正在加载视频...

视频加载失败

加载此视频时出现问题。这可能是由于临时网络问题，或视频可能不可用。

Can LLMs keep correct medical judgment when the context is misleading? LLMs ace medical exams. But real-world use cases rarely come with clean context the way exams do. So we investigated whether LLMs preserve correct medical judgment under misleading context. Our findings: > Across 11 frontier model configs, accuracy... show more

Hongjian Zhou

4,341 subscribers

52,730 次观看 • 1 个月前 •via X (Twitter)

Anya Rossi• Live Now

Private livecam show

0 条评论

暂无评论

原始帖子的评论将显示在这里

相关视频

LongWriter Unleashing 10,000+ Word Generation from Long Context LLMs discuss: Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation length is inherently bounded by the sample it has seen during supervised fine-tuning (SFT). In other words, their output limitation is due to the scarcity of long-output examples in existing SFT datasets. To address this, we introduce AgentWrite, an agent-based pipeline that decomposes ultra-long generation tasks into subtasks, enabling off-the-shelf LLMs to generate coherent outputs exceeding 20,000 words. Leveraging AgentWrite, we construct LongWriter-6k, a dataset containing 6,000 SFT data with output lengths ranging from 2k to 32k words. By incorporating this dataset into model training, we successfully scale the output length of existing models to over 10,000 words while maintaining output quality. We also develop LongBench-Write, a comprehensive benchmark for evaluating ultra-long generation capabilities. Our 9B parameter model, further improved through DPO, achieves state-of-the-art performance on this benchmark, surpassing even much larger proprietary models. In general, our work demonstrates that existing long context LLM already possesses the potential for a larger output window--all you need is data with extended output during model alignment to unlock this capability.

LongWriter Unleashing 10,000+ Word Generation from Long Context LLMs discuss: Current long context large language models (LLMs) can process inputs up to 100,000 tokens, yet struggle to generate outputs exceeding even a modest length of 2,000 words. Through controlled experiments, we find that the model's effective generation length is inherently bounded by the sample it has seen during supervised fine-tuning (SFT). In other words, their output limitation is due to the scarcity of long-output examples in existing SFT datasets. To address this, we introduce AgentWrite, an agent-based pipeline that decomposes ultra-long generation tasks into subtasks, enabling off-the-shelf LLMs to generate coherent outputs exceeding 20,000 words. Leveraging AgentWrite, we construct LongWriter-6k, a dataset containing 6,000 SFT data with output lengths ranging from 2k to 32k words. By incorporating this dataset into model training, we successfully scale the output length of existing models to over 10,000 words while maintaining output quality. We also develop LongBench-Write, a comprehensive benchmark for evaluating ultra-long generation capabilities. Our 9B parameter model, further improved through DPO, achieves state-of-the-art performance on this benchmark, surpassing even much larger proprietary models. In general, our work demonstrates that existing long context LLM already possesses the potential for a larger output window--all you need is data with extended output during model alignment to unlock this capability.

AK

50,995 次观看 • 1 年前

Does LLM really need to be a helpful assistant all the time? No. If you want to simulate people, “perfectly helpful” could be the wrong objective. Meet OdysSim, a journey toward LLMs beyond assistants, as behavioral foundation models (10B tokens of real human behavior; 23 sim benchmarks, finally in one place. new open models: outperform or on par with GPT-5.5, Gemini 3.1, or Claude Opus 4.7 in many behavior-sim dimensions). Human behavior simulation is becoming essential. Agent evaluation needs realistic users before real users show up. Medical and classroom training need realistic patients and students. Social science needs synthetic participants at scale. But real people are not ideal assistants. Real patients panic or ignore good advice. Real students misunderstand. Real customers are vague, picky, impatient, or simply leave. Human behavior is messy, diverse, and often imperfect. Frontier LLMs are getting better at math, code, and long-horizon tasks. They are NOT getting better at simulating human behavior. If anything, they drift the other way: more assistant-ish, more homogeneous, fewer of the errors and quirks real humans show. This is no accident. The whole pipeline is built for helpfulness and task success, not behavioral realism. And you can't prompt your way out of that. So we rethink the recipe from scratch and release: 🧠 The OdysSim corpus: 21.4M real human interactions (~10B tokens) from 62 sources, every conversation retrofitted with social grounding (who is talking, and why) 📏 SOUL-Index: 23 human-behavior benchmarks unified into one suite across 5 axes 🤖 OSim-8B: open weights; tops more SOUL-Index benchmarks than any frontier model, acts more like a real user than any of them on τ-bench (nearly matching real humans in the reaction dimension), and writes far more human-like text along the way.

Does LLM really need to be a helpful assistant all the time? No. If you want to simulate people, “perfectly helpful” could be the wrong objective. Meet OdysSim, a journey toward LLMs beyond assistants, as behavioral foundation models (10B tokens of real human behavior; 23 sim benchmarks, finally in one place. new open models: outperform or on par with GPT-5.5, Gemini 3.1, or Claude Opus 4.7 in many behavior-sim dimensions). Human behavior simulation is becoming essential. Agent evaluation needs realistic users before real users show up. Medical and classroom training need realistic patients and students. Social science needs synthetic participants at scale. But real people are not ideal assistants. Real patients panic or ignore good advice. Real students misunderstand. Real customers are vague, picky, impatient, or simply leave. Human behavior is messy, diverse, and often imperfect. Frontier LLMs are getting better at math, code, and long-horizon tasks. They are NOT getting better at simulating human behavior. If anything, they drift the other way: more assistant-ish, more homogeneous, fewer of the errors and quirks real humans show. This is no accident. The whole pipeline is built for helpfulness and task success, not behavioral realism. And you can't prompt your way out of that. So we rethink the recipe from scratch and release: 🧠 The OdysSim corpus: 21.4M real human interactions (~10B tokens) from 62 sources, every conversation retrofitted with social grounding (who is talking, and why) 📏 SOUL-Index: 23 human-behavior benchmarks unified into one suite across 5 axes 🤖 OSim-8B: open weights; tops more SOUL-Index benchmarks than any frontier model, acts more like a real user than any of them on τ-bench (nearly matching real humans in the reaction dimension), and writes far more human-like text along the way.

Xuhui Zhou

140,846 次观看 • 1 个月前

Call for Genuine Dialogue with Foreign Medical Graduates (FMGs) We are deeply concerned by the recent actions of the National Medical Commission National Medical Commission regarding the new policies affecting Foreign Medical Graduates #FMGs . Despite multiple attempts by #FMGs to engage in constructive dialogue about these policies, the doors have been closed to them, leaving many feeling unheard and marginalized. Key Issues Highlighted: 1.Lack of Engagement with FMGs: •When FMGs approached the NMC to discuss the recent circulars impacting their education and career pathways, they were met with a closed door. This lack of openness contradicts the NMC’s stated commitment to inclusivity and transparency. •On other occasions, the NMC has publicly invited dialogue with medical professionals on various issues. The exclusion of FMGs from such conversations suggests a discrepancy in how different members of the medical community are treated. 2.The Need for Authentic Communication: •Effective policymaking requires genuine engagement with all stakeholders. FMGs, who represent a significant portion of the medical workforce, deserve to have their voices heard and their concerns addressed in a fair and transparent manner. •The perception that the NMC is avoiding meaningful dialogue with FMGs undermines trust and suggests a lack of willingness to address the real challenges faced by these dedicated professionals. Our Stance: We at @udfaindia believe that a body like the NMC, which is designed to represent and listen to the medical community, must be open to all its members. Ignoring or sidelining any group is counterproductive and contrary to the principles of equality and fairness. Why This Matters: •Equity and Representation: All medical professionals, regardless of where they received their education, should have the opportunity to engage with regulatory bodies and influence policies that affect their careers and lives. •Transparency and Trust: For the NMC to maintain its credibility and legitimacy, it must foster an environment of openness and responsiveness, especially towards those who are most impacted by its decisions. •Potential for Unrest: If the NMC continues to act unilaterally without genuine dialogue, it risks alienating a significant portion of the medical community. This could lead to increased discontent and potential protests, which is not in anyone’s interest. Our Call to Action: •Open the Doors to Dialogue: We urge the NMC to actively engage with FMGs and their representatives to discuss the implications of recent policies and seek mutually acceptable solutions. •Demonstrate True Commitment: Show that the commitment to inclusivity and open dialogue is not just for show, but a fundamental principle guiding the NMC’s actions. •Listen and Represent: The NMC must fulfill its mandate to listen to and represent all members of the medical community, including FMGs who have contributed significantly to healthcare in India. Let’s Work Together for a Fair and Inclusive Medical Community. We remain hopeful that the NMC will reconsider its approach and embrace a more inclusive and open stance towards FMGs. It is only through unity and collaboration that we can ensure a just and effective healthcare system for all. #OpenDialogue #FairTreatment #SupportFMGs #NMCAccountability #FMGS National Medical Commission ALL FMGs ASSOCIATION(AFA) Free Press Journal Anil Sharda ravish ndtv Karn Pratap Singh

