Show HN: Cosmic REST API v2 and Brand New Docs https://ift.tt/2Z0fFnV

Show HN: Cosmic REST API v2 and Brand New Docs https://ift.tt/2MS4sU3 February 9, 2021 at 10:29PM

Show HN: Back-up Apple Notes data in Markdown and HTML format https://ift.tt/3aK9HNs

Show HN: Back-up Apple Notes data in Markdown and HTML format https://ift.tt/3d9o7K3 February 9, 2021 at 10:54PM

Time to Park? Know Your Meter

Time to Park? Know Your Meter
By Jonathan Streeter

Meters help businesses and others in high demand areas of San Francisco by ensuring that people driving cars will use just the amount of time they need when temporarily parking.  As such, the SFMTA recently made a simple but important update to how parking meters operate when paying for parking with a credit card.

Now, when paying by credit card, the meters will default to two hours of parking time. The previous default credit card charge was $0.25. In many areas of the city, that provides a few minutes of parking!  While the new default is 120 minutes, customers may still choose the precise amount of time they want to park by using the up and down buttons on the meter.  For example, if a customer wants to park for only 15 minutes, they may use the down button until the correct amount of time it is selected. Previously, a customer would have to use the up button to increase the amount of desired time.

The change to the credit card default was made for a few reasons:

•             The average time cars park at meters is around an hour and a half.  Most customers were required to press the “up” button repeatedly in order to get to their desired amount of parking time.

•             Before this change, parking meters saw a very high number of $0.25 transactions—many of those are likely customers who didn’t realize they would only be charged for just a few minutes of parking time if they did not use the up button to increase the amount of time needed.  When customers purchase the minimum amount of time without realizing it, they risk getting a ticket. By defaulting to an amount of time closer to the average time purchased, we hope to help more people avoid parking tickets for an expired meter.  Under the new default, we’ve seen a significant decline in $0.25 credit card transactions.

•             Customers who accidentally purchase only $0.25 worth of time often must immediately complete another transaction to purchase the amount of parking time they want.  When customers make two separate transactions, the city is charged two transaction fees.  Increasing the default time helps avoid double transactions and double fees. 

And please note: this change applies only to credit or debit card payments: the payment procedure for cash or pay-by-phone has not changed.

For more information visit our parking meter web page.

Published February 09, 2021 at 09:56PM
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Launch HN: SigNoz (YC W21) – Open-source alternative to DataDog https://ift.tt/2YZWsTl

Launch HN: SigNoz (YC W21) – Open-source alternative to DataDog Hi HN, Pranay and Ankit here. We’re founders of SigNoz ( https://signoz.io ), an open source observability platform. We are building an open-core alternative to DataDog for companies that are security and privacy conscious, and are concerned about huge bills they need to pay to SaaS observability vendors. Observability means being able to monitor your application components - from mobile and web front-ends to infrastructure, and being able to ask questions about their states. Things like latency, error rates, RPS, etc. Better observability helps developers find the cause of issues in their deployed software and solve them quickly. Ankit was leading an engineering team, where we became aware of the importance of observability in a microservices system where each service depended on the health of multiple other services. And we saw that this problem was getting more and more important, esp. in today’s world of distributed systems. The journey of SigNoz started with our own pain point. I was working in a startup in India. We didn’t use application monitoring (APM) tools like DataDog/NewRelic as it was very costly, though we badly needed it. We had many customers complaining about broken APIs or a payment not processing - and we had to get into war room mode to solve it. Having a good observability system would have allowed us to solve these issues much more quickly. Not having any solution which met our needs, we set out to do something about this. In our initial exploration, we tried setting up RED (Rate, Error and Duration) and infra metrics using Prometheus. But we soon realized that metrics can only give you an aggregate overview of systems. You need to debug why these metrics went haywire. This led us to explore Jaeger, an open source distributed tracing system. Key issues with Jaeger were that there was no concept of metrics in Jaegers, and datastores supported by Jaeger lacked aggregation capabilities. For example, if you had tags of “customer_type: premium” for your premium customers, you couldn’t find p99 latency experienced by them through Jaeger. We found that though there are many backend products - an open source product with UI custom-built for observability, which integrates metrics & traces, was missing. Also, some folks we talked to expressed concern about sending data outside of boundaries - and we felt that with increasing privacy regulations, this would become more critical. We thought there was scope for an open source solution that addresses these points. We think that currently there is a huge gap between the state of SaaS APM products and OSS products. There is a scope for open core products which is open source but also supports enterprise scale and comes with support and advanced features. Some of our key features - (1) Seamless UI to track metrics and traces (2) Ability to get metrics for business-relevant queries, e.g. latency faced by premium customers (3) Aggregates on filtered traces, etc. We plan to focus next on building native alert managers, support for custom metrics and then logs ( waiting for open telemetry logs to mature more in this). More details about our roadmap here ( https://ift.tt/2NeRYFC ) We are based on Golang & React. The design of SigNoz is inspired by streaming data architecture. Data is ingested to Kafka and relevant info & meta-data is extracted by stream processing. Any number of processors can be built as per business needs. Processed data is ingested to real-time analytics datastore, Apache Druid, which powers aggregates on slicing and dicing of high dimensional data. In the initial benchmarks we did for self-hosting SigNoz, we found that it would be 10x more cost-effective than SaaS vendors ( https://ift.tt/3cWL5Ur ) We’ve launched this repo under MIT license so any developer can use the tool. The goal is to not charge individual developers & small teams. We eventually plan on making a licensed version where we charge for features that large companies care about like advanced security, single sign-on, advanced integrations and support. You can check out our repo at https://ift.tt/38ZkjXK We have a ton of features in mind and would love you to try it and let us know your feedback! February 9, 2021 at 09:59PM

