their own internal development platform, so we’re building the software we’d want to buy. Following the example of other great products like Replit, our toolkit is based on integration across the stages of an app’s lifecycle - from dev to production. Coherence provides one integrated dashboard with cloud development environments, automatic CI/CD, and full-stack environments for each branch (as well as production), all with one simple configuration. A developer can write, test, review, and deploy code from one interface with best practices built in. Devs are intuitively navigated to their own cloud resources when needed, eliminating time spent searching consoles for every log, metric, or configuration setting you might need. We currently support containerized full-stack web apps on AWS and Google Cloud. Unlike the PaaS category defined by Heroku, our goal is to help teams leverage the platforms provided by the best cloud providers, keeping the benefits of running in your own cloud account: unlimited extensibility, cost savings, compliance with audit requirements, and freedom to easily exit our platform without an impact to your systems. You can play around with a Coherence sandbox and get a taste of the developer experience at app.withcoherence.com/sandbox. If you like what you can see, you can also deploy an app into your AWS or GCP with one of our starter templates for a new project: app.withcoherence.com. We’re happy to support onboarding on a call or via Slack and we’d love to discuss what it would look like to migrate one of your existing apps to Coherence. We’re excited to get your feedback, so please feel free to drop us a note at hn@withcoherence.com October 6, 2022 at 06:10PM

Show HN: Low-Code ABAC – Permissions even a monkey can use (on top of OPA) https://ift.tt/jgaOp0t

Show HN: Low-Code ABAC – Permissions even a monkey can use (on top of OPA) https://ift.tt/BPMGUOm October 6, 2022 at 07:34PM

Show HN: I created a simple and fast Broken Link Checker for WordPress https://ift.tt/4moN693

Show HN: I created a simple and fast Broken Link Checker for WordPress There aren't any reliable free broken link checkers out there, so I decided to create one. Right now, it is a Python script that can be run on any platform. It is multi-threaded and has a lot of room for improvement. Feel free to check out the code and point out the mistakes or leave suggestions. I am newbie programmer :) https://ift.tt/y1wPT0E October 6, 2022 at 05:08PM

Show HN: Soso.ooo – Google Search Alternative https://ift.tt/zwy5HZN

Show HN: Soso.ooo – Google Search Alternative https://soso.ooo October 6, 2022 at 12:55PM

Show HN: GreenSwapp – API to track CO2 of food products at scale https://ift.tt/xrmIDMi

Show HN: GreenSwapp – API to track CO2 of food products at scale Hi guys! We’re Ajay & Jainit, and we’re super excited to launch GreenSwapp to the HN community! GreenSwapp is an API that tracks product and recipe-wise carbon footprints at scale, for food products. Tracking carbon footprints of individual food products is hard because you’d have to trace it all the way back to the farm and account for all the energy inputs till the point of sale. Each such analysis takes about 6 months to do, and in many cases, it is hard to even map this journey. For food products that typically have multiple ingredients (i.e. they are recipes), you have to do this for each ingredient. Supermarkets have about 30k unique products per store. If you do the math, you’ll see that this quickly becomes impossible to do manually, in a reasonable amount of time. After my Masters’ in Sustainability & Engineering, I (Ajay) worked as a climate & impact consultant for more than a decade. I first discovered this problem 5 years ago (as I ran my own sustainability consultancy), when I saw some of my large food brand & retail clients struggling to identify their highest impact products & suppliers. More recently, we found that food platforms (such as Uber Eats & Yelp) want to track the climate impact of their recipes and restaurants too. We do the hard work of estimating carbon emissions using a combination of product meta-data / supply chain attributes (such as origin location, farming type, packaging type etc.) and aggregated product carbon footprints from peer-reviewed journals. Our core dataset is a comprehensive list of food types found in supermarkets. We use an ML model (semantic search using sentence transformers) to match queried product names to our standard list of product types and retrieve their median supply chain carbon footprints. This enables us to track impact at scale (thousands of products in minutes), making it easy and cheap for you to track product-level emissions. This would allow you to easily a) report emissions, b) identify high impact products, brands, and suppliers in your inventory, and c) communicate impact with consumers. What if my product is more sustainable? We identify every supply chain attribute that might reduce a product’s impact (such as organic farming, better packaging material, locally grown) and apply a correction factor (also obtained from peer-reviewed life cycle analyses papers). You can read more on our algorithm here - https://ift.tt/mW6LFdr You can try our API for free on any milk product (e.g. - Oatly oat drink barista edition) or any type of pizza (e.g. - Pepperoni pizza). Here are the API docs - https://ift.tt/TujrK2Y . You can get your trial API key here - https://ift.tt/mW6LFdr . Our full API works with any food product you can find in the supermarket and any recipe. We charge a data license fee of $0.99 per product per year for supermarket products and $0.99 per ingredient per recipe per year for dishes. Here is a cost estimator tool - greenswapp.com/pricing. If you want to look up more than 100 products and 10 recipes, you also pay an annual platform fee (still figuring out the right price point). We’d love to get your feedback on our API. We are also trying to identify the biggest use case for this product. So please let us know what you’re using it for by commenting on this post - is it to use in a new app you’re building, get your company to use it at their cafeteria, reduce your company’s emissions, offset it, etc.? Any other ideas on how we can make this better would be wonderful too. Thanks a ton! https://ift.tt/TujrK2Y October 5, 2022 at 10:00PM