I Switched to UTC and Never Looked Back
16 by adamci | 0 comments on Hacker News.
Sabtu, 31 Mei 2025
Verstappen on battling McLarens: My time to shine
Max Verstappen joked he will try to put on a show for the cameras in Turn 1 against the dominant McLarens.
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Jumat, 30 Mei 2025
Transfer rumors, news: Liverpool duo wanted by Saudi clubs
Saudi Pro League clubs are showing interest in Darwin Nunez and Luis Diaz. Transfer Talk has the latest.
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Kamis, 29 Mei 2025
NFF adjusts college HOF coaching eligibility bar
The National Football Foundation has lowered the eligibility criteria for coaches to be considered for induction into the College Football Hall of Fame beginning in 2027.
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Rabu, 28 Mei 2025
NBA playoff futures: Thunder big favorites to take title, Pacers move up
With the conference finals underway, here are the updated odds to win the NBA championship and Finals MVP.
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Norris' win in Monaco has reset F1, McLaren title fight
Preseason title favorite Lando Norris was losing steam to teammate Oscar Piastri. That's no longer the case.
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Selasa, 27 Mei 2025
How 2024-25's All-NBA Team is rated in NBA 2K25
Learn how strong the All-NBA Teams of 2025 are in 2K Games' simulation.
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Senin, 26 Mei 2025
Emerging Valkyries pickups to stream
Veronica Burton and Kayla Thornton highlight some of the top fantasy women's basketball streamers for the week of May 26.
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Minggu, 25 Mei 2025
Happy repossessed truck back, Paul wins in Paris
Tommy Paul was pleased to come back to win his first-round match at the French Open on Sunday, of course, but perhaps not as thrilled as he was to discuss regaining the truck that was repossessed back home in Florida.
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Sabtu, 24 Mei 2025
Follow live: Updates, highlights and more from Liberty-Fever
Follow live as the Breanna Stewart and the New York Liberty take on Caitlin Clark and the Indiana Fever on the road.
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Congratulations, you've been promoted to the Premier League! Now what?
The last six teams to come up to the Premier League have gone straight back down. How can Leeds, Burnley and Sunderland break that cycle next season?
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Jumat, 23 Mei 2025
Experts weigh in: How will Djokovic and Swiatek fare? Can Alcaraz defend?
Alcaraz and Swiatek were last year's French Open champs, but Sinner, Gauff and many more could contend.
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Kamis, 22 Mei 2025
New top story on Hacker News: Show HN: Pi Co-pilot – Evaluation of AI apps made easy
Show HN: Pi Co-pilot – Evaluation of AI apps made easy
6 by achintms | 0 comments on Hacker News.
Hey HN — 2 months ago we shared our first product with the HN community ( https://ift.tt/B15tYUl ). Despite receiving lots of traffic from HN, we didn’t see any traction or retention. One of our major takeaways was that our product was too complicated. So we’ve spent the last 2 months iterating towards a much more focused product that tries to do just one thing really well. Today, we’d like to share our second launch with HN. Our original idea was to help software engineers build high-quality LLM applications by integrating their domain knowledge into a scoring system, which could then drive everything from prompt tuning to fine-tuning, RL, and data filtering. But what we quickly learned (with the help of HN – thank you!) is that most people aren’t optimizing as their first, second, or even third step — they’re just trying to ship something reasonable using system prompts and off-the-shelf models. In looking to build a product that’s useful to a wider audience, we found one piece of the original product that most people _did_ notice and want: the ability to check that the outputs of their AI apps look good. Whether you’re tweaking a prompt, switching models, or just testing a feature, you still need a way to catch regressions and evaluate your changes. Beyond basic correctness, developers also wanted to measure more subtle qualities — like whether a response feels friendly. So we rebuilt the product around this single use case: helping developers define and apply subjective, nuanced evals to their LLM outputs. We call it Pi Co-pilot. You can start with any/all of the below: - a few good/bad examples - a system prompt, or app description - an old eval prompt you wrote The co-pilot helps you turn that into a scoring spec — a set of ~10–20 concrete questions that probe the output against dimensions of quality you care about (e.g. “is it verbose?”, “does it have a professional tone?”, etc). For each question, it selects either: - a fast encoder-based model (trained for scoring) – Pi scorer. See our original post [1] for more details on why this is a good fit for scoring compared to the “LLM as a judge” pattern. - or generates Python functions when that makes more sense (word count, regex etc.) You iterate over examples, tweak questions, adjust scoring behavior, and quickly reach a spec that reflects your actual taste — not some generic benchmark or off-the-shelf metrics. Then you can plug the scoring system into your own workflow: Python, TypeScript, Promptfoo, Langfuse, Spreadsheets, whatever. We provide easy integrations with these systems. We took inspiration from tools like v0 and Bolt: natural language on the left, structured artifacts on the right. That pattern felt intuitive — explore conversationally, and let the underlying system crystallize it into things you can inspect and use (scoring spec, examples and code). Here is a loom demo of this: https://ift.tt/5BiKCa1 We’d appreciate feedback from the community on whether this second iteration of our product feels more useful. We are offering $10 of free credits (about 25M input tokens), so you can try out the Pi co-pilot for your use-cases. No sign-in required to start exploring: https://withpi.ai Overall stack: Co-pilot next.js and Vercel on GCP. Models: 4o on Azure, fine tuned Llama & ModernBert on GCP. Training: Runpod and SFCompute. – Achint (co-founder, Pi Labs)
6 by achintms | 0 comments on Hacker News.
