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What if pathology foundation models could do more with less? GigaPath-Flash and GigaTIME-Flash cut computational demands while maintaining strong performance, opening the door to larger studies and broader exploration.

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Microsoft Research

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18,261 просмотров • 19 дней назад •via X (Twitter)

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Фото профиля Fiducial.ai
Fiducial.ai18 дней назад

Population-scale runs need a versioned feature manifest for the encoder, tiling and scanner preprocessing. The compute saving loses value if cohorts silently mix incompatible representations.

Фото профиля Neeraj Kumar
Neeraj Kumar19 дней назад

How do the Flash models hold up on external cohorts and rare morphology classes? Compute savings matter only if calibration survives scanner and site shift.

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🚨 You can use GPT-6 Astra as the orchestrator and DeepSeek V4.1 Flash as the agents to cut costs by more than 88% while maintaining the same level of quality. And now it’s even better, since DeepSeek V4.1 Flash is one of the best models for cybersecurity at an incredibly low price
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🚨 You can use GPT-6 Astra as the orchestrator and DeepSeek V4.1 Flash as the agents to cut costs by more than 88% while maintaining the same level of quality. And now it’s even better, since DeepSeek V4.1 Flash is one of the best models for cybersecurity at an incredibly low price

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introducing Gemini 3.6 Flash: our latest model that balances speed with intelligence to deliver strong performance in agentic and multimodal tasks built directly on developer and customer feedback from 3.5 Flash available now in AI Studio and the Gemini API

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Our new Gemini 3.5 Flash is our strongest agentic and coding model yet, delivering frontier-level performance at 4x the speed of comparable frontier models at less than half the cost. We had Tulsee Doshi break down what you need to know about it.
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Blank Bio (Blank Bio) is building foundation models to power a computational toolkit for RNA therapeutics, starting with mRNA design and expanding to target ID, biomarker discovery, and more. Congrats on the launch, Jonny, Phil Fradkin & Ian Shi!
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Blank Bio (Blank Bio) is building foundation models to power a computational toolkit for RNA therapeutics, starting with mRNA design and expanding to target ID, biomarker discovery, and more. Congrats on the launch, Jonny, Phil Fradkin & Ian Shi!

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What if we could RECREATE the REFLECTION of the FLASH seen in the GLASS during the Charlie Kirk assassination? Literally recreate it on site. The timing of the flash seen in the reflection of the glass is what makes it so interesting. What else would cause a flash that bright in the middle of the day? This could at least determine a lot of good information and confirm or deny theories that are out there. Just to get an idea of where the FLASH came from, which could identify a possible shooter or 2nd shooter location. The reason I say this is because it lines up perfectly with the sound of the gun, meaning you see the flash first, then you hear the shot, which would make sense because light travels faster than sound. This way we could get an idea of where it came from in the first place and determine if it's a good lead or not and then check the crowd or building locations. It could be recreated with a camera flash or something with bright enough lumens. This could clear up at least one theory or confirm it and lead us in the right direction of where the shooter could have been. Please comment with your thoughts and ideas. We need to get someone out there to the location with the Proper equipment. At least we could confirm or deny some theories. Video in bottom left shows the flash.
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What if we could RECREATE the REFLECTION of the FLASH seen in the GLASS during the Charlie Kirk assassination? Literally recreate it on site. The timing of the flash seen in the reflection of the glass is what makes it so interesting. What else would cause a flash that bright in the middle of the day? This could at least determine a lot of good information and confirm or deny theories that are out there. Just to get an idea of where the FLASH came from, which could identify a possible shooter or 2nd shooter location. The reason I say this is because it lines up perfectly with the sound of the gun, meaning you see the flash first, then you hear the shot, which would make sense because light travels faster than sound. This way we could get an idea of where it came from in the first place and determine if it's a good lead or not and then check the crowd or building locations. It could be recreated with a camera flash or something with bright enough lumens. This could clear up at least one theory or confirm it and lead us in the right direction of where the shooter could have been. Please comment with your thoughts and ideas. We need to get someone out there to the location with the Proper equipment. At least we could confirm or deny some theories. Video in bottom left shows the flash.

