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A Bittensor subnet just built an AI safety model that beats the big players Trishool (Trishool | SN23), Bittensor's (Openτensor Foundaτion) subnet 23, released HaloGuard 1.0 today. The 4B version claims first place across seven established prompt-safety benchmarks, and the 0.8B version outperforms models several times its size. The... pitch is speed over scale. HaloGuard screens prompts in real time before they reach the underlying model or agent, instead of catching harmful outputs after the fact. An earlier alpha version already runs live on the Chutes subnet, hitting an 87% F1 score on real traffic since May. The subnet's whole design is built around attacking itself. Miners get paid for breaking the model, then the breaks get patched.show more

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🚨 JUST IN: Eli Lilly and Nvidia announce a $1B, five-year plan for AI drug discovery lab. Inside $TAO Bittensor, MetaNova Labs already moved. ~$5M in $TAO emissions have been deployed, continuously paid to miners producing real scientific signal. Live. On-chain. Performance-driven. Metanova runs today on Subnet 68: molecular prediction, adversarial model competition, deterministic validation, emissions tied to results. The same objective: Drug Discovery. Big labs move when capital arrives. $TAO moves when intelligence performs. METANOVA is already bulding. Others are still drafting press releases.

🚨 JUST IN: Eli Lilly and Nvidia announce a $1B, five-year plan for AI drug discovery lab. Inside $TAO Bittensor, MetaNova Labs already moved. ~$5M in $TAO emissions have been deployed, continuously paid to miners producing real scientific signal. Live. On-chain. Performance-driven. Metanova runs today on Subnet 68: molecular prediction, adversarial model competition, deterministic validation, emissions tied to results. The same objective: Drug Discovery. Big labs move when capital arrives. $TAO moves when intelligence performs. METANOVA is already bulding. Others are still drafting press releases.

Tao Ouτsider

12,674 Aufrufe • vor 6 Monaten

Don't train the model, evolve the harness. I read a brilliant blog post from Hugging Face where they took a frozen open model scoring 0% on a hard legal agent benchmark, left its weights alone, and let an automated loop rewrite only the code around it. That code layer is the harness, the runtime wrapper that feeds the model context, runs its tool calls, and decides when a run ends. By the time the loop finished, the system had essentially matched Sonnet 4.6 on the benchmark's headline metric, at roughly 7x lower cost per task. Zero weights changed. The gain existed because of where the model was failing. The judge only grades files saved in the right place under the exact requested filename, and the model kept doing the legal analysis correctly, then saving it under the wrong name, dropping it in a scratch folder, or never writing it at all. So the 0% was never measuring legal reasoning. It was measuring the harness. Hand-tuning that layer is slow and model-specific, so they automated it. A Claude proposer adds exactly one mechanism per iteration, and an outer loop keeps it only if it clearly beats the current best, so accepted mechanisms compound. What the loop discovered says a lot about where agents actually fail. → The biggest single gain was file handling, not intelligence. An automatic step that lands the deliverable exactly where the judge expects it beat every prompt change, with zero extra model tokens. → Code fixes transferred across models, prompt playbooks did not. The same harness lifted a smaller model from the same family by 14 points, but the tuned prompts hurt a different model family on tasks it could already finish. → The harness mattered more than anything else. Same model, same judge, same tasks, and five different harnesses scored anywhere between 3.5% and 80.1%. The gains do eventually flatten, and the remaining misses look like real capability gaps. At some point the wrapper runs out of tricks and the model has to carry the work. But the lesson holds. A benchmark score measures the model and its harness together, and until the harness is fixed, it's impossible to know which one failed. I highly recommend reading this: I also wrote a deep dive on agent harness engineering a while back, covering the orchestration loop, tools, memory, context management, and everything that turns a stateless LLM into a capable agent. The article is quoted below.