Hey HN — 2 months ago we shared our first product with the HN community ( https://ift.tt/B15tYUl ). Despite receiving lots of traffic from HN, we didn’t see any traction or retention. One of our major takeaways was that our product was too complicated. So we’ve spent the last 2 months iterating towards a much more focused product that tries to do just one thing really well. Today, we’d like to share our second launch with HN. Our original idea was to help software engineers build high-quality LLM applications by integrating their domain knowledge into a scoring system, which could then drive everything from prompt tuning to fine-tuning, RL, and data filtering. But what we quickly learned (with the help of HN – thank you!) is that most people aren’t optimizing as their first, second, or even third step — they’re just trying to ship something reasonable using system prompts and off-the-shelf models. In looking to build a product that’s useful to a wider audience, we found one piece of the original product that most people _did_ notice and want: the ability to check that the outputs of their AI apps look good. Whether you’re tweaking a prompt, switching models, or just testing a feature, you still need a way to catch regressions and evaluate your changes. Beyond basic correctness, developers also wanted to measure more subtle qualities — like whether a response feels friendly. So we rebuilt the product around this single use case: helping developers define and apply subjective, nuanced evals to their LLM outputs. We call it Pi Co-pilot. You can start with any/all of the below: - a few good/bad examples - a system prompt, or app description - an old eval prompt you wrote The co-pilot helps you turn that into a scoring spec — a set of ~10–20 concrete questions that probe the output against dimensions of quality you care about (e.g. “is it verbose?”, “does it have a professional tone?”, etc). For each question, it selects either: - a fast encoder-based model (trained for scoring) – Pi scorer. See our original post [1] for more details on why this is a good fit for scoring compared to the “LLM as a judge” pattern. - or generates Python functions when that makes more sense (word count, regex etc.) You iterate over examples, tweak questions, adjust scoring behavior, and quickly reach a spec that reflects your actual taste — not some generic benchmark or off-the-shelf metrics. Then you can plug the scoring system into your own workflow: Python, TypeScript, Promptfoo, Langfuse, Spreadsheets, whatever. We provide easy integrations with these systems. We took inspiration from tools like v0 and Bolt: natural language on the left, structured artifacts on the right. That pattern felt intuitive — explore conversationally, and let the underlying system crystallize it into things you can inspect and use (scoring spec, examples and code). Here is a loom demo of this: https://ift.tt/5BiKCa1 We’d appreciate feedback from the community on whether this second iteration of our product feels more useful. We are offering $10 of free credits (about 25M input tokens), so you can try out the Pi co-pilot for your use-cases. No sign-in required to start exploring: https://withpi.ai Overall stack: Co-pilot next.js and Vercel on GCP. Models: 4o on Azure, fine tuned Llama & ModernBert on GCP. Training: Runpod and SFCompute. – Achint (co-founder, Pi Labs)
Rebuilding Blackhawks hire Blashill as head coach
Jeff Blashill, the ex-Red Wings coach who has been an assistant with the Lightning for the past three seasons, will be the Blackhawks' next head coach, a source told ESPN.