Call for Genuine Dialogue with Foreign Medical Graduates (FMGs) We are deeply concerned by the recent actions of the National Medical Commission National Medical Commission regarding the new policies affecting Foreign Medical Graduates #FMGs . Despite multiple attempts by #FMGs to engage in constructive dialogue about these policies, the doors have been closed to them, leaving many feeling unheard and marginalized. Key Issues Highlighted: 1.Lack of Engagement with FMGs: •When FMGs approached the NMC to discuss the recent circulars impacting their education and career pathways, they were met with a closed door. This lack of openness contradicts the NMC’s stated commitment to inclusivity and transparency. •On other occasions, the NMC has publicly invited dialogue with medical professionals on various issues. The exclusion of FMGs from such conversations suggests a discrepancy in how different members of the medical community are treated. 2.The Need for Authentic Communication: •Effective policymaking requires genuine engagement with all stakeholders. FMGs, who represent a significant portion of the medical workforce, deserve to have their voices heard and their concerns addressed in a fair and transparent manner. •The perception that the NMC is avoiding meaningful dialogue with FMGs undermines trust and suggests a lack of willingness to address the real challenges faced by these dedicated professionals. Our Stance: We at @udfaindia believe that a body like the NMC, which is designed to represent and listen to the medical community, must be open to all its members. Ignoring or sidelining any group is counterproductive and contrary to the principles of equality and fairness. Why This Matters: •Equity and Representation: All medical professionals, regardless of where they received their education, should have the opportunity to engage with regulatory bodies and influence policies that affect their careers and lives. •Transparency and Trust: For the NMC to maintain its credibility and legitimacy, it must foster an environment of openness and responsiveness, especially towards those who are most impacted by its decisions. •Potential for Unrest: If the NMC continues to act unilaterally without genuine dialogue, it risks alienating a significant portion of the medical community. This could lead to increased discontent and potential protests, which is not in anyone’s interest. Our Call to Action: •Open the Doors to Dialogue: We urge the NMC to actively engage with FMGs and their representatives to discuss the implications of recent policies and seek mutually acceptable solutions. •Demonstrate True Commitment: Show that the commitment to inclusivity and open dialogue is not just for show, but a fundamental principle guiding the NMC’s actions. •Listen and Represent: The NMC must fulfill its mandate to listen to and represent all members of the medical community, including FMGs who have contributed significantly to healthcare in India. Let’s Work Together for a Fair and Inclusive Medical Community. We remain hopeful that the NMC will reconsider its approach and embrace a more inclusive and open stance towards FMGs. It is only through unity and collaboration that we can ensure a just and effective healthcare system for all. #OpenDialogue #FairTreatment #SupportFMGs #NMCAccountability #FMGS National Medical Commission ALL FMGs ASSOCIATION(AFA) Free Press Journal Anil Sharda ravish ndtv Karn Pratap Singh

Dr.Arun Kumar

13,523 次观看 • 2 年前

Dear Tarun Chitra 1. We are the original creators of DeSci back in 2016. What DeSci has become today is largely unrelated with its original model of producing rigorous peer-reviewed scientific studies published in reputable medical journals. The model we introduced. We are tirelessly fighting against pseudoscience, and we are showing the world that people can understand the difference between legit science and pseudoscience with the success of $INNBCV. Yes, meritocracy is possible in crypto. Even against all odds. 2. We are the only project in the entire crypto space that ever funded, performed, and published highly innovative HIV cure research ( We are the project that produced the first peer-reviewed study on blockchain-based biomedical data storage in the world’s most reputable scientific network, Springer Nature ( $INNBCV is not for the privileged few; it is for the many. We resisted all the pressure from those who wanted us to provide big allocations to VIPs of other DAOs “because it is good for the marketing” and put our users first, ensuring a fair launch, a launch for the people, and they turned $70k into $2,000,000. $INNBCV shows that you can have a sustainable model, provided you are backed by actual science. And thanks to the amazing guys at daos.fun baoskee and Solana community. Behind our project there is the sweat and blood of years of work to produce publications in the most reputable medical journals. Just to put things into perspective, it took us 3 years to publish our latest work in Springer Nature. 3. Unlike many other projects, we had no ICO/VCs, meaning we had to prove ourselves every single day because we are only supported by our community. If we deliver products, we survive; it is either publish or perish for us, and that’s why we have such a close connection to our community. $INNBCV is a struggler, $INNBCV is a survivor, $INNBCV is not for the privilege of the few but for the people. Our community makes it possible by supporting us. You guys are the real heroes.

Dear Tarun Chitra 1. We are the original creators of DeSci back in 2016. What DeSci has become today is largely unrelated with its original model of producing rigorous peer-reviewed scientific studies published in reputable medical journals. The model we introduced. We are tirelessly fighting against pseudoscience, and we are showing the world that people can understand the difference between legit science and pseudoscience with the success of $INNBCV. Yes, meritocracy is possible in crypto. Even against all odds. 2. We are the only project in the entire crypto space that ever funded, performed, and published highly innovative HIV cure research ( We are the project that produced the first peer-reviewed study on blockchain-based biomedical data storage in the world’s most reputable scientific network, Springer Nature ( $INNBCV is not for the privileged few; it is for the many. We resisted all the pressure from those who wanted us to provide big allocations to VIPs of other DAOs “because it is good for the marketing” and put our users first, ensuring a fair launch, a launch for the people, and they turned $70k into $2,000,000. $INNBCV shows that you can have a sustainable model, provided you are backed by actual science. And thanks to the amazing guys at daos.fun baoskee and Solana community. Behind our project there is the sweat and blood of years of work to produce publications in the most reputable medical journals. Just to put things into perspective, it took us 3 years to publish our latest work in Springer Nature. 3. Unlike many other projects, we had no ICO/VCs, meaning we had to prove ourselves every single day because we are only supported by our community. If we deliver products, we survive; it is either publish or perish for us, and that’s why we have such a close connection to our community. $INNBCV is a struggler, $INNBCV is a survivor, $INNBCV is not for the privilege of the few but for the people. Our community makes it possible by supporting us. You guys are the real heroes.

InnovativeBioresearch🇮🇹

10,867 次观看 • 1 年前

Here's what The Browser Company's AI eng & ML teams are working on for Dia right now: (This is a pitch to come work for us; info at end) 🤖 COMPUTER USE – we've built our own bespoke APIs on top of Chromium to optimize latency, accuracy, and cost of computer-using agents. Demo attached. Big breakthroughs here in recent weeks. 🛡️ ON-DEVICE MODELS – we've built our own custom infra to run everything from encoder-only models to full LLMs on device. It's cross-platform, supports LoRa adapters, and optimized for the GPU. This system preserves privacy and enables fast inference times. 🧠 MEMORY – with your permission, Dia automatically tailors your AI experiences to you, personally, based on the tabs you open while browsing normally every day. We're also bringing vertical memory to specific features. ♻️ DATA FLYWHEELS – our Fall/Winter P0 is to double-down on training custom models based on implicit signals from daily use of Dia. Dia should get smarter and more useful the more people use it. Whether via RL, auto-generated prompts, or otherwise. If this work sounds interesting to you please visit our jobs page or email careers@thebrowser.company. Hiring nearly every related role -- from ML engineers to people prototyping with AI and context/prompt writers -- everyone encouraged to apply!!

Here's what The Browser Company's AI eng & ML teams are working on for Dia right now: (This is a pitch to come work for us; info at end) 🤖 COMPUTER USE – we've built our own bespoke APIs on top of Chromium to optimize latency, accuracy, and cost of computer-using agents. Demo attached. Big breakthroughs here in recent weeks. 🛡️ ON-DEVICE MODELS – we've built our own custom infra to run everything from encoder-only models to full LLMs on device. It's cross-platform, supports LoRa adapters, and optimized for the GPU. This system preserves privacy and enables fast inference times. 🧠 MEMORY – with your permission, Dia automatically tailors your AI experiences to you, personally, based on the tabs you open while browsing normally every day. We're also bringing vertical memory to specific features. ♻️ DATA FLYWHEELS – our Fall/Winter P0 is to double-down on training custom models based on implicit signals from daily use of Dia. Dia should get smarter and more useful the more people use it. Whether via RL, auto-generated prompts, or otherwise. If this work sounds interesting to you please visit our jobs page or email [email protected]. Hiring nearly every related role -- from ML engineers to people prototyping with AI and context/prompt writers -- everyone encouraged to apply!!

Josh Miller

67,937 次观看 • 11 个月前

This might be the greatest counting video ever. I don’t want to spoil it, so watch it first, then come on back and let’s discuss… ____ There are so many fantastic things happening here as our hero (age: 2 years, 7 months) counts his plastic eggs. In this video we get a sense that he is well on his way to mastering two important early math skills: rote counting and one-to-one correspondence. Rote counting is the process of remembering and reciting numbers in their correct order. It’s a memorized skill - but an important one that helps children to understand that numbers exist in a fixed sequence. One to one correspondence is the process of matching each spoken number to one (and only one) physical object. This little guy pulls it off perfectly, counting with accuracy to 11. His inclusion of 8:30 between 8 and 9 was not only the best laugh I’ve had all week, but a telling stroke of genius as well. Where did it come from? I’d be willing to wager it’s his bedtime - which he’s learned comes between 8 and 9pm. So smart! 🧠 This little mathematician was shared to TT by smaxwell18.