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⚡️🔬📣 Excited to share our new nature article building and evaluating PathChat, a multimodal generative AI copilot and chatbot for human pathology. Article: Open Access Link: We leverage our previous success in building foundation models for computational pathology such as UNI / CONCH and combine it with the advancements of large vision language models and generative AI to enable PathChat to answer diverse pathology-related queries. We assessed PathChat using both multiple choice diagnostic questions and open-ended questions. Congratulations to Max Lu Bowen Chen @DFKW_MD Richard J. Chen and everyone else who contributed to this work. Also see blog post from Max Lu about this work: , also teasing the development and preview of PathChat 2, a successor to PathChat 1 bringing new capabilities and substantially improved performance to the state-of-the-art.

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🏠 Everyone talks about building more homes. Almost no one talks about whether federal mortgage policy is adding fuel to the fire. If more government-backed financing creates more buyers while housing remains limited, what happens to prices? Allen Mendenhall is a Research Fellow in Economic Policy Studies and Senior Advisor for the Free Enterprise Initiative at The Heritage Foundation. John Gibbs is the Director of Economic Policy Studies at the Heritage Foundation.

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Introducing Gemini 3.5 Flash ⚡️ Normally, Gemini 3.5 Flash is 4x faster than other models with frontier performance. For a limited time, Antigravity is serving it 12x faster thanks to custom inference tricks, delivering incredible speed for your workflows. 🚀 See the performance in this demo: generating pixel art from photos, orchestrating multi-agent workflows to write and register sprites, and spawning browser subagents to auto-test rendering: 👇

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Our most intelligent workhorse model yet for coding and agents has arrived ⚡ Meet Gemini 3.7 Flash. — Crush that seemingly endless to-do list. Gemini Spark in the Google Gemini now uses 3.7 Flash. The new model can equip your personal AI agent to work even smarter for you by seamlessly handling complex, multi-step tasks across your Google Workspace apps like Gmail, Google Calendar and Google Docs — Enjoy a smoother build experience. The model thinks more diligently, putting more effort into multi-step planning and tool calls. A more disciplined execution means less manual oversight and fewer retries across engineering workflows — Build more, spend less. 3.7 Flash is available through the end of the year with an introductory price of half the original 3.6 Flash cost per million tokens ($0.75/1M input tokens and $3.75/1M output tokens)

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deepseek v4.1 flash vs secret model tested both with the same prompt at the highest reasoning available > v4.1 flash took 2 hours to finish both tests and cost $2.60 > secret model finished both in 30 minutes, can’t reveal cost yet v4.1 flash spent a lot of time overthinking and running random tests, which slowed it down thanks AIHubmix for providing api access so i could test v4.1 flash hoping that team will cut down the overthinking before the full release, but for now here are the results which model did better here?
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deepseek v4.1 flash vs secret model tested both with the same prompt at the highest reasoning available > v4.1 flash took 2 hours to finish both tests and cost $2.60 > secret model finished both in 30 minutes, can’t reveal cost yet v4.1 flash spent a lot of time overthinking and running random tests, which slowed it down thanks AIHubmix for providing api access so i could test v4.1 flash hoping that team will cut down the overthinking before the full release, but for now here are the results which model did better here?

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The reason why people are sick usually has a lot less to do with physiology and a lot more to do with pathology in our politics. Air quality, water quality, whether you can walk in your community safely, whether or not you can see a doctor if you get sick in the first place.
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The reason why people are sick usually has a lot less to do with physiology and a lot more to do with pathology in our politics. Air quality, water quality, whether you can walk in your community safely, whether or not you can see a doctor if you get sick in the first place.

