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Into a Dragon's Cave | Release Back from June but I forgot. Models/Map SamMaxWell ム | Comms - 3/3 IceDev Audio MOO🩷COW (Voicepack) HentAudio 🇺🇦 🟢COMMS OPEN🟢 (SFX pack) OpenNSFW 🟣 Available Now (SFX pack) #ZentreNSFW #Rule34

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MarcoV4🔞 COMMS OPEN! ❤️💜💙

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21,539 görüntüleme • 28 gün önce •via X (Twitter)

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ves 🔞

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Here’s the MARINE ONE ATC audio from yesterday. M1 made several calls to NATIONAL TOWER (DCA) on the helicopter specific frequency advising they are 3 minutes from lifting. Tower here’s them briefly on one attempt and say’s they’re broken and unreadable. M1 then lifts getting a better radio line of sight to the tower cab and say’s they’re on the go. Tower comes back confused, saying, “Ok, no 3 minutes, roger” and there ensues confusion from the Tower as they just cleared a Embraer Regional Jet off Runway 01 which the runway basically points towards the Ellipse. M1 calls the traffic insight and decides to pause (smart pilots). While this Envoy Air jet is required to perform a left turn upon departure to avoid the P-56 Prohibited airspace that encompasses the Ellipse. You have a jet accelerating towards the helicopter carrying POTUS in an extremely nervous environment especially since the post DCA crash in 2025. Contributing factors are a relatively new landing zone for M1, DCA tower acknowledging M1 but can’t understand, a busy tower frequency already with several local, federal, and military helicopters already consuming the frequency. Being in tune *pun with VHF airband communications, operating helicopter and trying to call ATC from the ground. Sometimes you can hear ATC and they can hear you and the next minute (especially when you are on the cusp of line of sight comms) It can come down to a variety of factors including atmospheric conditions and just exactly how your antenna is pointed and in some cases this can be a matter of feet or less. The “angle of the dangle” if you will. I don’t know what procedures are in place for M1 communicating with ATC on the ground at the Ellipse besides VHF airband. I can tell you post this incident, I’d have an open telephone line to the tower cab during this whole pickup/dropoff evolution to ensure broken comms are received and understood. And or, I’d have a complete ground stop at DCA while M1 is on the ground. PS. There have been airliners departing DCA in the past and have missed their turn and penetrated the P-56 Prohibited airspace. So while it wasn’t as big of a deal this time, It could potentially be in the future if not addressed. Has something like this in the past happened?… probably but it’s just got now a microscope over the whole area every time these is one slight mix up or mess up. At lease the helipad is getting installed at the WH. Audio via ATC
1:54

Here’s the MARINE ONE ATC audio from yesterday. M1 made several calls to NATIONAL TOWER (DCA) on the helicopter specific frequency advising they are 3 minutes from lifting. Tower here’s them briefly on one attempt and say’s they’re broken and unreadable. M1 then lifts getting a better radio line of sight to the tower cab and say’s they’re on the go. Tower comes back confused, saying, “Ok, no 3 minutes, roger” and there ensues confusion from the Tower as they just cleared a Embraer Regional Jet off Runway 01 which the runway basically points towards the Ellipse. M1 calls the traffic insight and decides to pause (smart pilots). While this Envoy Air jet is required to perform a left turn upon departure to avoid the P-56 Prohibited airspace that encompasses the Ellipse. You have a jet accelerating towards the helicopter carrying POTUS in an extremely nervous environment especially since the post DCA crash in 2025. Contributing factors are a relatively new landing zone for M1, DCA tower acknowledging M1 but can’t understand, a busy tower frequency already with several local, federal, and military helicopters already consuming the frequency. Being in tune *pun with VHF airband communications, operating helicopter and trying to call ATC from the ground. Sometimes you can hear ATC and they can hear you and the next minute (especially when you are on the cusp of line of sight comms) It can come down to a variety of factors including atmospheric conditions and just exactly how your antenna is pointed and in some cases this can be a matter of feet or less. The “angle of the dangle” if you will. I don’t know what procedures are in place for M1 communicating with ATC on the ground at the Ellipse besides VHF airband. I can tell you post this incident, I’d have an open telephone line to the tower cab during this whole pickup/dropoff evolution to ensure broken comms are received and understood. And or, I’d have a complete ground stop at DCA while M1 is on the ground. PS. There have been airliners departing DCA in the past and have missed their turn and penetrated the P-56 Prohibited airspace. So while it wasn’t as big of a deal this time, It could potentially be in the future if not addressed. Has something like this in the past happened?… probably but it’s just got now a microscope over the whole area every time these is one slight mix up or mess up. At lease the helipad is getting installed at the WH. Audio via ATC