This might be the greatest counting video ever. I don’t want to spoil it, so watch it first, then come on back and let’s discuss… ____ There are so many fantastic things happening here as our hero (age: 2 years, 7 months) counts his plastic eggs. In this video we get a sense that he is well on his way to mastering two important early math skills: rote counting and one-to-one correspondence. Rote counting is the process of remembering and reciting numbers in their correct order. It’s a memorized skill - but an important one that helps children to understand that numbers exist in a fixed sequence. One to one correspondence is the process of matching each spoken number to one (and only one) physical object. This little guy pulls it off perfectly, counting with accuracy to 11. His inclusion of 8:30 between 8 and 9 was not only the best laugh I’ve had all week, but a telling stroke of genius as well. Where did it come from? I’d be willing to wager it’s his bedtime - which he’s learned comes between 8 and 9pm. So smart! 🧠 This little mathematician was shared to TT by smaxwell18.

Dan Wuori

115,189 次观看 • 1 年前

Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.

Free NVIDIA GPU with 16 GB VRAM GPU for Running Local LLMs! If you want to master local LLMs but you're waiting until you can afford a $1,500 GPU, you're honestly not going to make it. The open source AI ecosystem is moving way too fast for you to wait on your budget to catch up. Especially when you can build a bleeding edge inference engine from scratch right now, completely for free. You don't need a heavy local rig to start. Google is literally letting you use an enterprise grade NVIDIA Tesla T4 GPU for $0/hour. At standard cloud computing rates (~$0.20/hr), Google Colab’s 4 hour daily free tier hands you roughly $24 worth of data center tier GPU compute every single month. And most people just waste it. Let’s talk about the hardware you get access to for free. The NVIDIA Tesla T4 is an absolute workhorse: - Architecture: NVIDIA Turing (TU104) - VRAM: 16GB GDDR6 (320 GB/s bandwidth) - Compute: 320 Tensor Cores | 2560 CUDA Cores - Performance: 130 TOPS INT8 | 8.1 TFLOPS FP32 - Power: Sipping energy at a max 70W TDP This is the exact same hardware I used to run DeepMind's Gemma 4 26B A4B QAT MoE at a 250,000 context window without a single Out Of Memory (OOM) crash. If you have a web browser and 10 minutes, you have everything you need. I’ve put together a fully documented, cell by cell Google Colab notebook that teaches you exactly how to do this. Here is what the notebook actually teaches you: - How to provision an Ubuntu Linux environment with CUDA 13.0 and verify your driver stack. - How to pull the source code and compile the latest llama.cpp C++ binaries from scratch, specifically optimizing the build for your exact GPU using the -DCMAKE_CUDA_ARCHITECTURES=native flag. - How to directly download quantized local LLMs (GGUF format) straight from HuggingFace using the CLI. - How to manage 16GB VRAM limits, offload neural network layers to the GPU, and push massive context windows. Compile raw llama.cpp, ollama run a model, or spin up the LM Studio CLI. Pick whatever stack you are comfortable with. just start building. No hardware. No credit card. No excuses. Bookmark this post right now so you don't lose the tutorial. Even if you don't have time to run it today, you are going to want this workflow in your engineering toolkit. The link to the free Colab Notebook is in the comments below. Lemme know if you need more tutorials like this.

Alok

178,744 次观看 • 20 天前

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

This Chinese developer launched Llama 70B locally on a MacBook on a plane and for a full 11 hours without internet ran client projects. He was sitting by the window on a transatlantic flight with a MacBook Pro M4 with 64 GB of memory. WiFi on board cost $25 for the flight. He declined. No cloud API, no connection to Anthropic or OpenAI servers, no internet at all. Just a local Llama 3.3 70B on bf16 and his own orchestrator script. The model runs through llama.cpp. Generation speed, 71 tokens per second. Context around 60,000 tokens. Memory usage, 48.6 GiB out of 64. Battery at takeoff, 3 hours 21 minutes. And he gave the orchestrator this system prompt before takeoff: "You are an offline orchestrator running on a single MacBook. There is no network. The only resources you have are local files in /Users/dev/work, the Llama 70B inference server at localhost:8080, and a battery budget of 3 hours 21 minutes. Process the queue at /Users/dev/work/queue.jsonl (one client task per line). For each task: draft → run local evals → save artefact to /Users/dev/work/done/. Save context checkpoints every 12 tasks so you can resume after a battery swap. Stop only on empty queue or when battery drops below 5%." So the system knows exactly what resources it is running on. It knows it has no connection to the outside world for the next 11 hours. It knows it has finite memory and a finite battery. It knows the human will not intervene until the plane lands. The system runs in 1 loop. Takes a task from the queue, runs it through inference, saves the artifact, writes a checkpoint. Task after task, just like that. And only when the battery drops below 5% does the orchestrator automatically pause, waits for the laptop to switch to the backup power bank, and continues from the last checkpoint. Here is what the system actually writes in his log during the flight: "saved context checkpoint 8 of 12 (pos_min = 488, pos_max = 50118, size = 62.813 MiB)" "restored context checkpoint (pos_min = 488, pos_max = 50118)" "prompt processing progress: n_tokens = 50 / 60 818" "task 37016 done | tps = 71 s tokens text → /Users/dev/work/done/proposal_westside.md" Outside the window, clouds, blue sky, and no WiFi. On the tray, 1 MacBook, an open terminal on 2 screens, and an inference server on localhost. From what I have observed, this is the cleanest offline AI workflow I have seen in the past year: 11 hours of flight, $0 for WiFi, and the entire client queue closed before landing.

Blaze

1,839,572 次观看 • 2 个月前

This is the second of two posts I’m making about the teaching of Andy Stanley because I have a burden to warn people to be like the Bereans "examining the Scriptures daily to see if these things were so." (Acts 17:11). I have attached a short video clip where Stanley says we don’t know Jesus rose from the dead because the bible tells us so. He says it’s the other way around. Now how do we know Jesus bodily rose from the dead? Did any of us see it happen? No, it happened 2000 years ago. But haven’t people researched the resurrection and found all sorts of evidence consistent with Him rising from the dead. Yes, but such evidence, though powerful, and we can certainly use in our witnessing, does not ultimately prove He rose from the dead. In fact, no matter what evidence we point to in geology, biology, astronomy etc., none of this proves in an ultimate sense the bible is true. Now it is certainly true such evidence properly interpreted does confirm the bible’s history. For instance, the molecule of heredity DNA is a complex information system and language system. No one has seen matter produce information or a language from matter by natural processes. Our observations and experience show information and language have to come from an intelligence. Such evidence confirms an intelligence behind life. This certainly confirms the first verse of the bible, “In the beginning God created… .” But nonetheless, it’s not absolute proof. Think about it. We are finite beings living in the present. We don’t know everything. We don’t know how much we don’t know or do know in relation to whatever there is to know! When we try to interpret evidence of the present in relation to the past, how do we know we have all the relevant information to make the correct interpretation. Some information we don’t have could totally change our interpretation. That has certainly happened with scientists solving crimes using circumstantial evidence. When new evidence comes along, some of the interpretations change and certain people thought to be guilty were found they were innocent after-all. We need to have all the information needed. But we can’t know everything. However we have a book that claims over three thousand times to be the Word of God. This book claims that God moved people by His spirit to write His Word, what He wants revealed to us about life, the universe and history. This book, the bible, tells us that God knows everything, He is infinite in knowledge and wisdom. He has all information. “In whom are hidden all the treasures of wisdom and knowledge” (Colossians 2:3). If God’s Word is what it claims to be (and it is), then the infinite Creator God has revealed to us the key information we need to know to have the ability to correctly interpret this world in relation to the past, present and its purpose and meaning. “Therefore, we never stop thanking God that when you received his message from us, you didn’t think of our words as mere human ideas. You accepted what we said as the very word of God—which, of course, it is. And this word continues to work in you who believe” (1 Thessalonians 2:13). This means if we build our thinking on God’s Word, we build a Christian worldview to enable us to look at the world through “biblical glasses” and have the ability to correctly interpret and understand it. And Genesis 1-11 is the history that is foundational to the rest of the bible and thus our worldview. So, when we start with God’s Word we learn that the God revealed to us in the bible is the One Who created all things. Now by just looking at the world, for instance at DNA, we might deduce that there’s an intelligence behind life. But we would not know who that intelligence is unless revealed to us. The bible reveals who that intelligence is, God the father, Son, and Holy Spirit. But, if we just looked at the world with all its death, suffering and disease, we could assume that the intelligence behind life must be an ogre to make such a violent disease ridden suffering world. But when we start from God’s Word, we understand there was no death or disease to start with, but these entered the world because of sin. We also find man has a problem called sin which alienated him from God. We even find in Genesis that God promised someone would come to save us from our sin and restore our relationship with God (Genesis 3:15; 3:21). We learn later on that “someone” is Jesus--the one who became flesh for us, the babe in a manger 2000 years ago. It's important to understand we can’t know anything absolutely unless an absolute authority has revealed to us what we need to know. Now Andy Stanley claims we don’t know Jesus rose from the dead because the bible tells us so, but claims it’s the other way round, that because Jesus rose from the dead, we can believe what is says about this in the bible. In reality, Andy Stanley is actually claiming he knows all information about the resurrection to know it’s true so then he can proclaim what the bible says about the resurrection is true. This is not so. And remember from the previous post, Andy Stanley accepts man’s view of evolution and millions of years as true to declare what Genesis records about creation is not all true. So why shouldn't people take the word of others who claim Jesus didn't rise from the dead to then declare the account of the resurrection in the gospels can't be true. Stanley, as a finite fallen human being with very limited knowledge, starts outside the bible to go to the bible to make pronouncements over God’s written Word. No wonder he rejects a literal Genesis. Sadly, this is the case for the majority of our church leaders and Christian academics, particularly when it comes to Genesis. Think about it. Really Stanley is acting in accord with our sin nature because of what happened in Genesis 3. Part of our sin nature concerns us wanting to be our own god. Consider Genesis 3:1 and Genesis 3:5, the temptation by the devil. Satan tempted Adam and Eve to question God’s Word, and be want to be like God to decide good and evil, truth, etc., for themselves. In actuality, the statements Andy Stanley is making about Christianity and God’s Word are reflecting this sin problem we have. He is letting his sin nature master over him in this instance instead of letting God’s Word tell us clearly what we should believe. And I would say that about all Christians who reinterpret parts of the bible (like Genesis) because of beliefs from outside the bible. Now the whole bible is actually about Jesus, from the very first verse, that Jesus is the Creator (Colossians 1:16 “For by him all things were created,”), & and he is the Savior (Revelation 5:9), to the very last verse, “He who testifies to these things says, “Surely I am coming soon.” Amen. Come, Lord Jesus! The grace of the Lord Jesus be with all. Amen” (Revelation 22:20–21) God reveals all we need to know about Jesus in His Word. We know Jesus rose from the dead because the bible tells us so! We know we are sinners because the bible tells us so. We know we can be saved through faith in Christ because the bible tells us so. We know we need to repent of sin because the bible tells us so. As Christians we know we will spend eternity in Heaven because the bible tells us so. “So faith comes from hearing, and hearing through the word of God” (Romans 10:17). I still remember singing the chorus as a child Jesus loves me this I know, for the bible tells me so. Yes, the bible tells me so – that’s how know who Jesus is, that He is the Creator, that he died and rose from the dead, and that He is our Savior.