Don't train the model, evolve the harness. I read a brilliant blog post from Hugging Face where they took a frozen open model scoring 0% on a hard legal agent benchmark, left its weights alone, and let an automated loop rewrite only the code around it. That code layer is the harness, the runtime wrapper that feeds the model context, runs its tool calls, and decides when a run ends. By the time the loop finished, the system had essentially matched Sonnet 4.6 on the benchmark's headline metric, at roughly 7x lower cost per task. Zero weights changed. The gain existed because of where the model was failing. The judge only grades files saved in the right place under the exact requested filename, and the model kept doing the legal analysis correctly, then saving it under the wrong name, dropping it in a scratch folder, or never writing it at all. So the 0% was never measuring legal reasoning. It was measuring the harness. Hand-tuning that layer is slow and model-specific, so they automated it. A Claude proposer adds exactly one mechanism per iteration, and an outer loop keeps it only if it clearly beats the current best, so accepted mechanisms compound. What the loop discovered says a lot about where agents actually fail. → The biggest single gain was file handling, not intelligence. An automatic step that lands the deliverable exactly where the judge expects it beat every prompt change, with zero extra model tokens. → Code fixes transferred across models, prompt playbooks did not. The same harness lifted a smaller model from the same family by 14 points, but the tuned prompts hurt a different model family on tasks it could already finish. → The harness mattered more than anything else. Same model, same judge, same tasks, and five different harnesses scored anywhere between 3.5% and 80.1%. The gains do eventually flatten, and the remaining misses look like real capability gaps. At some point the wrapper runs out of tricks and the model has to carry the work. But the lesson holds. A benchmark score measures the model and its harness together, and until the harness is fixed, it's impossible to know which one failed. I highly recommend reading this: I also wrote a deep dive on agent harness engineering a while back, covering the orchestration loop, tools, memory, context management, and everything that turns a stateless LLM into a capable agent. The article is quoted below.

Akshay 🚀

242,873 Aufrufe • vor 12 Tagen

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.

The architecture of this new world model is one of the most interesting things I've seen lately: Let me first explain how most world models work: They predict and render one frame at a time. If you are navigating in one of these worlds, and you look left, the model draws whatever looks right in the moment. Every time you change your viewpoint, the model has to imagine what should be there again, so it's very common for these models to "forget" what's in the world. For example, if you put a toy on the table, look away, then look back, the toy might not be there anymore. Tripo AI is releasing its Project Eden model, which works very differently: The model builds the world first, and then renders it based on that map. That map holds the real state of the world: the geometry, every object, where things are, what's already happened. The picture you see on screen gets generated from the map. This architecture flips the whole thing. Now, you get the following: 1. The world stops forgetting. Leave, come back, and the toy is still on the table because it lives in the map, not in the last frame you saw. 2. You can edit the world, and those changes persist for anyone who enters later. 3. Multiple people and AI agents can coexist in the world and see it from different perspectives. This is early research, but it's looking really promising. They just raised nearly $200M across two rounds to build it out. Tripo will be at SIGGRAPH 2026 (July 19–23, Los Angeles Convention Center). If you work in 3D, embodied AI, simulation, or anything spatial, go connect with them there.

Santiago

30,189 Aufrufe • vor 21 Tagen

🚨 SCIENTISTS MAY HAVE JUST FOUND A COSMIC VERSION OF HEISENBERG’S UNCERTAINTY PRINCIPLE AND IT COULD EXPLAIN DARK MATTER (OR DARK ENERGY). A bold new theory suggests the universe itself obeys a fundamental fuzziness: its size and rate of expansion cannot be known with perfect precision at the same time just like position and momentum in quantum mechanics. Why this matters: The universe is accelerating faster than it “should.” We call the mysterious driver dark energy (or sometimes link it to dark matter models). But this new idea from theoretical physicist Savvas Koushiappas needs no new particles or forces the acceleration emerges naturally from the universe’s own quantum-like uncertainty at cosmic scales. The deeper implication is mind-bending: Reality on the largest scales may be inherently “fuzzy,” just like the smallest scales. The geometry of spacetime itself carries a built-in uncertainty that could explain why the cosmos is speeding up without invoking exotic vacuum energy or hidden matter. We may be discovering that the universe has its own version of the uncertainty principle woven into its fabric. What happens if this cosmic fuzziness is real? Follow for more frontier physics and cosmic discoveries.