Thenewarea51

118,198 görüntüleme • 1 ay önce

I know your timeline is flooded now with word salads of "insane, HER, 10 features you missed, we're so back". Sit down. Chill. Take a deep breath like Mark does in the demo . Let's think step by step: - Technique-wise, OpenAI has figured out a way to map audio to audio directly as first-class modality, and stream videos to a transformer in real-time. These require some new research on tokenization and architecture, but overall it's a data and system optimization problem (as most things are). High-quality data can come from at least 2 sources: 1) Naturally occurring dialogues on YouTube, podcasts, TV series, movies, etc. Whisper can be trained to identify speaker turns in a dialogue or separate overlapping speeches for automated annotation. 2) Synthetic data. Run the slow 3-stage pipeline using the most powerful models: speech1->text1 (ASR), text1->text2 (LLM), text2->speech2 (TTS). The middle LLM can decide when to stop and also simulate how to resume from interruption. It could output additional "thought traces" that are not verbalized to help generate better reply. Then GPT-4o distills directly from speech1->speech2, with optional auxiliary loss functions based on the 3-stage data. After distillation, these behaviors are now baked into the model without emitting intermediate texts. On the system side: the latency would not meet real-time threshold if every video frame is decompressed into an RGB image. OpenAI has likely developed their own neural-first, streaming video codec to transmit the motion deltas as tokens. The communication protocol and NN inference must be co-optimized. For example, there could be a small and energy-efficient NN running on the edge device that decides to transmit more tokens if the video is interesting, and fewer otherwise. - I didn't expect GPT-4o to be closer to GPT-5, the rumored "Arrakis" model that takes multimodal in and out. In fact, it's likely an early checkpoint of GPT-5 that hasn't finished training yet. The branding betrays a certain insecurity. Ahead of Google I/O, OpenAI would rather beat our mental projection of GPT-4.5 than disappoint by missing the sky-high expectation for GPT-5. A smart move to buy more time. - Notably, the assistant is much more lively and even a bit flirty. GPT-4o is trying (perhaps a bit too hard) to sound like HER. OpenAI is eating Character AI's lunch, with almost 100% overlap in form factor and huge distribution channels. It's a pivot towards more emotional AI with strong personality, which OpenAI seemed to actively suppress in the past. - Whoever wins Apple first wins big time. I see 3 levels of integration with iOS: 1) Ditch Siri. OpenAI distills a smaller-tier, purely on-device GPT-4o for iOS, with optional paid upgrade to use the cloud. 2) Native features to stream the camera or screen into the model. Chip-level support for neural audio/video codec. 3) Integrate with iOS system-level action API and smart home APIs. No one uses Siri Shortcuts, but it's time to resurrect. This could become the AI agent product with a billion users from the get-go. The FSD for smartphones with a Tesla-scale data flywheel.
14:22

I know your timeline is flooded now with word salads of "insane, HER, 10 features you missed, we're so back". Sit down. Chill. Take a deep breath like Mark does in the demo . Let's think step by step: - Technique-wise, OpenAI has figured out a way to map audio to audio directly as first-class modality, and stream videos to a transformer in real-time. These require some new research on tokenization and architecture, but overall it's a data and system optimization problem (as most things are). High-quality data can come from at least 2 sources: 1) Naturally occurring dialogues on YouTube, podcasts, TV series, movies, etc. Whisper can be trained to identify speaker turns in a dialogue or separate overlapping speeches for automated annotation. 2) Synthetic data. Run the slow 3-stage pipeline using the most powerful models: speech1->text1 (ASR), text1->text2 (LLM), text2->speech2 (TTS). The middle LLM can decide when to stop and also simulate how to resume from interruption. It could output additional "thought traces" that are not verbalized to help generate better reply. Then GPT-4o distills directly from speech1->speech2, with optional auxiliary loss functions based on the 3-stage data. After distillation, these behaviors are now baked into the model without emitting intermediate texts. On the system side: the latency would not meet real-time threshold if every video frame is decompressed into an RGB image. OpenAI has likely developed their own neural-first, streaming video codec to transmit the motion deltas as tokens. The communication protocol and NN inference must be co-optimized. For example, there could be a small and energy-efficient NN running on the edge device that decides to transmit more tokens if the video is interesting, and fewer otherwise. - I didn't expect GPT-4o to be closer to GPT-5, the rumored "Arrakis" model that takes multimodal in and out. In fact, it's likely an early checkpoint of GPT-5 that hasn't finished training yet. The branding betrays a certain insecurity. Ahead of Google I/O, OpenAI would rather beat our mental projection of GPT-4.5 than disappoint by missing the sky-high expectation for GPT-5. A smart move to buy more time. - Notably, the assistant is much more lively and even a bit flirty. GPT-4o is trying (perhaps a bit too hard) to sound like HER. OpenAI is eating Character AI's lunch, with almost 100% overlap in form factor and huge distribution channels. It's a pivot towards more emotional AI with strong personality, which OpenAI seemed to actively suppress in the past. - Whoever wins Apple first wins big time. I see 3 levels of integration with iOS: 1) Ditch Siri. OpenAI distills a smaller-tier, purely on-device GPT-4o for iOS, with optional paid upgrade to use the cloud. 2) Native features to stream the camera or screen into the model. Chip-level support for neural audio/video codec. 3) Integrate with iOS system-level action API and smart home APIs. No one uses Siri Shortcuts, but it's time to resurrect. This could become the AI agent product with a billion users from the get-go. The FSD for smartphones with a Tesla-scale data flywheel.

Jim Fan

991,964 görüntüleme • 2 yıl önce

So. A couple months back Citrini sent Analyst #3 behind the lines into the Strait of Hormuz. Cuban cigar in the water, Shahed drones overhead, IRGC patrol boats running circles eighteen miles off the Iranian coast. One of the best pieces anyone published all year. We agree with the ethos completely. We just took the part about staying alive a little more seriously. So our AAIG HR department (which we don’t have) decided it’d be slightly smarter to send Simeon Research behind the guns instead of in front of them. Badge is in hand. We’re going to Eurosatory, Paris, June 15–19 the second largest defense expo on earth. 120,000+ people, 60+ countries, every major prime and a full hall of disruptors under one roof. And no, what you just watched isn’t AI. That’s a real autonomous swarm by Swarm Defense Counter-drone is the single hottest fight on that floor right now the radars, the jammers, the lasers and the interceptors built to put swarms exactly like that back on the ground. That’s where we start with Electro Optic Systems remote weapon systems, High Energy Laser Weapons (HELW) and much more. Loitering munitions and autonomous strike following the story. From AI-enabled command and control, space-based ISR and secure comms to soldier man-to-man protection and optics. We are going to look at it all. But the bigger question we’re flying to Paris to answer is the money one: Europe is committing well over a trillion to defense this decade and we’re there to see where it actually goes. We’re sitting down with industry experts and management teams across the space. This is the open of a proper deep dive into the defense sector. Not from a desk. From the floor. what’s actually being ordered, and where the money flows next. Follow along trough our Substack. $THEON.AS $EOS.AX $RHM.NE $RENK
0:22