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Rabu, 21 Mei 2025
Selasa, 20 Mei 2025
NBA playoff futures: Thunder favored to win championship, followed by Knicks
An updated listing of the odds to win the NBA championship and Finals MVP heading into the conference finals.
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Senin, 19 Mei 2025
Hamilton is optimistic after Imola, but Leclerc showcases Ferrari's ills
Lewis Hamilton was giddy after Imola, but that shouldn't overshadow Charles Leclerc's frustrations at Ferrari.
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Minggu, 18 Mei 2025
New top story on Hacker News: Show HN: Model2vec-Rs – Fast Static Text Embeddings in Rust
Show HN: Model2vec-Rs – Fast Static Text Embeddings in Rust
10 by Tananon | 0 comments on Hacker News.
Hey HN! We’ve just open-sourced model2vec-rs, a Rust crate for loading and running Model2Vec static embedding models with zero Python dependency. This allows you to embed text at (very) high throughput; for example, in a Rust-based microservice or CLI tool. This can be used for semantic search, retrieval, RAG, or any other text embedding usecase. Main Features: - Rust-native inference: Load any Model2Vec model from Hugging Face or your local path with StaticModel::from_pretrained(...). - Tiny footprint: The crate itself is only ~1.7 mb, with embedding models between 7 and 30 mb. Performance: We benchmarked single-threaded on a CPU: - Python: ~4650 embeddings/sec - Rust: ~8000 embeddings/sec (~1.7× speedup) First open-source project in Rust for us, so would be great to get some feedback!
10 by Tananon | 0 comments on Hacker News.
Hey HN! We’ve just open-sourced model2vec-rs, a Rust crate for loading and running Model2Vec static embedding models with zero Python dependency. This allows you to embed text at (very) high throughput; for example, in a Rust-based microservice or CLI tool. This can be used for semantic search, retrieval, RAG, or any other text embedding usecase. Main Features: - Rust-native inference: Load any Model2Vec model from Hugging Face or your local path with StaticModel::from_pretrained(...). - Tiny footprint: The crate itself is only ~1.7 mb, with embedding models between 7 and 30 mb. Performance: We benchmarked single-threaded on a CPU: - Python: ~4650 embeddings/sec - Rust: ~8000 embeddings/sec (~1.7× speedup) First open-source project in Rust for us, so would be great to get some feedback!
Sabtu, 17 Mei 2025
Scuffling O's fire manager Hyde after slow start
The Baltimore Orioles fired manager Brandon Hyde on Saturday, hoping a new leader can spark a turnaround of the most disappointing team in Major League Baseball.
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Jumat, 16 Mei 2025
New top story on Hacker News: Show HN: Workflow Use – Deterministic, self-healing browser automation (RPA 2.0)
Show HN: Workflow Use – Deterministic, self-healing browser automation (RPA 2.0)
12 by gregpr07 | 4 comments on Hacker News.
Hey HN – Gregor & Magnus here again. A few months ago, we launched Browser Use ( https://ift.tt/Z8Q3U10 ), which let LLMs perform tasks in the browser using natural language prompts. It was great for one-off tasks like booking flights or finding products—but we soon realized enterprises have somewhat different needs: They typically have one workflow with dynamic variables (e.g., filling out a form and downloading a PDF) that they want to reliably run a million times without breaking. Pure LLM agents were slow, expensive, and unpredictable for these high-frequency tasks. So we just started working on Workflow Use: - You show the browser what to do (by manually recording steps; show don’t tell). - An LLM converts these recordings into deterministic scripts with variables (scripts include AI steps as well, where it’s 100% agentic) - Scripts run reliably, 10x faster, and ~90% cheaper than Browser Use. - If a step breaks, workflow will fallback to Browser Use and agentically run the step. (This self-healing functionality is still very early.) This project just kicked off, so lots of things will break, it’s definitely not production-ready yet, and plenty of stuff is still missing (like a solid editor and proper self-healing). But we wanted to share early, get feedback, and figure out what workflows you’d want to automate this way. Try it out and let us know what you think!
12 by gregpr07 | 4 comments on Hacker News.
