mikeortman

Its available for me now in gemini.google.com.... but its failing so bad at accurate audio transcription.

Its transcribing the meeting but hallucinates badly... both in fast and thinking mode. Fast mode only transcribed about a fifth of the meeting before saying its done. Thinking mode completely changed the topic and made up ENTIRE conversations. Gemini 2.5 actually transcribed it decently, just occasional missteps when people talked over each other.

I'm concerned.

I think technically they can. It’s because they’re downgrading them to a free service. I’m going to venture to say that it’s a money grab on a previously generous gift by slack.

Apple claiming that Liquid Glass is a technique only Apple can achieve, will be replicated, or at least indistinguishably replicated, in pure CSS... within 48 hours of today, out of spite

Just the absence of a license generally means the creator has all right reserved by default. You don’t need a license in every file because in much of the world copyright is given by default to the creator. A licensed file is permission to do something with that copyright material.

Just be careful! In SOME jurisdictions, you can get in trouble for 'stealing' if you take back something that was stolen. Possession vs Ownership are 2 different things. For instance, the thief may have stolen something, sold it to someone who bought it in good-faith, and you take it back from that person, it's technically theft!

File a police report, go through the right channels. If you know its yours, call the police department non-emergency and explain the situation

Artificially aiming to increase natural attrition through requiring employees to make like-altering decisions (or else) is evil. At the bare minimum, it should be seen as a layoff.

It's wild to nitpick the licensing like this. I get why its conter-intuitive and in violation of Github's guidelines, but it's winamp, folk. It has no intrinsic value these days to update or fork outside of giving people the opportunity to learn from the tricks they had to do to make stuff work. There are solutions significantly better and open source today. 'Canceling' winamp in 2024 was not on my life's bucket list after the year 2000.

There is hypocrisy here around internet archive, it's totally OK to store copy-write content on the archive, but its not OK when a company does so on their own.

Comments are disabled or moderated. I tried as well.

You didn't provide details, you provided a defense, which is weak at best.

This checkbox will absolutely not look good in front of a jury. It's petty at best.

I feel like we are living in an episode of the Simpsons at this point

I'm glad we are starting to lean into cloud-agnostic or building back the on-prem/dedicated systems again.

I think the very concept of this is to open source a common stack, instead of relying on a middleman like Porter, which also costs a TON of money at business tier

It baffles me why people would be so unnecessarily critical on someone's project they are showing off in a show-and-tell fashion. It's a neat project with value.

I'm fairly sure weather.gov's api doesn't have an SLA of any kind. I highly recommend going through the push vs polling approach if possible and store the data as it comes in live in your own DB. AWS Open Data + MADIS are good sources that are... for reasons... far more stable

Yeah, they did... but being blunt, IBM had 8 years to do a lot of damage. Not speaking of the engineers or the tech behind it, I'm talking about escalating the ad revenue and general junk on their website. MOST of the page load time on weather.com and AccuWeather is ad and tracking. It's an ungodly number of requests and will drain a data plan's usage limits surprisingly quickly.

Just opening weather.com will send almost 1000 requests , transfer 10.3MB. Every 30 seconds or so it will make about 300 requests + 2MB of transfer for new ads. It's... insane

It's hard to get accurate numbers as many weather sites are privately held. But to give some info:

Weather Company was sold for $2B to IBM in 2015 (and recently sold to private equity for undisclosed amount). Tomorrow.IO has a $1B+ valuation pre-IPO SPAC.

https://www.forbes.com/sites/johnkoetsier/2021/04/08/ibms-we...

Hi, hobbyist here! This is a huge area where government meteorologists and "Big Weather" differ and you can help close that gap!

For context:

The governments of the world provides these big weather companies (weather.com (cough IBM), Accuweather (cough IBM cough), etc) a metric shit ton of their data completely for free (by law) including data transfer. These are things like radar, satellite, ground station data, forecasts, composite models, etc. These companies profit substantially on it, as in billions of dollars. You as citizens also can get this data completely for free as well! MADIS is a system the government is working on to make that data access easier by bringing together many of these systems together and removing the bureaucratic redundancy and abstracting out the aging infrastructure. This is literally terabytes of data per day you can grab with almost no questions asked. That data is then processed privately and resold and repackaged to the end user, and you probably interact with this privatized data the most.