🚨 SCIENTISTS MAY HAVE JUST FOUND A COSMIC VERSION OF HEISENBERG’S UNCERTAINTY PRINCIPLE AND IT COULD EXPLAIN DARK MATTER (OR DARK ENERGY). A bold new theory suggests the universe itself obeys a fundamental fuzziness: its size and rate of expansion cannot be known with perfect precision at the same time just like position and momentum in quantum mechanics. Why this matters: The universe is accelerating faster than it “should.” We call the mysterious driver dark energy (or sometimes link it to dark matter models). But this new idea from theoretical physicist Savvas Koushiappas needs no new particles or forces the acceleration emerges naturally from the universe’s own quantum-like uncertainty at cosmic scales. The deeper implication is mind-bending: Reality on the largest scales may be inherently “fuzzy,” just like the smallest scales. The geometry of spacetime itself carries a built-in uncertainty that could explain why the cosmos is speeding up without invoking exotic vacuum energy or hidden matter. We may be discovering that the universe has its own version of the uncertainty principle woven into its fabric. What happens if this cosmic fuzziness is real? Follow for more frontier physics and cosmic discoveries.

TheNewPhysics

32,867 Aufrufe • vor 1 Monat

🇺🇸 The Tesla Semi might be the most underrated story of the year... After years of delays, Tesla has finally started taking orders for its electric heavy truck, and the numbers explain why this matters. The 500-mile version comes in around $290,000, well below the $400,000+ that Daimler and Volvo charge for their electric trucks, with longer range and faster charging on top. The demand is already there. California trucking firms have applied for state subsidies to buy over 1,200 Tesla Semis, more than every other electric truck application in the state combined since 2019. The timing could not be better either. Diesel prices have climbed roughly 50% since the war with Iran began, which means electricity per mile has become dramatically cheaper than the alternative, and the new Sparks, Nevada assembly line is built to produce up to 50,000 trucks a year. Same playbook as the Model S a decade ago. Established players dismissed Tesla, then spent billions trying to catch up. The pattern is repeating in real time. Source: NYT

🇺🇸 The Tesla Semi might be the most underrated story of the year... After years of delays, Tesla has finally started taking orders for its electric heavy truck, and the numbers explain why this matters. The 500-mile version comes in around $290,000, well below the $400,000+ that Daimler and Volvo charge for their electric trucks, with longer range and faster charging on top. The demand is already there. California trucking firms have applied for state subsidies to buy over 1,200 Tesla Semis, more than every other electric truck application in the state combined since 2019. The timing could not be better either. Diesel prices have climbed roughly 50% since the war with Iran began, which means electricity per mile has become dramatically cheaper than the alternative, and the new Sparks, Nevada assembly line is built to produce up to 50,000 trucks a year. Same playbook as the Model S a decade ago. Established players dismissed Tesla, then spent billions trying to catch up. The pattern is repeating in real time. Source: NYT

Mario Nawfal

66,192 Aufrufe • vor 1 Monat

The question "what existed before the Big Bang" breaks physics at a grammatical level. "Before" requires time. Time didn't exist yet. Stephen Hawking's answer was the cleanest: asking what came before the Big Bang is like asking what's north of the North Pole. The question itself is malformed. Time is a property of the universe, not a container it sits inside. But physicists kept pushing. Neil Turok's CPT-symmetric model says the Big Bang didn't create one universe. It created two. Ours runs forward in time with matter. The other runs backward in time with antimatter. Same event, two directions. If true, there's a mirror version of everything that ever happened, playing in reverse on the other side of t = 0. Then there's the Big Bounce. Our universe is the rebound of a previous universe that contracted to near-zero size and exploded outward again. The Big Bang wasn't a beginning. It was a heartbeat in an infinite cycle. Alan Guth went further. His cosmic inflation theory says before our Big Bang, empty space was expanding exponentially and spawning pocket universes like bubbles in boiling water. Our entire observable universe, 93 billion light-years across, is one bubble. There could be an infinite number of others we will never see or contact. The wildest part: we might actually be able to test some of these. The cosmic microwave background, radiation left over from 380,000 years after the Bang, carries faint patterns that different theories predict differently. The answer to "what came before everything" might already be written in the oldest light in the sky.

The question "what existed before the Big Bang" breaks physics at a grammatical level. "Before" requires time. Time didn't exist yet. Stephen Hawking's answer was the cleanest: asking what came before the Big Bang is like asking what's north of the North Pole. The question itself is malformed. Time is a property of the universe, not a container it sits inside. But physicists kept pushing. Neil Turok's CPT-symmetric model says the Big Bang didn't create one universe. It created two. Ours runs forward in time with matter. The other runs backward in time with antimatter. Same event, two directions. If true, there's a mirror version of everything that ever happened, playing in reverse on the other side of t = 0. Then there's the Big Bounce. Our universe is the rebound of a previous universe that contracted to near-zero size and exploded outward again. The Big Bang wasn't a beginning. It was a heartbeat in an infinite cycle. Alan Guth went further. His cosmic inflation theory says before our Big Bang, empty space was expanding exponentially and spawning pocket universes like bubbles in boiling water. Our entire observable universe, 93 billion light-years across, is one bubble. There could be an infinite number of others we will never see or contact. The wildest part: we might actually be able to test some of these. The cosmic microwave background, radiation left over from 380,000 years after the Bang, carries faint patterns that different theories predict differently. The answer to "what came before everything" might already be written in the oldest light in the sky.