Hey HN – Gregor & Magnus here again. A few months ago, we launched Browser Use ( https://ift.tt/Z8Q3U10 ), which let LLMs perform tasks in the browser using natural language prompts. It was great for one-off tasks like booking flights or finding products—but we soon realized enterprises have somewhat different needs: They typically have one workflow with dynamic variables (e.g., filling out a form and downloading a PDF) that they want to reliably run a million times without breaking. Pure LLM agents were slow, expensive, and unpredictable for these high-frequency tasks. So we just started working on Workflow Use: - You show the browser what to do (by manually recording steps; show don’t tell). - An LLM converts these recordings into deterministic scripts with variables (scripts include AI steps as well, where it’s 100% agentic) - Scripts run reliably, 10x faster, and ~90% cheaper than Browser Use. - If a step breaks, workflow will fallback to Browser Use and agentically run the step. (This self-healing functionality is still very early.) This project just kicked off, so lots of things will break, it’s definitely not production-ready yet, and plenty of stuff is still missing (like a solid editor and proper self-healing). But we wanted to share early, get feedback, and figure out what workflows you’d want to automate this way. Try it out and let us know what you think!
NWSL: Game should've stopped after King collapse
The National Women's Soccer League said that it would abandon future games featuring life-threatening emergencies to players.
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Playoff futures: Odds improving for Timberwolves as champs, Edwards as Finals MVP
An updated listing of the odds to win the NBA championship and Finals MVP entering Friday.
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Kamis, 15 Mei 2025
QB Williams sought to avoid Bears, book details
Quarterback Caleb Williams looked to find a way around the NFL's collective bargaining agreement in an effort to avoid being drafted by the Chicago Bears, details from a forthcoming book reveal.
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Rabu, 14 Mei 2025
Madrid down to 10 from first team vs. Mallorca
Injuries to Brahim DÃaz and Andriy Lunin left Real Madrid with just 10 available first-team players for Real Madrid's crucial LaLiga game against Real Mallorca on Wednesday.
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Cristiano Ronaldo's son gets Portugal U15 start
Cristiano Ronaldo's eldest son, Cristiano Ronaldo Júnior, had his first start for Portugal under-15s on Wednesday in a 1-1 draw against Greece.
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FIFA targets $1bn return from Women's World Cup
FIFA is targeting $1 billion in revenue from the Women's World Cup, global football governing body's president Gianni Infantino said on Tuesday at the Saudi Arabia-U.S. Investment Forum 2025 in Riyadh.
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Sampdoria suffer historic relegation to Serie C
Sampdoria have been relegated to the third tier of Italian football for the first time in their 78-year history.
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Selasa, 13 Mei 2025
USWNT celebrates 40 years with beautifully complementary kits
To celebrate 40 years of the U.S. women's national team, Nike has unveiled kits defined by stars and stripes.
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Senin, 12 Mei 2025
Jordan joins NBC's NBA coverage as contributor
NBC announced Monday that Hall of Famer Michael Jordan will be a special contributor to its NBA coverage beginning this fall.
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Leafs' Domi fined $5K for hit to Panthers' Barkov
Maple Leafs forward Max Domi was fined $5,000 -- the maximum amount allowed by the league's CBA -- for boarding Panthers captain Aleksander Barkov in Game 4.
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MLS Power Rankings: Minnesota routs Miami to climb the order
After rocking Miami 4-1, Minnesota is closing in on the Herons in the top 10 of ESPN's MLS Power Rankings.
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Minggu, 11 Mei 2025
Forest's Nuno defends owner after on-pitch clash
Nottingham Forest owner Evangelos Marinakis stormed onto the pitch to remonstrate with coach Nuno Espirito Santo after Sunday's Premier League draw against Leicester City.
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Sabtu, 10 Mei 2025
How Chelsea women made history with unbeaten WSL season
Chelsea wrapped up the Women's Super League title a few weeks ago, but they finished the season with an unbeaten record. Here's how they did it.
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Jumat, 09 Mei 2025
New top story on Hacker News: Show HN: Hydra (YC W22) – Serverless Analytics on Postgres
Show HN: Hydra (YC W22) – Serverless Analytics on Postgres
19 by coatue | 5 comments on Hacker News.