This is the second of two posts I’m making about the teaching of Andy Stanley because I have a burden to warn people to be like the Bereans "examining the Scriptures daily to see if these things were so." (Acts 17:11). I have attached a short video clip where Stanley says we don’t know Jesus rose from the dead because the bible tells us so. He says it’s the other way around. Now how do we know Jesus bodily rose from the dead? Did any of us see it happen? No, it happened 2000 years ago. But haven’t people researched the resurrection and found all sorts of evidence consistent with Him rising from the dead. Yes, but such evidence, though powerful, and we can certainly use in our witnessing, does not ultimately prove He rose from the dead. In fact, no matter what evidence we point to in geology, biology, astronomy etc., none of this proves in an ultimate sense the bible is true. Now it is certainly true such evidence properly interpreted does confirm the bible’s history. For instance, the molecule of heredity DNA is a complex information system and language system. No one has seen matter produce information or a language from matter by natural processes. Our observations and experience show information and language have to come from an intelligence. Such evidence confirms an intelligence behind life. This certainly confirms the first verse of the bible, “In the beginning God created… .” But nonetheless, it’s not absolute proof. Think about it. We are finite beings living in the present. We don’t know everything. We don’t know how much we don’t know or do know in relation to whatever there is to know! When we try to interpret evidence of the present in relation to the past, how do we know we have all the relevant information to make the correct interpretation. Some information we don’t have could totally change our interpretation. That has certainly happened with scientists solving crimes using circumstantial evidence. When new evidence comes along, some of the interpretations change and certain people thought to be guilty were found they were innocent after-all. We need to have all the information needed. But we can’t know everything. However we have a book that claims over three thousand times to be the Word of God. This book claims that God moved people by His spirit to write His Word, what He wants revealed to us about life, the universe and history. This book, the bible, tells us that God knows everything, He is infinite in knowledge and wisdom. He has all information. “In whom are hidden all the treasures of wisdom and knowledge” (Colossians 2:3). If God’s Word is what it claims to be (and it is), then the infinite Creator God has revealed to us the key information we need to know to have the ability to correctly interpret this world in relation to the past, present and its purpose and meaning. “Therefore, we never stop thanking God that when you received his message from us, you didn’t think of our words as mere human ideas. You accepted what we said as the very word of God—which, of course, it is. And this word continues to work in you who believe” (1 Thessalonians 2:13). This means if we build our thinking on God’s Word, we build a Christian worldview to enable us to look at the world through “biblical glasses” and have the ability to correctly interpret and understand it. And Genesis 1-11 is the history that is foundational to the rest of the bible and thus our worldview. So, when we start with God’s Word we learn that the God revealed to us in the bible is the One Who created all things. Now by just looking at the world, for instance at DNA, we might deduce that there’s an intelligence behind life. But we would not know who that intelligence is unless revealed to us. The bible reveals who that intelligence is, God the father, Son, and Holy Spirit. But, if we just looked at the world with all its death, suffering and disease, we could assume that the intelligence behind life must be an ogre to make such a violent disease ridden suffering world. But when we start from God’s Word, we understand there was no death or disease to start with, but these entered the world because of sin. We also find man has a problem called sin which alienated him from God. We even find in Genesis that God promised someone would come to save us from our sin and restore our relationship with God (Genesis 3:15; 3:21). We learn later on that “someone” is Jesus--the one who became flesh for us, the babe in a manger 2000 years ago. It's important to understand we can’t know anything absolutely unless an absolute authority has revealed to us what we need to know. Now Andy Stanley claims we don’t know Jesus rose from the dead because the bible tells us so, but claims it’s the other way round, that because Jesus rose from the dead, we can believe what is says about this in the bible. In reality, Andy Stanley is actually claiming he knows all information about the resurrection to know it’s true so then he can proclaim what the bible says about the resurrection is true. This is not so. And remember from the previous post, Andy Stanley accepts man’s view of evolution and millions of years as true to declare what Genesis records about creation is not all true. So why shouldn't people take the word of others who claim Jesus didn't rise from the dead to then declare the account of the resurrection in the gospels can't be true. Stanley, as a finite fallen human being with very limited knowledge, starts outside the bible to go to the bible to make pronouncements over God’s written Word. No wonder he rejects a literal Genesis. Sadly, this is the case for the majority of our church leaders and Christian academics, particularly when it comes to Genesis. Think about it. Really Stanley is acting in accord with our sin nature because of what happened in Genesis 3. Part of our sin nature concerns us wanting to be our own god. Consider Genesis 3:1 and Genesis 3:5, the temptation by the devil. Satan tempted Adam and Eve to question God’s Word, and be want to be like God to decide good and evil, truth, etc., for themselves. In actuality, the statements Andy Stanley is making about Christianity and God’s Word are reflecting this sin problem we have. He is letting his sin nature master over him in this instance instead of letting God’s Word tell us clearly what we should believe. And I would say that about all Christians who reinterpret parts of the bible (like Genesis) because of beliefs from outside the bible. Now the whole bible is actually about Jesus, from the very first verse, that Jesus is the Creator (Colossians 1:16 “For by him all things were created,”), & and he is the Savior (Revelation 5:9), to the very last verse, “He who testifies to these things says, “Surely I am coming soon.” Amen. Come, Lord Jesus! The grace of the Lord Jesus be with all. Amen” (Revelation 22:20–21) God reveals all we need to know about Jesus in His Word. We know Jesus rose from the dead because the bible tells us so! We know we are sinners because the bible tells us so. We know we can be saved through faith in Christ because the bible tells us so. We know we need to repent of sin because the bible tells us so. As Christians we know we will spend eternity in Heaven because the bible tells us so. “So faith comes from hearing, and hearing through the word of God” (Romans 10:17). I still remember singing the chorus as a child Jesus loves me this I know, for the bible tells me so. Yes, the bible tells me so – that’s how know who Jesus is, that He is the Creator, that he died and rose from the dead, and that He is our Savior.

Ken Ham

233,976 次观看 • 3 年前

We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:

We’re excited to introduce ShinkaEvolve: An open-source framework that evolves programs for scientific discovery with unprecedented sample-efficiency. Blog: Code: Like AlphaEvolve and its variants, our framework leverages LLMs to find state-of-the-art solutions to complex problems, but using orders of magnitude fewer resources! Many evolutionary AI systems are powerful but act like brute-force engines, burning thousands of samples to find good solutions. This makes discovery slow and expensive. We took inspiration from the efficiency of nature. ‘Shinka’ (進化) is Japanese for evolution, and we designed our system to be just as resourceful. On the classic circle packing optimization problem, ShinkaEvolve discovered a new state-of-the-art solution using only 150 samples. This is a big leap in efficiency compared to previous methods that required thousands of evaluations. We applied ShinkaEvolve to a diverse set of hard problems with real-world applications: 1/ AIME Math Reasoning: It evolved sophisticated agentic scaffolds that significantly outperform strong baselines, discovering an entire Pareto frontier of solutions trading performance for efficiency. 2/ Competitive Programming: On ALE-Bench (a benchmark for NP-Hard optimization problems), ShinkaEvolve took the best existing agent's solutions and improved them, turning a 5th place solution on one task into a 2nd place leaderboard rank in a competitive programming competition. 3/ LLM Training: We even turned ShinkaEvolve inward to improve LLMs themselves. It tackled the open challenge of designing load balancing losses for Mixture-of-Experts (MoE) models. It discovered a novel loss function that leads to better expert specialization and consistently improves model performance and perplexity. ShinkaEvolve achieves its remarkable sample-efficiency through three key innovations that work together: (1) an adaptive parent sampling strategy to balance exploration and exploitation, (2) novelty-based rejection filtering to avoid redundant work, and (3) a bandit-based LLM ensemble that dynamically picks the best model for the job. By making ShinkaEvolve open-source and highly sample-efficient, our goal is to democratize access to advanced, open-ended discovery tools. Our vision for ShinkaEvolve is to be an easy-to-use companion tool to help scientists and engineers with their daily work. We believe that building more efficient, nature-inspired systems is key to unlocking the future of AI-driven scientific research. We are excited to see what the community builds with it! Learn more in our technical report:

Sakana AI

359,537 次观看 • 10 个月前

The human brain is truly a marvel of nature. If you horribly reductive, and boiled it down to a language model, you'd be looking at roughly 100 trillon parameters running as a sparse MoE architecture Only about 1-5% of neurons fire at any given moment, meaning the brain "activates" maybe 1-5 trillion parameters per inference step. For context, the largest AI models we've built probably top out around 5 trillion parameters. The brain is roughly 100x larger. Even its active params at any given moment are larger than almost every model in existence today. Here's what melts my brain (pun intnended) though Your brain does all of this on about 20 watts of power, less than a dim light bulb. Training a frontier AI model consumes enough electricity to power small cities for months. Running inference across data centers pulls megawatts. Your brain runs 24/7 for 80+ years on the equivalent of a phone charger. We haven't come close to matching the brain's scale. And we're not even in the same universe when it comes to efficiency. Evolution spent 500 million yrs optimizing the most energy-efficient intelligence architecture ever known. we're trying to brute force our way there with compute and electricity. Nature is still the best engineer in the room.

The human brain is truly a marvel of nature. If you horribly reductive, and boiled it down to a language model, you'd be looking at roughly 100 trillon parameters running as a sparse MoE architecture Only about 1-5% of neurons fire at any given moment, meaning the brain "activates" maybe 1-5 trillion parameters per inference step. For context, the largest AI models we've built probably top out around 5 trillion parameters. The brain is roughly 100x larger. Even its active params at any given moment are larger than almost every model in existence today. Here's what melts my brain (pun intnended) though Your brain does all of this on about 20 watts of power, less than a dim light bulb. Training a frontier AI model consumes enough electricity to power small cities for months. Running inference across data centers pulls megawatts. Your brain runs 24/7 for 80+ years on the equivalent of a phone charger. We haven't come close to matching the brain's scale. And we're not even in the same universe when it comes to efficiency. Evolution spent 500 million yrs optimizing the most energy-efficient intelligence architecture ever known. we're trying to brute force our way there with compute and electricity. Nature is still the best engineer in the room.

am.will

130,733 次观看 • 3 个月前

📍Theory of Space (accepted at #ICLR2026) Theory of Mind → hidden mental states Theory of Space → hidden spatial beliefs from passive observers “What do I know?” to active explorers “What don’t I know, and how do I reduce that uncertainty?” Theory of Space is to evaluate if foundation models can actively construct, revise, and exploit internal spatial beliefs. We quantify Active-Passive Gap. Not just measure task accuracy, but how much uncertainty is reduced per step, and how many steps are needed in total for agents to build stable spatial beliefs. Exploration should prioritize information gain and reduce uncertainty per step. Instead, we observe LLMs/VLMs explore redundantly with stalled belief updates. Key findings: 1. Active agents perform worse than rule based programs 2. Cognitive Map Failures & Belief Drift (beliefs about previously observed objects degrades over time; new updates corrupt earlier correct perceptions) 3. Poor Vision Identification & Belief Inertia in Belief Revision Website: Code: Data: Theory of Space is a joint effort of Northwestern Engineering, Stanford AI Lab, Allen School, Cornell Computer Science. Led by the amazing WilliamZhang, jointly done with Zihan Huang, yue wang, Jieyu Zhang, Lester Xue, @wzihanw, Qineng Wang, Keshigeyan Chandrasegaran, Ruohan Zhang, Yejin Choi, Ranjay Krishna, Jiajun Wu, Fei-Fei Li

📍Theory of Space (accepted at #ICLR2026) Theory of Mind → hidden mental states Theory of Space → hidden spatial beliefs from passive observers “What do I know?” to active explorers “What don’t I know, and how do I reduce that uncertainty?” Theory of Space is to evaluate if foundation models can actively construct, revise, and exploit internal spatial beliefs. We quantify Active-Passive Gap. Not just measure task accuracy, but how much uncertainty is reduced per step, and how many steps are needed in total for agents to build stable spatial beliefs. Exploration should prioritize information gain and reduce uncertainty per step. Instead, we observe LLMs/VLMs explore redundantly with stalled belief updates. Key findings: 1. Active agents perform worse than rule based programs 2. Cognitive Map Failures & Belief Drift (beliefs about previously observed objects degrades over time; new updates corrupt earlier correct perceptions) 3. Poor Vision Identification & Belief Inertia in Belief Revision Website: Code: Data: Theory of Space is a joint effort of Northwestern Engineering, Stanford AI Lab, Allen School, Cornell Computer Science. Led by the amazing WilliamZhang, jointly done with Zihan Huang, yue wang, Jieyu Zhang, Lester Xue, @wzihanw, Qineng Wang, Keshigeyan Chandrasegaran, Ruohan Zhang, Yejin Choi, Ranjay Krishna, Jiajun Wu, Fei-Fei Li

Manling Li

52,794 次观看 • 5 个月前

How long should my baby use a pacifier? I get this question a lot and it’s a tough one - both because there isn’t a single correct answer and because (like feeding and sleep) the topic brings out lots of strong opinions. But if you ask me, the family in this video has the right idea. Infants are born with a strong sucking reflex and pacifiers can help them to soothe and sleep. There’s even some evidence to suggest that sleeping with pacifiers might reduce the risk of Sudden Infant Death Syndrome (SIDS). In short: for babies (up to a year), I’m a big fan. But it’s not uncommon to see children with pacifiers well into toddlerhood, and in some cases, even beyond. And here I’d raise some important cautions. Children who rely heavily on pacifiers may be more prone to middle ear infections. And dentists note that prolonged pacifier use can affect your child’s teeth and create bite issues. Perhaps most importantly is their potential to impact expressive language development. Your child’s ability to speak is an important one. After a point, language shapes not only the content of our thinking, but the very structure of our cognition. By otherwise occupying the mouth over long periods of time, pacifiers may slow language development by limiting opportunities for expression. Speaking with a pacifier in the mouth can also lead to distortion of speech sounds (even when they aren’t in the mouth). All told, I’m an advocate for beginning to wean off of pacifiers at around a year of age - which is why this video spoke to me. We see an infant appropriately using one and big sister demonstrating her expressive language, her mouth unencumbered and free to chatter away happily. The transition can be difficult - but not nearly as challenging as for a child who has become dependent over a period of years. Do/did you use pacifiers with your child? Why or why not? How did you help transition away from their use? This sweet siblings were shared to IG by _lullabye_luxuries_.

How long should my baby use a pacifier? I get this question a lot and it’s a tough one - both because there isn’t a single correct answer and because (like feeding and sleep) the topic brings out lots of strong opinions. But if you ask me, the family in this video has the right idea. Infants are born with a strong sucking reflex and pacifiers can help them to soothe and sleep. There’s even some evidence to suggest that sleeping with pacifiers might reduce the risk of Sudden Infant Death Syndrome (SIDS). In short: for babies (up to a year), I’m a big fan. But it’s not uncommon to see children with pacifiers well into toddlerhood, and in some cases, even beyond. And here I’d raise some important cautions. Children who rely heavily on pacifiers may be more prone to middle ear infections. And dentists note that prolonged pacifier use can affect your child’s teeth and create bite issues. Perhaps most importantly is their potential to impact expressive language development. Your child’s ability to speak is an important one. After a point, language shapes not only the content of our thinking, but the very structure of our cognition. By otherwise occupying the mouth over long periods of time, pacifiers may slow language development by limiting opportunities for expression. Speaking with a pacifier in the mouth can also lead to distortion of speech sounds (even when they aren’t in the mouth). All told, I’m an advocate for beginning to wean off of pacifiers at around a year of age - which is why this video spoke to me. We see an infant appropriately using one and big sister demonstrating her expressive language, her mouth unencumbered and free to chatter away happily. The transition can be difficult - but not nearly as challenging as for a child who has become dependent over a period of years. Do/did you use pacifiers with your child? Why or why not? How did you help transition away from their use? This sweet siblings were shared to IG by _lullabye_luxuries_.

