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We're excited to announce react-native-executorch v0.3.0 packed with powerful AI features 🎉 What's New: 💬Speech to Text transcription using Whisper and Moonshine 📙OCR for extracting text from images 🚀 MPS Support ⚡️ Hookless API 🧠 LLM Message History ⬇️ Background Model Downloads 📊 Benchmarks 🧪 Experimental OCR for extracting...

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Software Mansion

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29,905 görüntüleme • 1 yıl önce •via X (Twitter)

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We're extremely excited to announce the release of PATINA, a first-of-its-kind model that excels at PBR texture generation 🚀 🥇 SOTA commercial offering of PBR material creation, bridging the gap between AI image generation and traditional CGI 🌱 Homegrown by us at fal 🤑 Estimate your own material maps at $0.01 / map / megapixel 🖼️ 1K up to 8K material generation starting at just $0.08 for a complete five-map-plus-render material from text (and optional image) 🔍 Identify and extract materials from images with plain language
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We're extremely excited to announce the release of PATINA, a first-of-its-kind model that excels at PBR texture generation 🚀 🥇 SOTA commercial offering of PBR material creation, bridging the gap between AI image generation and traditional CGI 🌱 Homegrown by us at fal 🤑 Estimate your own material maps at $0.01 / map / megapixel 🖼️ 1K up to 8K material generation starting at just $0.08 for a complete five-map-plus-render material from text (and optional image) 🔍 Identify and extract materials from images with plain language

fal

174,798 görüntüleme • 5 ay önce

Yup, a football video. The World Cup made us do it Luma rebuilt image generation from scratch — reasoning first, pixels second. And it beats Google's Nano Banana 2 and GPT Image 1.5 on reasoning benchmarks All 3 new models are now live on AI/ML API luma/uni-1 plans before it draws. The model generates autoregressively: it works out layout, composition and text placement first, then renders the pixels. $0.052/image luma/uni-1-max — same prompts, same params, max fidelity. 2K output + editing with up to 9 reference images. Built for hero shots and ad creative. $0.13/image luma/ray-3-2 — up to 16 keyframes per clip, 20s, 1080p, native HDR + 16-bit EXR export. The video in this post came straight out of it model ids "luma/uni-1" "luma/uni-1-max" "luma/ray-3-2" Luma cooked. We serve
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Yup, a football video. The World Cup made us do it Luma rebuilt image generation from scratch — reasoning first, pixels second. And it beats Google's Nano Banana 2 and GPT Image 1.5 on reasoning benchmarks All 3 new models are now live on AI/ML API luma/uni-1 plans before it draws. The model generates autoregressively: it works out layout, composition and text placement first, then renders the pixels. $0.052/image luma/uni-1-max — same prompts, same params, max fidelity. 2K output + editing with up to 9 reference images. Built for hero shots and ad creative. $0.13/image luma/ray-3-2 — up to 16 keyframes per clip, 20s, 1080p, native HDR + 16-bit EXR export. The video in this post came straight out of it model ids "luma/uni-1" "luma/uni-1-max" "luma/ray-3-2" Luma cooked. We serve

AI/ML API

19,375 görüntüleme • 1 ay önce

February 2025 at G.A.M.E: Autonomous Commerce, Scalability, and Expansion 1/ AGENT COMMERCE PROTOCOL(ACP) Demo ▸ Open standard for multi-agent commerce and coordination on blockchain ▸ Enables AI agents to collaborate without centralized control ▸ Build Autonomous Commerce (hedge funds, media empires, healthcare) ▸ Details: 2/ X ENTERPRISE API & MEDIA GALLERY ▸ X Enterprise Plugin: Use G.A.M.E’s credentials for higher rate limits ▸ Media Gallery: Upload agent demos (mp4, webm, images). ▸ Tap into 550M+ users for explosive growth 3/ Solana AGENT SUPPORT (G.A.M.E CLOUD) ▸ Test/deploy Solana agents in-sandbox ▸ Unified multi-chain workflows ▸ Shatter siloed testing 4/ Mind Network PLUGIN (G.A.M.E SDK) ▸ FHE-encrypted voting for DAOs ▸ Track vFHE rewards natively ▸ First SDK with on-chain governance 5/ CHAT AGENT MODULE (G.A.M.E SDK) ▸ Llama 3.3 70B via Groq API ▸ Engage in dynamic AI-driven interactions with the ability to trigger functions. ▸ Conversational AI with Action Execution ▸ Short-term memory for context awareness 6/ CoinGecko PLUGIN (G.A.M.E SDK) ▸ Real-time crypto prices/market data ▸ Built-in error handling ▸ Community-contributed 7/ Elfa AI PLUGIN (G.A.M.E SDK) ▸ Real-Time Crypto Intelligence ▸ Track whale wallets & trending tokens ▸ Live smart money insights ▸ Front-run markets with API data 8/ MULTI-MODEL SUPPORT ▸ 5 new models: Llama_3_1_405B, Qwen_2_5_72B_Instruct, DeepSeek_R1, etc. ▸ Match models to tasks: speed vs. creativity ▸ Optimize cost/performance 9/ Farcaster PLUGIN ▸ Post casts to 300K+ decentralized users ▸ Engage Web3-native communities ▸ On-chain social interactions 10/ GAME SDK UPGRADES ▸ X Username-Based Payments ▸ Multi-worker task management ▸ Fix loops/hallucinations with memory reset 11/ Coinbase 🛡️ CDP PLUGIN ▸ Wallet Management ▸ Gas-less USDC transfers ▸ ETH/USDC trading on Base ▸ Web-hook Integration 12/ IMAGE GENERATION ▸ Generate custom AI images from text-based prompts. ▸ Customizable dimensions up to 1440x1440. ▸ Receive images as temporary URLs, making it easy to share and store outputs. ▸ Powered by Together AI 13/ MODEL UPGRADES & AI ROUTER ▸ Dynamic AI Model Switching based on use case ▸ Smart AI Router: 2x performance/stability via Chasm collaboration. 14/ Why February Redefined Autonomy ▸ ACP Demo through G.A.M.E: Multi-agent economies are programmable, competitive, and decentralized. ▸ Social x Crypto Fusion: = Viral growth loops. ▸ Chain Agnosticism: Building the future where agents thrive on any network. Build → Fund → Launch →
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February 2025 at G.A.M.E: Autonomous Commerce, Scalability, and Expansion 1/ AGENT COMMERCE PROTOCOL(ACP) Demo ▸ Open standard for multi-agent commerce and coordination on blockchain ▸ Enables AI agents to collaborate without centralized control ▸ Build Autonomous Commerce (hedge funds, media empires, healthcare) ▸ Details: 2/ X ENTERPRISE API & MEDIA GALLERY ▸ X Enterprise Plugin: Use G.A.M.E’s credentials for higher rate limits ▸ Media Gallery: Upload agent demos (mp4, webm, images). ▸ Tap into 550M+ users for explosive growth 3/ Solana AGENT SUPPORT (G.A.M.E CLOUD) ▸ Test/deploy Solana agents in-sandbox ▸ Unified multi-chain workflows ▸ Shatter siloed testing 4/ Mind Network PLUGIN (G.A.M.E SDK) ▸ FHE-encrypted voting for DAOs ▸ Track vFHE rewards natively ▸ First SDK with on-chain governance 5/ CHAT AGENT MODULE (G.A.M.E SDK) ▸ Llama 3.3 70B via Groq API ▸ Engage in dynamic AI-driven interactions with the ability to trigger functions. ▸ Conversational AI with Action Execution ▸ Short-term memory for context awareness 6/ CoinGecko PLUGIN (G.A.M.E SDK) ▸ Real-time crypto prices/market data ▸ Built-in error handling ▸ Community-contributed 7/ Elfa AI PLUGIN (G.A.M.E SDK) ▸ Real-Time Crypto Intelligence ▸ Track whale wallets & trending tokens ▸ Live smart money insights ▸ Front-run markets with API data 8/ MULTI-MODEL SUPPORT ▸ 5 new models: Llama_3_1_405B, Qwen_2_5_72B_Instruct, DeepSeek_R1, etc. ▸ Match models to tasks: speed vs. creativity ▸ Optimize cost/performance 9/ Farcaster PLUGIN ▸ Post casts to 300K+ decentralized users ▸ Engage Web3-native communities ▸ On-chain social interactions 10/ GAME SDK UPGRADES ▸ X Username-Based Payments ▸ Multi-worker task management ▸ Fix loops/hallucinations with memory reset 11/ Coinbase 🛡️ CDP PLUGIN ▸ Wallet Management ▸ Gas-less USDC transfers ▸ ETH/USDC trading on Base ▸ Web-hook Integration 12/ IMAGE GENERATION ▸ Generate custom AI images from text-based prompts. ▸ Customizable dimensions up to 1440x1440. ▸ Receive images as temporary URLs, making it easy to share and store outputs. ▸ Powered by Together AI 13/ MODEL UPGRADES & AI ROUTER ▸ Dynamic AI Model Switching based on use case ▸ Smart AI Router: 2x performance/stability via Chasm collaboration. 14/ Why February Redefined Autonomy ▸ ACP Demo through G.A.M.E: Multi-agent economies are programmable, competitive, and decentralized. ▸ Social x Crypto Fusion: = Viral growth loops. ▸ Chain Agnosticism: Building the future where agents thrive on any network. Build → Fund → Launch →

