Mopeds can only operate on streets, they cant go in bikelanes or sidewalks. They are subject to the same restrictions as cars (licensure, registration) except that they uniquely have a speed limit
Not having a speed limit is unique only to private car ownership
Bit funny how the article states that the systems are unobtrusive as to be essentially useless, which ignores the fact that these systems are defanged to appease entitled speeders.
Governments around the world impose various requirements on vehicle speeds, most notably ebikes being capped at 20mph for "safety reasons". Meanwhile private cars can travel at over 100mph for no apparent reason other than to not upset drivers at the expense of everyone else on the road.
It looks really interesting, but as an experienced climber I'm not sure if just watching a video of my avatar climbing would really help with skill acquisition.
Also, this claims that the wall type or video quality doesn't matter, but I have a hard time understanding how the model would be able to understand that a small crimp could possibly be dual textured and therefore has only a few specific ways of approaching it.
So it seems that this is more for visualizing a climb (which is a skill most climbers should develop) and not really for dialing in some sort of microbeta for a problem.
Comparing YC to Bell Labs, which had deep ties to academia, misses the entire philosophy of YC and Paul Graham. He constantly talks about academia being a hive of orthodoxy, and positions "smart people" as those who can think around it.
By his logic, Bell Labs would be one of the largest hives of orthodox thinking to ever exist, and its the complete antithesis of vc heterodox thought
It's not a "Tangential remark" if your tangent is 10 times longer than your commentary on the article!
> Please don't complain about tangential annoyances—e.g. article or website formats, name collisions, or back-button breakage. They're too common to be interesting.
> Supportive. We build models to support our users, not replace them. We are focused on efficient, specialized, and practical AI performance – not a quest for god-like intelligence. We develop tools that help everyday people and everyday firms use AI to unlock creativity, boost their productivity, and open up new economic opportunities.
Refreshing take on the peak alarmism we see from tech "thought leaders"
I work in this space (podcast ads) and the podcast ads tech maturity is years behind other media, like video, due to RSS. This lag is also reflected in that advertisers generally spend less on podcasting.
RSS makes it very difficult to measure the performance of an ad. Most players in this space wrap a publishers hosted podcast link with their own tracking link, and the main metric of engagement is a download. Downloads themselves are a pretty crude measurement and don’t hold up to something like minutes watched/listened.
Placing ads into the audio directly also just isn’t as performant as streaming it into the media like in video. Publishers and advertisers want more fine grained control over where the ad is placed, when it’s placed, and how it’s consumed by the listener. Podcast ads currently don’t deliver on that.
> The US-wide heat map aligns pretty well with agricultural and industrial pollution, not necessarily wildfires. Let's not turn every last thing into a climate change debate.
Does agricultural and industrial pollution not contribute to climate change?
Stream quality and catalog sizes are both legal issues with music rights holders and have nothing to do with streaming innovation. Unless you consider Apple having a boatload of money to make legal issues go away a kind of innovation.
Because the aggrieved aren’t satisfied only by Elon taking over twitter and taking it in a different direction, they also believe that those currently at twitter deserve harsh punishment for whatever role they had in twitter.
There have been a lot of people cheering twitters downfall because they feel personally or politically vindicated. But it’s especially bewildering to see how many people here on Hn completely cheer on the absolute disrespect and humiliation of twitter employees as a good thing.
Without getting into the weeds of it, I'd say smooth out the rough edges in your development experience and make it behave as similar to prod as possible. If there's less friction there's less incentive to cut corners and make hacks imo.
Some pain points:
- Does it take forever to spin up infra to run a single test?
- Is grabbing test data a manual process? This can be a huge pain especially if the test data is binary like avro or parquet. Test inputs and results should be human friendly
- Does setting up a testing environment require filling out tons of yaml files and manual steps?
- Things built at the wrong level of abstraction! This always irks me to experience. Keep your abstractions clean between which tools in your data stack do what. When people start inlining task-specific logic at the DAG level in airflow, or let their individual tasks figure out triggering or scheduling decisions is when things just become confusing.
Right now my workflow allows me to run a prod job (google cloud dataflow) from my local machine. It consumes prod data and writes to a test-prefixed path. With unit tests on the scala code + successful run of the dataflow job + validation and metrics thrown on the prod job I can feel pretty comfortable with the correctness of the pipeline.
IMO Data engineering is already a specialized form of software engineering. However what people interpret as DE's being slow to adopt best practices from traditional software engineering is more about the unique difficulties of working with data (especially at scale) and less about the awareness or desire to use best practices.
Speaking from my DE experience at Spotify and previously in startup land, the biggest challenge is the slow and distant feedback loop. The vast majority of data pipelines don't run on your machine and don't behave like they do on a local machine. They run as massively distributed processes and their state is opaque to the developer.
Validating the correctness of a large scale data pipeline can be incredibly difficult as the successful operation of a pipeline doesn't conclusively determine whether the data is actually correct for the end user. People working seriously in this space understand that traditional practices here like unit testing only go so far. And integration testing really needs to work at scale with easily recyclable infrastructure (and data) to not be a massive drag on developer productivity. Even getting the correct kind of data to be fed into a test can be very difficult if the ops/infra of the org isn't designed for it.
The best data tooling isn't going to look exactly like traditional swe tooling. Tools that vastly reduce the feedback loop of developing (and debugging) distributed pipelines running in the cloud and also provide means of validating the output on meaningful data is where tooling should be going. Trying to shoehorn traditional SWE best practices will really only take off once that kind of developer experience is realized.
The trueup data casts a pretty wide net. Seeing Microsoft on that list and makes the impression that layoffs are hitting FAANGs in a similar way to high growth startups, but those layoffs were due to Microsoft pulling out of Russia.
Not having a speed limit is unique only to private car ownership