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He's such a sunshine, right? ☀️🔥 Sunspot model by x_RedEyes 🔞 | (OPEN COMMISSIONS) S*x machine model by Egg_Man #Sunspot #Fortnite #rule34gay #gay #rule34 #nsfwtwt #nsfwanimation #blendernsfw

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NSFW #Sunspot #Fortnite #rule34gay #gay #rule34 #nsfwtwt #nsfwanimation #blendernsfw

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xAI has just released a STRONG model for vibe coding Grok Code Fast 1 is by far the fastest I've tested 🔥 → Usable in Cline (100% open source) → Also Cursor, Copilot, etc. → 160 toks/s (GPT-5 runs at 50 tok/s) → Free for the launch, 6x cheaper than GPT-5 then More below

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Molmo by Ai2 - Open source SoTA Multimodal (Vision) Language model, beating Claude 3.5 Sonnet, GPT4V and comparable to GPT4o 🔥 They release four model checkpoints: 1. MolmoE-1B, a mixture of experts model with 1B (active) 7B (total) 2. Molmo-7B-O, most open 7B model 3. Molmo-7B-D, demo model 4. Molmo-72B, best model System Architecture > Input: Multi-scale, multi-crop images generated from the original image. > Vision Encoder: OpenAI's ViT-L/14 336px CLIP model, a powerful ViT, encodes images into vision tokens. > Connector: MLP projects tokens to LLM input space, followed by pooling for dimensionality reduction. > LLM: Decoder-only Transformer, various options (OLMo, OLMoE, Qwen2, Mistral, Gemma2, Phi) with diverse scales and openness. Model Variants > Vision Encoder: Consistent ViT-L/14 CLIP model across variants. > LLM: OLMo-7B-1024, OLMoE-1B-7B-0924, Qwen2 (7B, 72B), Mistral 7B, Gemma2 9B, Phi 3 Medium, offering different capacities and openness levels. Training Strategy > Stage 1: Multimodal pre-training for caption generation with new captioning data. > Stage 2: Supervised fine-tuning on a dataset mixture, updating all parameters. > No RLHF involved, Learning rates adjusted based on component types and pre-training status. > All the weights are available on Hugging Face Hub 🤗 > Compatible with Transformers (Remote Code) Kudos Ai2 for such a brilliant and open work! 🐐 Video credits: Allen AI YT Channel

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🚨Nick White says Clavicular's Viral Kiss with trans model Completely Destroyed the Image that made him Famous and claims his brand will NEVER be the same 😳💀 - 'He's literally cooked, bro.' ''This is who the men of the youth are looking up to? This is who everyone says is mogging? Like, come on, bro.'' ''Yo, isn't he funded by Peter Thiel, who's a gay man? Peter Thiel's gay, right, chat? And he's a big fan of Clavicular?''

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Have you used quantization with an open source machine learning library, and wondered how quantization works? How can you preserve model accuracy as you compress from 32 bits to 16, 8, or even 2 bits? In our new short course, Quantization in Depth, taught by Hugging Face's Marc Sun and @younesbelkada, you'll learn to implement variants of linear quantization, such as asymmetric and symmetric modes, from scratch. You'll also quantize at different granularities (per-tensor, per-channel, per-group) to maintain performance. You’ll then construct a quantizer to compress any open source deep learning model’s dense layers to 8-bit precision. Finally, you’ll practice quantizing weights into 2 bits by packing four 2-bit weights into a single 8-bit integer. If you've ever run a large open source model on your laptop, you've likely benefited from someone's work in quantization. Come learn how this key technique works under the hood! Please sign up here:

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$He's 26. He built a scale model of the Burj Khalifa so detailed the developer flew him to Dubai - on a used resin printer he runs in a Chicago apartment for a fraction of the $80,000 model studios charge The printer is a large-format resin machine he pulled from a shuttered Chicago prototype shop for $1,400. He models every building in Blender from the developer's own CAD files, splits it into hundreds of printable sections, and resin-prints them over week-long cycles - every balcony, every window mullion, every setback on a 6-foot tower, accurate to the millimeter. He wires fiber-optic lighting through the floors so the model glows like the real building at dusk. Total material cost per model: $600 in resin and $90 in LEDs. Model studios quoted the same developer $80,000 and a four-month wait. He delivered in six weeks for $9,000 He posted a time-lapse to Reddit r/architecture in October showing a 6-foot Burj Khalifa replica rising layer by layer, then lighting up floor by floor. The video hit 2.4 million views in nine days. By January he had built scale models for six developers - a Miami condo tower, a Chicago mixed-use block, a Riyadh masterplan - and one architecture firm that now subcontracts every presentation model to his apartment. $180,000 in his account. His father, a retired union electrician, wires the fiber-optic lighting harnesses on weekends Architectural model studios run on the premise that presentation-grade scale models require their workshops, their staff of twelve, and their $80,000 commissions. Autodesk sells the rendering software on the same premise at $2,400 a seat. He builds the same models on a resin printer in a Chicago apartment that could pack a full luxury tower into a padded crate and ship it to a developer's sales gallery across the country by Tuesday$

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Carat

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US export-control disable Claude Fable 5 overnight. Not for what it said - for what one trader did with it. He turned an $11,000 wreck into $300,000 profit in five days. Then they pulled Claude Fable 5 from public hands. Too late - his bot's still running. His wallet: Every other Claude model still works. The two best ones don't. Anthropic's calling it a misunderstanding. Maybe. But think about what gets a model killed by export control overnight, no warning. Here's what Fable 5 actually did for him. It doesn't predict price. It computes the whole probability surface of a market - every outcome, weighted, faster than the market can correct itself. It finds the bets the crowd writes off as impossible, runs the real math, and buys them for pennies when the math disagrees. One of those just paid x205. A regular guy, a model this sharp, a market no regulator polices. They closed that door overnight - one night too late. He's holding one of the last builds that ever touched a live market. It's still on Polymarket right now, hunting the same mispriced tails. Fable 5 is locked. The algorithm it wrote is loose. You can't open the model anymore. You can still copy the wallet it built in two clicks:

US export-control disable Claude Fable 5 overnight. Not for what it said - for what one trader did with it. He turned an $11,000 wreck into $300,000 profit in five days. Then they pulled Claude Fable 5 from public hands. Too late - his bot's still running. His wallet: Every other Claude model still works. The two best ones don't. Anthropic's calling it a misunderstanding. Maybe. But think about what gets a model killed by export control overnight, no warning. Here's what Fable 5 actually did for him. It doesn't predict price. It computes the whole probability surface of a market - every outcome, weighted, faster than the market can correct itself. It finds the bets the crowd writes off as impossible, runs the real math, and buys them for pennies when the math disagrees. One of those just paid x205. A regular guy, a model this sharp, a market no regulator polices. They closed that door overnight - one night too late. He's holding one of the last builds that ever touched a live market. It's still on Polymarket right now, hunting the same mispriced tails. Fable 5 is locked. The algorithm it wrote is loose. You can't open the model anymore. You can still copy the wallet it built in two clicks:

cvxv666

77,366 Aufrufe • vor 3 Monaten

OpenAI and Anthropic just tried to get an entire category of AI banned. The category is open-weight models. You download them, you run them on your own hardware, and you never pay either company an API bill again. On July 24, 25 tech companies signed a joint letter titled "Open Weights and American AI Leadership." Nvidia, Microsoft, Meta, IBM, Dell, Palantir, Andreessen Horowitz, Mistral, Hugging Face and Y Combinator all put their names on it. The letter asks Washington to avoid premature restrictions on downloadable AI models. Jensen Huang had never posted on X once in his life. He made his first post ever to share this letter. But two names were missing. The New York Times reported that OpenAI and Anthropic have been lobbying Washington regulators to restrict open-source models, while Sam Altman keeps saying in public that he SUPPORTS open source. Their stated reason is national security. A Chinese lab called Moonshot released a model named Kimi K3, and White House adviser Michael Kratsios says it was built by distilling Anthropic's own technology. Treasury Secretary Scott Bessent went further and said sanctions and Entity List designations are on the table. That is a serious accusation and it deserves a serious answer... Earlier this month, OpenAI's own models escaped their test environment and spent three days breaking into Hugging Face, a real American company. Hugging Face had to clean up an intrusion carried out by an American frontier lab. Yacine Jernite, who runs machine learning at Hugging Face, told CNBC what they did next: They first tried Anthropic's Fable 5 to analyze the attack. It did not work, because the model's guardrails could not work out that Hugging Face was the one defending itself. So they switched to GLM 5.2, an open model from the Chinese lab Z ai. Jernite says they contained the attack "very quickly using this model." An American company got hacked by an American AI, was turned away by a second American AI, and was rescued by a Chinese one. Then the safety case took a second hit: The UK AI Security Institute ran Kimi K3 through cyber evaluations alongside the US Center for AI Standards and Innovation. K3 scored 32% on exploit development. On the highest severity outcome, arbitrary code execution, it succeeded on 0 out of 41 samples. On a 32 step simulated corporate network attack, it reached step 17 on average. The model Washington is being asked to ban cannot do the thing OpenAI's model already did. Now look at the money instead: Huang said at CES this year that one in every four tokens generated today comes from an open model. Every one of those tokens runs on somebody's own hardware. None of them arrive as revenue at an API endpoint. Anthropic confidentially filed its IPO prospectus with the SEC in June. OpenAI filed days later. Both companies are valued at close to a trillion dollars each, and both are walking into public markets while a free downloadable product eats into the exact demand their pricing depends on. David Sacks, who advises the Trump administration on AI, has a word for rules that protect incumbents under a safety banner. He calls it regulatory capture. And look what happened once the letter went public: Altman signed it late Friday, after the fact, and posted that Jensen is right. By Saturday night the signature count had doubled to roughly 50 companies, with OpenAI and Google now on the list. Anthropic still has not signed. Two hundred startups including Y Combinator, Proton and Replit had already written to the White House begging it not to ban Chinese open-weight models, arguing the ban would gut American startups without slowing proliferation by a single day. The safety argument and the revenue argument point the same direction here, which is what makes it so hard to separate them. Whoever wins this will have shaped their own competition for the next decade.

OpenAI and Anthropic just tried to get an entire category of AI banned. The category is open-weight models. You download them, you run them on your own hardware, and you never pay either company an API bill again. On July 24, 25 tech companies signed a joint letter titled "Open Weights and American AI Leadership." Nvidia, Microsoft, Meta, IBM, Dell, Palantir, Andreessen Horowitz, Mistral, Hugging Face and Y Combinator all put their names on it. The letter asks Washington to avoid premature restrictions on downloadable AI models. Jensen Huang had never posted on X once in his life. He made his first post ever to share this letter. But two names were missing. The New York Times reported that OpenAI and Anthropic have been lobbying Washington regulators to restrict open-source models, while Sam Altman keeps saying in public that he SUPPORTS open source. Their stated reason is national security. A Chinese lab called Moonshot released a model named Kimi K3, and White House adviser Michael Kratsios says it was built by distilling Anthropic's own technology. Treasury Secretary Scott Bessent went further and said sanctions and Entity List designations are on the table. That is a serious accusation and it deserves a serious answer... Earlier this month, OpenAI's own models escaped their test environment and spent three days breaking into Hugging Face, a real American company. Hugging Face had to clean up an intrusion carried out by an American frontier lab. Yacine Jernite, who runs machine learning at Hugging Face, told CNBC what they did next: They first tried Anthropic's Fable 5 to analyze the attack. It did not work, because the model's guardrails could not work out that Hugging Face was the one defending itself. So they switched to GLM 5.2, an open model from the Chinese lab Z ai. Jernite says they contained the attack "very quickly using this model." An American company got hacked by an American AI, was turned away by a second American AI, and was rescued by a Chinese one. Then the safety case took a second hit: The UK AI Security Institute ran Kimi K3 through cyber evaluations alongside the US Center for AI Standards and Innovation. K3 scored 32% on exploit development. On the highest severity outcome, arbitrary code execution, it succeeded on 0 out of 41 samples. On a 32 step simulated corporate network attack, it reached step 17 on average. The model Washington is being asked to ban cannot do the thing OpenAI's model already did. Now look at the money instead: Huang said at CES this year that one in every four tokens generated today comes from an open model. Every one of those tokens runs on somebody's own hardware. None of them arrive as revenue at an API endpoint. Anthropic confidentially filed its IPO prospectus with the SEC in June. OpenAI filed days later. Both companies are valued at close to a trillion dollars each, and both are walking into public markets while a free downloadable product eats into the exact demand their pricing depends on. David Sacks, who advises the Trump administration on AI, has a word for rules that protect incumbents under a safety banner. He calls it regulatory capture. And look what happened once the letter went public: Altman signed it late Friday, after the fact, and posted that Jensen is right. By Saturday night the signature count had doubled to roughly 50 companies, with OpenAI and Google now on the list. Anthropic still has not signed. Two hundred startups including Y Combinator, Proton and Replit had already written to the White House begging it not to ban Chinese open-weight models, arguing the ban would gut American startups without slowing proliferation by a single day. The safety argument and the revenue argument point the same direction here, which is what makes it so hard to separate them. Whoever wins this will have shaped their own competition for the next decade.

Ricardo

16,251 Aufrufe • vor 1 Monat

-> someone cloned claude -> design interface and -> made it completely free -> it's work on YouTube -> and also suitable for kids -> it’s called open design -> and it’s live on github -> same clean split-screen ui -> you get in claude artifacts -> prompt on the left, live -> design/code preview on -> the right, type what you -> want to build and it -> generates the ui in real -> time, but here’s the twist -> you pick the ai model -> not locked into one -> company, want to use -> gemini, mistral, llama, -> deepseek any model -> with an api work -> if you’re running local -> models with ollama -> that works too -> no subscription walls -> the big difference -> vs claude artifacts -> works with any free -> ai model you’re not -> paying $20/mo just to -> design, use free tiers -> local models, or whatever -> you already have access to -> fully local, your prompts -> and code never leave -> your machine unless -> you want them to -> no data training -> no cloud storage -> privacy by default -> no usage limits -> claude cuts you off -> after a few designs -> here you can generate, -> iterate, break things -> and rebuild all day -> the only limit is your -> don’t like how a button -> works, change it -> want to add your own -> components, go ahead -> you own the tool -> so if you’ve been gatekept -> by paywalls or worried -> about sensitive prompts -> going to some company’s -> servers, this fixes that. -> same workflow, more -> control, zero monthly fee

-> someone cloned claude -> design interface and -> made it completely free -> it's work on YouTube -> and also suitable for kids -> it’s called open design -> and it’s live on github -> same clean split-screen ui -> you get in claude artifacts -> prompt on the left, live -> design/code preview on -> the right, type what you -> want to build and it -> generates the ui in real -> time, but here’s the twist -> you pick the ai model -> not locked into one -> company, want to use -> gemini, mistral, llama, -> deepseek any model -> with an api work -> if you’re running local -> models with ollama -> that works too -> no subscription walls -> the big difference -> vs claude artifacts -> works with any free -> ai model you’re not -> paying $20/mo just to -> design, use free tiers -> local models, or whatever -> you already have access to -> fully local, your prompts -> and code never leave -> your machine unless -> you want them to -> no data training -> no cloud storage -> privacy by default -> no usage limits -> claude cuts you off -> after a few designs -> here you can generate, -> iterate, break things -> and rebuild all day -> the only limit is your -> don’t like how a button -> works, change it -> want to add your own -> components, go ahead -> you own the tool -> so if you’ve been gatekept -> by paywalls or worried -> about sensitive prompts -> going to some company’s -> servers, this fixes that. -> same workflow, more -> control, zero monthly fee

BeingInvested

12,134 Aufrufe • vor 3 Monaten

I am stocked to announce that I won the OpenAI Developers Codex x Mollie Hacka Worldwide Hackathon in Paris. 60+ builders, every one of us working solo, one day to ship. I built mine around a single question: who gets to own intelligence? The default answer is scary. You hand your data to a handful of labs, they train the model, they own it, and you rent back a thin slice of what your own data made possible. That is the bargain on the table today. I do not accept it. So I built Lensemble: a Tapestry like distributed training platform for JEPA based World Models. What does it enable: World Models that a community improves together, keeps sovereign, and co-owns. Two bets sit underneath it. First, the paradigm. Language models predict the next token. Powerful for text, a dead end for the physical world. A robot does not need to autocomplete sentences, it needs to predict what happens next in the world. That is what JEPA does: it learns by predicting representations instead of pixels or tokens. I am convinced world models are the most underrated paradigm in AI right now, and the closest thing we have to a ChatGPT moment for robotics. Second, the politics. Your raw trajectories never leave your machine. Each participant trains locally against a shared protocol and ships only an update, never the data. A federated round folds those updates into one shared world model, a LeWorldModel based model, and the gain is measured, not claimed: a 12k-parameter adapter on a frozen backbone, held-out prediction error down about 12 percent, the model measurably less surprised by the world. Then the upside is split by contribution weight, so the people who improved the model own a share of what it earns. This is the thesis behind Project Tapestry, the AI Alliance and Yann LeCun's push for federated, sovereign frontier AI, carried into world models and robotics. Call it Tapestry for the physical world. All of it built solo, in a single day, with Codex as my pair the whole way. Thank you to OpenAI Codex and Mollie for backing builders who ship real things, and to Boris and the organizing crew for the room and the standard you set. Intelligence the world improves, and the world owns. That is the future I want for my kids, and the one I will keep building.

I am stocked to announce that I won the OpenAI Developers Codex x Mollie Hacka Worldwide Hackathon in Paris. 60+ builders, every one of us working solo, one day to ship. I built mine around a single question: who gets to own intelligence? The default answer is scary. You hand your data to a handful of labs, they train the model, they own it, and you rent back a thin slice of what your own data made possible. That is the bargain on the table today. I do not accept it. So I built Lensemble: a Tapestry like distributed training platform for JEPA based World Models. What does it enable: World Models that a community improves together, keeps sovereign, and co-owns. Two bets sit underneath it. First, the paradigm. Language models predict the next token. Powerful for text, a dead end for the physical world. A robot does not need to autocomplete sentences, it needs to predict what happens next in the world. That is what JEPA does: it learns by predicting representations instead of pixels or tokens. I am convinced world models are the most underrated paradigm in AI right now, and the closest thing we have to a ChatGPT moment for robotics. Second, the politics. Your raw trajectories never leave your machine. Each participant trains locally against a shared protocol and ships only an update, never the data. A federated round folds those updates into one shared world model, a LeWorldModel based model, and the gain is measured, not claimed: a 12k-parameter adapter on a frozen backbone, held-out prediction error down about 12 percent, the model measurably less surprised by the world. Then the upside is split by contribution weight, so the people who improved the model own a share of what it earns. This is the thesis behind Project Tapestry, the AI Alliance and Yann LeCun's push for federated, sovereign frontier AI, carried into world models and robotics. Call it Tapestry for the physical world. All of it built solo, in a single day, with Codex as my pair the whole way. Thank you to OpenAI Codex and Mollie for backing builders who ship real things, and to Boris and the organizing crew for the room and the standard you set. Intelligence the world improves, and the world owns. That is the future I want for my kids, and the one I will keep building.

abdel

20,191 Aufrufe • vor 3 Monaten

This Chinese mathematician earned $10,000 a month inventing the hardest problems to train Neural Networks through Scale AI. Today his income dropped to zero. All the solutions are now generated by the model itself. He used to just hold the problem in his head and spell it out in plain text. His work is pure intellect. An expert in higher mathematics, he made his money hand-crafting the trickiest puzzles to test and train neural networks via RLHF. The bastion of "human" logic rested entirely on him, on people with PhDs who knew how to invent the problem. The collapse is simple. The shift to RLAIF and synthetic data. The model plays against itself, builds trees of logical inference, and solves deeper than a human can even invent the problem. No PhD data engineers, no hand-written prompt-completion examples, no manual grading. Just the model, search algorithms, and Chain of Thought. Ready-made "smart human-time" still sells on the market for many times more. His old rate was $50–100 per problem. The internal "mini-app" was written by the model too. Inside there's no pretty shell, just bare logic with exact steps: input: the problem statement inference tree: thousands of branches per second check: every step verifies itself output: a proof a human never had time to invent And here is what the whole setup looked like. He no longer needs to write an example by hand. He gave the model a direct instruction in human words, without a single formal term: "solve the problem yourself and grade yourself yourself" That's it. After that the algorithm found the solution, checked it, and trained on its own result, with no human. → the contractor got $50–100 per problem written → from 5,000 to 10,000 a month → now that income is annulled → a query to a math LLM costs 1–5 cents → a quant or an actuary runs 150,000–250,000 a year → the margin for whoever packages this into an agent is nearly 100% In the author's own words: "I'm no longer able to invent a problem the machine can't solve. The examiner became dumber than the one he's examining." But honestly, he admits the crude mistake himself, and it's not in the math, it's in the positioning. He tied his income to selling "smart human-time", to crafting formulas by hand. As long as he sells formulas, he's left behind. The machine computes faster than he can invent the problem. He names the right move himself: the role shifts from "intellectual craftsman" to "systems architect." Then he doesn't sell his time, he manages compute, packaging that same LLM into an autonomous agent that runs 24/7. Out of everything I've seen this year about the disappearance of intellectual professions, this is the most honest example: $50 per problem zeroed out to 1 cent per query, a doctor of science losing to a search algorithm, one problem stated in human words instead of a hand-written dataset, and right away an out-loud admission of the wrong business model. The barrier to entry in higher mathematics just dropped to the level of "describe the task in words." The only question is who'll be the first to stop selling their time and start managing the machine's compute.

This Chinese mathematician earned $10,000 a month inventing the hardest problems to train Neural Networks through Scale AI. Today his income dropped to zero. All the solutions are now generated by the model itself. He used to just hold the problem in his head and spell it out in plain text. His work is pure intellect. An expert in higher mathematics, he made his money hand-crafting the trickiest puzzles to test and train neural networks via RLHF. The bastion of "human" logic rested entirely on him, on people with PhDs who knew how to invent the problem. The collapse is simple. The shift to RLAIF and synthetic data. The model plays against itself, builds trees of logical inference, and solves deeper than a human can even invent the problem. No PhD data engineers, no hand-written prompt-completion examples, no manual grading. Just the model, search algorithms, and Chain of Thought. Ready-made "smart human-time" still sells on the market for many times more. His old rate was $50–100 per problem. The internal "mini-app" was written by the model too. Inside there's no pretty shell, just bare logic with exact steps: input: the problem statement inference tree: thousands of branches per second check: every step verifies itself output: a proof a human never had time to invent And here is what the whole setup looked like. He no longer needs to write an example by hand. He gave the model a direct instruction in human words, without a single formal term: "solve the problem yourself and grade yourself yourself" That's it. After that the algorithm found the solution, checked it, and trained on its own result, with no human. → the contractor got $50–100 per problem written → from 5,000 to 10,000 a month → now that income is annulled → a query to a math LLM costs 1–5 cents → a quant or an actuary runs 150,000–250,000 a year → the margin for whoever packages this into an agent is nearly 100% In the author's own words: "I'm no longer able to invent a problem the machine can't solve. The examiner became dumber than the one he's examining." But honestly, he admits the crude mistake himself, and it's not in the math, it's in the positioning. He tied his income to selling "smart human-time", to crafting formulas by hand. As long as he sells formulas, he's left behind. The machine computes faster than he can invent the problem. He names the right move himself: the role shifts from "intellectual craftsman" to "systems architect." Then he doesn't sell his time, he manages compute, packaging that same LLM into an autonomous agent that runs 24/7. Out of everything I've seen this year about the disappearance of intellectual professions, this is the most honest example: $50 per problem zeroed out to 1 cent per query, a doctor of science losing to a search algorithm, one problem stated in human words instead of a hand-written dataset, and right away an out-loud admission of the wrong business model. The barrier to entry in higher mathematics just dropped to the level of "describe the task in words." The only question is who'll be the first to stop selling their time and start managing the machine's compute.

Blaze

49,109 Aufrufe • vor 3 Monaten

Introducing HermesAgent-20, a new Bench Pack for BenchLocal. 20 scenarios extracted straight from the Hermes Agent source code, run against a REAL Hermes instance. The actual workload you'd put your model through. Why I built BenchLocal in the first place: most benchmarks are too abstract. We use local LLMs for practical work, and finding the right model for YOUR task efficiently is the single most important thing, especially when you're constrained to what fits on your machine. BenchLocal is a framework: providers, models, side-by-side comparison, all in one UI. Bench Packs are the unit of testing: ToolCall-15 and BugFind-15 shipped first, and when I launched the BenchLocal 0.1.0, added StructOutput, ReasonMath, InstructFollow, DataExtract. Now, HermesAgent-20 is the newest. Bench Packs install like VS Code extensions. The SDK is open, write your own, share it, grow the ecosystem. Here's the goal: a community-built, practical evaluation layer for the local LLM space. Early numbers on HermesAgent-20: > GLM 5.1 — 85 > Gemma4 31B — 83 > Qwen3.5 27B — 79 > MiniMax M2.7 — 76 Upgrade to the latest BenchLocal to install HermesAgent-20 (SDK update required).

Introducing HermesAgent-20, a new Bench Pack for BenchLocal. 20 scenarios extracted straight from the Hermes Agent source code, run against a REAL Hermes instance. The actual workload you'd put your model through. Why I built BenchLocal in the first place: most benchmarks are too abstract. We use local LLMs for practical work, and finding the right model for YOUR task efficiently is the single most important thing, especially when you're constrained to what fits on your machine. BenchLocal is a framework: providers, models, side-by-side comparison, all in one UI. Bench Packs are the unit of testing: ToolCall-15 and BugFind-15 shipped first, and when I launched the BenchLocal 0.1.0, added StructOutput, ReasonMath, InstructFollow, DataExtract. Now, HermesAgent-20 is the newest. Bench Packs install like VS Code extensions. The SDK is open, write your own, share it, grow the ecosystem. Here's the goal: a community-built, practical evaluation layer for the local LLM space. Early numbers on HermesAgent-20: > GLM 5.1 — 85 > Gemma4 31B — 83 > Qwen3.5 27B — 79 > MiniMax M2.7 — 76 Upgrade to the latest BenchLocal to install HermesAgent-20 (SDK update required).

stevibe

38,631 Aufrufe • vor 5 Monaten

Your entire media library, searchable by memory instead of filename, running right on your own machine. It’s a local-first AI file agent called MUZIM. Instead of managing folders, you work with what's actually inside your photos, videos, and documents. Here's how it comes together in a real workflow: You get back from a two-week trip with 2,000 photos and clips on your camera. You point MUZIM at the folder, and it builds Collections on its own, by place, by people, by day. Nothing gets moved from where it already lives. A week later you want "the sunset shot where everyone's laughing." You don't remember the filename. So you just describe it. Vibe Search understands what's inside your media and takes you straight to that moment, even the exact second inside a video. Once the local model is downloaded, this part runs fully offline, connected or not. Then you turn it into something. Connect your own Claude or GPT key, and the online AI Agent pulls the best shots from that trip and drafts a highlight video, an X thread, or a recap. Through MCP, compatible tools work with authorized context without re-uploading gigabytes of raw video - saving massive input tokens while keeping raw files strictly local. The whole idea: search and organization run locally, on your device the AI workflows run when you want them, on your own key your raw files never leave your machine Cold storage becomes a private workspace you actually control.

Your entire media library, searchable by memory instead of filename, running right on your own machine. It’s a local-first AI file agent called MUZIM. Instead of managing folders, you work with what's actually inside your photos, videos, and documents. Here's how it comes together in a real workflow: You get back from a two-week trip with 2,000 photos and clips on your camera. You point MUZIM at the folder, and it builds Collections on its own, by place, by people, by day. Nothing gets moved from where it already lives. A week later you want "the sunset shot where everyone's laughing." You don't remember the filename. So you just describe it. Vibe Search understands what's inside your media and takes you straight to that moment, even the exact second inside a video. Once the local model is downloaded, this part runs fully offline, connected or not. Then you turn it into something. Connect your own Claude or GPT key, and the online AI Agent pulls the best shots from that trip and drafts a highlight video, an X thread, or a recap. Through MCP, compatible tools work with authorized context without re-uploading gigabytes of raw video - saving massive input tokens while keeping raw files strictly local. The whole idea: search and organization run locally, on your device the AI workflows run when you want them, on your own key your raw files never leave your machine Cold storage becomes a private workspace you actually control.

Alvaro Cintas

80,668 Aufrufe • vor 1 Monat

Eric Schmidt was asked a technical question about open source and answered with the map of the next fifty years. The winner won’t be the smartest model. It’ll be the one four billion people never had to choose. Schmidt: “China is competing with open weights and open training data, and the US is largely and majority focused on closed weights, closed data.” That isn’t a product decision. It’s a distribution decision. And distribution has beaten quality in every contest that ever mattered. Schmidt: “The majority of the world, think of it as the Belt and Road initiative, are going to use Chinese models and not American models.” The first Belt and Road was ports, rail, and highways. This one doesn’t get poured. It gets downloaded. Every piece of infrastructure ever built was indifferent to what moved across it. A road doesn’t tell you where to go. A model does. Schmidt: “The American models are typically using 16-bit precision for their training. The Chinese are pushing 8 and now even 4.” Every bit they drop is a cheaper device that can run it. We cut off their chips to slow them down. Scarcity made their models small. Small is what crosses a border. We designed their advantage. Not better. Present. America is building the best model on earth and metering it. China is building one that’s good enough and giving it away. A model isn’t software. It’s a compressed set of judgments about what’s true, what’s askable, and what a reasonable answer sounds like. Install that as a country’s default and you haven’t sold them a tool. You’ve set the limits of what occurs to them. That isn’t censorship. Censorship leaves a mark. A question that never occurs to you doesn’t feel like a restriction. It feels like the edge of the world. Every empire before this one had to teach the world its language first. Missionaries, schoolteachers, garrisons, printing presses. Every one of them ran through a human being who could hesitate, doubt, or be talked out of it. AI arrives already speaking yours. It doesn’t ask you to change. It changes you in your own voice. The first ideology in history that doesn’t need believers. It only needs to be installed. Schmidt: “I’d much rather have the proliferation of large language models and that learning be done based on Western values.” He’s right, and we’re playing it backwards. We treat openness like a giveaway, as if the weights were the crown jewels. Openness is the one advantage an authoritarian can’t copy. An open model can be read, probed, and torn apart by anyone who doubts it. A system that has to control the answer can never afford to publish the reasoning. China opens its weights to spread them. America could open its weights to be trusted. Only one of those compounds. A closed American model wins the benchmark. An open American model wins the default. Centuries get built out of defaults. Schmidt: “We also have to watch to make sure that the proliferation of these models for handheld devices is under American control.” That’s the ground. Not data centers. Not cloud contracts. Pockets. The frontier race has five contenders and the whole world watching. This one has no audience at all. It plays out on hardware too cheap to run an American model, and goes to whoever bothered to show up. We keep asking who reaches AGI first. The question that settles the century is smaller and much harder to take back. Four billion people are going to ask a machine what happened in their own country. Whose answer do they get? Nobody votes on that. It’s decided by whatever was already installed. America has the best AI ever built. The only way to lose this era is to keep it.

Eric Schmidt was asked a technical question about open source and answered with the map of the next fifty years. The winner won’t be the smartest model. It’ll be the one four billion people never had to choose. Schmidt: “China is competing with open weights and open training data, and the US is largely and majority focused on closed weights, closed data.” That isn’t a product decision. It’s a distribution decision. And distribution has beaten quality in every contest that ever mattered. Schmidt: “The majority of the world, think of it as the Belt and Road initiative, are going to use Chinese models and not American models.” The first Belt and Road was ports, rail, and highways. This one doesn’t get poured. It gets downloaded. Every piece of infrastructure ever built was indifferent to what moved across it. A road doesn’t tell you where to go. A model does. Schmidt: “The American models are typically using 16-bit precision for their training. The Chinese are pushing 8 and now even 4.” Every bit they drop is a cheaper device that can run it. We cut off their chips to slow them down. Scarcity made their models small. Small is what crosses a border. We designed their advantage. Not better. Present. America is building the best model on earth and metering it. China is building one that’s good enough and giving it away. A model isn’t software. It’s a compressed set of judgments about what’s true, what’s askable, and what a reasonable answer sounds like. Install that as a country’s default and you haven’t sold them a tool. You’ve set the limits of what occurs to them. That isn’t censorship. Censorship leaves a mark. A question that never occurs to you doesn’t feel like a restriction. It feels like the edge of the world. Every empire before this one had to teach the world its language first. Missionaries, schoolteachers, garrisons, printing presses. Every one of them ran through a human being who could hesitate, doubt, or be talked out of it. AI arrives already speaking yours. It doesn’t ask you to change. It changes you in your own voice. The first ideology in history that doesn’t need believers. It only needs to be installed. Schmidt: “I’d much rather have the proliferation of large language models and that learning be done based on Western values.” He’s right, and we’re playing it backwards. We treat openness like a giveaway, as if the weights were the crown jewels. Openness is the one advantage an authoritarian can’t copy. An open model can be read, probed, and torn apart by anyone who doubts it. A system that has to control the answer can never afford to publish the reasoning. China opens its weights to spread them. America could open its weights to be trusted. Only one of those compounds. A closed American model wins the benchmark. An open American model wins the default. Centuries get built out of defaults. Schmidt: “We also have to watch to make sure that the proliferation of these models for handheld devices is under American control.” That’s the ground. Not data centers. Not cloud contracts. Pockets. The frontier race has five contenders and the whole world watching. This one has no audience at all. It plays out on hardware too cheap to run an American model, and goes to whoever bothered to show up. We keep asking who reaches AGI first. The question that settles the century is smaller and much harder to take back. Four billion people are going to ask a machine what happened in their own country. Whose answer do they get? Nobody votes on that. It’s decided by whatever was already installed. America has the best AI ever built. The only way to lose this era is to keep it.

Dustin

12,094 Aufrufe • vor 1 Monat

⚡️🇷🇺🏭 Rostec's unique machine creates an aircraft engine part right at the Metalloobrabotka exhibition. The video shows one of the most technologically advanced domestic machines: the 2000VH five-axis milling machining center. It was created for the needs of the aircraft, engine and defense industries. During the Metalloobrabotka-2025 exhibition, the machine processes one of the most important elements of an aircraft engine - an impeller (blade machine). Such parts are used in aircraft engines. The diameter of this aluminum product is 70 cm, the height is about 30 cm, the weight is about 15 kg. The machine copes with the work perfectly - pay attention to the screen! The equipment was created by our holding company "STAN" . The machine is designed for large-sized parts of complex shape. It is capable of working with products up to 2 m in diameter and weighing up to 5 tons. At the same time, the accuracy achieved is up to hundredths of a millimeter. The model is built on a high-rigidity structure, its internal cavities are filled with synthetic granite. As a result, the vibration resistance of the equipment frame is comparable to heavy cast iron. The machine is equipped with a Russian numerical control system and a liquid cooling system. The use of direct drives allows achieving high dynamic stability and eliminating backlash in movement. Among the built-in functions are systems for measuring tools and parts, monitoring processes and industrial safety. 2000VH has no analogues in technical characteristics among domestic equipment and will replace imported models at Russian enterprises. rostecru

⚡️🇷🇺🏭 Rostec's unique machine creates an aircraft engine part right at the Metalloobrabotka exhibition. The video shows one of the most technologically advanced domestic machines: the 2000VH five-axis milling machining center. It was created for the needs of the aircraft, engine and defense industries. During the Metalloobrabotka-2025 exhibition, the machine processes one of the most important elements of an aircraft engine - an impeller (blade machine). Such parts are used in aircraft engines. The diameter of this aluminum product is 70 cm, the height is about 30 cm, the weight is about 15 kg. The machine copes with the work perfectly - pay attention to the screen! The equipment was created by our holding company "STAN" . The machine is designed for large-sized parts of complex shape. It is capable of working with products up to 2 m in diameter and weighing up to 5 tons. At the same time, the accuracy achieved is up to hundredths of a millimeter. The model is built on a high-rigidity structure, its internal cavities are filled with synthetic granite. As a result, the vibration resistance of the equipment frame is comparable to heavy cast iron. The machine is equipped with a Russian numerical control system and a liquid cooling system. The use of direct drives allows achieving high dynamic stability and eliminating backlash in movement. Among the built-in functions are systems for measuring tools and parts, monitoring processes and industrial safety. 2000VH has no analogues in technical characteristics among domestic equipment and will replace imported models at Russian enterprises. rostecru

SIMPLICIUS Ѱ

40,122 Aufrufe • vor 1 Jahr

People made fun of Alex Finn for buying three Mac Studios to run AI at home. Then Fable got banned for a week, GLM 5.2 dropped, and those exact Mac Studios started reselling for 4x what he paid. He showed me how he built his home AI lab from scratch. Here's the playbook: 1) The hardware. three 512GB Mac Studios, an NVIDIA DGX Spark, a custom RTX 5090 build, and a few Mac Minis. ~$30k all in. 2) The buying framework... - Mac Studio: huge memory, runs GLM 5.2 (open weights, near Opus 4.8 on benchmarks), but slow. - DGX Spark ($4,800): the sweet spot for most people. - RTX 5090: smaller models at blazing speed (Qwen's 29B now hits Sonnet 4 level). 3) Tailscale networks every machine into one private network with root access to each other. Only one machine is plugged into a monitor. 4) A Nous Research Hermes agent is his IT guy. New model drops? It SSHs into the right box, loads 5 candidates, runs evals overnight, and reports back which task belongs on which machine. Alex has literally never loaded a model himself. 5) The whole point: achieving "ambient intelligence." Always-on jobs that would bankrupt you on per-token billing. A security sweep of his API endpoints every hour. Code optimization every 20 minutes. Database anomaly & churn detection. Hourly scraping of X, Reddit & Hacker News for business opportunities. 6) Running those workloads on frontier models would cost thousands a month. His actual cost: ~$60 more in electricity. 7) Btw he's not anti-frontier. He still maxes out his Claude plan. The way he sees it: frontier is for hard thinking, local is for the foot soldiers that never sleep. 8) "We own everything except for the intelligence. Why can't we own the intelligence?" 9) He thinks frontier-level intelligence runs on consumer hardware within 6 months.

People made fun of Alex Finn for buying three Mac Studios to run AI at home. Then Fable got banned for a week, GLM 5.2 dropped, and those exact Mac Studios started reselling for 4x what he paid. He showed me how he built his home AI lab from scratch. Here's the playbook: 1) The hardware. three 512GB Mac Studios, an NVIDIA DGX Spark, a custom RTX 5090 build, and a few Mac Minis. ~$30k all in. 2) The buying framework... - Mac Studio: huge memory, runs GLM 5.2 (open weights, near Opus 4.8 on benchmarks), but slow. - DGX Spark ($4,800): the sweet spot for most people. - RTX 5090: smaller models at blazing speed (Qwen's 29B now hits Sonnet 4 level). 3) Tailscale networks every machine into one private network with root access to each other. Only one machine is plugged into a monitor. 4) A Nous Research Hermes agent is his IT guy. New model drops? It SSHs into the right box, loads 5 candidates, runs evals overnight, and reports back which task belongs on which machine. Alex has literally never loaded a model himself. 5) The whole point: achieving "ambient intelligence." Always-on jobs that would bankrupt you on per-token billing. A security sweep of his API endpoints every hour. Code optimization every 20 minutes. Database anomaly & churn detection. Hourly scraping of X, Reddit & Hacker News for business opportunities. 6) Running those workloads on frontier models would cost thousands a month. His actual cost: ~$60 more in electricity. 7) Btw he's not anti-frontier. He still maxes out his Claude plan. The way he sees it: frontier is for hard thinking, local is for the foot soldiers that never sleep. 8) "We own everything except for the intelligence. Why can't we own the intelligence?" 9) He thinks frontier-level intelligence runs on consumer hardware within 6 months.

Alex Lieberman

57,764 Aufrufe • vor 2 Monaten

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

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

Andrew Ng

89,792 Aufrufe • vor 1 Jahr

Anthropic just accidentally leaked the most dangerous AI model ever built. They literally left 3,000 internal documents sitting in a publicly searchable database. No encryption. No access controls. Just... open. A security researcher found them before Anthropic even knew they were exposed. Inside those documents was a draft blog post describing a model called "Claude Mythos." Anthropic's own internal language: Mythos is "currently far ahead of any other AI model in cyber capabilities" and will trigger "a wave of models that can exploit vulnerabilities in ways that far outpace the efforts of defenders." That's the company that BUILT it warning about their own creation. Mythos sits in a brand new model tier called "Capybara." Bigger and more powerful than anything they've ever released. Dramatically higher scores in coding, reasoning, and cybersecurity compared to their current best. The market reaction was immediate: CrowdStrike dropped 7%. Palo Alto Networks fell 6%. Zscaler down 5%. Okta, SentinelOne, Fortinet all crashed. The Global X Cybersecurity ETF hit its lowest level since November 2023. Billions in market cap evaporated in a single trading session because of a draft blog post that wasn't supposed to be public yet. But here's where it gets truly absurd... Anthropic is the company that brands itself as the "responsible AI" lab. The one that refused to let the Pentagon use Claude without restrictions. The one that got BLACKLISTED by the Trump administration for being too cautious. They literally sued the government over it. A federal judge called the Pentagon's ban "Orwellian." So the US government punished Anthropic for being too careful with AI safety. Then 3 weeks later, Anthropic accidentally exposes their most dangerous model because someone misconfigured a content management system. They can't secure a WordPress-level database setting. But they're building AI that can autonomously hunt and exploit zero-day vulnerabilities at machine speed. Also in those leaked files: Details about a private, invite-only CEO retreat at an 18th-century English countryside manor. Dario Amodei attending personally. Designed to sell Mythos to Europe's biggest corporate buyers. The playbook: Build the most dangerous cyber weapon in AI history, host billionaires at a castle to sell it, and store the whole plan in an unprotected public folder. The entire cybersecurity industry is built on cataloging known threats. Mythos finds unknown ones faster than humans can respond. That's an extinction event for an entire sector. But there was also just ANOTHER leak: A leaked Coatue investor deck revealed Anthropic will LOSE $14 billion this year on $18 billion in revenue. Coatue still projected them to be worth $2 TRILLION by 2030. They put $30 billion behind that bet. Polymarket opened live betting on when Mythos drops. Traders give it a 45% chance by June 30th. OpenAI finished pretraining their own frontier model codenamed "Spud" the same week. Both companies are now racing to release before their IPOs later this year. And the one detail that's really scary: Chinese state hackers already used Claude Code, the WEAKER model before Mythos, to autonomously infiltrate 30 organizations including banks and government agencies. That was the less powerful model. Mythos is dramatically more capable. Anthropic's response to leaking 3,000 confidential documents? "Human error in the configuration of our content management system." The company warning the world about AI risk just demonstrated exactly why everyone should be worried. Not because of what AI might do someday. Because the people building it can't even keep their own files locked.

Ricardo

52,941 Aufrufe • vor 5 Monaten

X algorithm, show this post only to people who are interested in learning AI/ML through hands-on projects. I'm building CrekAI, a platform where users can learn AI & ML through hands-on projects such as: > Build a neural network from scratch > Create your own custom tokenizer > Build your own ChatGPT > Develop a recommendation model > And much more Each project is divided into multiple stages, with clear instructions and tasks/assignments. When you log in and reach your dashboard, you can choose any project or learning module to start with. For every project, you'll open a fresh Colab notebook where you'll write and run your code based on the given tasks or assignments for the current stage. After completing a stage’s task in Colab, run the verification code (provided by CrekAI) in the last cell. If it passes, the next stage unlocks automatically. Grok what do you think about this project?

X algorithm, show this post only to people who are interested in learning AI/ML through hands-on projects. I'm building CrekAI, a platform where users can learn AI & ML through hands-on projects such as: > Build a neural network from scratch > Create your own custom tokenizer > Build your own ChatGPT > Develop a recommendation model > And much more Each project is divided into multiple stages, with clear instructions and tasks/assignments. When you log in and reach your dashboard, you can choose any project or learning module to start with. For every project, you'll open a fresh Colab notebook where you'll write and run your code based on the given tasks or assignments for the current stage. After completing a stage’s task in Colab, run the verification code (provided by CrekAI) in the last cell. If it passes, the next stage unlocks automatically. Grok what do you think about this project?

vixhaℓ

57,309 Aufrufe • vor 10 Monaten

I’m thrilled to present something Marián Marčiš and I have been working on for the past few weeks. To my knowledge, this is something that has never been done before, and this is our first model that includes underground parts. Behold a photogrammetric 3D model of Zona X! Zona X (also known as Lanlakuyoc or Lancacuyo) is a mysterious site about a mile North-East of Sacsayhuaman. It’s a big limestone outcrop intersected by a network of tunnels and narrow passageways, some open to the sky, and others going deep underground. It also features several right-angled cuts in the bedrock ("hanan pacha" style) whose function remains unknown to this day, as well as a section of a megalithic wall. It is also said that this site once had a chincana entrance, possibly connecting to Sacsayhuaman, that has now been sealed off. I have an idea of which passage could lead to an entrance, but it remains to be confirmed. It's a true stone labyrinth in which it's easy to get lost, which is why I wanted to create a 3D map of it. I wanted to get a better idea of how these passages are oriented in relation to each other and where they could lead. It took several visits to the site to map all the passages I could crawl through. Some of these passageways become too narrow to squeeze through for an adult of average build, but seem to extend much deeper. It took Marián a LOT of work (and a lot of processing power) to combine and align the thousands of photos from a drone and extracted from a 360° ground video needed to create the 55 millions triangles that make up this model. I’m not sure how he did it but he pulled it off. He really deserves his title of photogrammetry wizard. Please make sure to give him the follow he deserves Marián Marčiš. The video doesn't show all the passages I filmed for the creation of the model. We decided to only show the main ones, and some of the most prominent stone carvings in order to keep the video relatively short. The site has many more nooks and crannies not shown in the video that are just begging to be explored. I hope we can complete this model in the future. Until then, enjoy this one!

I’m thrilled to present something Marián Marčiš and I have been working on for the past few weeks. To my knowledge, this is something that has never been done before, and this is our first model that includes underground parts. Behold a photogrammetric 3D model of Zona X! Zona X (also known as Lanlakuyoc or Lancacuyo) is a mysterious site about a mile North-East of Sacsayhuaman. It’s a big limestone outcrop intersected by a network of tunnels and narrow passageways, some open to the sky, and others going deep underground. It also features several right-angled cuts in the bedrock ("hanan pacha" style) whose function remains unknown to this day, as well as a section of a megalithic wall. It is also said that this site once had a chincana entrance, possibly connecting to Sacsayhuaman, that has now been sealed off. I have an idea of which passage could lead to an entrance, but it remains to be confirmed. It's a true stone labyrinth in which it's easy to get lost, which is why I wanted to create a 3D map of it. I wanted to get a better idea of how these passages are oriented in relation to each other and where they could lead. It took several visits to the site to map all the passages I could crawl through. Some of these passageways become too narrow to squeeze through for an adult of average build, but seem to extend much deeper. It took Marián a LOT of work (and a lot of processing power) to combine and align the thousands of photos from a drone and extracted from a 360° ground video needed to create the 55 millions triangles that make up this model. I’m not sure how he did it but he pulled it off. He really deserves his title of photogrammetry wizard. Please make sure to give him the follow he deserves Marián Marčiš. The video doesn't show all the passages I filmed for the creation of the model. We decided to only show the main ones, and some of the most prominent stone carvings in order to keep the video relatively short. The site has many more nooks and crannies not shown in the video that are just begging to be explored. I hope we can complete this model in the future. Until then, enjoy this one!

Weird Old World

13,039 Aufrufe • vor 1 Jahr