Bigpeachpie

Overall, I'm very high on Lorcana balancing as reflected in metagame health. We've had 4 sets where either a significant build or play shift came late to refresh the meta. This set's storyline supports that w/ lots of rank changes throughout and I can't wait for the next!

@XtremeHD57 @SafetyBlade_HS @SnapDecks I've just been clicking auto deck and it got me from 10 or whatever I was at to 70 in a few days

@SafetyBlade_HS @SnapDecks Make some decks without a bunch of new cards. That’s why I can’t return to this game. It’s so aggravating

Cool Decks Time 😎 To be clear these were all very good for me and the subject of my recent streams 1. What if arishem was cool 2. Eson is 100% cool 3. Good deck but cool stuff instead Been doing some crazy deck building lately and wanted to share before the OTA @SnapDecks

@JamesMcPherson @minarchis1 @AdamKoffler That's not a starter salary though. The issue is you should be able to get a starter home on a starter salary, a mid tier home on a mid tier salary, etc. You cannot.

@minarchis1 @AdamKoffler Well, since those are the places where "starter homes" are $700k, then it works out.

Here’s Dave Ramsey’s advice on buying a house in the year 2026: - Have 20% for a down payment to avoid private mortgage insurance (PMI) - Do a 15-year fixed rate loan - Make sure your monthly payment isn’t more than 1/4th of your take home pay Let’s put that math to the test 👇🏼

@JamesMcPherson @minarchis1 @AdamKoffler Bro your point that people are too aggressive with what a starter home is is true, but you're way off base for what the income to house ratio costs are and have been historically.

@JamesMcPherson @AdamKoffler Ok - then do a market with expensive employees like San Fran where median (avg was too slow to pull from census) house is $1.3M and median salary household salary is $150K, not a fictional $350K salary to $500K house. Same discrepancy in most markets, and that's the issue.

If you are going to use expensive housing markets, then you need to use expensive employees. $100k is the absolute minimum to be called a “six figure salary” and is low for the same place that a $700k house is a starter home. Push it up to a more reasonable $350k/year and $500k house and you are discovering who is buying a house right now.

@Slytherin_OU @AdamKoffler Yeah probably at a time when CDs were 15+ %, even savings accounts had +15% in the 80s specifically, so the 16% needs to be contextualized. Sounds like you were on easy mode

@AdamKoffler My first house had a 16% rate. It's not the end of the world. Entirely too much complaining

@donnyyoung They specialize in lemons and their actual business is the parent company they funnel money to for junk loans (or something, don't remember the details). Carvana is just a "vehicle" for liquidity Lots of people get lucky with Carvana, lots of people don't

Carvana is kind of unbelievable. I bought a van online last week. It showed up at my house. Had a few mechanical issues… They’ve got a 7-day return policy, so my wife and I just picked a different one online. Drove to their location in town. Swapped it out. In and out in 30 minutes. No pressure or awkward waiting. No “let me talk to my manager.” It felt like returning something to Amazon… except it’s a car. Honest question… How are traditional dealerships still competing with this? Would you ever buy a car without stepping foot on a lot? - This is the kind of thing my reselling business is helping fund. Follow me @donnyyoung as I share my journey. #carvana @Carvana

@gothburz Can you tell me how this works if there is no sense of desperation? Many job seekers have this, but not all. For ex, you are good at what you do, content with what you have, and in an in-demand industry. You're comfortable already, but shopping/getting recruited because why not

I run Compensation Analytics for a Fortune 500 company. My job is to calculate the lowest salary you'll accept. Not the salary you deserve. Not the salary the role requires. Not the market rate. The minimum number that keeps you from walking. I know this number before you walk in. Sometimes before you apply. We buy data. Your payroll processor shares your salary history with Equifax through a product called The Work Number. More than 800 million employment and income records. Updated every pay cycle. Equifax sells it to us through a "verification of income" API. The word "verification" means we know what you made at your last three jobs, whether you got a raise, and when you didn't. That's market intelligence. We layer signals. Credit card utilization. Payday loan activity. Past-due balances. Delinquent debt. Address changes. There are about 500 vendors that aggregate this data now. An audit by the Washington Center for Equitable Growth flagged 20 as high-risk for enabling algorithmic wage discrimination. Sixteen of the twenty plug directly into payroll and HR systems. We use nine. The dashboard has a field called "candidate tolerance threshold." That's the number. The lowest salary you'll accept. We set the offer at 3% to 6% above it. Enough to feel like negotiation. Not enough to change your life. That's compensation design. The academic term is "surveillance wages." The industry term is "compensation optimization." A law professor named Veena Dubal found that when multiple employers in the same market use the same vendors, it functions as de facto price-fixing of labor. Same mechanism as the RealPage rental pricing scandal. Same logic. Same outcome. RealPage coordinates rents. Our vendors coordinate salaries. Different commodity. Same extraction. That's the market. Here's what the algorithm sees when you apply. Your last three salaries. Your debt-to-income ratio. How quickly you accepted your previous offer. Your zip code. Whether you've used a payday lender in the last two years. It calculates your reservation wage and sets the offer just above. Your performance doesn't set your salary. Your desperation does. A new VP of Total Rewards asked me why the algorithm used payday loan history. I explained that payday usage correlates with financial fragility, and financial fragility predicts acceptance velocity. She asked if that was legal. I said it was standard. She asked whose standard. I showed her the vendor's compliance page. She transferred to a different division. That's organizational learning. Colorado introduced a bill to ban the practice. HB25-1264. It would prohibit using payday loan history, location data, and search behavior to set algorithmic pay offers. The companies lobbied against it. The same companies that told their employees they don't use surveillance wages. A state representative asked the obvious question: "If these companies don't pay surveillance wages, then what is the problem of codifying in law that you're not allowed to?" The lobbyists provided written testimony. They said the bill would create "compliance burden." They did not answer the question. That's advocacy. The data flows in one direction. We know your salary trajectory. You don't know ours. We know what you'll settle for. You think you're negotiating. The algorithm already accounted for your counter. It budgeted for exactly one round. There is a freeze option. You can go to Equifax's website and freeze your Work Number file. Most people don't know it exists. We don't mention it in the offer letter. We don't mention it in the onboarding packet. We don't mention it in the benefits portal. We don't mention it anywhere. That's by design. The system requires your ignorance to function. If everyone froze their data, compensation optimization would have nothing to optimize. I froze mine the week I started this job. I work in Compensation Analytics. I know what the tools see. I just build them for everyone else.