Hi HN, Hydra cofounders (Joe and JD) here ( https://www.hydra.so/ )! We enable realtime analytics on Postgres without requiring an external analytics database. Traditionally, this was unfeasible: Postgres is a rowstore database that’s 1000X slower at analytical processing than a columnstore database. (A quick refresher for anyone interested: A rowstore means table rows are stored sequentially, making it efficient at inserting / updating a record, but inefficient at filtering and aggregating data. At most businesses, analytical reporting scans large volumes of events, traces, time-series data. As the volume grows, the inefficiency of the rowstore compounds: i.e. it's not scalable for analytics. In contrast, a columnstore stores all the values of each column in sequence.) For decades, it was a requirement for businesses to manage these differences between the row and columnstore’s relative strengths, by maintaining two separate systems. This led to large gaps in both functionality and syntax, and background knowledge of engineers. For example, here are the gaps between Redshift (a popular columnstore) and Postgres (rowstore) features: ( https://ift.tt/dMhUA9t... ). We think there’s a better, simpler way: unify the rowstore and columnstore – keep the data in one place, stop the costs and hassle of managing an external analytics database. With Hydra, events, traces, time-series data, user sessions, clickstream, IOT telemetry, etc. are now accessible as a columnstore right alongside my standard rowstore tables. Our solution: Hydra separates compute from storage to bring the analytics columnstore with serverless processing and automatic caching to your postgres database. The term "serverless" can be a bit confusing, because a server always exists, but it means compute is ephemeral and spun up and down automatically. The database automatically provisions and isolates dedicated compute resources for each query process. Serverless is different from managed compute, where the user explicitly chooses to allocate and scale CPU and memory continuously, and potentially overpay during idle time. How is serverless useful? It's important that every analytics query has its own resources per process. The major hurdles with running analytics on Postgres is 1) Rowstore performance 2) Resource contention. #2 is very often overlooked - but in practice, when analytics queries are run they tend to hog resources (RAM and CPU) from Postgres transactional work. So, a slightly expensive analytics query has the ability to slow down the entire database: that's why serverless is important: it guarantees the expensive queries are isolated and run on dedicated database resources per process. why is hydra so fast at analytics? ( https://ift.tt/eCZscBb ) 1) columnstore by default 2) metadata for efficient file-skipping and retrieval 3) parallel, vectorized execution 4) automatic caching what’s the killer feature? hydra can quickly join columnstore tables with standard row tables within postgres with direct sql. example: “segment events as a table.” Instead of dumping segment event data into a s3 bucket or external analytics database, use hydra to store and join events (clicks, signups, purchases) with user profile data within postgres. know your users in realtime: “what events predict churn?” or “which user will likely convert?” is immediately actionable. Thanks for reading! We would love to hear your feedback and if you'd like to try Hydra now, we offer a $300 credit and 14-days free per account. We're excited to see how bringing the columnstore and rowstore side-by-side can help your project.
19 by coatue | 5 comments on Hacker News.
Hi HN, Hydra cofounders (Joe and JD) here ( https://www.hydra.so/ )! We enable realtime analytics on Postgres without requiring an external analytics database. Traditionally, this was unfeasible: Postgres is a rowstore database that’s 1000X slower at analytical processing than a columnstore database. (A quick refresher for anyone interested: A rowstore means table rows are stored sequentially, making it efficient at inserting / updating a record, but inefficient at filtering and aggregating data. At most businesses, analytical reporting scans large volumes of events, traces, time-series data. As the volume grows, the inefficiency of the rowstore compounds: i.e. it's not scalable for analytics. In contrast, a columnstore stores all the values of each column in sequence.) For decades, it was a requirement for businesses to manage these differences between the row and columnstore’s relative strengths, by maintaining two separate systems. This led to large gaps in both functionality and syntax, and background knowledge of engineers. For example, here are the gaps between Redshift (a popular columnstore) and Postgres (rowstore) features: ( https://ift.tt/dMhUA9t... ). We think there’s a better, simpler way: unify the rowstore and columnstore – keep the data in one place, stop the costs and hassle of managing an external analytics database. With Hydra, events, traces, time-series data, user sessions, clickstream, IOT telemetry, etc. are now accessible as a columnstore right alongside my standard rowstore tables. Our solution: Hydra separates compute from storage to bring the analytics columnstore with serverless processing and automatic caching to your postgres database. The term "serverless" can be a bit confusing, because a server always exists, but it means compute is ephemeral and spun up and down