So. A couple months back Citrini sent Analyst #3 behind the lines into the Strait of Hormuz. Cuban cigar in the water, Shahed drones overhead, IRGC patrol boats running circles eighteen miles off the Iranian coast. One of the best pieces anyone published all year. We agree with the ethos completely. We just took the part about staying alive a little more seriously. So our AAIG HR department (which we don’t have) decided it’d be slightly smarter to send Simeon Research behind the guns instead of in front of them. Badge is in hand. We’re going to Eurosatory, Paris, June 15–19 the second largest defense expo on earth. 120,000+ people, 60+ countries, every major prime and a full hall of disruptors under one roof. And no, what you just watched isn’t AI. That’s a real autonomous swarm by Swarm Defense Counter-drone is the single hottest fight on that floor right now the radars, the jammers, the lasers and the interceptors built to put swarms exactly like that back on the ground. That’s where we start with Electro Optic Systems remote weapon systems, High Energy Laser Weapons (HELW) and much more. Loitering munitions and autonomous strike following the story. From AI-enabled command and control, space-based ISR and secure comms to soldier man-to-man protection and optics. We are going to look at it all. But the bigger question we’re flying to Paris to answer is the money one: Europe is committing well over a trillion to defense this decade and we’re there to see where it actually goes. We’re sitting down with industry experts and management teams across the space. This is the open of a proper deep dive into the defense sector. Not from a desk. From the floor. what’s actually being ordered, and where the money flows next. Follow along trough our Substack. $THEON.AS $EOS.AX $RHM.NE $RENK

Markos

47,524 görüntüleme • 3 ay önce

New framework: Kick down your robot, it will get back up every time 🥋 Chinese startup RoboParty is a Beijing startup founded April 2025 by Huang Yi, originally shipping ROBOTO ORIGIN, the world's first full-stack open-source bipedal humanoid. They released UFO: Unsupervised Reinforcement Learning Framework for Humanoid Control. DEFINITIONS -> what differs is where the learning signal comes from: - SUPERVISED: humans supply the right answers (labels), the model imitates them. - UNSUPERVISED: no answer key, the model finds structure in raw data on its own. - REINFORCEMENT LEARNING: no answer key either, the model tries things and a reward scores each attempt. → UNSUPERVISED RL: trial and error where the agent invents its own rewards, instead of engineers hand-writing one per task. REPRESENTATION LEARNING: compress raw states into a useful internal map. TEMPORAL DISTANCE: distance on that map is "how many steps from A to B." CONTRASTIVE: trained by pulling together what's close in time, pushing apart what isn't. -> CONTRASTIVE TEMPORAL-DISTANCE REPRESENTATION LEARNING: the model builds an internal map of body states where distance means how many steps it takes to get from one to another. It is trained by contrast: states that occur close together in a movement get pulled together in the map, randomly paired states get pushed apart. UFO is an open-source training framework that teaches humanoid robots skills, like getting up, walking, goal-reaching, teleoperation, without reference motions -> no motion-capture or human-video demonstrations to imitate. Its core is TeCH, a contrastive temporal-distance representation-learning algorithm: the robot explores, builds pseudo-goals by temporal rolling, and learns goal-conditioned policies from a single unified progress reward. One framework trains five different robots (Unitree G1/H1, RoboParty RP0/RP1, AgiBot X2) with automatic config conversion in ~2–3 hours per robot! The real novelty here "no demonstrations at all". No data-collection arms race,the dominant humanoid-locomotion recipe is tracking: imitate mocap/retargeted-human reference trajectories. The robot self-generates goals from its own exploration and learns from a progress reward, needing zero reference motion data. Everybody else is fighting over data acquisition, while this team just teleports out of the race entirely (inb4 "competition is for losers 💀 ). This strategy reminds me of the DeepSeek playbook applied to robots: open-source the whole stack to become the global default and commoditize everyone else. RoboParty is giving away hardware and now control software (UFO) to be the Android of humanoids. Yet another reason for the US to ban Chinese open models perhaps 🥶 ? What I also really like about this approach is the cross-embodiment infrastructure, one framework trains Unitree G1/H1, RoboParty RP0/RP1, and AgiBot X2 with automatic configuration conversion. Just like Physical Intelligence, RoboParty seems to place itself as a neutral hardware agnostic middle man. Also woth mentioning: their ability ot perform stable skill injection, e.g. adding a cartwheel without forgetting how to walk. A common failure of RL humanoid policies is that teaching a new agile skill destabilizes the existing ones (catastrophic forgetting). UFO claims you can inject rare motions (cartwheel) without collapsing learned behavior. If it holds, that's a significant incremental/continual skill-learning! But again, I have to underline it: no arXiv, no external validation, no success-rate numbers. -> robotics badely needs an independent unbiased evaluator imho. Still, look at that cool demo: robot is getting kicked and pushed around (serious disturbance) during teleoperation (controlled the person at the back wearing the VR headset), and still managed to always get back up. This is some serious demonstration of stability and robustness!
0:49