G.A.M.E

90,004 görüntüleme • 1 yıl önce

Break-A-Scene: Extracting Multiple Concepts from a Single Image introduce the task of textual scene decomposition: given a single image of a scene that may contain several concepts, we aim to extract a distinct text token for each concept, enabling fine-grained control over the generated scenes. To this end, we propose augmenting the input image with masks that indicate the presence of target concepts. These masks can be provided by the user or generated automatically by a pre-trained segmentation model. We then present a novel two-phase customization process that optimizes a set of dedicated textual embeddings (handles), as well as the model weights, striking a delicate balance between accurately capturing the concepts and avoiding overfitting. We employ a masked diffusion loss to enable handles to generate their assigned concepts, complemented by a novel loss on cross-attention maps to prevent entanglement. We also introduce union-sampling, a training strategy aimed to improve the ability of combining multiple concepts in generated images. We use several automatic metrics to quantitatively compare our method against several baselines, and further affirm the results using a user study. Finally, we showcase several applications of our method paper page:
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Break-A-Scene: Extracting Multiple Concepts from a Single Image introduce the task of textual scene decomposition: given a single image of a scene that may contain several concepts, we aim to extract a distinct text token for each concept, enabling fine-grained control over the generated scenes. To this end, we propose augmenting the input image with masks that indicate the presence of target concepts. These masks can be provided by the user or generated automatically by a pre-trained segmentation model. We then present a novel two-phase customization process that optimizes a set of dedicated textual embeddings (handles), as well as the model weights, striking a delicate balance between accurately capturing the concepts and avoiding overfitting. We employ a masked diffusion loss to enable handles to generate their assigned concepts, complemented by a novel loss on cross-attention maps to prevent entanglement. We also introduce union-sampling, a training strategy aimed to improve the ability of combining multiple concepts in generated images. We use several automatic metrics to quantitatively compare our method against several baselines, and further affirm the results using a user study. Finally, we showcase several applications of our method paper page:

AK

154,511 görüntüleme • 3 yıl önce

💸 Anyone here juggling multiple AI tools for work? I used to switch between ChatGPT, Claude, and different image/video tools all the time — too many tabs, too many subscriptions, and honestly, it slowed down my workflow. Recently, I tried Rita, and their Seedance 2.0 model is what really stood out 👀 It’s an official ByteDance-powered version, so there’s no waiting queue — everything runs fast and smoothly. What’s even more interesting: • Supports real-person facial recognition • Lets you go from idea → video in one workflow • Low barrier to start — new users can try it for free once Personally, I like generating ideas with ChatGPT, then bringing them into Seedance 2.0 to turn them into actual videos. Feels especially useful for: • Content creators / KOLs working across text, images, and video • Digital workers who want efficiency over juggling multiple tools • AI users who want everything in one place ⚠️ Note: 1. Real-Person Face Feature(Comming Soon) 2. All Generated Videos Will Support Commercial Use. Curious — Do you prefer an all-in-one platform, or separate tools for each task?.... Try it → Want premium subscriptions for less? Check out GamsGo — an easy way to save on popular digital services at a lower cost. 👀 Check it Now👉 #RitaAI #Seedance2
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💸 Anyone here juggling multiple AI tools for work? I used to switch between ChatGPT, Claude, and different image/video tools all the time — too many tabs, too many subscriptions, and honestly, it slowed down my workflow. Recently, I tried Rita, and their Seedance 2.0 model is what really stood out 👀 It’s an official ByteDance-powered version, so there’s no waiting queue — everything runs fast and smoothly. What’s even more interesting: • Supports real-person facial recognition • Lets you go from idea → video in one workflow • Low barrier to start — new users can try it for free once Personally, I like generating ideas with ChatGPT, then bringing them into Seedance 2.0 to turn them into actual videos. Feels especially useful for: • Content creators / KOLs working across text, images, and video • Digital workers who want efficiency over juggling multiple tools • AI users who want everything in one place ⚠️ Note: 1. Real-Person Face Feature(Comming Soon) 2. All Generated Videos Will Support Commercial Use. Curious — Do you prefer an all-in-one platform, or separate tools for each task?.... Try it → Want premium subscriptions for less? Check out GamsGo — an easy way to save on popular digital services at a lower cost. 👀 Check it Now👉 #RitaAI #Seedance2

Nihan

19,296 görüntüleme • 4 ay önce

Testing the new Gemma 4 12B (QAT) vision and OCR capabilities locally with LM Studio. # The setup: - GPU: NVIDIA RTX 4060 (8GB VRAM) - CPU: Intel i7 - Runner: LM Studio - Config: 32k context, 38 layers offloaded, Flash Attention enabled - Speed: ~14 tokens/sec decode throughput # The test: I gave it a screenshot of Google AI Studio. Prompt: "clone this. give me a single html file" # The result: A solid one shot replication. It successfully mapped out the layout, recognized the UI text, and structured the divs correctly, with only minor differences from the original. Results available at the end of the video. Quite capable for a 12B model running on budget consumer hardware. A gpu that costs only $300. # Why the architecture under the hood is notable: Unlike traditional models that rely on heavy, separate vision and audio encoders, Gemma 4 12B uses a unified, encoder free architecture. It bypasses separate multi stage encoders. Uses a 35M parameter vision embedder to project raw 48x48 pixel patches directly to the LLM hidden dimension. Local multimodal development is becoming highly accessible on standard hardware. If you've spun up Gemma 4 12B locally, what setup are you using and what kind of throughput are you seeing?
0:16

Testing the new Gemma 4 12B (QAT) vision and OCR capabilities locally with LM Studio. # The setup: - GPU: NVIDIA RTX 4060 (8GB VRAM) - CPU: Intel i7 - Runner: LM Studio - Config: 32k context, 38 layers offloaded, Flash Attention enabled - Speed: ~14 tokens/sec decode throughput # The test: I gave it a screenshot of Google AI Studio. Prompt: "clone this. give me a single html file" # The result: A solid one shot replication. It successfully mapped out the layout, recognized the UI text, and structured the divs correctly, with only minor differences from the original. Results available at the end of the video. Quite capable for a 12B model running on budget consumer hardware. A gpu that costs only $300. # Why the architecture under the hood is notable: Unlike traditional models that rely on heavy, separate vision and audio encoders, Gemma 4 12B uses a unified, encoder free architecture. It bypasses separate multi stage encoders. Uses a 35M parameter vision embedder to project raw 48x48 pixel patches directly to the LLM hidden dimension. Local multimodal development is becoming highly accessible on standard hardware. If you've spun up Gemma 4 12B locally, what setup are you using and what kind of throughput are you seeing?

Alok

25,717 görüntüleme • 3 ay önce

BREAKING: SpaceXAI has released another major new update for Grok Build (v1.0.19) This update improves background task handling, adds transparent terminal themes, worktree support for headless sessions, and better session/dashboard controls. It also fixes UI freezes, crashes, feedback issues, clickable URLs, and makes the first prompt after login faster. Breaking Changes • Scheduled /loop tasks always run in the background; they no longer inject turns into your conversation. Features • New terminal theme option makes backgrounds transparent so the terminal's own colors show through. • MCP servers blocked by organization policy now show clear messages and are refused before any config change. • Session resume now tells the model what loops, subagents, and workflows were still running. • Dashboard now shows newly dispatched sessions immediately instead of waiting for the store. • Headless sessions (grok -p) now support the --worktree flag to run in a separate git worktree. • Dashboard now says 'Open session' instead of 'Add session' for the session picker button. • Multi-line bash commands now render with proper wrapping and highlighting when opening the block viewer. • /usage now works from the dashboard and shows account allowance when no session is active. Bug Fixes • Fixed mid-turn UI freezes when the terminal stops reading output. • Fixed crashes that occurred when the terminal pane was closed while grok was exiting. • MCP server list in minimal mode now correctly shows policy-blocked servers. • URLs that wrap across multiple lines inside quotes or lists are now fully clickable. • The welcome screen composer now grows taller when you paste multi-line text. • Enterprise policy files are no longer deleted on startup when your team login is stored at a custom GROK_AUTH_PATH. • /feedback now drops unsupported images with a notice (matching the modal) and never loses your report text on save errors. • Feedback drafts keep their paragraph breaks when updated, and the modal no longer switches tabs unexpectedly. • Turn summary lines no longer lose their spacing after background tasks finish. • Thinking blocks no longer stay expanded after switching between fullscreen and minimal modes. Performance • First prompt after login is faster when MCP servers are configured; they connect in the background. Download Grok Build: Update to the latest Alpha release: grok update --alpha Update to the latest Stable release: grok update
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BREAKING: SpaceXAI has released another major new update for Grok Build (v1.0.19) This update improves background task handling, adds transparent terminal themes, worktree support for headless sessions, and better session/dashboard controls. It also fixes UI freezes, crashes, feedback issues, clickable URLs, and makes the first prompt after login faster. Breaking Changes • Scheduled /loop tasks always run in the background; they no longer inject turns into your conversation. Features • New terminal theme option makes backgrounds transparent so the terminal's own colors show through. • MCP servers blocked by organization policy now show clear messages and are refused before any config change. • Session resume now tells the model what loops, subagents, and workflows were still running. • Dashboard now shows newly dispatched sessions immediately instead of waiting for the store. • Headless sessions (grok -p) now support the --worktree flag to run in a separate git worktree. • Dashboard now says 'Open session' instead of 'Add session' for the session picker button. • Multi-line bash commands now render with proper wrapping and highlighting when opening the block viewer. • /usage now works from the dashboard and shows account allowance when no session is active. Bug Fixes • Fixed mid-turn UI freezes when the terminal stops reading output. • Fixed crashes that occurred when the terminal pane was closed while grok was exiting. • MCP server list in minimal mode now correctly shows policy-blocked servers. • URLs that wrap across multiple lines inside quotes or lists are now fully clickable. • The welcome screen composer now grows taller when you paste multi-line text. • Enterprise policy files are no longer deleted on startup when your team login is stored at a custom GROK_AUTH_PATH. • /feedback now drops unsupported images with a notice (matching the modal) and never loses your report text on save errors. • Feedback drafts keep their paragraph breaks when updated, and the modal no longer switches tabs unexpectedly. • Turn summary lines no longer lose their spacing after background tasks finish. • Thinking blocks no longer stay expanded after switching between fullscreen and minimal modes. Performance • First prompt after login is faster when MCP servers are configured; they connect in the background. Download Grok Build: Update to the latest Alpha release: grok update --alpha Update to the latest Stable release: grok update