The frustration I have much of the additional "value" these weather data brokers provide is by linking up with each other with data contracts. These private companies have a much much higher detail on the ground than the government by being able to partner with companies that make common internet-connected personal radar stations and reselling that data to each other. The government doesn't have that privilege to buy limitless data. NOAA/NWS, for example, is extremely underfunded so if they had to privilege to buy it they probably couldn't come to an agreement to buy it. As a result, they can't use that data to improve the accuracy of alerts/warnings/forcasts, the same exact tools that the big weather companies make all their money from. It's a shit cycle and totally unfair IMO.

So please contribute if you can!!

Sidebar: I'm a founder of a self-bootstrapped startup to build a better weather data broker that doesn't cost an arm and a leg. If that's something you are passionate about solving, feel free to reach out :)

This speaks on long term outlooks at synoptic scale, we really should put some energy on researching mesoscale long term outlooks, or even 4 hour short term. It's a difficult problem to solve as the variables are quite complex, but the reward can be substantial -- on-land severe weather impacts less people but often is deadlier and can cause huge financial loss in areas that may not expect it.

Apple's response, in a nutshell:

- A weird callout to the nationality of the company right out of the gate

- Spotify will be nothing without us (insert crazy ex memes)

- "Our engineering helps ensure that Spotify’s apps can work seamlessly with Siri, CarPlay, Apple Watch, AirPlay, Widgets, and more." -- yeah, because those are your products, Apple. You make your money on those products being tightly integrated

- A bizarre quantization of the apple ecosystem by total number of "APIs" Apple gives them access to (250k)

- A claim of insider coercion between Spotify and the EU Commission that made it difficult for Apple to win

- They are going to appeal

Its verryyyy clear that Google rushed these models. They have a fiscal obligation to try and stay ahead of the curve and make as much money as possible on these investments. The CEO isn't honestly mad that the model is racist, he's mad that the model might lose the company contracts because its racist. Why would another company risk their image or lawsuits if they make a chatbot that says something illegal or denies rights to protected classes?

Racism is a bias (to say the least), and a bias is a pattern, and ML models are trained to find patterns. The model itself, is doing a really good job at that. The hard part and the part that takes a long time and money is training a model to learn from certain patterns and to not just ignore, but to actively find the patterns it shouldn't learn from. Google's greed chose not to invest enough time and money into that, and its biting them.

Bless the engineers/ICs and low-level PMs/managers doing their best, this isn't their fault in the slightest.

The dataset IS free to download, but running a query against it on Google Cloudis what costs $$$. BigQuery is basically renting servers to scan through the data, which is the fee

That... is not how lenses work, right? They are showing a totally flat glass surface that close to your eyes? I can't imagine your eyes going to be able to focus on the entire width of your FoV of text and images of that size... without it at least giving you a headache

This has sooo much promise, I can't describe how excited I am and will definitely be contributing if I can.

This would make it easy to implement a fancy optimizer. Each component tends to have certain acceptable thresholds for their dependencies like input voltages, current limits, external resistor/capacitor/inductor values/ratings, etc.

Then, each component could have different implementations. You can have different manufacturers produce the exact same inductor/capacitor/resistor in different packages. You can link it up to your existing BOM or hook into vendor APIs to get pricing.

Imagine optimizing for cost, removing redundancy, simplifying footprints, and prioritizing in-stock inventory over new order components.

This could be a huge deal, looking forward to its progress

This was the biggest win for me. One time asked ChatGPT if it could "write python to convert a docx to pdf using any python package of your choosing" and its response was a paragraph telling me doing so is difficult, followed by a python function, with just an inline comment //implement conversion here

It blows my mind that many people worked there, only 8% of Microsoft Gaming?

because its a PR piece :) PR for the public, defense for the courts

I have a hunch a lot of companies that layoff in December are also motivated by the bonus structure. Many companies structure their bonus pay around the new year in line with their fiscal calendar, either paying at the beginning of the year or entitling them to the bonus paid out later in the year.