New framework: Kick down your robot, it will get back up every time 🥋 Chinese startup RoboParty is a Beijing startup founded April 2025 by Huang Yi, originally shipping ROBOTO ORIGIN, the world's first full-stack open-source bipedal humanoid. They released UFO: Unsupervised Reinforcement Learning Framework for Humanoid Control. DEFINITIONS -> what differs is where the learning signal comes from: - SUPERVISED: humans supply the right answers (labels), the model imitates them. - UNSUPERVISED: no answer key, the model finds structure in raw data on its own. - REINFORCEMENT LEARNING: no answer key either, the model tries things and a reward scores each attempt. → UNSUPERVISED RL: trial and error where the agent invents its own rewards, instead of engineers hand-writing one per task. REPRESENTATION LEARNING: compress raw states into a useful internal map. TEMPORAL DISTANCE: distance on that map is "how many steps from A to B." CONTRASTIVE: trained by pulling together what's close in time, pushing apart what isn't. -> CONTRASTIVE TEMPORAL-DISTANCE REPRESENTATION LEARNING: the model builds an internal map of body states where distance means how many steps it takes to get from one to another. It is trained by contrast: states that occur close together in a movement get pulled together in the map, randomly paired states get pushed apart. UFO is an open-source training framework that teaches humanoid robots skills, like getting up, walking, goal-reaching, teleoperation, without reference motions -> no motion-capture or human-video demonstrations to imitate. Its core is TeCH, a contrastive temporal-distance representation-learning algorithm: the robot explores, builds pseudo-goals by temporal rolling, and learns goal-conditioned policies from a single unified progress reward. One framework trains five different robots (Unitree G1/H1, RoboParty RP0/RP1, AgiBot X2) with automatic config conversion in ~2–3 hours per robot! The real novelty here "no demonstrations at all". No data-collection arms race,the dominant humanoid-locomotion recipe is tracking: imitate mocap/retargeted-human reference trajectories. The robot self-generates goals from its own exploration and learns from a progress reward, needing zero reference motion data. Everybody else is fighting over data acquisition, while this team just teleports out of the race entirely (inb4 "competition is for losers 💀 ). This strategy reminds me of the DeepSeek playbook applied to robots: open-source the whole stack to become the global default and commoditize everyone else. RoboParty is giving away hardware and now control software (UFO) to be the Android of humanoids. Yet another reason for the US to ban Chinese open models perhaps 🥶 ? What I also really like about this approach is the cross-embodiment infrastructure, one framework trains Unitree G1/H1, RoboParty RP0/RP1, and AgiBot X2 with automatic configuration conversion. Just like Physical Intelligence, RoboParty seems to place itself as a neutral hardware agnostic middle man. Also woth mentioning: their ability ot perform stable skill injection, e.g. adding a cartwheel without forgetting how to walk. A common failure of RL humanoid policies is that teaching a new agile skill destabilizes the existing ones (catastrophic forgetting). UFO claims you can inject rare motions (cartwheel) without collapsing learned behavior. If it holds, that's a significant incremental/continual skill-learning! But again, I have to underline it: no arXiv, no external validation, no success-rate numbers. -> robotics badely needs an independent unbiased evaluator imho. Still, look at that cool demo: robot is getting kicked and pushed around (serious disturbance) during teleoperation (controlled the person at the back wearing the VR headset), and still managed to always get back up. This is some serious demonstration of stability and robustness!

Léo

36,112 görüntüleme • 1 ay önce

I spent $4,699 on a DGX Spark and used it as a chatbot for six months. Not proud of it. But I think most Spark owners are doing the same thing, so let me tell you what changed. The moment was dumb. I had a recording of a client call, needed notes, and my first instinct was to open a browser tab and upload it to some transcription SaaS. Then I looked at the box on my desk. 128 GB of unified memory. Sitting there. Running a chat model I'd asked maybe 30 questions that day. And I thought: I have a supercomputer and I'm about to pay a stranger to listen to my client call. So I tried to do it locally. And here's where it got annoying: - my LLM can't hear audio - the ASR model I found is great but it's a CLI script - the transcript came out in two languages, need translation - summary needs the LLM again - now I want a mind map, that's another tool - oh and here's a scanned PDF from the same meeting, OCR, another tool Copy paste copy paste. Output saved to ~/Downloads/final_v2_REAL.txt. That's when it clicked: tokens per second was never the point of this box. A 24 GB gaming card forces you to pick ONE model, so everyone picks the biggest generalist and asks it to do everything. But 128 GB doesn't force that. You can keep six specialists loaded at once. A 1.7B speech model transcribes better than a 35B chat model ever will. A tiny translation model just translates and doesn't get creative. The big LLM does the thinking, and only the thinking. The hardware was never the problem. The missing piece was the thing that ties them together. So I built it. SparklingKit. (the name is a tribute to the Spark. also: a spark is one flash, sparkling is when it keeps going) What it actually is: → transcription, OCR, translation, image grounding, text-to-image, mind maps, chat. one panel. → six models hand-picked to fit together in 128 GB with room left over → queue-first: drop a 3-hour recording, go get coffee, check from your phone → nothing is a dead end: finish a transcript and "translate / map / chat with it" are already waiting → visual workflows: chain the steps once, run them forever → everything is a file. one folder. no database. copy it to back up. point Obsidian at it. → nothing leaves your machine unless you send it. The thing I care about most: if you stop using SparklingKit tomorrow, nothing happens to your data. It was already a folder of markdown and json. There's no lock-in to escape. Spark owners: you bought room, not a faster chatbot. This is how I'm using the room. No Spark? The panel runs anywhere Docker runs. Point it at your own endpoints. It's open source, and it's version one. Rough edges exist. Issues and PRs very welcome. Full details and a one-line install at If you've ever uploaded something private to a cloud AI and felt weird about it, this is for you.
1:23