automatically. The database automatically provisions and isolates dedicated compute resources for each query process. Serverless is different from managed compute, where the user explicitly chooses to allocate and scale CPU and memory continuously, and potentially overpay during idle time. How is serverless useful? It's important that every analytics query has its own resources per process. The major hurdles with running analytics on Postgres is 1) Rowstore performance 2) Resource contention. #2 is very often overlooked - but in practice, when analytics queries are run they tend to hog resources (RAM and CPU) from Postgres transactional work. So, a slightly expensive analytics query has the ability to slow down the entire database: that's why serverless is important: it guarantees the expensive queries are isolated and run on dedicated database resources per process. why is hydra so fast at analytics? ( https://ift.tt/eCZscBb ) 1) columnstore by default 2) metadata for efficient file-skipping and retrieval 3) parallel, vectorized execution 4) automatic caching what’s the killer feature? hydra can quickly join columnstore tables with standard row tables within postgres with direct sql. example: “segment events as a table.” Instead of dumping segment event data into a s3 bucket or external analytics database, use hydra to store and join events (clicks, signups, purchases) with user profile data within postgres. know your users in realtime: “what events predict churn?” or “which user will likely convert?” is immediately actionable. Thanks for reading! We would love to hear your feedback and if you'd like to try Hydra now, we offer a $300 credit and 14-days free per account. We're excited to see how bringing the columnstore and rowstore side-by-side can help your project.
Tiger's son, Charlie, fails to qualify for U.S. Open
Sixteen-year-old Charlie Woods, son of Tiger Woods, failed in his second attempt to qualify for the U.S. Open.
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Kamis, 08 Mei 2025
Stanley Cup playoff odds: Rantanen new Conn Smythe fave after hat trick
Every playoff team's odds to win the 2025 Stanley Cup and favorites for the Conn Smythe Trophy for playoff MVP.
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Rabu, 07 Mei 2025
Selasa, 06 Mei 2025
Shiffrin to join Denver NWSL ownership group
Two-time Olympic alpine ski gold medalist Mikaela Shiffrin has joined the ownership group of Denver's expansion National Women's Soccer League team.
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Our final college softball rankings of the 2025 regular season: Top 25 poll and what to watch
Check out our Top 25 poll, plus the play and player of the week.
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Betting buzz: Stars, Jets to face off after braving remarkably long live odds
The Winnipeg Jets and Dallas Stars face off in the second round after braving long live odds in Game 7's.
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NBA playoff odds: Big changes after Game 1 wins by Knicks, Nuggets
An updated listing of the odds to win the NBA championship and Finals MVP.
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Senin, 05 Mei 2025
Gruden joins arena team as part-owner, adviser
Jon Gruden joined the Nashville Kats of the Arena Football One League as a part-owner and in what the club called "consulting and advisory roles in all aspects of the team's football and business operations."
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Waiver wire: Why settle for one rising star when you can have two?
Tristan H. Cockcroft outlines the players you should be claiming off the waiver wire and who you should be dropping from fantasy rosters.
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MLS: Vancouver remain top; Seattle, Philadelphia soar up
The Vancouver Whitecaps continue to dominate the top spot in the latest edition of ESPN's MLS Power Rankings.
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NWSL: Orlando Pride top, Naeher makes history
Despite losing to Portland, the defending champions remain atop the ranks.
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Minggu, 04 Mei 2025
Jets' Scheifele ruled out for Game 7 vs. Blues
Jets forward Mark Scheifele will not play in Game 7 against the Blues on Sunday due to an undisclosed injury that he suffered in Game 5, coach Scott Arniel said.
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Sabtu, 03 Mei 2025
Red Sox 1B Casas has ruptured patellar tendon
Red Sox first baseman Triston Casas has been placed on the injured list after rupturing his left patellar tendon Friday night against the Twins.
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Jumat, 02 Mei 2025
Cooper to return as Lightning coach next season
Lightning coach Jon Cooper will return next season, general manager Julien BriseBois said Friday, shutting down speculation that they might part ways after an early playoff exit.
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Relevent-USSF settlement: Are foreign leagues coming to America?
Settling its suit against U.S. Soccer, Relevent can now bring foreign games to the U.S. Is MLS under threat?
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Kamis, 01 Mei 2025
Draft tiers at forward/center
Andre Snellings goes through the different tiers at the forward/center position to help you in your 2025 fantasy women's basketball draft.
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