Dan Wuori

157,980 次观看 • 2 年前

This is gonna be so long omg but when it comes to translating in Korean, it’s very very important to take in consideration of 1) Culture 2) Seniority, the Closeness & Intimacy of the people involved in a conversation. Because this gives an entire different vibe for the context. And we have to keep in mind that there are Korean phrases and expressions that simply don’t have just One way, exact words that can be translated in English. Translators can find ways to convey the meaning, but it requires more explanation and understanding of the context while conveying it, and not relying on assumptions. How about “인상 덜 쓰기” or “인상 쓰다“ then? Here, a direct translation can simply be 인상 덜 쓰기 - be less grumpy/ frown less, etc 인상 쓰다 - being grumpy, frown, make a face, etc Here, I include an educational video of an English speaker talking with a Korean speaker where they’re discussing a similar expression, “인상 좀 쓰지마!“ (which in word by word in English, it sounds something like “Don’t frown/ Don’t make a face”) Now, as an English speaker, that sounds a bit harsh doesn’t it? That’s because such expression doesn’t exist in just One interpretation in English, so if we only read it as it is, without considering the context and the speakers’ closeness, we’re bound to misunderstand the context. Quoting some parts of this video, 💚: “Now, Tyler’s expression for today is ‘인상 좀 쓰지마!’. Have you ever heard of this expression? 🩶: I’ve heard something like that before 💚: Then doesn’t this mean there’s a same expression in English too right? 🩶: Oh! I’ve only heard it in Korea 💚: Ah really? The guy then explained that if you say something like this in english, it can be rude. so it’s better not to say it in English-speaking countries. He gave examples like “If you do that, you’ll get wrinkles”, and the other guy added something like “If you make an ugly face, you’ll have wrinkles” and said that “If someone (an English speaker) hears this they would think I must be crazy” Here, they’re talking about the cultural differences. A phrase like this, when expressed directly, without considering people’s closeness, and being translated exactly like that, would sound so negative to English speakers. *also notice how this phrase is being worded in various ways??? ‘HOW’ it can sound to someone, really depends on their culture, their closeness with the person, and context* So later on, he gave a few more examples like, “stop making such a long face”, and “stop wrinkling your forehead”. And concluded that: 💚: Well in that case, the phrase “인상 좀 쓰지마” is basically a phrase that doesn’t exist in English. 🩶: It’s an expression that we don’t use. It’s not that it doesn’t exist. (This part, he’s explaining that there’s no such a thing as word by word to explain this phrase in English, bcs we English speakers use different wordings that is more suitable for a situation’s nuances.) Then again, we have to consider speakers’ closeness as well because in Korea, it’s a huge thing. If you don’t understand how this works, you won’t understand the nuances of a conversation. You’re bound to misunderstand. If you refuse to understand it then, I don’t think you want to respect their culture. Because again, this is a huge thing.

This is gonna be so long omg but when it comes to translating in Korean, it’s very very important to take in consideration of 1) Culture 2) Seniority, the Closeness & Intimacy of the people involved in a conversation. Because this gives an entire different vibe for the context. And we have to keep in mind that there are Korean phrases and expressions that simply don’t have just One way, exact words that can be translated in English. Translators can find ways to convey the meaning, but it requires more explanation and understanding of the context while conveying it, and not relying on assumptions. How about “인상 덜 쓰기” or “인상 쓰다“ then? Here, a direct translation can simply be 인상 덜 쓰기 - be less grumpy/ frown less, etc 인상 쓰다 - being grumpy, frown, make a face, etc Here, I include an educational video of an English speaker talking with a Korean speaker where they’re discussing a similar expression, “인상 좀 쓰지마!“ (which in word by word in English, it sounds something like “Don’t frown/ Don’t make a face”) Now, as an English speaker, that sounds a bit harsh doesn’t it? That’s because such expression doesn’t exist in just One interpretation in English, so if we only read it as it is, without considering the context and the speakers’ closeness, we’re bound to misunderstand the context. Quoting some parts of this video, 💚: “Now, Tyler’s expression for today is ‘인상 좀 쓰지마!’. Have you ever heard of this expression? 🩶: I’ve heard something like that before 💚: Then doesn’t this mean there’s a same expression in English too right? 🩶: Oh! I’ve only heard it in Korea 💚: Ah really? The guy then explained that if you say something like this in english, it can be rude. so it’s better not to say it in English-speaking countries. He gave examples like “If you do that, you’ll get wrinkles”, and the other guy added something like “If you make an ugly face, you’ll have wrinkles” and said that “If someone (an English speaker) hears this they would think I must be crazy” Here, they’re talking about the cultural differences. A phrase like this, when expressed directly, without considering people’s closeness, and being translated exactly like that, would sound so negative to English speakers. also notice how this phrase is being worded in various ways??? ‘HOW’ it can sound to someone, really depends on their culture, their closeness with the person, and context So later on, he gave a few more examples like, “stop making such a long face”, and “stop wrinkling your forehead”. And concluded that: 💚: Well in that case, the phrase “인상 좀 쓰지마” is basically a phrase that doesn’t exist in English. 🩶: It’s an expression that we don’t use. It’s not that it doesn’t exist. (This part, he’s explaining that there’s no such a thing as word by word to explain this phrase in English, bcs we English speakers use different wordings that is more suitable for a situation’s nuances.) Then again, we have to consider speakers’ closeness as well because in Korea, it’s a huge thing. If you don’t understand how this works, you won’t understand the nuances of a conversation. You’re bound to misunderstand. If you refuse to understand it then, I don’t think you want to respect their culture. Because again, this is a huge thing.

✰ 𝐚𝐥𝐢𝐬𝐚 ✰

12,631 次观看 • 6 个月前

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:

雪踏乌云

112,114 次观看 • 8 天前

Is Using a Nebulizer with Food Grade Hydrogen Peroxide preventive and healing for upper respiratory symptoms? The earlier you get this in the better, so if you start feeling run down or stuffy take action sooner. ***THIS IS NOT MEDICAL ADVICE. Not only can nebulizing save you money, but also time from healing and potentially from the damage/side effects that antibiotics can have. I used a nasal mist sprayer with a 50/50 mix of 3% HP to distilled water and ate raw chopped garlic too when I had pneumonia and I relieved congestion, coughing or anything chest related. Just aches and pains. I’m all about prevention and using natural medicines first! “The inhalation of HP by nebulization has been shown to be extremely effective for the rapid elimination of any pathogen presence in the sinuses, nose, throat, and deep into the lungs.” -Dr. Levy HOW TO PREPARE HYDROGEN PEROXIDE FOR NEBULIZING 1. Add 2 tsp of 3% food grade hydrogen peroxide to 8 oz of saline water (this makes.1% dilution), if you are starting with 12% hydrogen peroxide, add 1/2 tsp to 8 oz of saline water. 2. Transfer this mix to a glass dropper bottle. 3. Use about 2-3 mL or (1/2 tsp) of this mix for each nebulizing session. 4. You can keep this solution refrigerated for a long time and continue to reuse it. Praying for your health and abundance of goodness 🙌🏼

Sensitive content

Is Using a Nebulizer with Food Grade Hydrogen Peroxide preventive and healing for upper respiratory symptoms? The earlier you get this in the better, so if you start feeling run down or stuffy take action sooner. ***THIS IS NOT MEDICAL ADVICE. Not only can nebulizing save you money, but also time from healing and potentially from the damage/side effects that antibiotics can have. I used a nasal mist sprayer with a 50/50 mix of 3% HP to distilled water and ate raw chopped garlic too when I had pneumonia and I relieved congestion, coughing or anything chest related. Just aches and pains. I’m all about prevention and using natural medicines first! “The inhalation of HP by nebulization has been shown to be extremely effective for the rapid elimination of any pathogen presence in the sinuses, nose, throat, and deep into the lungs.” -Dr. Levy HOW TO PREPARE HYDROGEN PEROXIDE FOR NEBULIZING 1. Add 2 tsp of 3% food grade hydrogen peroxide to 8 oz of saline water (this makes.1% dilution), if you are starting with 12% hydrogen peroxide, add 1/2 tsp to 8 oz of saline water. 2. Transfer this mix to a glass dropper bottle. 3. Use about 2-3 mL or (1/2 tsp) of this mix for each nebulizing session. 4. You can keep this solution refrigerated for a long time and continue to reuse it. Praying for your health and abundance of goodness 🙌🏼

Cleanse Parasites .com 🧹🪱 Herbal Cleanse Co.

46,711 次观看 • 3 个月前

Cal AI might be the most viral health app this year. 8M+ downloads, projected to do $30M revenue this year. Built by two teenagers. Everyone's using it. But nobody's talking about the fact that their AI is completely broken... To the point where users are manually correcting EVERY meal. • Bowl of grapes: 60 cal estimate (actually ~260) • 4 boiled eggs: 1,010 cal estimate (actually ~300) • Meat portions: consistently off by 50% These aren't my words, but the reviews you can see for yourself on App Store and other places. If the whole USP is saving time vs manual logging, and you still have to correct everything... what's the point? What Cal AI did get right: Distribution. • Viral TikTok content • Smart influencer partnerships • 8M downloads in under a year They absolutely crushed GTM. But the product doesn't work lol. The gap between the "90% accuracy" claim vs the actual user experience kills trust. And in health apps, trust is everything. This is the problem with AI apps across the board right now. Everyone's racing to ship fast and go viral. Nobody's asking: "Does this actually solve the problem?" Distribution > product quality is a losing game. As a result, you're bound to encounter problems: 1. Training data doesn't match real-world variety 2. No depth sensing for portion size 3. Poor training data on homemade meals 4. Zero context (is that chicken grilled or fried?) You only get fast & inaccurate answers. This is why, when I started building my own recipe app, I looked at Cal and other AI nutrition apps and noticed that being accurate was the biggest factor. Here's how we're building Nonna differently: ✓ Multi-model AI (different models for different foods) ✓ User feedback loop to improve estimates ✓ Manual override that actually trains the system ✓ Ship when it works, not when it's "good enough" If the AI can't nail it, we're not shipping it. But accuracy alone is boring. So we're also adding some additional features that make you want to use it daily: • Fridge Story: shareable infographic of your fridge contents • Mystery Ingredient: weekly cooking challenges • Cuisine Spin: random inspiration when you're stuck • Expectation vs Reality: before/after photo collages Tl;dr: Distribution gets downloads. Product keeps users. Cal got millions of downloads. How many people still use it daily after manually correcting every meal for a week? Viral marketing with a broken product = expensive way to disappoint people.