I spent $4,699 on a DGX Spark and used it as a chatbot for six months. Not proud of it. But I think most Spark owners are doing the same thing, so let me tell you what changed. The moment was dumb. I had a recording of a client call, needed notes, and my first instinct was to open a browser tab and upload it to some transcription SaaS. Then I looked at the box on my desk. 128 GB of unified memory. Sitting there. Running a chat model I'd asked maybe 30 questions that day. And I thought: I have a supercomputer and I'm about to pay a stranger to listen to my client call. So I tried to do it locally. And here's where it got annoying: - my LLM can't hear audio - the ASR model I found is great but it's a CLI script - the transcript came out in two languages, need translation - summary needs the LLM again - now I want a mind map, that's another tool - oh and here's a scanned PDF from the same meeting, OCR, another tool Copy paste copy paste. Output saved to ~/Downloads/final_v2_REAL.txt. That's when it clicked: tokens per second was never the point of this box. A 24 GB gaming card forces you to pick ONE model, so everyone picks the biggest generalist and asks it to do everything. But 128 GB doesn't force that. You can keep six specialists loaded at once. A 1.7B speech model transcribes better than a 35B chat model ever will. A tiny translation model just translates and doesn't get creative. The big LLM does the thinking, and only the thinking. The hardware was never the problem. The missing piece was the thing that ties them together. So I built it. SparklingKit. (the name is a tribute to the Spark. also: a spark is one flash, sparkling is when it keeps going) What it actually is: → transcription, OCR, translation, image grounding, text-to-image, mind maps, chat. one panel. → six models hand-picked to fit together in 128 GB with room left over → queue-first: drop a 3-hour recording, go get coffee, check from your phone → nothing is a dead end: finish a transcript and "translate / map / chat with it" are already waiting → visual workflows: chain the steps once, run them forever → everything is a file. one folder. no database. copy it to back up. point Obsidian at it. → nothing leaves your machine unless you send it. The thing I care about most: if you stop using SparklingKit tomorrow, nothing happens to your data. It was already a folder of markdown and json. There's no lock-in to escape. Spark owners: you bought room, not a faster chatbot. This is how I'm using the room. No Spark? The panel runs anywhere Docker runs. Point it at your own endpoints. It's open source, and it's version one. Rough edges exist. Issues and PRs very welcome. Full details and a one-line install at If you've ever uploaded something private to a cloud AI and felt weird about it, this is for you.

stevibe

55,152 görüntüleme • 15 gün önce

Mike is back and better than ever. Mike ( has undergone a major refresh packed with new features, design improvements, and quality-of-life enhancements. And as always, everything is open source. Here is a summary of the major updates: 1. Legal research Case law hallucinations are a massive problem. To address this, Mike's assistant can now produce grounded citations backed by case law from CourtListener. The integration is tight and highly intuitive. Every answer includes links to cited cases, along with supporting quotations from judicial opinions to substantiate each proposition made by the AI. The assistant also researches cases in much the same way a lawyer would. It retrieves potentially relevant authorities, verifies the accuracy of citations, and searches for keywords that align with the research topic. Support for additional jurisdictions will be added soon. 2. Improved user interface (UI) The UI has been redesigned around Apple's Liquid Glass design principles, giving the application a cleaner and more minimalist feel down to every modal and button. Mike is designed to be a place of calm where lawyers can focus amidst the chaos of legal practice. 3. Version control Lawyers are familiar with saving documents as new versions in document management systems such as iManage and NetDocuments. Mike now brings this workflow into projects by allowing users to save new versions on top of existing documents with a simple drag-and-drop action. This introduces order and clarity to matters where redlines and document versions can quickly become confusing, especially when there are five different files named "Final Execution Version." 4. Data control Users can now export their data and permanently delete their data directly from the Settings page. 5. Security Multi-factor authentication (MFA) is now available. Users can enable MFA to protect sensitive actions within Mike, such as data deletion, and can also optionally require MFA during login. There have also been numerous backend security improvements since the previous release. 6. MCP connectors This was one of the most requested features. Users can now add MCP connectors directly from Settings. All tokens and secrets are encrypted at rest. In the demo video, I showcase the Neimo MCP from k-ID, a global age verification and compliance platform. Mike can also be connected to other MCP servers, including email systems. The setup process is currently somewhat manual, but one-click authentication and onboarding will be added in a future release. Mike now has a strong open-source community behind it and is improving every single day. The summary above highlights just some of the work that has been shipped over the past month. I'll also be doing more demos to showcase Mike's capabilities. See you soon.
7:03

Mike is back and better than ever. Mike ( has undergone a major refresh packed with new features, design improvements, and quality-of-life enhancements. And as always, everything is open source. Here is a summary of the major updates: 1. Legal research Case law hallucinations are a massive problem. To address this, Mike's assistant can now produce grounded citations backed by case law from CourtListener. The integration is tight and highly intuitive. Every answer includes links to cited cases, along with supporting quotations from judicial opinions to substantiate each proposition made by the AI. The assistant also researches cases in much the same way a lawyer would. It retrieves potentially relevant authorities, verifies the accuracy of citations, and searches for keywords that align with the research topic. Support for additional jurisdictions will be added soon. 2. Improved user interface (UI) The UI has been redesigned around Apple's Liquid Glass design principles, giving the application a cleaner and more minimalist feel down to every modal and button. Mike is designed to be a place of calm where lawyers can focus amidst the chaos of legal practice. 3. Version control Lawyers are familiar with saving documents as new versions in document management systems such as iManage and NetDocuments. Mike now brings this workflow into projects by allowing users to save new versions on top of existing documents with a simple drag-and-drop action. This introduces order and clarity to matters where redlines and document versions can quickly become confusing, especially when there are five different files named "Final Execution Version." 4. Data control Users can now export their data and permanently delete their data directly from the Settings page. 5. Security Multi-factor authentication (MFA) is now available. Users can enable MFA to protect sensitive actions within Mike, such as data deletion, and can also optionally require MFA during login. There have also been numerous backend security improvements since the previous release. 6. MCP connectors This was one of the most requested features. Users can now add MCP connectors directly from Settings. All tokens and secrets are encrypted at rest. In the demo video, I showcase the Neimo MCP from k-ID, a global age verification and compliance platform. Mike can also be connected to other MCP servers, including email systems. The setup process is currently somewhat manual, but one-click authentication and onboarding will be added in a future release. Mike now has a strong open-source community behind it and is improving every single day. The summary above highlights just some of the work that has been shipped over the past month. I'll also be doing more demos to showcase Mike's capabilities. See you soon.