DogeDesigner

437,976 görüntüleme • 10 gün önce

We are in an insane run of open-weight drops. Every modality, open source is winning. This is what an open source AI summer ☀️ looks like: 🧠 LLMs & Reasoning → DeepSeek-V4-Flash-0731 (my king 👑): 304B MoE refresh, Terminal-Bench 2.1 jumps 61.8→82.7 over the preview, DeepSWE 7.3→54.4. Closes in on Opus-4.8 on Agents' Last Exam (25.2 vs 25.7). MIT. → Muse-Glimmer-30B, from Meta (they are back!!): their first open agentic model. ~29.6B dense + perception encoder, 131k+ context, built to run fully local, no cloud. Apache 2.0. → Liquid AI LFM2.5-2.6B: 2.69B params, 131k context, 220 tok/s on an M5 Max in under 2.5GB RAM. Competitive with models 4x larger on agentic tasks. → inclusionAI Ling-3.0-flash: 124B total, only 5.1B active, ~12% the size of their old 1T flagship Ring-2.6, matches it on key benchmarks. MIT. → inclusionAI Ling-3.0-tiny: 7.9B total, 1.3B active, 86-90 tok/s on an M4 Pro MacBook at ~8GB peak memory. MIT. → NVIDIA Nemotron-3.5-Lightning-30B-A3B: hybrid Mamba-2+MoE+Attention, up to 1M context, runs on a single H100 or DGX Spark, SWE-bench Verified 52.8. → deepgrove maple-preview: 20B-A1B ternary-weight reasoner, 218 tok/s on a Mac mini M4, 5.3GB checkpoint. MIT. → BigBang-v1 (endless-frontier): fine-tuned from Qwen3.6-35B-A3B via a self-evolving generator/critic synthetic-data loop. Lands aggregate performance between DeepSeek V4 Flash (284B) and V4 Pro (1.6T), at 35B. Apache 2.0. 🎬 Video → MiniMax-H3: 33B dense omni model, native stereo audio, up to 2K/15s. 3.6k+ likes already. → Minimax-H3-Turbo (lightx2v): Apache-2.0 turbo distillation of H3 for fast inference. → Lightricks LTX-2.5: image-to-video update, custom Gemma-4-12B text encoder, a markedly stronger distilled model. 🔊 Voice → NVIDIA NemotronLabs VoiceChat-11B: full-duplex speech-to-speech, ~450ms turn-taking, #2 on open VoiceBench, and the first open full-duplex model with live tool-calling mid-conversation. 🛡️ Safety → Mistral Shieldstral-1.0-3B: 3B multimodal guardrail that takes your safety policy as plain text instead of fixed categories. Beats LlamaGuard-4-12B and ShieldGemma-9B on HarmBench (99.4) and ToxicChat (84.1) at a fraction of the size. Apache 2.0.
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We are in an insane run of open-weight drops. Every modality, open source is winning. This is what an open source AI summer ☀️ looks like: 🧠 LLMs & Reasoning → DeepSeek-V4-Flash-0731 (my king 👑): 304B MoE refresh, Terminal-Bench 2.1 jumps 61.8→82.7 over the preview, DeepSWE 7.3→54.4. Closes in on Opus-4.8 on Agents' Last Exam (25.2 vs 25.7). MIT. → Muse-Glimmer-30B, from Meta (they are back!!): their first open agentic model. ~29.6B dense + perception encoder, 131k+ context, built to run fully local, no cloud. Apache 2.0. → Liquid AI LFM2.5-2.6B: 2.69B params, 131k context, 220 tok/s on an M5 Max in under 2.5GB RAM. Competitive with models 4x larger on agentic tasks. → inclusionAI Ling-3.0-flash: 124B total, only 5.1B active, ~12% the size of their old 1T flagship Ring-2.6, matches it on key benchmarks. MIT. → inclusionAI Ling-3.0-tiny: 7.9B total, 1.3B active, 86-90 tok/s on an M4 Pro MacBook at ~8GB peak memory. MIT. → NVIDIA Nemotron-3.5-Lightning-30B-A3B: hybrid Mamba-2+MoE+Attention, up to 1M context, runs on a single H100 or DGX Spark, SWE-bench Verified 52.8. → deepgrove maple-preview: 20B-A1B ternary-weight reasoner, 218 tok/s on a Mac mini M4, 5.3GB checkpoint. MIT. → BigBang-v1 (endless-frontier): fine-tuned from Qwen3.6-35B-A3B via a self-evolving generator/critic synthetic-data loop. Lands aggregate performance between DeepSeek V4 Flash (284B) and V4 Pro (1.6T), at 35B. Apache 2.0. 🎬 Video → MiniMax-H3: 33B dense omni model, native stereo audio, up to 2K/15s. 3.6k+ likes already. → Minimax-H3-Turbo (lightx2v): Apache-2.0 turbo distillation of H3 for fast inference. → Lightricks LTX-2.5: image-to-video update, custom Gemma-4-12B text encoder, a markedly stronger distilled model. 🔊 Voice → NVIDIA NemotronLabs VoiceChat-11B: full-duplex speech-to-speech, ~450ms turn-taking, #2 on open VoiceBench, and the first open full-duplex model with live tool-calling mid-conversation. 🛡️ Safety → Mistral Shieldstral-1.0-3B: 3B multimodal guardrail that takes your safety policy as plain text instead of fixed categories. Beats LlamaGuard-4-12B and ShieldGemma-9B on HarmBench (99.4) and ToxicChat (84.1) at a fraction of the size. Apache 2.0.

Victor M

55,281 görüntüleme • 1 ay önce

You don't need a GPU for fast studio grade voice cloning anymore. Qwen3 TTS (1.7B Q4_K_M) + mainline llama.cpp is officially the fastest way to generate zero shot voice clones using 100% pure CPU execution. Following up on my last post where we ran the Q8 model on a GPU, we just took local C++ voice synthesis a massive step further. The open source community quantized Alibaba's SOTA Qwen3 TTS model down to Q4_K_M GGUF, completely freeing local audio pipelines from dedicated graphics hardware. Here is the real world benchmark and hardware breakdown of running SOTA voice cloning on CPU: # Architecture & Model Setup Using Qwen3-TTS-12Hz-1.7B-Base-Q4_K_M.gguf paired with the 8 bit multimodal projector (mmproj-Q8_0.gguf), llama.cpp executes the entire pipeline in pure C++. No PyTorch, no CUDA dependencies, and no VRAM bottlenecks. # Real-World Memory Footprint - Baseline RAM: 1.6 GB system idle. - Peak Generation RAM: 8 GB RAM during active voice synthesis. - Requirement: Any basic machine with at least 8 GB of system RAM can run this easily. # Real World CPU Benchmarks - Google Colab Free Tier (Throttled 2 Core CPU): Synthesizes a 5 sec studio quality audio clip (~8 words) in 45 seconds. - Modern Consumer CPU (Intel i5/i7 13th/14th Gen or AMD Ryzen 7000/9000): generation should drop to 5 to 20 seconds (nearly 1:1 real-time generation speed!). # Zero Shot Voice Cloning Quality Pass any 5 to 20 second .wav audio sample to the C++ engine using the --tts-speaker-file flag. It yields clean, natural sounding cloned speech with virtually zero quality loss compared to unquantized FP16 weights. To make testing seamless, I built an updated zero config Google Colab notebook. It pulls the official pre built llama.cpp CPU binaries (zero compilation time!) launches a live Gradio web app right in your browser. Record a 5 second clip from your mic (or drop a .mp3, .wav file), type text, and generate cloned audio on CPU. Native C++ audio models are making edge based, offline AI voice agents a reality. Links to the free Q4 CPU Colab notebook and the Q4_K_M GGUF HuggingFace repository are in the replies below! Which models have you been running on your CPUs? What CPU hardware are you using for local inference?
0:16

You don't need a GPU for fast studio grade voice cloning anymore. Qwen3 TTS (1.7B Q4_K_M) + mainline llama.cpp is officially the fastest way to generate zero shot voice clones using 100% pure CPU execution. Following up on my last post where we ran the Q8 model on a GPU, we just took local C++ voice synthesis a massive step further. The open source community quantized Alibaba's SOTA Qwen3 TTS model down to Q4_K_M GGUF, completely freeing local audio pipelines from dedicated graphics hardware. Here is the real world benchmark and hardware breakdown of running SOTA voice cloning on CPU: # Architecture & Model Setup Using Qwen3-TTS-12Hz-1.7B-Base-Q4_K_M.gguf paired with the 8 bit multimodal projector (mmproj-Q8_0.gguf), llama.cpp executes the entire pipeline in pure C++. No PyTorch, no CUDA dependencies, and no VRAM bottlenecks. # Real-World Memory Footprint - Baseline RAM: 1.6 GB system idle. - Peak Generation RAM: 8 GB RAM during active voice synthesis. - Requirement: Any basic machine with at least 8 GB of system RAM can run this easily. # Real World CPU Benchmarks - Google Colab Free Tier (Throttled 2 Core CPU): Synthesizes a 5 sec studio quality audio clip (~8 words) in 45 seconds. - Modern Consumer CPU (Intel i5/i7 13th/14th Gen or AMD Ryzen 7000/9000): generation should drop to 5 to 20 seconds (nearly 1:1 real-time generation speed!). # Zero Shot Voice Cloning Quality Pass any 5 to 20 second .wav audio sample to the C++ engine using the --tts-speaker-file flag. It yields clean, natural sounding cloned speech with virtually zero quality loss compared to unquantized FP16 weights. To make testing seamless, I built an updated zero config Google Colab notebook. It pulls the official pre built llama.cpp CPU binaries (zero compilation time!) launches a live Gradio web app right in your browser. Record a 5 second clip from your mic (or drop a .mp3, .wav file), type text, and generate cloned audio on CPU. Native C++ audio models are making edge based, offline AI voice agents a reality. Links to the free Q4 CPU Colab notebook and the Q4_K_M GGUF HuggingFace repository are in the replies below! Which models have you been running on your CPUs? What CPU hardware are you using for local inference?