Cal AI might be the most viral health app this year. 8M+ downloads, projected to do $30M revenue this year. Built by two teenagers. Everyone's using it. But nobody's talking about the fact that their AI is completely broken... To the point where users are manually correcting EVERY meal. • Bowl of grapes: 60 cal estimate (actually ~260) • 4 boiled eggs: 1,010 cal estimate (actually ~300) • Meat portions: consistently off by 50% These aren't my words, but the reviews you can see for yourself on App Store and other places. If the whole USP is saving time vs manual logging, and you still have to correct everything... what's the point? What Cal AI did get right: Distribution. • Viral TikTok content • Smart influencer partnerships • 8M downloads in under a year They absolutely crushed GTM. But the product doesn't work lol. The gap between the "90% accuracy" claim vs the actual user experience kills trust. And in health apps, trust is everything. This is the problem with AI apps across the board right now. Everyone's racing to ship fast and go viral. Nobody's asking: "Does this actually solve the problem?" Distribution > product quality is a losing game. As a result, you're bound to encounter problems: 1. Training data doesn't match real-world variety 2. No depth sensing for portion size 3. Poor training data on homemade meals 4. Zero context (is that chicken grilled or fried?) You only get fast & inaccurate answers. This is why, when I started building my own recipe app, I looked at Cal and other AI nutrition apps and noticed that being accurate was the biggest factor. Here's how we're building Nonna differently: ✓ Multi-model AI (different models for different foods) ✓ User feedback loop to improve estimates ✓ Manual override that actually trains the system ✓ Ship when it works, not when it's "good enough" If the AI can't nail it, we're not shipping it. But accuracy alone is boring. So we're also adding some additional features that make you want to use it daily: • Fridge Story: shareable infographic of your fridge contents • Mystery Ingredient: weekly cooking challenges • Cuisine Spin: random inspiration when you're stuck • Expectation vs Reality: before/after photo collages Tl;dr: Distribution gets downloads. Product keeps users. Cal got millions of downloads. How many people still use it daily after manually correcting every meal for a week? Viral marketing with a broken product = expensive way to disappoint people.

Denislav Jeliazkov

37,989 次观看 • 8 个月前

Yesterday, during a flight from Antalya to Kazan, we encountered turbulence and literally 20 minutes later, the captain announced that one of the passengers was unwell and asked for help from medical workers. I intuitively approached the girl, the guy accompanying her was confused, the staff measured her blood pressure, brought ice, then hot tea, ammonia is prohibited, they gave her some snuff. I am not a doctor, but my very first client in long-term work was a girl with panic attacks and we have come a long way to her self-regulation without medical intervention. The passenger had a panic attack, in 15 minutes I was able to help her and understood what doctors feel when they save people's lives 🫶 It is clear that the attack was not life-threatening, but frightened empty eyes, a pale face, icy hands and feet, a body shaking in an uncontrollable tremor finally began to breathe, her face turned red, tears flowed and the girl came to her senses. I am surprised why flight attendants are not trained in panic Panic attacks, because they have all the attributes you need - a craft bag or a simple paper airline bag, put it on tightly from the bridge of the nose to the chin and breathe in a count, for example, 22/1, 22/2, etc., clearly pronounce it to the person in the attack. After the initial symptom relief, ask to find 7 objects of the same color, and do this 5-7 times, changing the color of the objects. Practice the "here and now" exercise daily. And of course, knowing the tendency to attacks, go to work with a psychosomatic therapist and be prepared for the flight, having anti-anxiety medications with you. I explained the principles of work to the flight attendants, they treated me to delicious coffee on board as a thank you) But there is no better gratitude than a little or a very happy client who has received the skills and resources to manage their condition and their life 💟

Yesterday, during a flight from Antalya to Kazan, we encountered turbulence and literally 20 minutes later, the captain announced that one of the passengers was unwell and asked for help from medical workers. I intuitively approached the girl, the guy accompanying her was confused, the staff measured her blood pressure, brought ice, then hot tea, ammonia is prohibited, they gave her some snuff. I am not a doctor, but my very first client in long-term work was a girl with panic attacks and we have come a long way to her self-regulation without medical intervention. The passenger had a panic attack, in 15 minutes I was able to help her and understood what doctors feel when they save people's lives 🫶 It is clear that the attack was not life-threatening, but frightened empty eyes, a pale face, icy hands and feet, a body shaking in an uncontrollable tremor finally began to breathe, her face turned red, tears flowed and the girl came to her senses. I am surprised why flight attendants are not trained in panic Panic attacks, because they have all the attributes you need - a craft bag or a simple paper airline bag, put it on tightly from the bridge of the nose to the chin and breathe in a count, for example, 22/1, 22/2, etc., clearly pronounce it to the person in the attack. After the initial symptom relief, ask to find 7 objects of the same color, and do this 5-7 times, changing the color of the objects. Practice the "here and now" exercise daily. And of course, knowing the tendency to attacks, go to work with a psychosomatic therapist and be prepared for the flight, having anti-anxiety medications with you. I explained the principles of work to the flight attendants, they treated me to delicious coffee on board as a thank you) But there is no better gratitude than a little or a very happy client who has received the skills and resources to manage their condition and their life 💟

Kerry Holmes

26,453 次观看 • 5 个月前

🔊Church: WE’RE LIVING IN THE FINAL STAGE! Acceleration is happening - Satan is accelerating deception, and God is accelerating EXPOSURE!! The scale of evil being revealed through the Epstein files is proof that the restrainer, the Holy Spirit, is losing His grip on evil. In His mercy, God is exposing darkness so people can see what has been operating behind the scenes and who they are truly following. This is the final call to come to Jesus!!! For those who reject Him, the next chapter is the 7-Year Tribulation — the worst time in human history! The atrocities we are seeing now will be nothing compared to what the world will endure during those days!! KEEP LOOKING UP!! OUR SAVIOR IS AT THE DOOR! We need to talk about the dark season we’re living in! 👉🏼The Epstein files have revealed some of the most revolting and gut-wrenching evil we have ever seen. I hope you’re not digging too deeply into them or reading too much, because the details are extremely disturbing. ⚡I will not go into details, but everywhere we look, deeply disturbing layers of satanism, corruption, abuse, deception, and darkness are surfacing. Many wealthy and powerful figures — presidents, royalty, tech moguls — along with major institutions such as the United Nations, Harvard University, Massachusetts Institute of Technology (MIT), Goldman Sachs, the International Peace Institute, and others, have come under intense scrutiny. Things that have operated for decades behind closed doors are now being brought into the open. This is NOT random! We need to pause and think: 👉🏼Why is so much darkness being exposed? 👉🏼Why are these extremely disturbing revelations surfacing across governments, institutions, and elites? 👉🏼Why is corruption no longer hidden, but now openly displayed? First, let’s quickly review the prophetic timeline and framework that ties all of this together: ✅Increasing deception ✅Moral and spiritual decline ✅Rise of global instability and lawlessness ✅Collapse of trust in worldly systems ✅Exposure of evil ⏹️The Rapture of the Church ⏹️Rise of the Antichrist (false savior) ⏹️7 year peace treaty with Israel ⏹️The 7-Year Tribulation/Wrath of God ⏹️Execution of a global system of control ⏹️The Second Coming of Christ ⏹️The Millennial Reign of Christ WHERE DOES THIS EXPOSURE OF EVIL FIT? Let’s step back and look at history — the Bible shows a very consistent pattern: 💣Before the Flood 👉🏼Wickedness filled the earth 💣Before the destruction of Sodom and Gomorrah 👉🏼Moral corruption and depravity reached its peak 💣Before Israel went into captivity 👉🏼prophets exposed corruption, idolatry, and rebellion 💣Before the fall of Jerusalem (70 AD) 👉🏼Warnings, tensions, and spiritual blindness intensified 📜From this, we can see that before major biblical judgments, God consistently reveals and exposes evil. Therefore, before the greatest and final judgment in history falls upon the earth, all evil must be brought into the light. That is exactly what we are witnessing today. This exposure of evil is unfolding in the time leading up to the 7-Year Tribulation, following the same pattern Scripture has shown throughout history. ⚠️Just to clarify: Just as Noah became an heir of righteousness by faith, and Lot is called righteous, we — the Church — have been justified by faith in His blood, which makes us righteous. Therefore, we will not experience the wrath of God. We will not be here for the 7-year-tribulation 📖“For God did not appoint us to wrath, but to obtain salvation through our Lord Jesus Christ.” 1 Thess 5:9 WHY NOW? The exposure of evil prepares humanity for The Savior — and for a false “savior.” Before people will follow a new leader or system, they must first lose complete faith in the existing leadership and structures. Right now, governments, media, medical systems, financial systems, and even religious institutions are being exposed and started to become widely distrusted. This is not random collapse — IT’S DEMOLITION! GOD IS PULLING BACK THE VEIL!! Jesus says: 📖“For nothing is secret that will not be revealed, nor anything hidden that will not be known and come to light.” Luke 8:17 💙Exposure is actually an act of mercy before judgment. God reveals corruption so that people can see who they have been following and place their faith in Jesus. Victims can also be vindicated. In a sense, God is separating light from darkness, shattering the illusion that human leaders are saviors and showing the world what it’s like to live in a world without Him. WE CAN SEE THE RESTRAINER LOOSENING Paul describes a “restrainer” that holds back full-scale lawlessness UNTIL a certain time: 📖“For the mystery of lawlessness is already at work; only He who now restrains will do so until He is taken out of the way.” 2 Thess 2:7 👉🏼The mystery of lawlessness has always existed, it’s always been there, but something is shifting right now in our world, we can feel that the restraints are weakening, shame is diminishing, evil is becoming bolder, it doesn’t hide anymore. Sin today is not only practiced, but celebrated, defended, and even legislated! ❌I wish I could unhear this TEDx talk at the University of Würzburg in Germany, where medical student argued that pedophilia is an “unchangeable sexual orientation” and that society should accept people with that attraction. This is the restrainer losing its grip on evil, and evil becoming bolder!!! WHAT WILL HAPPEN NEXT? 🌍The world will begin to see global control as safety. The evil surfacing right now is so extreme that even those who embrace wickedness find it unacceptable. In their fear, they will accept global oversight as a measure of protection. 👉🏼We know the Beast system requires global economic integration, centralized identification, digital transactions, and behavioral compliance. People will not accept this if they feel free — but when they feel endangered, they will gladly trade freedom for security. Fear will demand control, control will be marketed as protection, and protection will come at the cost of freedom. This conditions humanity to say: “Just fix it. I don’t care how!! Just do it!!” This mindset is the perfect mindset for welcoming the Beast system!!! 🔊We are not watching random headlines! We are witnessing God accelerate the exposure of evil on a massive scale — a world being shaken and destabilized, with numerous elites stepping down or being compromised. The world is seeing what has been hidden behind the veil — true evil at work! Soon, people will be looking for someone (a.k.a the Antichrist) to step up and bring peace! They will be searching for a savior — and this is where the enemy will accelerate his maximum deception plan! 🔊Church: A global separation is happening, and light and darkness are no longer blending quietly!!! But the same God who is revealing corruption is the God who is redeeming the very last souls of the Church age of grace! There is no middle ground left!!! 🔊If the Restrainer is beginning to lift His restraint, how close are we to the Rapture when that restraint is fully removed??!! Time is almost UP!! KEEP PRAYING! KEEP PREACHING THE GOSPEL! KEEP LOOKING UP FOR THE ONE TRUE SAVIOR, JESUS CHRIST — KING OF KINGS AND LORD OF LORDS! Maranatha!! COME, LORD JESUS, COME!!! 🤍