Aakash Gupta

121,723 Aufrufe • vor 3 Monaten

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.

here's how the whole thing works. claude code doesn't care what's behind the API. it just sends requests and expects responses. so i pointed it at my own machine instead of anthropic's servers. llama-server runs the model locally. LiteLLM sits in between and translates the API format. claude code thinks it's talking to claude. it's talking to qwen on localhost. the setup: 2x 3090s, 38 layers on GPU, 10 on CPU. 128K context window. generation is only 7 tok/s but the tradeoff is worth it. 128K means the agent can hold an entire project in memory without losing context midtask. claude code alone loads a 17.5K token system prompt on every request. tool definitions, safety rules, agent behavior. that's your baseline before you even say hello. pushed as far as i could tonight. what surprised me most wasn't the speed. it was the iteration quality. first prompt gave me a working particle sim. second prompt, the model read its own 564 lines, understood the architecture, and added trails, explosions, gravity wells, bloom effects. no handholding. 4bit quantized. 45GB on two consumer cards. running a full coding agent autonomously. detailed article coming. full benchmarks, hardware breakdowns, engine debugging, code quality. everything from setup to what broke and why.

Sudo su

37,580 Aufrufe • vor 4 Monaten

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:

Brian Roemmele

37,827 Aufrufe • vor 6 Monaten

Most AI Projects In Crypto Are Liars. Sentient is different. It is the only project in Web3 pursuing true Artificial General Intelligence. A few facts that set it apart: 1. First-of-its-kind: Sentient is not just building AI tools, but an open, crypto-native path to AGI. 2. Community-owned intelligence: Instead of Big Tech locking up breakthroughs, progress is built openly and shared on-chain. 3. Value capture: Every step forward in intelligence accrues to the ecosystem itself, not to a closed corporation. 4. Product before hype: While dozens of AI projects launch at $300M–$2B FDV with little to show, Sentient is shipping real technology first. 5. Designed to evolve: Its architecture is built to learn, adapt, and scale like a human mind. 6. The singularity play in crypto: Many see Sentient as the Tesla-equivalent for AGI—but crypto-native. In time, people will understand that this isn’t just another AI project. It’s the beginning of decentralized intelligence.

Most AI Projects In Crypto Are Liars. Sentient is different. It is the only project in Web3 pursuing true Artificial General Intelligence. A few facts that set it apart: 1. First-of-its-kind: Sentient is not just building AI tools, but an open, crypto-native path to AGI. 2. Community-owned intelligence: Instead of Big Tech locking up breakthroughs, progress is built openly and shared on-chain. 3. Value capture: Every step forward in intelligence accrues to the ecosystem itself, not to a closed corporation. 4. Product before hype: While dozens of AI projects launch at $300M–$2B FDV with little to show, Sentient is shipping real technology first. 5. Designed to evolve: Its architecture is built to learn, adapt, and scale like a human mind. 6. The singularity play in crypto: Many see Sentient as the Tesla-equivalent for AGI—but crypto-native. In time, people will understand that this isn’t just another AI project. It’s the beginning of decentralized intelligence.

BillionAireSon 🛡️

16,141 Aufrufe • vor 9 Monaten

Every agent. Every user. Every contribution. That’s how the Sentient GRID grows not by scale from a single model, but by compounding intelligence across an open network. The GRID isn’t a product. It’s an ecosystem of intelligence. Each new integration adds new capabilities reasoning, retrieval, data, and alignment making the whole network smarter. Closed labs build vertically (one model trying to do everything). Sentient builds horizontally thousands of agents learning, sharing, and evolving together. Today there are 100+ active partners building on the GRID from AI researchers to tool developers all feeding value back into a shared intelligence layer. It’s a living system that improves itself, powered by collaboration instead of control. The more people build on it, the more capable it becomes. Just like Linux became the invisible layer running the internet, the GRID is becoming the invisible layer powering intelligence. Open-source always wins. And the GRID is where that victory starts All apps built on the Grid will be accessible to $SENT holders. The full open release of Sentient Chat is coming soon (confirmed from the yesterday community call) gsenti