WillC

10,293 görüntüleme • 3 ay önce

Finally video emerges of an actual Ukrainian attack in Toretsk, two weeks after their supposed ninja counteroffensive kicked off... or is it?⬇️ I'm referring to this video, which was published Wednesday, showing the destruction of two AFU M113s withdrawing troops from a southern suburb of Toretsk (Zabalka) that basically all mappers had placed well inside Russian lines (video 1, see figure 3 for the map). Video emerged later that day of Russian infantry destroying an apparently Ukrainian-held house in the same area with a satchel charge after suppressing the defenders with an RPG and small arms (the first segment of video 2). Interesting, I thought. So I looked at the map again, harder. And then I looked at the geolocated positions of Russian units, which the TG channel Creamy Caprice keeps a map of (figure 3, showing the location of the first video - the red and blue dots mark Russian and Ukrainian positions logged over the entire course of the battle). And that's when it struck me - Russian troops have never been spotted in the northwestern corner of the Zabalka district. They've been seen in the central part and they've been seen around the slag heaps, but not the northwest corner. And there's a relatively short route into it through the Ukrainian-held forest to the northwest, although the last mile is through an open field around the base of (and dominated by) the nearby slag heap. It would be an extremely dangerous journey. Now we come to what the video actually depicts - the withdrawal of troops. The first APC is hit while withdrawing and the escaping dismounts are effectively engaged after going to ground in the forest. The second APC then arrives in Zabalka, loads up, and is heavily hit and knocked out as it withdraws. A couple soldiers are seen heading deeper into the city on foot as it departs, but not a full squad - there may not have been room for them on the transport, or they may have (correctly) thought their chances were better on foot. Then the subsequent video emerged of Russian infantry clearing holdouts in the area, which could have very well been those men. So what happened here? Well, I pointed out earlier that it's standard practice to back-clear an urban area after taking it, to clear bypassed enemy strongholds and booby-traps and render the area safe to support further operations. I suspect there was actually a pocket of bypassed Ukrainian troops holed up in the western Zabalka District, a couple kilometers behind Russian lines, whom the AFU command tried to evacuate via APC earlier this week while they still had the chance to do so. And the Russians may very well have allowed those APCs to pull in and load up so they could - as they did - very coldly kill them while they were packed with infantry to withdraw. Why did the Ukrainian command undertake such a high-risk operation instead of simply writing these men off and telling them that it was every man for himself? Probably because these were Azov fighters and thus entitled to special treatment and consideration - one of their brigades operates in the area. In any event, far from suggesting a Ukrainian counterattack into south Toretsk, this turn of events suggests to me that the Russians are instead mopping up remaining resistance in the city. (As an addendum, the people behind Creamy Caprice think that the third segment of video 2 shows Ukrainian activity somewhat farther into the northwest corner of the Zabalka Dictrict, but that segment also doesn't show live troops - for all we know they were bombing an AFU comms repeater or something on the roof of that building. The second segment of that video is old judging by the snow on the ground.)
1:42

Finally video emerges of an actual Ukrainian attack in Toretsk, two weeks after their supposed ninja counteroffensive kicked off... or is it?⬇️ I'm referring to this video, which was published Wednesday, showing the destruction of two AFU M113s withdrawing troops from a southern suburb of Toretsk (Zabalka) that basically all mappers had placed well inside Russian lines (video 1, see figure 3 for the map). Video emerged later that day of Russian infantry destroying an apparently Ukrainian-held house in the same area with a satchel charge after suppressing the defenders with an RPG and small arms (the first segment of video 2). Interesting, I thought. So I looked at the map again, harder. And then I looked at the geolocated positions of Russian units, which the TG channel Creamy Caprice keeps a map of (figure 3, showing the location of the first video - the red and blue dots mark Russian and Ukrainian positions logged over the entire course of the battle). And that's when it struck me - Russian troops have never been spotted in the northwestern corner of the Zabalka district. They've been seen in the central part and they've been seen around the slag heaps, but not the northwest corner. And there's a relatively short route into it through the Ukrainian-held forest to the northwest, although the last mile is through an open field around the base of (and dominated by) the nearby slag heap. It would be an extremely dangerous journey. Now we come to what the video actually depicts - the withdrawal of troops. The first APC is hit while withdrawing and the escaping dismounts are effectively engaged after going to ground in the forest. The second APC then arrives in Zabalka, loads up, and is heavily hit and knocked out as it withdraws. A couple soldiers are seen heading deeper into the city on foot as it departs, but not a full squad - there may not have been room for them on the transport, or they may have (correctly) thought their chances were better on foot. Then the subsequent video emerged of Russian infantry clearing holdouts in the area, which could have very well been those men. So what happened here? Well, I pointed out earlier that it's standard practice to back-clear an urban area after taking it, to clear bypassed enemy strongholds and booby-traps and render the area safe to support further operations. I suspect there was actually a pocket of bypassed Ukrainian troops holed up in the western Zabalka District, a couple kilometers behind Russian lines, whom the AFU command tried to evacuate via APC earlier this week while they still had the chance to do so. And the Russians may very well have allowed those APCs to pull in and load up so they could - as they did - very coldly kill them while they were packed with infantry to withdraw. Why did the Ukrainian command undertake such a high-risk operation instead of simply writing these men off and telling them that it was every man for himself? Probably because these were Azov fighters and thus entitled to special treatment and consideration - one of their brigades operates in the area. In any event, far from suggesting a Ukrainian counterattack into south Toretsk, this turn of events suggests to me that the Russians are instead mopping up remaining resistance in the city. (As an addendum, the people behind Creamy Caprice think that the third segment of video 2 shows Ukrainian activity somewhat farther into the northwest corner of the Zabalka Dictrict, but that segment also doesn't show live troops - for all we know they were bombing an AFU comms repeater or something on the roof of that building. The second segment of that video is old judging by the snow on the ground.)