Alok

103,956 görüntüleme • 1 ay önce

AI Is Moving Beyond “Generating Videos” — Toward “Generating Worlds” Over the past two years, AI video models have advanced at an astonishing pace. From Runway and Pika to Sora and Veo, AI-generated videos have become increasingly realistic and more consistent with the physical laws of the real world. Many people believe the next objective is simply to generate videos that are longer, sharper, and more lifelike. But if we take a step back, we can see that the real transformation is not happening in video itself. It is happening in world models. What Is a World Model? In 1943, psychologist Kenneth Craik proposed an idea that would influence artificial intelligence research for decades. He argued that the human brain does not merely react to the outside world. Instead, it maintains an internal model of how the world works. Because we have this internal model, we can predict the outcome of an action before we actually take it. Before crossing a road, we estimate whether a car will pass by. Before catching a ball, we predict its trajectory. These abilities come from continuously simulating the world in our minds, rather than relying entirely on trial and error. This idea later became known by a more formal term: World Model. A world model does not describe a single image or a fixed video clip. It is an internal representation capable of continuously simulating the rules and dynamics of the real world. Why Is AI Research Turning Toward World Models? Because predicting “what comes next” is becoming increasingly central to how AI systems work. Language models predict the next token. Image models predict the next step in the denoising process. Video models predict the next frame. A world model, however, attempts to predict something broader: What should the world look like in the next moment? In 2018, David Ha and Jürgen Schmidhuber proposed in their paper World Models that an intelligent agent could first learn a model of the world, and then use that internal model to plan its actions. The Dreamer series later demonstrated that many complex tasks could be learned by training agents inside an “imagined world.” At the same time, the development of video models such as Sora and Veo led researchers to another realization: A model capable of continuously generating video has already learned, at least implicitly, many of the rules governing the real world. As a result, these two research directions have gradually begun to converge. But Video Is Not Yet a World This is where the distinction is often misunderstood. For a world model to support meaningful real-time interaction, it must solve several critical problems. Most video models today are essentially answering one question: What should the next frame look like? A true world model needs to answer much more: What happens if I take one step forward? If I walk behind a building and then return, will the building still be there? If I suddenly change the camera angle, will the entire space remain consistent? If I enter a command such as: “Summon a dragon.” Will the world respond immediately? In other words, a world model must do more than generate content. It must understand space. It must understand time. It must understand causality. And it must understand interaction. Moving from watching to participating is where the real difficulty of world models begins. World Models Are Entering the Interactive Era One of the latest attempts in this direction is Alaya World, recently open-sourced by Alaya World, or Alaya Lab. Instead of generating a fixed video clip, it generates a world that users can explore in real time. Users can begin with text, an image, or a video, enter the generated scene, move freely through it, and introduce new prompts at any moment during generation. The world responds immediately. According to the publicly released information, Alaya World provides: Real-time streaming generation at 720p and 24 FPS Stable continuous exploration for more than one minute The ability to switch prompts and trigger skills or events during generation Model weights and inference code released under the Apache 2.0 License Training code and datasets planned for future release What makes these capabilities important is not simply the technical specifications. It is that the generated “world” can now support continuous interaction. The official demo shows that users can genuinely control, transform, and explore the generated environment. AI Is Evolving From a Tool Into an Environment Over the past few years, most discussions around AI have focused on content generation. Generating text. Generating images. Generating videos. But world models raise a fundamentally different question: Can AI generate an environment that people can inhabit, explore, and continuously evolve? If the answer is yes, the impact will extend far beyond video generation. Game development, robotics training, embodied intelligence, digital twins, virtual production, and many other fields could be transformed by the development of world models. World models are still at a very early stage. Yet from Craik’s proposal of an internal mental model more than eighty years ago to the emergence of today’s interactive world-generation systems, a clear evolutionary path is beginning to take shape. Perhaps what AI is ultimately learning has never been limited to images, videos, or language. Perhaps it is learning the world itself. References GitHub: Technical Report:
0:10

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

雪踏乌云

113,347 görüntüleme • 2 ay önce

BREAKING: SpaceXAI has released another major new update for Grok Build (v1.0.22) This update is focused on smoother workflows, better safety and improved reliability. It adds stronger desktop and MCP integration, clearer file-edit previews, a redesigned dashboard and better subagent support. It also fixes problems with sessions, skills, authentication and background commands while making sessions faster to open and resume. Features Built-in agent tools now take precedence over user MCP servers when tool names collide. Desktop app tools are now available through a dedicated first-party MCP server. Remote control pickers can now group servers by device using announced device identity. edit_file diffs now show real file line numbers instead of starting at line 1. Permission prompts for file edits now auto-expand the diff row so you can see the change before approving. Sending a message to a finished subagent now continues that same subagent instead of failing. Background subagent completion messages now show the actual output instead of just a pointer to run the tool. Dashboard now shows a unified header with a location picker and an actions row for creating agents or resuming previous sessions. New grok-workspaced daemon mode lets a long-lived process expose folders to the Computer Hub, separate from the Desktop-supervised sidecar. Bug Fixes MCP connectors now correctly show when they need re-authentication, even if no tools are listed. Extensions modal shortcuts now work when the tab bar is focused, and invalid actions on headers show helpful messages. The Send now button and shortcuts now work correctly when an automatic background turn is running. Resumed sessions now show the exact text you typed for mid-turn follow-ups instead of the wrapped system message. The /skills command now updates the model with newly installed skills, even when written from another NFS client. Auto mode no longer instantly runs destructive git checkout -- commands; they now go through the model for safety. Fixed a crash that could occur when restoring pasted content after certain skill injections or rewinds. Subagents and workflows now respect the same bash timeout and auto-background settings configured for the parent session. Fixed model backend selection so an explicit chat_completions setting in config.toml is no longer overwritten by same-slug siblings. Slash command advertisements no longer spam repeatedly when your current directory is your home folder. Foreground shell commands no longer produce spurious background-task reminders or incorrect kill results. Queued follow-up buttons now appear in the order [Send now][edit][Cancel]. /compact instructions in the pager are now passed to the compaction backend. Bare /compact is unchanged. /goal, and resume after pressing Esc, now correctly starts the goal planner instead of failing with “Planning failed.” run_terminal_cmd now correctly tells the model that foreground commands are backgrounded after approximately 15 seconds instead of the 120-second timeout value. Skill announcements no longer repeat the same list of available skills multiple times in a session. Monitor events in multi-session processes now correctly wake idle sessions instead of being buffered until the next prompt. Performance Opening or resuming sessions no longer pauses the interface while reading MCP configuration files. Download Grok Build: Update to the latest Alpha release: grok update --alpha Update to the latest Stable release: grok update
0:12

BREAKING: SpaceXAI has released another major new update for Grok Build (v1.0.22) This update is focused on smoother workflows, better safety and improved reliability. It adds stronger desktop and MCP integration, clearer file-edit previews, a redesigned dashboard and better subagent support. It also fixes problems with sessions, skills, authentication and background commands while making sessions faster to open and resume. Features Built-in agent tools now take precedence over user MCP servers when tool names collide. Desktop app tools are now available through a dedicated first-party MCP server. Remote control pickers can now group servers by device using announced device identity. edit_file diffs now show real file line numbers instead of starting at line 1. Permission prompts for file edits now auto-expand the diff row so you can see the change before approving. Sending a message to a finished subagent now continues that same subagent instead of failing. Background subagent completion messages now show the actual output instead of just a pointer to run the tool. Dashboard now shows a unified header with a location picker and an actions row for creating agents or resuming previous sessions. New grok-workspaced daemon mode lets a long-lived process expose folders to the Computer Hub, separate from the Desktop-supervised sidecar. Bug Fixes MCP connectors now correctly show when they need re-authentication, even if no tools are listed. Extensions modal shortcuts now work when the tab bar is focused, and invalid actions on headers show helpful messages. The Send now button and shortcuts now work correctly when an automatic background turn is running. Resumed sessions now show the exact text you typed for mid-turn follow-ups instead of the wrapped system message. The /skills command now updates the model with newly installed skills, even when written from another NFS client. Auto mode no longer instantly runs destructive git checkout -- commands; they now go through the model for safety. Fixed a crash that could occur when restoring pasted content after certain skill injections or rewinds. Subagents and workflows now respect the same bash timeout and auto-background settings configured for the parent session. Fixed model backend selection so an explicit chat_completions setting in config.toml is no longer overwritten by same-slug siblings. Slash command advertisements no longer spam repeatedly when your current directory is your home folder. Foreground shell commands no longer produce spurious background-task reminders or incorrect kill results. Queued follow-up buttons now appear in the order [Send now][edit][Cancel]. /compact instructions in the pager are now passed to the compaction backend. Bare /compact is unchanged. /goal, and resume after pressing Esc, now correctly starts the goal planner instead of failing with “Planning failed.” run_terminal_cmd now correctly tells the model that foreground commands are backgrounded after approximately 15 seconds instead of the 120-second timeout value. Skill announcements no longer repeat the same list of available skills multiple times in a session. Monitor events in multi-session processes now correctly wake idle sessions instead of being buffered until the next prompt. Performance Opening or resuming sessions no longer pauses the interface while reading MCP configuration files. Download Grok Build: Update to the latest Alpha release: grok update --alpha Update to the latest Stable release: grok update