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Three years ago, Gemini started by understanding the world. With Gemini 2, models learned to think and reason. Late last year, Gemini 3 brought any idea to life. Today, we’re continuing that journey with our Gemini 3.5 series, starting with Gemini 3.5 Flash, delivering frontier performance for agents and coding.

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What if you could trade stocks, crypto, and more across global markets with less friction, all in one place? That’s what #8uiltForPerfectTrades looks like. See the full story ⬇️

Bitget

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glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns
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glm 5.3 vs qwen 3.8 vs gemini 3.7 vs deepseek v4 flash four models designed and built three structures each on a physics-backed site, with no dimensions anywhere in the brief the setup: our own agent loop on OpenRouter, a construction site as the tool set – footings, walls, arches, roofs, scaffold, a lamp. the site enforces physics and nothing else: unsupported brick falls, a roof needs walls under it, a worker reaches 3.2 m above whatever he stands on, an arch needs centring until the keystone is set, concrete cures before it carries. no budget ceiling – material cost is tallied and reported, never blocked. tasks: 1. house – a plot and a palette, no plan. shape, height and material are the model's call 2. lighthouse – a headland cut by a gully, with a rock stack standing 30 m offshore. the lamp must burn, it must be the highest thing built, and the keeper must be able to walk to it 3. bridge – a river with one islet and banks at different heights. cross it however you want models: Z.ai glm 5.3 flash, Qwen qwen 3.8 flash, Google DeepMind gemini 3.7 flash, DeepSeek v4 flash vision all twelve objects were finished and signed off by the models themselves. tallest lighthouse is qwen's at 38.4 m, planted on the offshore stack with a bridge run out to it – the only model that read the site that way. deepseek signed off its bridge on an empty riverbed: 0 bricks, 107 minutes, $1.16m of material tallied - total cost, three builds #1 glm 5.3 flash – $0.201 #2 gemini 3.7 flash – $0.871 #3 qwen 3.8 flash – $1.058 #4 deepseek v4 flash – $1.567 - wall clock, three builds #1 gemini 3.7 flash – 91m #2 glm 5.3 flash – 228m #3 deepseek v4 flash – 502m #4 qwen 3.8 flash – 912m - total tokens #1 gemini 3.7 flash – 3,567,052 #2 glm 5.3 flash – 4,732,748 #3 qwen 3.8 flash – 13,469,333 #4 deepseek v4 flash – 18,230,076 - defects logged by the site #1 deepseek v4 flash – 59 #2 gemini 3.7 flash – 132 #3 glm 5.3 flash – 221 #4 qwen 3.8 flash – 350 - material tallied across three builds #1 gemini 3.7 flash – $359,884 #2 glm 5.3 flash – $583,358 #3 deepseek v4 flash – $1,327,484 #4 qwen 3.8 flash – $2,188,625 observations: • glm is the cheap one and nothing here is close – $0.201 for three buildings, $0.042 per million tokens, 6x under gemini's rate • what glm spends it on is bulk, not care: 166,228 bricks in one house and 156 defect weight, the worst single object in the set • gemini is the efficiency line – 91 minutes and 3.57m tokens for all three and an eighth of qwen's clock • gemini also builds the smallest of everything. its lighthouse is 22.5 m against qwen's 38.4, its house 6.9 m against 19.3 • qwen is the maximalist: 1.18m bricks, $2.19m of material, tallest on all three tasks, and 912 minutes – 15 hours – to get there conclusion: twelve finished objects for $3.80 all in, and a 7.8x price spread between the cheapest model and the priciest! follow thehype. for 24/7 ai news, analysis and breakdowns

thehype.

26,360 просмотров • 22 дней назад

If you ever want a crash course on what not to do during a Flash Flood. Just visit Houston, Texas. This was the scene off i45 and despite literally dozens of vehicles stalled out more continued to try leading to water rescues. #TXwx
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If you ever want a crash course on what not to do during a Flash Flood. Just visit Houston, Texas. This was the scene off i45 and despite literally dozens of vehicles stalled out more continued to try leading to water rescues. #TXwx

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