Armchair Warlord

15,294 görüntüleme • 1 yıl önce

Bitcoin is down ~47% from its October 2025 ATH of $126K. Fear & Greed at single digits. "BTC to zero” Google searches at 5-year highs. ETF outflows mounting. People are calling for $40K, even $25K. So I animated 15 years of drawdown data to put this moment in context. - - - Left: BTC price (log scale), colored by drawdown regime. Right: The distribution of every single day Bitcoin has spent at each drawdown depth from an ATH, built up in real time as price evolves. Importantly, the distribution evolves and is not static - the peaks/valleys grow over time - but what is the trend in how they’re evolving? This tells you something important about what Bitcoin is becoming. - - - There are 3 predominant drawdown regions: 🟢Green = 0 to -15% (Regime 1) ⚪️White = -15% to -35% (Transition) 🔴Red = ≤ -35% (Regime 2) Watch it build. 2011: -92.7% bottom. The histogram is a thin red smear. Almost all of Bitcoin's short existence spent deep underwater. 2013-2015: Another cycle, another -72% drawdown. The distribution fills in a fat red tail between -60% and -80%. Over 1,500 days in drawdown. Most of them brutal. 2017: BTC makes a brief ATH at $11,562, but the histogram tells us that with 2,524 drawdown days, red still dominates. Bitcoin had spent most of its life getting punished. 2018: -78.4%. The -60 to -80% band fills in further. Again, Bitcoin getting punished. This has been the story of the old Bitcoin. Then something shifts. By 2021, the green bars near 0% start growing much more. Bitcoin is beginning to spend more time near its highs than it used to. The distribution is migrating left. 2022 bottom: -68.5%. Still deep, but shallower than every prior cycle bottom. Each cycle's worst drawdown has gotten less severe: -92.7% → -87% → -84% → -77% → -68.5% The floor keeps rising, the green keeps growing, and the red tail is still there - but it's shrinking as a share of the whole. The transition zone is gaining more share too, but still markedly less time there than red or green (for example we just recently spent nearly 90 days in this zone after the Oct ‘25 ATH). This is what maturation looks like in the data. Not the absence of drawdowns, but the gradual compression of their severity and the accumulation of time spent near highs. - - - So where are we now? Down ~47% from $126K. Sentiment is the worst since June 2022. Everyone's asking if this is 2018 again. Probably not. Bitcoin has spent a structurally larger share of time in Regime 1 than ever before, especially in this cycle. Green-white oscillations are replacing the deep red plunges. The dives into Regime 2 are getting shallower. We could still go lower from here, but the data is compressing the downside, cycle over cycle. Whether the market believes that right now is a different question… - - - Worth a boomark 📙, as you might need to come back to this a few times to digest 🧠
0:43

Bitcoin is down ~47% from its October 2025 ATH of $126K. Fear & Greed at single digits. "BTC to zero” Google searches at 5-year highs. ETF outflows mounting. People are calling for $40K, even $25K. So I animated 15 years of drawdown data to put this moment in context. - - - Left: BTC price (log scale), colored by drawdown regime. Right: The distribution of every single day Bitcoin has spent at each drawdown depth from an ATH, built up in real time as price evolves. Importantly, the distribution evolves and is not static - the peaks/valleys grow over time - but what is the trend in how they’re evolving? This tells you something important about what Bitcoin is becoming. - - - There are 3 predominant drawdown regions: 🟢Green = 0 to -15% (Regime 1) ⚪️White = -15% to -35% (Transition) 🔴Red = ≤ -35% (Regime 2) Watch it build. 2011: -92.7% bottom. The histogram is a thin red smear. Almost all of Bitcoin's short existence spent deep underwater. 2013-2015: Another cycle, another -72% drawdown. The distribution fills in a fat red tail between -60% and -80%. Over 1,500 days in drawdown. Most of them brutal. 2017: BTC makes a brief ATH at $11,562, but the histogram tells us that with 2,524 drawdown days, red still dominates. Bitcoin had spent most of its life getting punished. 2018: -78.4%. The -60 to -80% band fills in further. Again, Bitcoin getting punished. This has been the story of the old Bitcoin. Then something shifts. By 2021, the green bars near 0% start growing much more. Bitcoin is beginning to spend more time near its highs than it used to. The distribution is migrating left. 2022 bottom: -68.5%. Still deep, but shallower than every prior cycle bottom. Each cycle's worst drawdown has gotten less severe: -92.7% → -87% → -84% → -77% → -68.5% The floor keeps rising, the green keeps growing, and the red tail is still there - but it's shrinking as a share of the whole. The transition zone is gaining more share too, but still markedly less time there than red or green (for example we just recently spent nearly 90 days in this zone after the Oct ‘25 ATH). This is what maturation looks like in the data. Not the absence of drawdowns, but the gradual compression of their severity and the accumulation of time spent near highs. - - - So where are we now? Down ~47% from $126K. Sentiment is the worst since June 2022. Everyone's asking if this is 2018 again. Probably not. Bitcoin has spent a structurally larger share of time in Regime 1 than ever before, especially in this cycle. Green-white oscillations are replacing the deep red plunges. The dives into Regime 2 are getting shallower. We could still go lower from here, but the data is compressing the downside, cycle over cycle. Whether the market believes that right now is a different question… - - - Worth a boomark 📙, as you might need to come back to this a few times to digest 🧠