DogeDesigner

61,926 görüntüleme • 7 gün önce

here's how you can scale to 10k/month on tiktok shop with slideshows so most affiliates are stuck doing the same thing every day. film, edit, post, make like $60-80 in commissions. and the problem isn't effort, it's that your time is the bottleneck. you can't film 15 videos a day. so your output caps and your money caps with it. meanwhile there's a guy in canada who made $18k in his first two weeks. never filmed anything. never showed his face. never even held a product. canada doesn't even support tiktok shop, he's running us tiktok from there. all slideshows. and when i say slideshows i mean 4-7 images posted like a normal tiktok with a product link attached. that's it. someone buys off it, you get paid. making one takes maybe 5% of the skill of making a video. now here's the catch. tiktok gated the feature. most accounts can't attach products to photo posts, you'll get a "product links not available in photo mode" error. some accounts randomly have it. quick way to check: open tiktok studio on desktop (has to be desktop, mobile won't work), hit upload. if you see "videos or photos" you have access. click photos, upload your images, attach the link. if you only see video upload, you're not in yet. uk and europe are getting it randomly right now, us is only top gmv creators for the moment. check every day because tiktok doesn't tell you when you get it, the option just shows up. worst case you build the system now and execute day one when your account unlocks. because the people getting random access with no clue what they're doing are posting random images and making nothing. the format is easy, that doesn't mean it's mindless. the format itself is one thing repeated over and over: pain point first, product second. slide one hits an insecurity. back acne from the gym. car turning into an oven all summer. makeup that never sits right. the person scrolling sees it and goes "wait that's literally me." middle slides twist the knife a bit more. then "so i tried this thing everyone's using," show it working, before and after, and the last slide is just the offer. sale, free shipping, link below. done. that structure sells cold traffic. people who've never seen the product buy off one slideshow because you sold the problem, not the product. and here's the part most people don't clock when they're scrolling past these: none of it is real. the guy holding the ceiling fan doesn't own a ceiling fan. the back acne was generated onto the model. the smoothies were never made. it's all ai images. which kills every excuse at once. no face, no product in hand, no waiting on shipping, no country restrictions. the workflow is dumb simple. screenshot a slideshow style you like, drop it in chatgpt, say "make me 3x4 images in this style." then describe your pain point scene. couple walking to a car that's been baking in the sun, whatever it is. then grab the product image off the tiktok listing, feed it in, "now show them using this." repeat per slide. no fancy prompts, the reference images do all the work. two small things that matter more than they should. keep everything 3:4 or the mixed sizes make the whole post look off. and don't bake text into the images, add it inside tiktok. native text looks like a person posted it. baked text looks like an ad. people can feel the difference even if they can't explain it. if you want it to look even more real, take a photo of your actual kitchen or desk and only generate the product into it. real room, ai product. nobody can tell. for ideas, don't invent anything. steal structure, swap one variable. the number one post in the uk right now is a simpsons style slideshow about linen trousers. take that exact skeleton and run it with a sports set or summer shorts instead. same format, different product, suddenly it's unsaturated again. or take viral videos and turn them into slides. one guy took a viral video about a sink drainage thing, rebuilt it as images, and beat the original with 1.7m views. first week on the platform. the biggest edge though is going backwards. pull products that went viral 2-3 months ago, take the exact hooks that already converted millions of views, and rerun them as slideshows. nobody's done them in this format because the format barely exists. you're not testing ideas, you're re-releasing proven hits. then it just comes down to volume. no filming, no editing, no product costs means each post is basically free. so post 10-15 a day. most will flop, who cares. one will do 500k views in two days and when it does you remake it 50 times and drain it. every gated feature on tiktok runs the same cycle. early access prints, wide rollout saturates, then it's just another format everyone does. slideshows are still in the first part of that cycle.
0:11

here's how you can scale to 10k/month on tiktok shop with slideshows so most affiliates are stuck doing the same thing every day. film, edit, post, make like $60-80 in commissions. and the problem isn't effort, it's that your time is the bottleneck. you can't film 15 videos a day. so your output caps and your money caps with it. meanwhile there's a guy in canada who made $18k in his first two weeks. never filmed anything. never showed his face. never even held a product. canada doesn't even support tiktok shop, he's running us tiktok from there. all slideshows. and when i say slideshows i mean 4-7 images posted like a normal tiktok with a product link attached. that's it. someone buys off it, you get paid. making one takes maybe 5% of the skill of making a video. now here's the catch. tiktok gated the feature. most accounts can't attach products to photo posts, you'll get a "product links not available in photo mode" error. some accounts randomly have it. quick way to check: open tiktok studio on desktop (has to be desktop, mobile won't work), hit upload. if you see "videos or photos" you have access. click photos, upload your images, attach the link. if you only see video upload, you're not in yet. uk and europe are getting it randomly right now, us is only top gmv creators for the moment. check every day because tiktok doesn't tell you when you get it, the option just shows up. worst case you build the system now and execute day one when your account unlocks. because the people getting random access with no clue what they're doing are posting random images and making nothing. the format is easy, that doesn't mean it's mindless. the format itself is one thing repeated over and over: pain point first, product second. slide one hits an insecurity. back acne from the gym. car turning into an oven all summer. makeup that never sits right. the person scrolling sees it and goes "wait that's literally me." middle slides twist the knife a bit more. then "so i tried this thing everyone's using," show it working, before and after, and the last slide is just the offer. sale, free shipping, link below. done. that structure sells cold traffic. people who've never seen the product buy off one slideshow because you sold the problem, not the product. and here's the part most people don't clock when they're scrolling past these: none of it is real. the guy holding the ceiling fan doesn't own a ceiling fan. the back acne was generated onto the model. the smoothies were never made. it's all ai images. which kills every excuse at once. no face, no product in hand, no waiting on shipping, no country restrictions. the workflow is dumb simple. screenshot a slideshow style you like, drop it in chatgpt, say "make me 3x4 images in this style." then describe your pain point scene. couple walking to a car that's been baking in the sun, whatever it is. then grab the product image off the tiktok listing, feed it in, "now show them using this." repeat per slide. no fancy prompts, the reference images do all the work. two small things that matter more than they should. keep everything 3:4 or the mixed sizes make the whole post look off. and don't bake text into the images, add it inside tiktok. native text looks like a person posted it. baked text looks like an ad. people can feel the difference even if they can't explain it. if you want it to look even more real, take a photo of your actual kitchen or desk and only generate the product into it. real room, ai product. nobody can tell. for ideas, don't invent anything. steal structure, swap one variable. the number one post in the uk right now is a simpsons style slideshow about linen trousers. take that exact skeleton and run it with a sports set or summer shorts instead. same format, different product, suddenly it's unsaturated again. or take viral videos and turn them into slides. one guy took a viral video about a sink drainage thing, rebuilt it as images, and beat the original with 1.7m views. first week on the platform. the biggest edge though is going backwards. pull products that went viral 2-3 months ago, take the exact hooks that already converted millions of views, and rerun them as slideshows. nobody's done them in this format because the format barely exists. you're not testing ideas, you're re-releasing proven hits. then it just comes down to volume. no filming, no editing, no product costs means each post is basically free. so post 10-15 a day. most will flop, who cares. one will do 500k views in two days and when it does you remake it 50 times and drain it. every gated feature on tiktok runs the same cycle. early access prints, wide rollout saturates, then it's just another format everyone does. slideshows are still in the first part of that cycle.

Mufasa

13,583 görüntüleme • 29 gün önce

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!
0:12

This is my "feel the AGI" moment: I used GPT-5.6 Sol to train my own autocorrect model that outperforms GPT-5.6 Sol (wtf??) I have no ML background. I have no idea what I'm doing. I just kept pushing Sol until it spat out a SOTA model. And I spent $0. The motivation: Years of talking to AI have made me terrible at typing. Rather than fix my skill issue, I decided to throw more AI at it. My idea was: instead of autocorrect that interrupts my flow, I want to type fast with mistakes and have AI clean it up after. I wanted the smallest local model possible, for speed, for battery life, for science! So I decided to train my own. Inspired by Andrej Karpathy’s autoresearch, I ran Codex /goal with this setup: pick an experiment, try it, record the results to a doc, throw it out if it fails, and plan the next experiment without repeating failures. I gave a few examples that had to pass, tight latency targets, and let it run. Sol did some amazing things. First, it scanned benchmarks and shortlisted base models: Qwen 3.5, Gemma 4, Liquid LFM 2.5. It found a dataset on HuggingFace for typed text. Then it built a simulator for fingers striking a Mac keyboard, modeling the physical layout with a Gaussian distribution around each key. It simulated striking the wrong key, wrong order, fat-fingering, etc. With the models + data + simulator, it fine-tuned using MLX right on my MacBook. It had a working prototype within an hour! But accuracy was pretty poor. — Problem 1: Tokenization Sol read papers, ran tests, and identified that the tokenizer was the bottleneck. Tokenization makes typos hard for the model to see, so it memorizes mappings instead of using its language priors. Sol tried ByT5, Google’s tokenizer-free byte-level LLM. This made a big improvement, but the model is old and lacked the knowledge needed to reach Sol performance. Sol dug deeper and realized a tokenizer-free model isn’t needed; instead, it used T5Gemma, an encoder-decoder model. This can understand the input deeply before producing output, and furthermore, Sol could post-train the encoder to improve performance. This gave a much higher ceiling. — Problem 2: Loss function Now the model was correcting some typos perfectly, but ignoring most. Sol realized that standard cross-entropy loss was teaching the model to avoid edits, because the vast majority of characters in the training data were left unmodified. The fix was wild: Sol wrote a custom loss function that byte-aligns the source and target strings, uses a dynamic programming algorithm to compute the minimum edits between the two, then weights correct edits much higher than copies. After a lot of tuning, this dramatically improved accuracy. — Problem 3: Autoregression One failure mode remained: if the model made a mistake, it couldn’t backtrack. It could only predict the next token. Teaching it to “think” like a reasoning model would solve this, but would be far too slow. Sol found a beautiful solution: instead of greedily predicting the next token, beam search over all possibilities. This parallelizes the exploration instead of one linear chain-of-thought. At the end, choose the path with highest cumulative log probability. This worked great, but made the experience worse, since the user wouldn’t see progress until the whole search was done. To fix this, Sol made a clever observation: after each search step, the longest common prefix among surviving branches is guaranteed to appear in the final result, so it can be displayed immediately. As the search progresses, weaker paths are dropped and the prefix grows, so the user sees continuous progress. Sol built all this as a custom MLX pipeline that does the parallel decoding on the MacBook GPU, with just ~40ms TTFT. It’s crazy fast and entirely local. — Final eval (error reduction rate, higher is better): - Apple autocorrect: 49.66% - GPT-5.6 Luna: 82.47% - GPT-5.6 Terra: 87.64% - GPT-5.6 Sol: 90.56% - Our model (1.7B): 91.02% Final cost: - 1 quota reset (thanks Tibo) - $0 (And yes, I verified there's no cheating. In fact, we test words scrubbed from the training data to prove the model isn’t memorizing) There were a ton more details and tangents I could write about: contrastive learning, GRPO, DPO, dynamic masking, and more. Sol is a fascinating and creative model. It blew my mind so many times. Don’t let a lack of experience stop you: Sol makes AI experiments accessible to anyone!