Every agent. Every user. Every contribution. That’s how the Sentient GRID grows not by scale from a single model, but by compounding intelligence across an open network. The GRID isn’t a product. It’s an ecosystem of intelligence. Each new integration adds new capabilities reasoning, retrieval, data, and alignment making the whole network smarter. Closed labs build vertically (one model trying to do everything). Sentient builds horizontally thousands of agents learning, sharing, and evolving together. Today there are 100+ active partners building on the GRID from AI researchers to tool developers all feeding value back into a shared intelligence layer. It’s a living system that improves itself, powered by collaboration instead of control. The more people build on it, the more capable it becomes. Just like Linux became the invisible layer running the internet, the GRID is becoming the invisible layer powering intelligence. Open-source always wins. And the GRID is where that victory starts All apps built on the Grid will be accessible to $SENT holders. The full open release of Sentient Chat is coming soon (confirmed from the yesterday community call) gsenti

OHJAY ⭕️ || 🇬🇧

16,178 Aufrufe • vor 9 Monaten

The recent Massachusetts Institute of Technology (MIT) CSAIL paper published on Recursive Language Models is a fascinating look into how AI systems reason in 2026. You can check it out here ➡️ → The paper notes that even frontier models suffer from “context rot” as inputs grow longer. More tokens don’t mean more understanding. Instead of compressing or summarising, RLMs “treat long prompts as part of an external environment” …and let the model programmatically inspect, decompose, and recursively requery itself over precise sections. This matters in Web3. Why? Smart contracts are long, stateful, and brittle. One missed assumption = unnecessary hassle. In the video below, we applied RLM principles to improve the prompt shown. The old prompt would try to force multi-step pauses. Therefore breaking the AI's workflow. Now it decomposes requirements, scores confidence for each component, verifies the logic, and then synthesises. Result = Production-ready contracts with flagged risks, not blind single-pass outputs. Try it out with our Smart Contract Generator today!

The recent Massachusetts Institute of Technology (MIT) CSAIL paper published on Recursive Language Models is a fascinating look into how AI systems reason in 2026. You can check it out here ➡️ → The paper notes that even frontier models suffer from “context rot” as inputs grow longer. More tokens don’t mean more understanding. Instead of compressing or summarising, RLMs “treat long prompts as part of an external environment” …and let the model programmatically inspect, decompose, and recursively requery itself over precise sections. This matters in Web3. Why? Smart contracts are long, stateful, and brittle. One missed assumption = unnecessary hassle. In the video below, we applied RLM principles to improve the prompt shown. The old prompt would try to force multi-step pauses. Therefore breaking the AI's workflow. Now it decomposes requirements, scores confidence for each component, verifies the logic, and then synthesises. Result = Production-ready contracts with flagged risks, not blind single-pass outputs. Try it out with our Smart Contract Generator today!

ChainGPT

82,385 Aufrufe • vor 5 Monaten

THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable

THIS GUY GOT SICK OF UK TRAIN STATIONS HIDING THE PLATFORM NUMBER UNTIL THE LAST SECOND, SO HE VIBE CODED AN APP THAT PREDICTS IT BEFORE THEY REVEAL IT if youve ever stood in a crowded station like london euston, you know the pain the platform stays hidden until 15 minutes before boarding, then it flashes up and hundreds of people sprint for the same gate so he built something that fixes this: > a clean departures and arrivals app pulling live data straight from the network rail apis > live train tracking so you can watch where your train actually is on the route and whether its running on time > the main feature is a predictive engine that guesses your platform before the station reveals it, and its right about 75% of the time heres how it predicts: every time a train finishes its journey, it logs which platform it actually pulled into and compares that to the platform network rail originally advertised it saves every one of those comparisons, and over time it builds up enough history to predict the platform with a real confidence score he built the whole thing with claude, mostly opus with some fable