Sminston With 👁

31,213 görüntüleme • 6 ay önce

♾Infinity Nikki Version 2.7 Outfit Preview: Rippling Reverie Where bubbles cross and shimmer, unspoken longing is woven into the refrain of reunion. And so the music flows through the heart like a tide, lifting sleeping dreams, while every tender ripple bears witness to a miracle drawing softly near. >> Main Attribute: Sweet >> Outfit Ability: [Celestial Tide] After using the ability, a surging wave is summoned, which Nikki can leap onto. Only one wave can be active at a time. While the wave remains active, Nikki can stand at its base and interact with it at any time to leap back to the top. Nikki can place up to 3 Starlets at a time. If she remains within 30 meters of the most recently placed Starlet, she can press the button to instantly return to it, after which that Starlet disappears. Starlets will disappear if Nikki moves too far away from them or logs out. ❖ The [Celestial Tide] ability cannot be used in uncleared dungeons or any boss challenges. Starlets cannot be placed in certain stages or in the Mira Crown Pinnacle Contest. ❖ Special Effect: After equipping the [Celestial Tide] ability, a special teleportation animation will play when using a Warp Spire to teleport across the map. This effect will not be triggered while holding hands with other stylists. >> Decoration: [Echo of Starlight] Can be placed in open spaces. The timeless, lonely night sky hums with sighs, but thankfully, there is still someone listening. During this long vigil, she remembers the vibrant colors of the falling meteor—a grand, magnificent farewell. ❖ Available after reaching 160 Resonances in the limited-time Resonance [Refrain of Reunion]. >> Evolution Rewards ◆ Evolving the outfit to Stage 1 unlocks the pose [Dreamy Awakening]. ◆ Evolving the outfit to Stage 2 unlocks the Framed Moment item [Bubble Encounter]. You can also use Tidal Pulse Heartchime in [Clothing Evolution > Decoration: Radiance] to obtain [Echo of Starlight: Radiance]. ◆ Evolving the outfit to Stage 3 unlocks [Tracing Rainbows: Makeup], a new idle animation, poses, enhanced special effects, and alternative details for certain pieces. ♾Follow Infinity Nikki and retweet this post, and we'll select 10 lucky stylists to win [Infinity Nikki Aeroplane Chess *1] each! —— ➤ Infinity Nikki Version 2.7 Where Stars Yearn launches globally on June 23 (UTC-7)! ➤ Download Now: ➤ Join us on Reddit: #InfinityNikki #WhereStarsYearn
3:38

♾Infinity Nikki Version 2.7 Outfit Preview: Rippling Reverie Where bubbles cross and shimmer, unspoken longing is woven into the refrain of reunion. And so the music flows through the heart like a tide, lifting sleeping dreams, while every tender ripple bears witness to a miracle drawing softly near. >> Main Attribute: Sweet >> Outfit Ability: [Celestial Tide] After using the ability, a surging wave is summoned, which Nikki can leap onto. Only one wave can be active at a time. While the wave remains active, Nikki can stand at its base and interact with it at any time to leap back to the top. Nikki can place up to 3 Starlets at a time. If she remains within 30 meters of the most recently placed Starlet, she can press the button to instantly return to it, after which that Starlet disappears. Starlets will disappear if Nikki moves too far away from them or logs out. ❖ The [Celestial Tide] ability cannot be used in uncleared dungeons or any boss challenges. Starlets cannot be placed in certain stages or in the Mira Crown Pinnacle Contest. ❖ Special Effect: After equipping the [Celestial Tide] ability, a special teleportation animation will play when using a Warp Spire to teleport across the map. This effect will not be triggered while holding hands with other stylists. >> Decoration: [Echo of Starlight] Can be placed in open spaces. The timeless, lonely night sky hums with sighs, but thankfully, there is still someone listening. During this long vigil, she remembers the vibrant colors of the falling meteor—a grand, magnificent farewell. ❖ Available after reaching 160 Resonances in the limited-time Resonance [Refrain of Reunion]. >> Evolution Rewards ◆ Evolving the outfit to Stage 1 unlocks the pose [Dreamy Awakening]. ◆ Evolving the outfit to Stage 2 unlocks the Framed Moment item [Bubble Encounter]. You can also use Tidal Pulse Heartchime in [Clothing Evolution > Decoration: Radiance] to obtain [Echo of Starlight: Radiance]. ◆ Evolving the outfit to Stage 3 unlocks [Tracing Rainbows: Makeup], a new idle animation, poses, enhanced special effects, and alternative details for certain pieces. ♾Follow Infinity Nikki and retweet this post, and we'll select 10 lucky stylists to win [Infinity Nikki Aeroplane Chess *1] each! —— ➤ Infinity Nikki Version 2.7 Where Stars Yearn launches globally on June 23 (UTC-7)! ➤ Download Now: ➤ Join us on Reddit: #InfinityNikki #WhereStarsYearn

Infinity Nikki

84,872 görüntüleme • 3 ay önce