insane that we tolerate living in a society where you can go see one dentist and he's like "ya u have 13 cavities, will be $4000 to fix them all" then see another and he's like "that guy was full of shit and trying to scam you, you're fine" ???

@mmjukic "in hindsight" lol

In hindsight it was probably a bad idea to assume that the Iranian regime would fall so fast and so hard that no Iranians would be able to figure out how to assassinate any U.S. or Israeli officials over the next five years in revenge for the assassinations of their officials.

@GrantSlatton I read the movie before I watched the book but preferred the podcast

re: the movie Arrival, whether or not a person prefers the book or movie seems 100% dependent on which they experienced first Haven’t met a *single* person who saw the movie, read the novella, and said the novella was better, or vice versa

@Traverse_Tavern @BradenMTG @MilkGoesInLast Probably in the hot chocolate though

@BradenMTG @MilkGoesInLast Nah he spittin

there’s a special place in hell for people who make hot chocolate with water

@LorcanaHabibi I like that one guy with the swampert profile who just posts on Twitter for na and artabax for eu

Who’s your favourite NA/EU Disney Lorcana creators? #lorcana #whispersinthewell #DisneyLorcana

@alex_n1n @ExaltedFoks As a qbr deck person, they did specify "anything of value"

@ExaltedFoks try editing a QBR deck on a phone and tell me how that goes

You absolutely must become post-laptop. Everything of value can be accomplished on your phone

Continued training on junk social posts makes LLMs think worse, and causes irreversible reasoning decline and negative behaviors. Feeding LLMs lots of short, popular, or low-quality Twitter/X text causes lasting drops in reasoning, long-context use, and safety. This big concern here is that because many teams keep topping up models with web crawls. Feeding highly popular or shallow posts can cause lasting, hard to undo damage. They ran controlled tests that kept token counts and training steps the same. Junk came in 2 forms, M1 based on popularity and short length, and M2 based on clickbait style and shallow topics. Popularity of posts is a stronger predictor of harm than length. Training on very popular posts hurts step-by-step reasoning more, while training on very short posts mainly weakens long-context use. Across 4 instruction tuned models, junk exposure reduced reasoning, long context retrieval, and safety. With more junk, performance dropped in a dose like way. For example, ARC with chain of thought fell from 74.9 to 57.2, and RULER CWE fell from 84.4 to 52.3. They also measured personality style and saw higher narcissism and psychopathy with lower agreeableness. The main failure was thought skipping, the model answered without planning or it dropped steps. Instruction tuning and more clean pretraining helped a little, but they did not restore the baseline. Popularity predicted harm better than length, so curation during continual pretraining matters for safety.

@retirementkeys I would think with all the samples llms have access to it'd be more period accurate. Sounds like Bruno mars more than the 50s.

Banger youtu.be/71QH33IMGn8?si…

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@babieswithb Why is tummy time for the bottom quintile?

*coughs* tummy time *coughs*

@JQuigster What's the structure of you're 6-1 and won't make it? Just cut to top 8 or something?

Rd 7 vs 🔵🟣 1-2 (lost die roll) Two long slogs game 1 and 2 Game 3, 11 minutes left, I'm fighting. I could play for the draw, but drew uninkable in the Clarabelle turn and died immediately 😭 Win and in tomorrow morning.

CCS tournament thread! 450 players, 7 rounds today, 2 tomorrow if I can make it deep. Started off the day 2-0 OTD vs 🟣⚪! Rest of MUs below: