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Scientific analysis doesn’t stop when computation finishes. Results need to be clearly visualized and communicated to be shared and built upon. We’ve revamped visual outputs in Biomni Lab: • Automatic slide deck generation • Reports exportable to HTML, Word, or PDF with embedded figures • Substantial improvements to scientific... show more

Phylo

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30,066 Aufrufe • vor 6 Monaten •via X (Twitter)

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Introducing Genspark AI Slides 5.0. Our biggest upgrade to AI Slides yet. The biggest leap? Slide Skills. Reusable assets that bundle expert thinking and polished design, ready in one click. Here's everything new in 5.0: - 100+ Expert Skills built in. We've packed the thinking of the world's top experts, across consulting, sales, teaching, and more. Apply with one click, and their expertise becomes yours. - Build your own Skill. Upload a deck or PDF, describe the analysis steps, and lock in your team's style. - 5× faster, higher quality. Powered by next-gen architecture and the most powerful models. - Built-in data analysis. Crunches your Excel and Sheets data into presentation-ready insights. Every deck thinks like an expert. Try it now → 🎉 50% off AI Slides 5.0 credits, 14 days only

Introducing Genspark AI Slides 5.0. Our biggest upgrade to AI Slides yet. The biggest leap? Slide Skills. Reusable assets that bundle expert thinking and polished design, ready in one click. Here's everything new in 5.0: - 100+ Expert Skills built in. We've packed the thinking of the world's top experts, across consulting, sales, teaching, and more. Apply with one click, and their expertise becomes yours. - Build your own Skill. Upload a deck or PDF, describe the analysis steps, and lock in your team's style. - 5× faster, higher quality. Powered by next-gen architecture and the most powerful models. - Built-in data analysis. Crunches your Excel and Sheets data into presentation-ready insights. Every deck thinks like an expert. Try it now → 🎉 50% off AI Slides 5.0 credits, 14 days only

Genspark

210,765 Aufrufe • vor 3 Monaten

I built a tool to imagine how AI can help us create better, more transparent research. Instead of only seeing the tiny number of analyses the author chooses to ossify in a static pdf, imagine being able to talk to the papers you read and ask for different analyses. That's what I built. This early prototype uses our vote-by-mail extension. You get to see the initial analyses we thought made sense, but you can ask the LLM to show you way more! e.g., "what would the results look like if we excluded Utah" or "what would the results look like if we excluded Washington and used linear trends" etc. With research agents like Claude code, researchers are going to be able to automatically search across millions of potential analyses. Our current paradigm is not equipped to transmit this complexity effectively or make sure we don't end up with all p-hacked results. My lab and I are thinking about how we can transform the way we do and disseminate research to adjust for this new world. This visualizer is the first of what will hopefully be a wide range of ideas. Big props to Janet Malzahn for suggesting this one. Check out the tool at the link below.

I built a tool to imagine how AI can help us create better, more transparent research. Instead of only seeing the tiny number of analyses the author chooses to ossify in a static pdf, imagine being able to talk to the papers you read and ask for different analyses. That's what I built. This early prototype uses our vote-by-mail extension. You get to see the initial analyses we thought made sense, but you can ask the LLM to show you way more! e.g., "what would the results look like if we excluded Utah" or "what would the results look like if we excluded Washington and used linear trends" etc. With research agents like Claude code, researchers are going to be able to automatically search across millions of potential analyses. Our current paradigm is not equipped to transmit this complexity effectively or make sure we don't end up with all p-hacked results. My lab and I are thinking about how we can transform the way we do and disseminate research to adjust for this new world. This visualizer is the first of what will hopefully be a wide range of ideas. Big props to Janet Malzahn for suggesting this one. Check out the tool at the link below.

Andy Hall

70,816 Aufrufe • vor 7 Monaten

ClickUp to add artifacts with Brain2 👀 > It will be able to create slides, prototypes, websites, or dashboards. > Brain pulls from workspace context, so the output is built on real project data > Artifacts render inline in the channel and stay fully interactive. When Brain is asked to build a presentation, it will now return a finished, professionally done, navigable deck right in the thread, ready to share or drop into a task.

ClickUp to add artifacts with Brain2 👀 > It will be able to create slides, prototypes, websites, or dashboards. > Brain pulls from workspace context, so the output is built on real project data > Artifacts render inline in the channel and stay fully interactive. When Brain is asked to build a presentation, it will now return a finished, professionally done, navigable deck right in the thread, ready to share or drop into a task.

🚨 AI News | TestingCatalog

12,749 Aufrufe • vor 2 Monaten

$#JANCTION/#JASMY : Let’s not wait for the world to change, let’s change it ourselves.🌌 What if your gaming console, the #PlayStation you use for fun, could also help change the world? That’s exactly what #Jasmy Lab, through #Jasmy’s Layer 2 network, #Janction, is proposing in this bold and inspiring project. The idea is simple: millions of #PlayStations are sitting in homes all over the world, unused for large portions of the day. #Jasmy Lab wants to harness those idle moments so your console can contribute to meaningful, real-world projects. While you’re asleep or away at work, your #PlayStation could be helping to create visual effects for films, assist in medical research, or even contribute to training artificial intelligence. And here’s the best part: it requires no effort on your part. All you’d need to do is allow your console to join a vast distributed network, a massive team where everyone shares a small slice of their device’s power. In return, you’d be rewarded: points, vouchers, or even money could be earned based on how your console is used. #Jasmy Lab envisions a world where your PlayStation doesn’t just entertain you, it also earns for you, and serves a greater good. This initiative also aims to democratize access to services that have traditionally been expensive. Content creators, artists, and developers could use this network to power their projects at a fraction of the cost of current solutions. #Janction makes it possible for talented individuals, regardless of their resources, to create, innovate, and dream bigger. But Janction doesn’t stop there. This network could support scientific research, improve healthcare, build smarter cities, and even enable gaming without the need for the latest hardware. In short, #Janction wants to turn a familiar device,the game console, into a powerful tool that serves humanity in incredible ways. And to ensure everything is secure, fair, and transparent, #Jasmy Lab is building a system where every contribution is tracked, recognized, and protected. Gamers remain fully in control, with the freedom to opt in or out at any time. With #Janction, #Jasmy Lab isn’t just advancing technology, they’re proving that what we already have in our homes can become something far more powerful than we ever imagined. The future, according to them, might just begin where we least expect it: right in our living rooms.$

#JANCTION/#JASMY : Let’s not wait for the world to change, let’s change it ourselves.🌌 What if your gaming console, the #PlayStation you use for fun, could also help change the world? That’s exactly what #Jasmy Lab, through #Jasmy’s Layer 2 network, #Janction, is proposing in this bold and inspiring project. The idea is simple: millions of #PlayStations are sitting in homes all over the world, unused for large portions of the day. #Jasmy Lab wants to harness those idle moments so your console can contribute to meaningful, real-world projects. While you’re asleep or away at work, your #PlayStation could be helping to create visual effects for films, assist in medical research, or even contribute to training artificial intelligence. And here’s the best part: it requires no effort on your part. All you’d need to do is allow your console to join a vast distributed network, a massive team where everyone shares a small slice of their device’s power. In return, you’d be rewarded: points, vouchers, or even money could be earned based on how your console is used. #Jasmy Lab envisions a world where your PlayStation doesn’t just entertain you, it also earns for you, and serves a greater good. This initiative also aims to democratize access to services that have traditionally been expensive. Content creators, artists, and developers could use this network to power their projects at a fraction of the cost of current solutions. #Janction makes it possible for talented individuals, regardless of their resources, to create, innovate, and dream bigger. But Janction doesn’t stop there. This network could support scientific research, improve healthcare, build smarter cities, and even enable gaming without the need for the latest hardware. In short, #Janction wants to turn a familiar device,the game console, into a powerful tool that serves humanity in incredible ways. And to ensure everything is secure, fair, and transparent, #Jasmy Lab is building a system where every contribution is tracked, recognized, and protected. Gamers remain fully in control, with the freedom to opt in or out at any time. With #Janction, #Jasmy Lab isn’t just advancing technology, they’re proving that what we already have in our homes can become something far more powerful than we ever imagined. The future, according to them, might just begin where we least expect it: right in our living rooms.

NeoXtrix

10,594 Aufrufe • vor 1 Jahr

43 monkeys that were being used for vaccine development escaped from a lab in Yemassee, South Carolina on Wednesday and are currently on the loose. This video, taken near the facility on Wednesday afternoon, appears to show shadowy figures moving through the woods. CBS News could not confirm that the figures are the missing monkeys. People are advised not to try and capture, feed, or handle the monkeys. According to the CDC, some monkeys can carry B virus which can be deadly to people; “Even if a monkey doesn’t appear to be ill, it can still be infected. [The monkeys] are wild animals, even if they have been raised around people. Like any wild animals, they may bite, spit, or scratch when frightened. For these reasons, people should not attempt to capture, feed, or handle the escaped monkeys.”

43 monkeys that were being used for vaccine development escaped from a lab in Yemassee, South Carolina on Wednesday and are currently on the loose. This video, taken near the facility on Wednesday afternoon, appears to show shadowy figures moving through the woods. CBS News could not confirm that the figures are the missing monkeys. People are advised not to try and capture, feed, or handle the monkeys. According to the CDC, some monkeys can carry B virus which can be deadly to people; “Even if a monkey doesn’t appear to be ill, it can still be infected. [The monkeys] are wild animals, even if they have been raised around people. Like any wild animals, they may bite, spit, or scratch when frightened. For these reasons, people should not attempt to capture, feed, or handle the escaped monkeys.”

CBS News

1,281,783 Aufrufe • vor 1 Jahr

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

TBPN

29,721 Aufrufe • vor 6 Monaten

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

OpenAI's Kevin Weil 🇺🇸 says 24/7 robotic labs could automate scientific discovery using "reinforcement learning with a loop through the real world": "There’s a lot of science that can be totally automated. There’s no reason at this point that you need to have grad students pipetting one thing into another thing." "The idea is to have robotic labs that are online 24/7 and can scale in parallel. You have models reasoning for two days to find the most efficient experiments to run, once they get to a good point, they pass that to a robotic lab which can experiment in parallel at high volume." "The results pass back into a model which reasons about the results and then goes out and runs a different set of experiments. You’re doing reinforcement learning with a loop through the real world."

TBPN

98,711 Aufrufe • vor 7 Monaten

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

New Short Course: Getting Structured LLM Output! Learn how to get structured outputs from your LLM applications in this course, built in partnership with .txt, and taught by Will Kurt, a Founding Engineer, and , Developer Relations Engineer. It's challenging for software to automatically parse through an LLM's freeform text outputs. Structured outputs—like JSON—solve this by converting natural language into consistent, clear, data that a machine can read and process. This course teaches you how to generate structured outputs while building several use cases, including a social media analysis agent. You’ll learn about structured outputs and efficient ways to generate outputs in your defined schema or format. You’ll begin by using structured output APIs, then use re-prompting libraries like “instructor” to generate structured output. Finally, you’ll learn how constrained decoding works; this is a very clever technique in which constraints are applied on each subsequent token generated, blocking any tokens that don’t fit your defined schema. In detail, you’ll: - Learn why structured outputs are important, how they allow for scalable software development, and the different approaches to generate them, including vendor-provided APIs, re-prompting libraries, and structured generation. - Build a simple social media agent using OpenAI’s structured output API, learn how to define a model's desired structured output using Pydantic, and perform basic programming with your outputs, such as importing structured data into a data frame using pandas. - Learn how to use the open-source library "instructor," which checks the structured output of the model and re-prompts the model until it validates the desired output, and explore the limitations of this approach. - Understand how structured generation by the “outlines” library works by modifying LLM logits, on a per-generated-token basis based on the desired format, to give a particular output structure. - Learn how regular expressions, which outlines works with, are represented as finite-state machines, and how they can be used to develop a range of structured outputs beyond JSON. By the end of this course, you’ll have broadened your knowledge of the approaches you can use to get structured outputs from your LLM applications. Please sign up here:

Andrew Ng

89,792 Aufrufe • vor 1 Jahr

Meet BIOS, an AI Scientist built to orchestrate complex biomedical research. • Global SOTA on Data Analysis Benchmarks: BixBench 48.78% open-answer, 55.12% multiple-choice + refusal, 64.39% multiple-choice (no refusal) - outperforming systems like Edison Scientific and Kepler. • Human-in-the-Loop or Autonomous Mode: Intermediate checkpoints let researchers guide investigations mid-flight as insights emerge. No more waiting hours for batch runs + reruns to get results. Or, run in fully autonomous mode for extended investigations. • Persistent World State: Rather than losing context as conversations grow, world state ensures investigations build on insights within each research cycle and across sessions. • Subagent Swarm: BIOS orchestrates subagents specializing in research functions (Literature Review, Data Analysis, Novelty Detection) and, soon, research domains (microbiology, longevity, genomics). BIOS is available now in Beta with free + paid tiers, exclusive launch pricing and, for limited time, free full access to academic users with a .edu email address. Pro, Researcher and Lab subscription tiers offer discounted packages on monthly credits. Our usage-based pricing is competitive and in some cases significantly cheaper than leading scientific agents. Try BIOS and read our paper in the links below ↓

Meet BIOS, an AI Scientist built to orchestrate complex biomedical research. • Global SOTA on Data Analysis Benchmarks: BixBench 48.78% open-answer, 55.12% multiple-choice + refusal, 64.39% multiple-choice (no refusal) - outperforming systems like Edison Scientific and Kepler. • Human-in-the-Loop or Autonomous Mode: Intermediate checkpoints let researchers guide investigations mid-flight as insights emerge. No more waiting hours for batch runs + reruns to get results. Or, run in fully autonomous mode for extended investigations. • Persistent World State: Rather than losing context as conversations grow, world state ensures investigations build on insights within each research cycle and across sessions. • Subagent Swarm: BIOS orchestrates subagents specializing in research functions (Literature Review, Data Analysis, Novelty Detection) and, soon, research domains (microbiology, longevity, genomics). BIOS is available now in Beta with free + paid tiers, exclusive launch pricing and, for limited time, free full access to academic users with a .edu email address. Pro, Researcher and Lab subscription tiers offer discounted packages on monthly credits. Our usage-based pricing is competitive and in some cases significantly cheaper than leading scientific agents. Try BIOS and read our paper in the links below ↓

BIOS

69,397 Aufrufe • vor 7 Monaten

NEW LAWSUIT FILED! An NIH-funded #AuburnUniversity lab is torturing and killing kittens in cruel, wasteful experiments on your dime. The kittens are bred with severe genetic diseases, subjected to invasive procedures, and injected with needles into their skulls — before being killed at just a few months old. This video is only a glimpse into the horrors happening behind closed doors. When an Alabama resident and #Auburn alum demanded to see documentation on the lab’s spending and abuse, the university refused to comply. Now, WCW is supporting their Alabama Open Records Act lawsuit to force Auburn to turn over the receipts. Even worse, the lab recently received a $206,336 payout from an active NIH grant scheduled to run through at least May 31, 2026. NIH officials claim to be “working tirelessly” to phase out cat testing, but continue to write checks for barbaric experiments like this. #StopTheMoney. Stop the madness!

NEW LAWSUIT FILED! An NIH-funded #AuburnUniversity lab is torturing and killing kittens in cruel, wasteful experiments on your dime. The kittens are bred with severe genetic diseases, subjected to invasive procedures, and injected with needles into their skulls — before being killed at just a few months old. This video is only a glimpse into the horrors happening behind closed doors. When an Alabama resident and #Auburn alum demanded to see documentation on the lab’s spending and abuse, the university refused to comply. Now, WCW is supporting their Alabama Open Records Act lawsuit to force Auburn to turn over the receipts. Even worse, the lab recently received a $206,336 payout from an active NIH grant scheduled to run through at least May 31, 2026. NIH officials claim to be “working tirelessly” to phase out cat testing, but continue to write checks for barbaric experiments like this. #StopTheMoney. Stop the madness!

White Coat Waste

62,998 Aufrufe • vor 3 Monaten

Bill Gates is funding a technology that can erase entire species from Earth. Scientists around the world believe this should be banned and not available to anyone. It's called gene drive. It hijacks natural reproduction using CRISPR. Instead of passing a modified gene to half of an organism's offspring, gene drive forces it into all of them. The modification doesn't stop. It spreads through the entire wild population on its own. Generation after generation until the species collapses. Gates wants to deploy it on mosquitoes to eliminate malaria. On paper it sounds like a breakthrough. But Malone says a significant portion of the scientific community believes gene drive should never leave the lab. Because there is no off switch. Once it enters a wild ecosystem, no one can recall it, slow it down, or undo what it does. He pointed to something Brett Weinstein has warned about for years. Ecosystems are so deeply interconnected that wiping out one species can unravel chains of dependence that no model has ever fully mapped. We saw what one lab leak did with COVID. Gene drive is a lab release by design. — Robert Malone on Joe Rogan Experience (Joe Rogan Podcast News)

Bill Gates is funding a technology that can erase entire species from Earth. Scientists around the world believe this should be banned and not available to anyone. It's called gene drive. It hijacks natural reproduction using CRISPR. Instead of passing a modified gene to half of an organism's offspring, gene drive forces it into all of them. The modification doesn't stop. It spreads through the entire wild population on its own. Generation after generation until the species collapses. Gates wants to deploy it on mosquitoes to eliminate malaria. On paper it sounds like a breakthrough. But Malone says a significant portion of the scientific community believes gene drive should never leave the lab. Because there is no off switch. Once it enters a wild ecosystem, no one can recall it, slow it down, or undo what it does. He pointed to something Brett Weinstein has warned about for years. Ecosystems are so deeply interconnected that wiping out one species can unravel chains of dependence that no model has ever fully mapped. We saw what one lab leak did with COVID. Gene drive is a lab release by design. — Robert Malone on Joe Rogan Experience (Joe Rogan Podcast News)

Goku

127,991 Aufrufe • vor 5 Monaten

This is WILD! Nvidia just launched something that could compress the most expensive process in medicine from 12 years to 12 months. BioNeMo Agent Toolkit is an open, agent-ready platform that turns AI agents into autonomous scientific workers, giving them the ability to run real drug discovery workflows instead of just generating ideas. And more than 50 companies are already using it, including Anthropic, OpenAI, Eli Lilly, Databricks, Snowflake, Dassault Systèmes and Schrödinger. Here is what it actually does. Traditional drug discovery costs an average of $2.6 billion per drug and takes over a decade. Most of that time and money goes into screening millions of compounds, designing proteins that bind to disease targets and running countless lab experiments to validate whether something works. BioNeMo agents now do all of that computationally before a single lab experiment begins. The demo Nvidia shared makes the speed impossible to ignore. An agent was asked to design 10 protein binders for PD-L1, a critical cancer immunotherapy target and it completed the full design, co-folding, scoring, and 3D structural analysis on GPU in under 90 seconds. What used to require weeks of wet lab work and PhD-level expertise now runs as a callable tool inside an AI workflow. The four core capabilities are virtual drug screening, protein binder design, genomic analysis, and medical imaging each one compressing tasks that previously took weeks into minutes. The institutional validation behind this is unusually strong. Nvidia and Eli Lilly announced a joint investment of up to $1 billion over five years to build a co-innovation lab running entirely on BioNeMo. The University of Washington's Institute for Protein Design is already running RosettaFold3 at 2x faster performance than the prior generation. And the market this unlocks is enormous, and Nvidia is sitting right at the center of it. The AI drug discovery market is projected to grow from $2.9 billion in 2026 to $13.8 billion by 2033 and McKinsey estimates generative AI could deliver $60 to $110 billion in annual economic value to pharma. Bullish on drug discovery!

This is WILD! Nvidia just launched something that could compress the most expensive process in medicine from 12 years to 12 months. BioNeMo Agent Toolkit is an open, agent-ready platform that turns AI agents into autonomous scientific workers, giving them the ability to run real drug discovery workflows instead of just generating ideas. And more than 50 companies are already using it, including Anthropic, OpenAI, Eli Lilly, Databricks, Snowflake, Dassault Systèmes and Schrödinger. Here is what it actually does. Traditional drug discovery costs an average of $2.6 billion per drug and takes over a decade. Most of that time and money goes into screening millions of compounds, designing proteins that bind to disease targets and running countless lab experiments to validate whether something works. BioNeMo agents now do all of that computationally before a single lab experiment begins. The demo Nvidia shared makes the speed impossible to ignore. An agent was asked to design 10 protein binders for PD-L1, a critical cancer immunotherapy target and it completed the full design, co-folding, scoring, and 3D structural analysis on GPU in under 90 seconds. What used to require weeks of wet lab work and PhD-level expertise now runs as a callable tool inside an AI workflow. The four core capabilities are virtual drug screening, protein binder design, genomic analysis, and medical imaging each one compressing tasks that previously took weeks into minutes. The institutional validation behind this is unusually strong. Nvidia and Eli Lilly announced a joint investment of up to $1 billion over five years to build a co-innovation lab running entirely on BioNeMo. The University of Washington's Institute for Protein Design is already running RosettaFold3 at 2x faster performance than the prior generation. And the market this unlocks is enormous, and Nvidia is sitting right at the center of it. The AI drug discovery market is projected to grow from $2.9 billion in 2026 to $13.8 billion by 2033 and McKinsey estimates generative AI could deliver $60 to $110 billion in annual economic value to pharma. Bullish on drug discovery!

Milk Road AI

23,215 Aufrufe • vor 2 Monaten

EXCLUSIVE: Scene of Nancy Guthrie private lab testing. I’m outside DNA Labs International where the DNA Testing of evidence is happening right now. I traveled to Deerfield Beach, Florida where lab technicians have been testing critical evidence in the investigation — including the glove found nearly 2 miles from her home. We’ve heard a lot about the discrepancies of sending this type of DNA evidence to a private lab versus a lab used by the FBI, but the Pima County Sheriff, Chris Nanos, says this is their protocol, and a lab that they have used for years. There are only a limited number of private accredited DNA labs in the country capable of this level of forensic work. If possible, DNA Labs International will develop a DNA profile from the glove. According to independent DNA consultant Suzanna Ryan of Ryan Forensic, which is not connected to this case but can speak about how this all works, once a profile is developed it will most likely be sent to the Arizona Department of Public Safety. That’s where the quality control process begins: •A qualified DNA analyst or CODIS administrator verifies the data •The profile must meet strict standards before upload •Once approved, it is electronically transmitted through state systems to the Federal Bureau of Investigation •All systems work together within the national DNA database network CODIS This extra verification step can take time — but it’s critical to ensure the data is admissible and accurate. In this exclusive video, I take you to the scene of where this testing is being done. Because the lab sits on private property, we were asked to leave by property management — but this is where the forensic work is happening. More to come on my YouTube Channel The Ingle Edit and on NewsNation

Laura Ingle

202,689 Aufrufe • vor 6 Monaten

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Today, we’re pushing a major update to Edison Analysis, our data analysis agent, which is tuned for scientific research and SOTA across data analysis benchmarks. In contrast to Kosmos, which runs for 6-12 hours and produces tens of thousands of lines of code, Edison Analysis runs for seconds to minutes and is best for specific, well-defined computational tasks. It is available both on our platform under the Analysis tab, and via API, and costs only one credit per run, so it is available to users on both free and paid tiers. Edison Analysis is a modified version of the data analysis agent Kosmos uses in its trajectories. Try it out! One of the most important improvements over our previous data analysis agents has been the addition of a specialized data retrieval tool. Edison Analysis can either use this tool to access data, or can pull data down directly via API. To evaluate this tool, we ranked the most commonly used public data repositories across recent papers from BioRxiv, and created a new benchmark that measures the ability of a language agent system to retrieve raw data from those sources. Edison Analysis gets 71% on this benchmark, and we’ll be working to increase this over time. You can read more about our benchmarks in the our blog post, link below. Some features worth highlighting: 1. Edison Analysis produces a report on the analysis it runs, along with a Jupyter notebook that you can download to reproduce the analysis yourself. Every figure it produces is linked back to the specific lines of code used to produce the figure, to make it easy to reproduce. 2. It works well with both Python and R. 3. One of the best uses for Edison Analysis is to use it to retrieve datasets that you can then analyze with Kosmos. We have a bunch of major improvements to Edison Analysis coming in the next few months that we’re excited to share. In the meantime, congratulations to the team, especially Ludovico Mitchener, Jon Laurent, Conor Igoe , Alex Andonian, and many more.

Sam Rodriques

61,934 Aufrufe • vor 9 Monaten

We’re excited to announce the release of SciDraft, an AI-powered tool for drafting scientific manuscripts 🎉 By simply uploading your lab progress slides or an existing research proposal and interacting with the AI, you can generate a full manuscript draft in a remarkably short time. The Japanese version has already been released and has received exceptionally positive feedback from a large number of researchers. The access link is available via the Patreon link in the thread below, and you can start using SciDraft immediately after purchase! I’d be delighted if you give it a try 😊

We’re excited to announce the release of SciDraft, an AI-powered tool for drafting scientific manuscripts 🎉 By simply uploading your lab progress slides or an existing research proposal and interacting with the AI, you can generate a full manuscript draft in a remarkably short time. The Japanese version has already been released and has received exceptionally positive feedback from a large number of researchers. The access link is available via the Patreon link in the thread below, and you can start using SciDraft immediately after purchase! I’d be delighted if you give it a try 😊

Daichi Konno / 紺野大地

12,366 Aufrufe • vor 7 Monaten

My bet: in the near future, 80%⬆️ of CS research will be done by AI in collaboration with humans. However, today's research ecosystem is still built around the human, not the AI scientist. For example, the 8-page paper PDF is a lossy compression of months of branching exploration into a linear story, optimized for a human reviewer to skim in 30 minutes. It hides two structural taxes: 📖 Storytelling Tax — failures, rejected hypotheses, and dead ends get stripped. On RE-Bench (24,008 runs, 21 frontier models), failed runs = 90.2% of total compute cost, with a 113× median failed-to-success token ratio. Every lab independently rediscovers the same dead ends. 🔧 Engineering Tax — the gap between reviewer-sufficient prose and agent-sufficient spec. Across 8,921 PaperBench requirements (23 ICML'24 papers), only 45.4% are fully specified in the PDF. The rest is tacit lab knowledge. Tolerable when readers were human. Critical now that agents read, reproduce, and extend. We propose ARA: the Agent-Native Research Artifact — replace the narrative PDF with an agent-executable package, in 4 layers: 🧠 structured scientific logic ⚙️ executable code w/ full specs 🌳 exploration graph (every failure preserved) 📊 evidence grounding every claim

My bet: in the near future, 80%⬆️ of CS research will be done by AI in collaboration with humans. However, today's research ecosystem is still built around the human, not the AI scientist. For example, the 8-page paper PDF is a lossy compression of months of branching exploration into a linear story, optimized for a human reviewer to skim in 30 minutes. It hides two structural taxes: 📖 Storytelling Tax — failures, rejected hypotheses, and dead ends get stripped. On RE-Bench (24,008 runs, 21 frontier models), failed runs = 90.2% of total compute cost, with a 113× median failed-to-success token ratio. Every lab independently rediscovers the same dead ends. 🔧 Engineering Tax — the gap between reviewer-sufficient prose and agent-sufficient spec. Across 8,921 PaperBench requirements (23 ICML'24 papers), only 45.4% are fully specified in the PDF. The rest is tacit lab knowledge. Tolerable when readers were human. Critical now that agents read, reproduce, and extend. We propose ARA: the Agent-Native Research Artifact — replace the narrative PDF with an agent-executable package, in 4 layers: 🧠 structured scientific logic ⚙️ executable code w/ full specs 🌳 exploration graph (every failure preserved) 📊 evidence grounding every claim

Amber (Jiachen) Liu 🍊

130,564 Aufrufe • vor 4 Monaten

Pete Hegseth must be the next Secretary of Defense. We cannot let up until the confirmation. I, along with other prominent veterans, will be walking the halls of the US Senate next Wednesday to advocate for our generation. Pete Hegseth epitomizes our struggle. He is a combat veteran with a strong desire to force a meritocracy upon the military. He is not a general officer blinded by the status quo. He is an outsider. He is exactly what we need. Please reach out to me, DM, email, or through my website (I built a tab) if you would like to be in some of the break-out groups speaking with the US Senators and their staff’s next week. There are a couple of petitions circulating in support for Pete. I have signed both the Navy Seal and Green Beret documents. Scott Mann and others have promised me the outputs of these documents so I can continue to leverage the voice of the community. This is a fight that we cannot afford to lose. Don’t let them cancel US. RAH

Pete Hegseth must be the next Secretary of Defense. We cannot let up until the confirmation. I, along with other prominent veterans, will be walking the halls of the US Senate next Wednesday to advocate for our generation. Pete Hegseth epitomizes our struggle. He is a combat veteran with a strong desire to force a meritocracy upon the military. He is not a general officer blinded by the status quo. He is an outsider. He is exactly what we need. Please reach out to me, DM, email, or through my website (I built a tab) if you would like to be in some of the break-out groups speaking with the US Senators and their staff’s next week. There are a couple of petitions circulating in support for Pete. I have signed both the Navy Seal and Green Beret documents. Scott Mann and others have promised me the outputs of these documents so I can continue to leverage the voice of the community. This is a fight that we cannot afford to lose. Don’t let them cancel US. RAH

Stuart Scheller

20,289 Aufrufe • vor 1 Jahr

A nurse practitioner contacted me privately after my last video about the military needing doctors and nurses. They asked to stay anonymous, and I’m going to respect that. What they shared wasn’t about one doctor or one patient—it was about incentives in the system. There are large, well-known companies that process blood work for huge portions of the country. According to them, results often come back framed in a way that suggests something is always still wrong. The concern is this: if a patient is told they’ve improved, fixed the issue, or no longer need monitoring, that patient may not be a repeat customer. So instead, people stay on the hamster wheel—more tests, more follow-ups, more worry. This isn’t me saying every lab or every provider is corrupt. It is saying we need to talk about how profit-driven incentives can distort healthcare outcomes in the U.S. A system built on repeat customers will never be optimized for people getting better.

A nurse practitioner contacted me privately after my last video about the military needing doctors and nurses. They asked to stay anonymous, and I’m going to respect that. What they shared wasn’t about one doctor or one patient—it was about incentives in the system. There are large, well-known companies that process blood work for huge portions of the country. According to them, results often come back framed in a way that suggests something is always still wrong. The concern is this: if a patient is told they’ve improved, fixed the issue, or no longer need monitoring, that patient may not be a repeat customer. So instead, people stay on the hamster wheel—more tests, more follow-ups, more worry. This isn’t me saying every lab or every provider is corrupt. It is saying we need to talk about how profit-driven incentives can distort healthcare outcomes in the U.S. A system built on repeat customers will never be optimized for people getting better.

Anttsinc

48,047 Aufrufe • vor 7 Monaten