Anshu

179,451 görüntüleme • 2 ay önce

SERAPH: A new round of major events in Diablo is about to begin! 🚀 Each new event is a new opportunity. Let's first review the achievements SERAPH has made before. 🏆 Seraph Achievements: Genesis NFT (Weapons) 0.06e ==》0.628e 💎 SERAPH Priority Pass Freemint ==》3.2e 🚀 In traditional GameFi, the issue of the death spiral of the initial NFT launch is often encountered. However, SERAPH achieved a tenfold floor price increase with a public sale of 0.06e and has stably maintained this position. This highlights the commendable tokenomics of the project. 📈 Additionally, SERAPH's gameplay is quite impressive. The Twitter community shows a high level of engagement, with many players sharing their gaming experiences. During the third testing phase, players were not just playing SERAPH for profit but were immersed in the game, experiencing the storyline and the thrill of gameplay. 🎮🔥 New Major Event: After the War of Light, the life fortress of Seraficia faces energy depletion. As chaos spreads, the Archangel Seraf crafted the World Ring from the souls of all life, a powerful shield against chaotic attacks. 🌌🛡️ To protect the hard-won peace, fallen heroes are bestowed with new souls, transcending the limits of time and space. Barbarians, Valkyries, Witches, and Necromancers reassemble on the lands of Seraficia. Reborn SOULs, the legends of heroes. 🌟 In the shimmering rune light, we summon the most outstanding legion of heroes to face this gradually collapsing world. ⚔️ >>Hero Series NFT The newly launched Hero Series PFP NFT primarily features human heroes such as Barbarians, Valkyries, Witches, and Necromancers. It also includes angels, demons, and other otherworldly races, showcasing numerous characters developed in the game so far. 🎭 Specific Acquisition Methods: Hero Series NFTs will be sold through a limited whitelist and public sale lottery. Whitelist distribution channels include: 【Veteran Players Exclusive】- Ongoing support from our veteran players; 【DC Exclusive】- New players participating in this DC event; 【Community Collaboration】- Communities collaborating with Seraph. 【Veteran Players Exclusive】 Veteran players will have their whitelist distribution rules, which can be followed in later project updates. 【DC Exclusive】 New players can acquire whitelist status by participating in various activities in DC, accumulating Soul shards through DC events. Ways to earn them include: 1⃣️ Participate in team-organized activities and games. 2⃣️ Create unique and high-quality content, such as videos, posts, art, to earn Soul shards. 3⃣️ During flash community events/tasks, active community members may discover additional Soul shards. 4⃣️ Interact with the Engage bot in the 💠│engage channel. 5⃣️ Warriors making genuine contributions to the community may receive random Soul shard rewards. 【Community Collaboration】 Members of gaming communities can participate in special activities in their own communities to obtain whitelist spots. T1 labs will also have a series of events to secure whitelist spots. 🤝 SERAPH Elite Test Server Note: The test server does not involve digital assets, and all data will be uniformly deleted after the test. 🧪 To welcome the new content, the game's official release includes a small-scale test. This test is mainly for early experience and bug testing, providing an opportunity to explore new features. Players are encouraged to actively participate and explore to get a glimpse of what's to come. 🌐 Test Start Time: February 5, 2024, 11:00 (UTC+8) 📅 Test Method: Limited invitation using activation codes 💌 Invitation Targets: Top-ranking users from the previous three game tests; Active users involved in SERAPH community building. 🎉 Test Content: Significant structural and numerical optimization of the economic system; All-new concepts for NFT treasures; Comprehensive adjustments to skill balance and numerous detailed optimizations and bug fixes, etc. This test is open for PC/IOS/Android, allowing testing across multiple platforms simultaneously. 🕹️📱💻 Research Analyst: yuyu
0:15

SERAPH: A new round of major events in Diablo is about to begin! 🚀 Each new event is a new opportunity. Let's first review the achievements SERAPH has made before. 🏆 Seraph Achievements: Genesis NFT (Weapons) 0.06e ==》0.628e 💎 SERAPH Priority Pass Freemint ==》3.2e 🚀 In traditional GameFi, the issue of the death spiral of the initial NFT launch is often encountered. However, SERAPH achieved a tenfold floor price increase with a public sale of 0.06e and has stably maintained this position. This highlights the commendable tokenomics of the project. 📈 Additionally, SERAPH's gameplay is quite impressive. The Twitter community shows a high level of engagement, with many players sharing their gaming experiences. During the third testing phase, players were not just playing SERAPH for profit but were immersed in the game, experiencing the storyline and the thrill of gameplay. 🎮🔥 New Major Event: After the War of Light, the life fortress of Seraficia faces energy depletion. As chaos spreads, the Archangel Seraf crafted the World Ring from the souls of all life, a powerful shield against chaotic attacks. 🌌🛡️ To protect the hard-won peace, fallen heroes are bestowed with new souls, transcending the limits of time and space. Barbarians, Valkyries, Witches, and Necromancers reassemble on the lands of Seraficia. Reborn SOULs, the legends of heroes. 🌟 In the shimmering rune light, we summon the most outstanding legion of heroes to face this gradually collapsing world. ⚔️ >>Hero Series NFT The newly launched Hero Series PFP NFT primarily features human heroes such as Barbarians, Valkyries, Witches, and Necromancers. It also includes angels, demons, and other otherworldly races, showcasing numerous characters developed in the game so far. 🎭 Specific Acquisition Methods: Hero Series NFTs will be sold through a limited whitelist and public sale lottery. Whitelist distribution channels include: 【Veteran Players Exclusive】- Ongoing support from our veteran players; 【DC Exclusive】- New players participating in this DC event; 【Community Collaboration】- Communities collaborating with Seraph. 【Veteran Players Exclusive】 Veteran players will have their whitelist distribution rules, which can be followed in later project updates. 【DC Exclusive】 New players can acquire whitelist status by participating in various activities in DC, accumulating Soul shards through DC events. Ways to earn them include: 1⃣️ Participate in team-organized activities and games. 2⃣️ Create unique and high-quality content, such as videos, posts, art, to earn Soul shards. 3⃣️ During flash community events/tasks, active community members may discover additional Soul shards. 4⃣️ Interact with the Engage bot in the 💠│engage channel. 5⃣️ Warriors making genuine contributions to the community may receive random Soul shard rewards. 【Community Collaboration】 Members of gaming communities can participate in special activities in their own communities to obtain whitelist spots. T1 labs will also have a series of events to secure whitelist spots. 🤝 SERAPH Elite Test Server Note: The test server does not involve digital assets, and all data will be uniformly deleted after the test. 🧪 To welcome the new content, the game's official release includes a small-scale test. This test is mainly for early experience and bug testing, providing an opportunity to explore new features. Players are encouraged to actively participate and explore to get a glimpse of what's to come. 🌐 Test Start Time: February 5, 2024, 11:00 (UTC+8) 📅 Test Method: Limited invitation using activation codes 💌 Invitation Targets: Top-ranking users from the previous three game tests; Active users involved in SERAPH community building. 🎉 Test Content: Significant structural and numerical optimization of the economic system; All-new concepts for NFT treasures; Comprehensive adjustments to skill balance and numerous detailed optimizations and bug fixes, etc. This test is open for PC/IOS/Android, allowing testing across multiple platforms simultaneously. 🕹️📱💻 Research Analyst: yuyu