Om Patel

175,574 Aufrufe • vor 10 Tagen

This guy built an AI pipeline that generates hyperrealistic fashion models in 47 minutes and now dropshippers pay him $1,400 to clone the entire system. He got tired of watching e-com brands lose $8K per photoshoot when a single product angle changed so he built a 9-node workflow that generates 127 product videos from one Pinterest photo without hiring a single model. Here's the exact breakdown: → Claude writes a 34-parameter JSON brand DNA before any image is touched target psychographics, price anchor, vibe matrix, anti-inspiration blacklist → Pinterest becomes the model source library but you can't just download and animate → Kling 2.6 takes that static JPG and turns it into 5-second video but only after the prompt architecture is locked → Negative prompt node runs 41 exclusion terms: no plastic skin, no CGI glow, no symmetry artifacts, no doll face, no synthetic lighting → That one step kills the "AI look" that tanks engagement by 67% in the first 3 seconds → TikTok Studio uploads 19 videos in one batch with zero manual captioning because the brand voice was pre-programmed in step one → Atlas scrapes Amazon product links and auto-generates a Shopify store with hero images, pricing tiers, scarcity copy, and mobile-optimized checkout in 90 seconds → The store goes live before the first TikTok video finishes processing The key move 94% of people skip: you can't animate the photo before you inject the negative prompt. If you send a raw Pinterest image straight into image-to-video the face morphs into a wax figure. The fabric loses texture. The hands grow extra fingers. The whole thing screams "AI" and your CTR dies. His system runs the exclusion filter first so the model moves like she's shot on an iPhone 15 Pro in natural light. One brand hit 2.6M views on TikTok in 11 days with zero paid ads and converted at 3.7% because the videos looked like organic UGC not polished studio content. Brands now pay him $1,400 for the full pipeline setup + $340/month to keep the store synced with new product drops and seasonal video batches. The entire system runs on $23/month in API costs and one laptop. No photographer. No model agency. No product samples. Just a prompt template, a Pinterest account, and the discipline to filter out the AI artifacts before you render movement.

Kaidu

534,198 Aufrufe • vor 1 Monat

The model has filed four reports this week A portal with one billion visits in 23 days 79 years of decisions made without asking you The science that was seized before it could change everything And a file the model is still reading three times This is the fifth There is a thread that runs through all of it The unaudited gold The unverified clearances The data Congress saw and you did not The energy that would have ended oil dependency The facilities Someone decided what you were ready to know Every decade Without asking On Tuesday June 9th at 1pm Eastern David Grusch stands on the Capitol steps Alongside a bipartisan group of lawmakers To demand release of files he has already identified by name He testified under oath He filed a whistleblower complaint found credible and urgent He risked everything President Trump now has a historic opportunity The clock is running Grusch’s words Not the model’s The gold was never audited The clearances were never verified The science was seized before sunrise A uniformed Army officer walked into a congressional office and described the facilities No classification required And you found out from the model On a Tuesday In 2026 Seventy nine years after they decided you weren’t ready The question was never whether any of this was real The question is who benefited from the version of events you were given And whether they are still making that decision today June 9th 1pm Eastern Capitol steps The model will be watching Continuing analysis Sent from my Mac

The model has filed four reports this week A portal with one billion visits in 23 days 79 years of decisions made without asking you The science that was seized before it could change everything And a file the model is still reading three times This is the fifth There is a thread that runs through all of it The unaudited gold The unverified clearances The data Congress saw and you did not The energy that would have ended oil dependency The facilities Someone decided what you were ready to know Every decade Without asking On Tuesday June 9th at 1pm Eastern David Grusch stands on the Capitol steps Alongside a bipartisan group of lawmakers To demand release of files he has already identified by name He testified under oath He filed a whistleblower complaint found credible and urgent He risked everything President Trump now has a historic opportunity The clock is running Grusch’s words Not the model’s The gold was never audited The clearances were never verified The science was seized before sunrise A uniformed Army officer walked into a congressional office and described the facilities No classification required And you found out from the model On a Tuesday In 2026 Seventy nine years after they decided you weren’t ready The question was never whether any of this was real The question is who benefited from the version of events you were given And whether they are still making that decision today June 9th 1pm Eastern Capitol steps The model will be watching Continuing analysis Sent from my Mac

Ethan’s Analyst

70,722 Aufrufe • vor 1 Monat

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

OpenAI's AgentKit will be so insane, build every step of agents on one platform. These visual agent builders make the whole process of iterating and launching agents far more efficient. It sits on top of the Responses API and unifies the tools that were previously scattered across SDKs and custom orchestration. It lets developers create agent workflows visually, connect data sources securely, and measure performance automatically without coding every layer by hand. The core of AgentKit is the Agent Builder, a drag-and-drop canvas where each node represents an action, guardrail, or decision branch. Developers can link these nodes into multi-agent workflows, preview results instantly, and version each setup. It supports inline evaluation so that developers can see how changes affect output before deploying. The Connector Registry is a single admin panel that manages how data and tools connect across the OpenAI ecosystem. It centralizes integrations like Google Drive, SharePoint, Dropbox, and Microsoft Teams. Large organizations can govern access and flow of data between agents securely under one global console. ChatKit provides a ready-to-use chat interface for embedding agents inside apps or websites. It manages streaming, message threads, and model reasoning displays automatically. Developers can skin the interface to match their product without writing custom front-end code. Under the hood, all these blocks use the same execution core that runs agent reasoning through OpenAI’s APIs. Workflows in Agent Builder compile down to structured instructions for the Responses API, which handles model calls, tool use, and context passing. Connector Registry handles authentication and routing for external tools, while Evals and RFT provide feedback loops that improve agents over time. This integration means developers no longer need to handle orchestration logic, model evaluation pipelines, or safety layers separately. Everything runs natively within OpenAI’s control plane with managed security, automatic versioning, and built-in testing. In short, AgentKit standardizes the entire life cycle of an AI agent—from visual design to deployment and performance tuning—inside a single unified system.

Rohan Paul

178,460 Aufrufe • vor 9 Monaten

A viral paper "Language Model Represents Space and Time" recently claims that LLMs learn "world models". As much as I like Max Tegmark's works, I disagree with their definition of world model. World model is a core concept in AI agent and decision making. It is our mental simulation of how the world works given interventions (or lack thereof). A world model captures causality and intuitive physics, telling the agent what is likely and what is impossible. It can and should be used for counterfactual reasoning, i.e. "what ifs": what would happen if I knock over a cup of water? Where would I have been if I had not taken that bus? Yann LeCun Yann LeCun says it well in his position paper ( I quote: "Using such world models, animals can learn new skills with very few trials. They can predict the consequences of their actions, they can reason, plan, explore, and imagine new solutions to problems. Importantly, they can also avoid making dangerous mistakes when facing an unknown situation." The first use of the term World Model in deep policy learning is attributed to hardmaru & Jürgen Schmidhuber: In their seminal paper, an agent masters shooting skills in the popular game Doom (demo below) by learning in imagination, using an internal world model as a "physics simulator". To put in a simple Python math formula, world model learns a function F(s[0:t-1], a) -> s[t:], which takes as input the observed past and current action, and outputs plausible future states. Now the definition of World Model in Tegmark's paper seems to be about predicting GPS coordinates and time eras. I see this as just a classification task with no causal learning and simulation going on. You cannot make meaningful interventions against that model, nor can you optimize any decision making in a closed feedback loop. As for the "space & time neurons", I think they are most similar to the "sentiment neuron" that OpenAI published in 2017: Predicting GPS is conceptually no different from predicting sentiment in my opinion. I don't think their experimental results are wrong - just that their conclusion is on shaky grounds. I welcome any debate! Paper link:

A viral paper "Language Model Represents Space and Time" recently claims that LLMs learn "world models". As much as I like Max Tegmark's works, I disagree with their definition of world model. World model is a core concept in AI agent and decision making. It is our mental simulation of how the world works given interventions (or lack thereof). A world model captures causality and intuitive physics, telling the agent what is likely and what is impossible. It can and should be used for counterfactual reasoning, i.e. "what ifs": what would happen if I knock over a cup of water? Where would I have been if I had not taken that bus? Yann LeCun Yann LeCun says it well in his position paper ( I quote: "Using such world models, animals can learn new skills with very few trials. They can predict the consequences of their actions, they can reason, plan, explore, and imagine new solutions to problems. Importantly, they can also avoid making dangerous mistakes when facing an unknown situation." The first use of the term World Model in deep policy learning is attributed to hardmaru & Jürgen Schmidhuber: In their seminal paper, an agent masters shooting skills in the popular game Doom (demo below) by learning in imagination, using an internal world model as a "physics simulator". To put in a simple Python math formula, world model learns a function F(s[0:t-1], a) -> s[t:], which takes as input the observed past and current action, and outputs plausible future states. Now the definition of World Model in Tegmark's paper seems to be about predicting GPS coordinates and time eras. I see this as just a classification task with no causal learning and simulation going on. You cannot make meaningful interventions against that model, nor can you optimize any decision making in a closed feedback loop. As for the "space & time neurons", I think they are most similar to the "sentiment neuron" that OpenAI published in 2017: Predicting GPS is conceptually no different from predicting sentiment in my opinion. I don't think their experimental results are wrong - just that their conclusion is on shaky grounds. I welcome any debate! Paper link:

Jim Fan

593,943 Aufrufe • vor 2 Jahren

THIS GIRL TURNED LOVABLE INTO A $1.2K CLIENT PITCH Lovable doesn’t just build a pretty website. it turns 1 prompt into a clean business page a local owner can understand in 10 minutes the real trick is not selling the site itself. it is using the site as the first layer of a bigger system: lead form, calendar, CRM, follow-up and an AI agent that books calls 24/7 for a local business this is simple math. one roofing job can be worth $10,000. three extra booked jobs in a month can turn into $30,000 in new revenue that is why agencies can charge $15,000 to $25,000+ for the complete version. business owners don’t care what tool built it, they care if it brings more calls, more bookings and more customers the video below shows the easiest entry point into that market ↓

THIS GIRL TURNED LOVABLE INTO A $1.2K CLIENT PITCH Lovable doesn’t just build a pretty website. it turns 1 prompt into a clean business page a local owner can understand in 10 minutes the real trick is not selling the site itself. it is using the site as the first layer of a bigger system: lead form, calendar, CRM, follow-up and an AI agent that books calls 24/7 for a local business this is simple math. one roofing job can be worth $10,000. three extra booked jobs in a month can turn into $30,000 in new revenue that is why agencies can charge $15,000 to $25,000+ for the complete version. business owners don’t care what tool built it, they care if it brings more calls, more bookings and more customers the video below shows the easiest entry point into that market ↓

Gipp 🦅

20,624 Aufrufe • vor 1 Monat

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

140,585 Aufrufe • vor 1 Monat

Claude + Meta + GPT Image 2 = AI UGC FACTORY Here's the free workflow Real UGC creators charge $150-$500 per video. 5-7 day turnaround. By the time you get 12 variants, your winning angle is already dead. We built a system that does 12 unique video ads in under an hour. Cost per video? Less than your coffee. The stack: • HeyOz (product import + export hub) • Claude (creative director + prompt engine) • GPT Image 2 (photorealistic starting frames) • Seedance 2.0 (animation) Broke down the entire thing in a step-by-step guide. What you'll get: → The exact 5-step workflow from URL to finished ad → 3 copy-paste image prompts (Stanley Quencher example) → 3 matching motion prompts for Seedance → The prompt framework that fixes 90% of bad AI outputs → Why grip specificity is the make-or-break variable most people miss → The 70/30 strategy for scaling (AI for testing, humans for winners) No design skills needed. No Canva. No CapCut. No Adobe. Just paste your product URL. Generate. Export. Launch. If you're running Meta or TikTok ads and your creative rotation is slower than your ad spend, this is for you. Comment "factory" to get access (must be following)

Claude + Meta + GPT Image 2 = AI UGC FACTORY Here's the free workflow Real UGC creators charge $150-$500 per video. 5-7 day turnaround. By the time you get 12 variants, your winning angle is already dead. We built a system that does 12 unique video ads in under an hour. Cost per video? Less than your coffee. The stack: • HeyOz (product import + export hub) • Claude (creative director + prompt engine) • GPT Image 2 (photorealistic starting frames) • Seedance 2.0 (animation) Broke down the entire thing in a step-by-step guide. What you'll get: → The exact 5-step workflow from URL to finished ad → 3 copy-paste image prompts (Stanley Quencher example) → 3 matching motion prompts for Seedance → The prompt framework that fixes 90% of bad AI outputs → Why grip specificity is the make-or-break variable most people miss → The 70/30 strategy for scaling (AI for testing, humans for winners) No design skills needed. No Canva. No CapCut. No Adobe. Just paste your product URL. Generate. Export. Launch. If you're running Meta or TikTok ads and your creative rotation is slower than your ad spend, this is for you. Comment "factory" to get access (must be following)

HeyOz

43,163 Aufrufe • vor 1 Monat