Launch HN: Great Question (YC W21) – Customer research tools for software teams https://ift.tt/2YYY7J1

Launch HN: Great Question (YC W21) – Customer research tools for software teams Hi HN! I’m Ned and along with my co-founder PJ (pjmurraynz) we’re building Great Question ( https://ift.tt/2Z6TJr7 ) to make it easy to do customer research as part of every sprint or product release. The maxim of Y Combinator is “talk to customers, build something people want” yet relatively few software teams regularly engage in customer research. This was definitely the case for us in our last startup, and even when we sold that business to a place with a well resourced research team we were largely on our own. Without any real tools or processes to do customer research we ended up muddling through, but it was always ad hoc - and often skipped so we could just get a release out the door. Bad news. By talking to lots of customers (meta!) we learned that one of the biggest challenges teams face is in the logistics of research: finding customers to talk to, scheduling calls & paying incentives. The research community calls this Research Operations. We’re setting out to fix these problems by building tools that make it easy for small teams to do what companies like Facebook and Google do with massive teams of research coordinators. We help you do better customer research, more often in four ways: First, we help you build an on-demand pool of research subjects. These are customers who opt in to be notified about customer interview requests and surveys, or find out about beta product releases. They could also be customers you find in other forums or communities, through content marketing or direct outreach. Second, we let you book time with a customer in a couple of clicks, or send out a survey or prototype test. We give you templates to save you creating these things from scratch every time, but also to keep you following best practice. Templates like Product Market Fit surveys are live now with more advanced ones like Van Westendorp pricing surveys coming soon (email me for early access). Third, we handle all the messaging on platform to protect the privacy and consent of your users but also to manage what's called "participant fatigue", and handle any incentives to make sure you get the responses you need. Finally, we make it easy to share what you’re learning with your team. Store your notes, observations, video files and transcripts in one place. Post it to Slack, get an email digest of learnings & upcoming interviews, and find previous research reports in one central place. All of this is to say we’re building the tool we wish we had while building product at our last startup, and also in the belly of the beast after we got acquired. The tool that helps you go from having some big gnarly question to start getting answers in minutes, and which brings your team along for the journey. We use the tool religiously in-house and it's had a massive impact on not only our own product development process, but our first engineering hire (ex Twitch) has noted how much more connected he feels with our customers and the product he's building. What do you all think? We’d love your feedback on the product and our approach. In particular we’d love to know how customer research works at your company and the challenges you face making it happen! February 9, 2021 at 09:39PM

Show HN: Polar Signals Continuous Profiler – Systematic Performance Profiling https://ift.tt/3a3VlZh

Show HN: Polar Signals Continuous Profiler – Systematic Performance Profiling https://ift.tt/3jyVx5Z February 9, 2021 at 09:36PM

Show HN: "100 Page Python Intro" eBook https://ift.tt/2Z2Idgu

Show HN: "100 Page Python Intro" eBook https://ift.tt/3ryX8va February 9, 2021 at 06:03PM