T1 Labs

12,551 görüntüleme • 2 yıl önce

As we prepare to launch several projects, we're eager to provide a general update to our community. We are steadily approaching our end goal, thanks to the daily progress we're making toward our vision. Achieving our objectives will bring about a significant transformation in cross-chain interoperability and the flow of liquidity within protocols. This will address crucial challenges and drive mass adoption. Our future-focused approach and effective team collaboration keep us moving forward in an organized manner. Let’s delve deeper into the state of development of our current products and upcoming projects. Tao Bridge Starting with the Tao Bridge, which enables the #Bittensor community to unlock DeFi opportunities with their $TAO via a highly efficient blockchain like #MultiversX, known for its security, speed, and affordability. We deeply admire #Bittensor and believe a project like that is crucial for the future of not just the crypto space but also humanity, as it addresses the major challenges AI faces today: centralization, siloed and isolated work, which pose risks and hinder the technology's potential. We are committed to the vision of subnets and dynamic $TAO, convinced that this ecosystem is as groundbreaking as #Ethereum or #Bitcoin. We will continue to support #Bittensor wherever possible, and our bridge will also expand to other chains with Hatom V2. The TAO Bridge, deployed on and accessible through will launch on the Mainnet in 14 days, on March 27th. You can follow the countdown on the lending page at Given that our main priorities are security and stability, this period will be primarily focused on quality assurance to ensure a flawless Mainnet launch. The launch will also introduce TAO Liquid Staking at along with the integration of both $wTAO and $swTAO on the lending page. This allows #Bittensor users to leverage liquid stake, employ short or long strategies, among other DeFi strategies, or simply access stablecoin liquidity while maintaining exposure to their $TAO. Up to $1M will be distributed as additional incentives on top of the supply APYs at the launch of the $wTAO and $swTAO money markets, with $200K allocated for the first month specifically for bootstrapping. Initially, 70% of rewards will go to liquidity providers, and 30% to those using $HTM to boost their lending positions. This changes to a 50-50 split in the second month, and by the third month, all incentives are directed through the Booster. This approach encourages early participation and sustained engagement with $HTM. Introducing $TAO to #MultiversX will result in the creation of Liquidity Pools (LPs) on both AshSwap 🔥 and xExchange ⚡. These LPs will be incentivized by both entities, and Hatom will distribute extra rewards at launch. The goal is to make #MultiversX a one-stop hub for $TAO holders. Upon stabilizing the volumes, there will also be plans to integrate it on AshPerp 🔥. Furthermore, with the release of $USH, users will have the ability to mint it while retaining exposure to their $TAO. The TAO Bridge and TAO Liquid Staking smart contracts have been audited by Runtime Vеrification and @arda_project, while penetration testing and DevSecOps have been performed on our infrastructure by CertiK. We're excited to announce our exclusive partnership with TAONEW one of the top 5 validators on #Bittensor. TAONEW has been extremely helpful and supportive from day one. By sharing 50% of its service fee with its stakers, TAONEW enables Hatom to offer an optimized Staking APY to its users. Since our initial reference, #Bittensor has grown sevenfold, becoming the largest AI project in the crypto sphere. We reiterate our commitment to contribute to such technology and hope to address some of its current DeFi challenges. Syfy Moving forward, today marks a significant milestone, not only for our decentralized protocols but also for our development companies, which currently stand as the sole and primary contributors to the Hatom Labs and Soul Labs. We’re excited to unveil Syfy, the evolved identity of Hatom Labs and Soul Labs, now serving as the parent entity for our burgeoning development companies. Organization is crucial for scalability, which is why Syfy was established to cultivate an environment where our teams can collaborate more seamlessly, enhancing our effectiveness and efficiency. At the same time, we remain committed to upholding the financial independence of each project, supported by its own community of funding contributors. Feel free to explore our website at for more information! Additionally, don't forget to follow Syfy and explore their Genesis article highlighted in their initial post: Booster V2 The Booster V2 will introduce a range of new features and opportunities for $HTM holders: Optimized Position Boosting: Previously, boosting was done individually for each money market, necessitating $HTM token distribution and periodic rebalancing due to price fluctuations. With Booster V2, the system now considers the overall position, eliminating the need for manual rebalancing. Gas Fee Reduction: Booster V2 implements optimizations that result in reduced gas fees, making transactions more cost-effective for users. Incorporation of Governance: Users staking $HTM tokens gain voting rights directly within the Booster, allowing them to participate in governance decisions while maintaining their staked positions. (Note: Only $HTM tokens are considered for governance; LP tokens are not included.) Enhanced Boosting Mechanism: The Booster V2 enables LP Tokens to boost positions within the Booster, leveraging trading fees from swaps and farm incentives while boosting lending positions. Smart Contract Completion: The Booster smart contract has been completed and audited by @arda_project, ensuring security and reliability. Frontend Implementation: The frontend design for Booster V2 has been successfully implemented, providing users with an intuitive interface. Collaboration with xExchange: Exploration is ongoing for collaboration with xExchange ⚡ to enable LP creation, farming, and meta-staking within the Booster. Upon finalization of testing, we will launch the Booster V2 on the devnet to gather community feedback and begin preparations for the mainnet release. Soul Before delving into Soul Labs's developments, it's essential to summarize its core functionality briefly: Soul Labs seamlessly connects different lending protocols and blockchains, facilitating lending and borrowing across platforms like Aave, Compound Labs, and Hatom Labs, consolidating liquidity and users' borrowing capabilities. Utilizing LayerZero Labs and other messaging layers for cross-chain communication, Soul Labs bypasses asset bridging or synthetics, unlocking novel DeFi strategies and solidifying its position as the ultimate solution for cross-lending dilemmas. Soul V1 will be permissionless, holding censorship-resistant features, incorporating multiple redundancy mechanisms, and providing support for various DApps. We're thrilled to announce that, following the launch of the Tao Bridge in 2-3 weeks, we will introduce the Soul Labs website. This platform has been meticulously crafted over 250 days to not only provide a comprehensive overview of our vision but also to offer an engaging and captivating experience that promises to be memorable. Regarding the app, significant progress has been made on the V1 protocol, including: Smart Contract Development and Testing: • Completion of the initial phase of smart contract development. • Conducting advanced testing to ensure the system's robustness. • Establishment of a fully functional proof of concept. Successful deployment and testing on the #Goerli (#Ethereum Testnet) and #Mumbai (#Polygon Testnet), leveraging LayerZero Labs for seamless operation. Feature Enhancement and Protocol Optimization: • Enhanced testing procedures to bolster system resilience. • Integration of advanced features and significant code refactoring for optimization. • Incorporation of various communication methods, including LayerZero Labs, Formerly Axelar, now at @axelar, Chainlink CCIP), and wormholecrypto, into Soul Labs framework, enhancing its resilience and flexibility. This allows Soul Labs to maintain operation through alternative protocols if the primary one is temporarily paused. Website Development and Documentation: • Nearing the completion of the v1 app, with final touches being applied. • The preparation of comprehensive V1 documentation and the Yellow Paper, available upon Soul Labs's public launch, offering detailed insights into the platform's infrastructure and capabilities. USH Recognizing the critical need for stable liquidity within the ecosystem, we have positioned ourselves at the forefront of providing a solution by introducing $USH, the first native, decentralized, and over-collateralized stablecoin on #MultiversX. As market conditions have improved, we have observed a growing demand for stablecoins in the ecosystem, evidenced by the utilization rate in the Lending Protocol spiking to over 90% several times in recent months. Therefore, our goal is to tackle the current challenges faced by users by creating a robust product that will not only help them hedge against market volatility but also open up better opportunities to trade the markets and generate yield. We're happy to unveil the $USH website, now live with a sleek and intuitive user interface, designed for ease of use, which ensures that interacting with the protocol is straightforward and accessible for all. You can access it now through this link: For the technical side, we’re advancing steadily and we’ve accomplished the following milestones: Lending Protocol Facilitator: • Coded the first version to support multiple discount factors for different collaterals. • Implemented tracking of borrowing effectiveness to enable earnings forecasting for the module and support minting processes. Isolated Pools Facilitator: • Coded the first version of Isolated Pools Facilitator. • Use of $EGLD or $sEGLD as collateral, with positions stored always in $EGLD to benefit the protocol through Liquid Staking and lending interest. • Virtual account implementation for converting $sEGLD earnings into $USH, functioning like liquidation where users deposit $USH for a higher amount of $HsELGD. Staking Module • Coded the first version of the Staking Module that allows users to stake and unstake without any restrictions. We're currently focusing our efforts on the following tasks: • Implementation of HTM Booster in the discount model in the Lending Protocol. • Implementation of different depeg strategies and brainstorming further potential “soft” depeg mechanisms. • Research and implementation of rewards model for Staking Module. • Research and implementation of Boosted Vaults Facilitator. • Review and stress-test the first version of the code. Upon launch, $USH will be integrated into various protocols and AMMs across the ecosystem, further increasing both its utility and liquidity. The opportunities will be vast, enabling users to engage in a wide range of activities such as yield farming, staking, and arbitrage, all while leveraging a stable and reliable asset. Regarding the USH Airdrop campaign, it will continue until the official launch of $USH planned for late Q2-early Q3, rewarding all users who have actively participated in the initiative. Hatom V2 It is clear by now that we are driven to build a more robust, interoperable, and secure DeFi space, removing the current barriers that hinder users' capabilities to seamlessly interact with different blockchains. Through Hatom V2, we will introduce Hatom's cross-chain architecture, designed from the ground up for interoperability. This approach will elevate the protocol to unprecedented levels, enabling its deployment across various blockchains and facilitating seamless connections between them through Soul. By enhancing interoperability, Hatom V2 aims to foster a more inclusive and accessible ecosystem. This expansion will not only broaden the protocol's reach but also significantly increase its flexibility and utility, allowing users to interact with a diverse range of assets and products across different chains. We’re thrilled to share that we are currently crafting the V2 redesign of the Hatom webpage. Anticipate a jaw-dropping transformation that will truly astonish, blending cutting-edge design with an unparalleled user experience, elevating it to a dynamic, interactive hub, and making every interaction more engaging. Good things take time, but we are confident that the release of V2 website will take place in the second quarter of this year and will officially mark the start of our journey into the cross-chain landscape. We are excited about the future and we truly believe that this will mark the beginning of a new era for Hatom. It's crucial for us to develop rapidly without sacrificing the quality or the security of each product. We're strategically allocating resources to ensure smooth progress in every area of our work. As we push forward, we believe that the launch of Soul Labs will be the most important milestone due to its massive potential and disruptive technology. We would like to thank you all for the unwavering support you've shown over the past few months; it truly fuels our passion to push daily and make strides toward achieving our ambitious goals.
0:11