🔊Church: WE’RE LIVING IN THE FINAL STAGE! Acceleration is happening - Satan is accelerating deception, and God is accelerating EXPOSURE!! The scale of evil being revealed through the Epstein files is proof that the restrainer, the Holy Spirit, is losing His grip on evil. In His mercy, God is exposing darkness so people can see what has been operating behind the scenes and who they are truly following. This is the final call to come to Jesus!!! For those who reject Him, the next chapter is the 7-Year Tribulation — the worst time in human history! The atrocities we are seeing now will be nothing compared to what the world will endure during those days!! KEEP LOOKING UP!! OUR SAVIOR IS AT THE DOOR! We need to talk about the dark season we’re living in! 👉🏼The Epstein files have revealed some of the most revolting and gut-wrenching evil we have ever seen. I hope you’re not digging too deeply into them or reading too much, because the details are extremely disturbing. ⚡I will not go into details, but everywhere we look, deeply disturbing layers of satanism, corruption, abuse, deception, and darkness are surfacing. Many wealthy and powerful figures — presidents, royalty, tech moguls — along with major institutions such as the United Nations, Harvard University, Massachusetts Institute of Technology (MIT), Goldman Sachs, the International Peace Institute, and others, have come under intense scrutiny. Things that have operated for decades behind closed doors are now being brought into the open. This is NOT random! We need to pause and think: 👉🏼Why is so much darkness being exposed? 👉🏼Why are these extremely disturbing revelations surfacing across governments, institutions, and elites? 👉🏼Why is corruption no longer hidden, but now openly displayed? First, let’s quickly review the prophetic timeline and framework that ties all of this together: ✅Increasing deception ✅Moral and spiritual decline ✅Rise of global instability and lawlessness ✅Collapse of trust in worldly systems ✅Exposure of evil ⏹️The Rapture of the Church ⏹️Rise of the Antichrist (false savior) ⏹️7 year peace treaty with Israel ⏹️The 7-Year Tribulation/Wrath of God ⏹️Execution of a global system of control ⏹️The Second Coming of Christ ⏹️The Millennial Reign of Christ WHERE DOES THIS EXPOSURE OF EVIL FIT? Let’s step back and look at history — the Bible shows a very consistent pattern: 💣Before the Flood 👉🏼Wickedness filled the earth 💣Before the destruction of Sodom and Gomorrah 👉🏼Moral corruption and depravity reached its peak 💣Before Israel went into captivity 👉🏼prophets exposed corruption, idolatry, and rebellion 💣Before the fall of Jerusalem (70 AD) 👉🏼Warnings, tensions, and spiritual blindness intensified 📜From this, we can see that before major biblical judgments, God consistently reveals and exposes evil. Therefore, before the greatest and final judgment in history falls upon the earth, all evil must be brought into the light. That is exactly what we are witnessing today. This exposure of evil is unfolding in the time leading up to the 7-Year Tribulation, following the same pattern Scripture has shown throughout history. ⚠️Just to clarify: Just as Noah became an heir of righteousness by faith, and Lot is called righteous, we — the Church — have been justified by faith in His blood, which makes us righteous. Therefore, we will not experience the wrath of God. We will not be here for the 7-year-tribulation 📖“For God did not appoint us to wrath, but to obtain salvation through our Lord Jesus Christ.” 1 Thess 5:9 WHY NOW? The exposure of evil prepares humanity for The Savior — and for a false “savior.” Before people will follow a new leader or system, they must first lose complete faith in the existing leadership and structures. Right now, governments, media, medical systems, financial systems, and even religious institutions are being exposed and started to become widely distrusted. This is not random collapse — IT’S DEMOLITION! GOD IS PULLING BACK THE VEIL!! Jesus says: 📖“For nothing is secret that will not be revealed, nor anything hidden that will not be known and come to light.” Luke 8:17 💙Exposure is actually an act of mercy before judgment. God reveals corruption so that people can see who they have been following and place their faith in Jesus. Victims can also be vindicated. In a sense, God is separating light from darkness, shattering the illusion that human leaders are saviors and showing the world what it’s like to live in a world without Him. WE CAN SEE THE RESTRAINER LOOSENING Paul describes a “restrainer” that holds back full-scale lawlessness UNTIL a certain time: 📖“For the mystery of lawlessness is already at work; only He who now restrains will do so until He is taken out of the way.” 2 Thess 2:7 👉🏼The mystery of lawlessness has always existed, it’s always been there, but something is shifting right now in our world, we can feel that the restraints are weakening, shame is diminishing, evil is becoming bolder, it doesn’t hide anymore. Sin today is not only practiced, but celebrated, defended, and even legislated! ❌I wish I could unhear this TEDx talk at the University of Würzburg in Germany, where medical student argued that pedophilia is an “unchangeable sexual orientation” and that society should accept people with that attraction. This is the restrainer losing its grip on evil, and evil becoming bolder!!! WHAT WILL HAPPEN NEXT? 🌍The world will begin to see global control as safety. The evil surfacing right now is so extreme that even those who embrace wickedness find it unacceptable. In their fear, they will accept global oversight as a measure of protection. 👉🏼We know the Beast system requires global economic integration, centralized identification, digital transactions, and behavioral compliance. People will not accept this if they feel free — but when they feel endangered, they will gladly trade freedom for security. Fear will demand control, control will be marketed as protection, and protection will come at the cost of freedom. This conditions humanity to say: “Just fix it. I don’t care how!! Just do it!!” This mindset is the perfect mindset for welcoming the Beast system!!! 🔊We are not watching random headlines! We are witnessing God accelerate the exposure of evil on a massive scale — a world being shaken and destabilized, with numerous elites stepping down or being compromised. The world is seeing what has been hidden behind the veil — true evil at work! Soon, people will be looking for someone (a.k.a the Antichrist) to step up and bring peace! They will be searching for a savior — and this is where the enemy will accelerate his maximum deception plan! 🔊Church: A global separation is happening, and light and darkness are no longer blending quietly!!! But the same God who is revealing corruption is the God who is redeeming the very last souls of the Church age of grace! There is no middle ground left!!! 🔊If the Restrainer is beginning to lift His restraint, how close are we to the Rapture when that restraint is fully removed??!! Time is almost UP!! KEEP PRAYING! KEEP PREACHING THE GOSPEL! KEEP LOOKING UP FOR THE ONE TRUE SAVIOR, JESUS CHRIST — KING OF KINGS AND LORD OF LORDS! Maranatha!! COME, LORD JESUS, COME!!! 🤍

Maranatha777

78,311 次观看 • 5 个月前