As we prepare to launch several projects, we're eager to provide a general update to our community. We are steadily approaching our end goal, thanks to the daily progress we're making toward our vision. Achieving our objectives will bring about a significant transformation in cross-chain interoperability and the flow of liquidity within protocols. This will address crucial challenges and drive mass adoption. Our future-focused approach and effective team collaboration keep us moving forward in an organized manner. Let’s delve deeper into the state of development of our current products and upcoming projects. Tao Bridge Starting with the Tao Bridge, which enables the #Bittensor community to unlock DeFi opportunities with their $TAO via a highly efficient blockchain like #MultiversX, known for its security, speed, and affordability. We deeply admire #Bittensor and believe a project like that is crucial for the future of not just the crypto space but also humanity, as it addresses the major challenges AI faces today: centralization, siloed and isolated work, which pose risks and hinder the technology's potential. We are committed to the vision of subnets and dynamic $TAO, convinced that this ecosystem is as groundbreaking as #Ethereum or #Bitcoin. We will continue to support #Bittensor wherever possible, and our bridge will also expand to other chains with Hatom V2. The TAO Bridge, deployed on and accessible through will launch on the Mainnet in 14 days, on March 27th. You can follow the countdown on the lending page at Given that our main priorities are security and stability, this period will be primarily focused on quality assurance to ensure a flawless Mainnet launch. The launch will also introduce TAO Liquid Staking at along with the integration of both $wTAO and $swTAO on the lending page. This allows #Bittensor users to leverage liquid stake, employ short or long strategies, among other DeFi strategies, or simply access stablecoin liquidity while maintaining exposure to their $TAO. Up to $1M will be distributed as additional incentives on top of the supply APYs at the launch of the $wTAO and $swTAO money markets, with $200K allocated for the first month specifically for bootstrapping. Initially, 70% of rewards will go to liquidity providers, and 30% to those using $HTM to boost their lending positions. This changes to a 50-50 split in the second month, and by the third month, all incentives are directed through the Booster. This approach encourages early participation and sustained engagement with $HTM. Introducing $TAO to #MultiversX will result in the creation of Liquidity Pools (LPs) on both AshSwap 🔥 and xExchange ⚡. These LPs will be incentivized by both entities, and Hatom will distribute extra rewards at launch. The goal is to make #MultiversX a one-stop hub for $TAO holders. Upon stabilizing the volumes, there will also be plans to integrate it on AshPerp 🔥. Furthermore, with the release of $USH, users will have the ability to mint it while retaining exposure to their $TAO. The TAO Bridge and TAO Liquid Staking smart contracts have been audited by Runtime Vеrification and @arda_project, while penetration testing and DevSecOps have been performed on our infrastructure by CertiK. We're excited to announce our exclusive partnership with TAONEW one of the top 5 validators on #Bittensor. TAONEW has been extremely helpful and supportive from day one. By sharing 50% of its service fee with its stakers, TAONEW enables Hatom to offer an optimized Staking APY to its users. Since our initial reference, #Bittensor has grown sevenfold, becoming the largest AI project in the crypto sphere. We reiterate our commitment to contribute to such technology and hope to address some of its current DeFi challenges. Syfy Moving forward, today marks a significant milestone, not only for our decentralized protocols but also for our development companies, which currently stand as the sole and primary contributors to the Hatom Labs and Soul Labs. We’re excited to unveil Syfy, the evolved identity of Hatom Labs and Soul Labs, now serving as the parent entity for our burgeoning development companies. Organization is crucial for scalability, which is why Syfy was established to cultivate an environment where our teams can collaborate more seamlessly, enhancing our effectiveness and efficiency. At the same time, we remain committed to upholding the financial independence of each project, supported by its own community of funding contributors. Feel free to explore our website at for more information! Additionally, don't forget to follow Syfy and explore their Genesis article highlighted in their initial post: Booster V2 The Booster V2 will introduce a range of new features and opportunities for $HTM holders: Optimized Position Boosting: Previously, boosting was done individually for each money market, necessitating $HTM token distribution and periodic rebalancing due to price fluctuations. With Booster V2, the system now considers the overall position, eliminating the need for manual rebalancing. Gas Fee Reduction: Booster V2 implements optimizations that result in reduced gas fees, making transactions more cost-effective for users. Incorporation of Governance: Users staking $HTM tokens gain voting rights directly within the Booster, allowing them to participate in governance decisions while maintaining their staked positions. (Note: Only $HTM tokens are considered for governance; LP tokens are not included.) Enhanced Boosting Mechanism: The Booster V2 enables LP Tokens to boost positions within the Booster, leveraging trading fees from swaps and farm incentives while boosting lending positions. Smart Contract Completion: The Booster smart contract has been completed and audited by @arda_project, ensuring security and reliability. Frontend Implementation: The frontend design for Booster V2 has been successfully implemented, providing users with an intuitive interface. Collaboration with xExchange: Exploration is ongoing for collaboration with xExchange ⚡ to enable LP creation, farming, and meta-staking within the Booster. Upon finalization of testing, we will launch the Booster V2 on the devnet to gather community feedback and begin preparations for the mainnet release. Soul Before delving into Soul Labs's developments, it's essential to summarize its core functionality briefly: Soul Labs seamlessly connects different lending protocols and blockchains, facilitating lending and borrowing across platforms like Aave, Compound Labs, and Hatom Labs, consolidating liquidity and users' borrowing capabilities. Utilizing LayerZero Labs and other messaging layers for cross-chain communication, Soul Labs bypasses asset bridging or synthetics, unlocking novel DeFi strategies and solidifying its position as the ultimate solution for cross-lending dilemmas. Soul V1 will be permissionless, holding censorship-resistant features, incorporating multiple redundancy mechanisms, and providing support for various DApps. We're thrilled to announce that, following the launch of the Tao Bridge in 2-3 weeks, we will introduce the Soul Labs website. This platform has been meticulously crafted over 250 days to not only provide a comprehensive overview of our vision but also to offer an engaging and captivating experience that promises to be memorable. Regarding the app, significant progress has been made on the V1 protocol, including: Smart Contract Development and Testing: • Completion of the initial phase of smart contract development. • Conducting advanced testing to ensure the system's robustness. • Establishment of a fully functional proof of concept. Successful deployment and testing on the #Goerli (#Ethereum Testnet) and #Mumbai (#Polygon Testnet), leveraging LayerZero Labs for seamless operation. Feature Enhancement and Protocol Optimization: • Enhanced testing procedures to bolster system resilience. • Integration of advanced features and significant code refactoring for optimization. • Incorporation of various communication methods, including LayerZero Labs, Formerly Axelar, now at @axelar, Chainlink CCIP), and wormholecrypto, into Soul Labs framework, enhancing its resilience and flexibility. This allows Soul Labs to maintain operation through alternative protocols if the primary one is temporarily paused. Website Development and Documentation: • Nearing the completion of the v1 app, with final touches being applied. • The preparation of comprehensive V1 documentation and the Yellow Paper, available upon Soul Labs's public launch, offering detailed insights into the platform's infrastructure and capabilities. USH Recognizing the critical need for stable liquidity within the ecosystem, we have positioned ourselves at the forefront of providing a solution by introducing $USH, the first native, decentralized, and over-collateralized stablecoin on #MultiversX. As market conditions have improved, we have observed a growing demand for stablecoins in the ecosystem, evidenced by the utilization rate in the Lending Protocol spiking to over 90% several times in recent months. Therefore, our goal is to tackle the current challenges faced by users by creating a robust product that will not only help them hedge against market volatility but also open up better opportunities to trade the markets and generate yield. We're happy to unveil the $USH website, now live with a sleek and intuitive user interface, designed for ease of use, which ensures that interacting with the protocol is straightforward and accessible for all. You can access it now through this link: For the technical side, we’re advancing steadily and we’ve accomplished the following milestones: Lending Protocol Facilitator: • Coded the first version to support multiple discount factors for different collaterals. • Implemented tracking of borrowing effectiveness to enable earnings forecasting for the module and support minting processes. Isolated Pools Facilitator: • Coded the first version of Isolated Pools Facilitator. • Use of $EGLD or $sEGLD as collateral, with positions stored always in $EGLD to benefit the protocol through Liquid Staking and lending interest. • Virtual account implementation for converting $sEGLD earnings into $USH, functioning like liquidation where users deposit $USH for a higher amount of $HsELGD. Staking Module • Coded the first version of the Staking Module that allows users to stake and unstake without any restrictions. We're currently focusing our efforts on the following tasks: • Implementation of HTM Booster in the discount model in the Lending Protocol. • Implementation of different depeg strategies and brainstorming further potential “soft” depeg mechanisms. • Research and implementation of rewards model for Staking Module. • Research and implementation of Boosted Vaults Facilitator. • Review and stress-test the first version of the code. Upon launch, $USH will be integrated into various protocols and AMMs across the ecosystem, further increasing both its utility and liquidity. The opportunities will be vast, enabling users to engage in a wide range of activities such as yield farming, staking, and arbitrage, all while leveraging a stable and reliable asset. Regarding the USH Airdrop campaign, it will continue until the official launch of $USH planned for late Q2-early Q3, rewarding all users who have actively participated in the initiative. Hatom V2 It is clear by now that we are driven to build a more robust, interoperable, and secure DeFi space, removing the current barriers that hinder users' capabilities to seamlessly interact with different blockchains. Through Hatom V2, we will introduce Hatom's cross-chain architecture, designed from the ground up for interoperability. This approach will elevate the protocol to unprecedented levels, enabling its deployment across various blockchains and facilitating seamless connections between them through Soul. By enhancing interoperability, Hatom V2 aims to foster a more inclusive and accessible ecosystem. This expansion will not only broaden the protocol's reach but also significantly increase its flexibility and utility, allowing users to interact with a diverse range of assets and products across different chains. We’re thrilled to share that we are currently crafting the V2 redesign of the Hatom webpage. Anticipate a jaw-dropping transformation that will truly astonish, blending cutting-edge design with an unparalleled user experience, elevating it to a dynamic, interactive hub, and making every interaction more engaging. Good things take time, but we are confident that the release of V2 website will take place in the second quarter of this year and will officially mark the start of our journey into the cross-chain landscape. We are excited about the future and we truly believe that this will mark the beginning of a new era for Hatom. It's crucial for us to develop rapidly without sacrificing the quality or the security of each product. We're strategically allocating resources to ensure smooth progress in every area of our work. As we push forward, we believe that the launch of Soul Labs will be the most important milestone due to its massive potential and disruptive technology. We would like to thank you all for the unwavering support you've shown over the past few months; it truly fuels our passion to push daily and make strides toward achieving our ambitious goals.

Hatom Labs

203,486 görüntüleme • 2 yıl önce

This Chinese guy built a Second Brain in Obsidian and every morning gets 3 trading ideas that brought him $180,000 in 6 months. Inside he runs a pipeline of 6 workflows on N8N that automatically pulls every read article, listened podcast, and voice note into a shared Obsidian vault, and a neural network analyst every morning at 6:00 finds connections between the fresh and the old and puts the 3 strongest trading ideas for the day into the inbox. No analytics desk, no Bloomberg terminal, no Telegram chats with traders. Just a Mac Mini by the wall, an iPhone in the pocket, and 1 local Obsidian vault. And traditional quant funds keep entire teams of 8 people on salary for the same flow of insights, while his expenses are only subscriptions to Readwise, Whisper API, and N8N hosting. 6 pipelines process about 200 sources a day and close the monthly API bill at about $120. The Mac Mini itself stores the entire vault and keeps the neural network analyst running 24/7, and from the iPhone the owner drops any idea he hears on the go into a Telegram bot, and it lands in the vault inbox in just 30 seconds. The starting instruction that sits in the VAULT.md file at the root of his vault looks like this: "you are the AI analyst of a solo trader. you read his vault every morning at 6:00, find connections between fresh and old notes, and deliver 3 trading ideas he can verify in the hour before the market opens. pipelines: // Reader (pulls every article and highlight from Readwise, Twitter bookmarks, and Kindle into /notes) // Listener (transcribes podcasts through Airr and voice notes through Whisper, puts them in /notes) // Catcher (accepts any message from the Telegram bot and writes it to /inbox with a timestamp) // Connector (every night reads across the entire vault and updates the connection graph between 4,000 notes) // Briefer (at 6:00 AM writes a brief: 3 trading ideas for today plus the emerging thesis of the week, puts it in /inbox) // Mobile (lives in the iPhone, answers any question about the vault by voice, and confirms alerts while the owner is on the go). you wake the owner with a push notification only when a fresh note contradicts his active thesis or when 1 of the 3 morning ideas has a confidence score above 90%." This instruction immediately sets the role for the system and the limits of its autonomy. It knows it is supposed to connect new with old on its own. It knows it is supposed to prepare 3 trading ideas every morning on its own. It knows it connects the live trader only when a thesis is contradicted or an ultra-confident idea appears. → Reader pulls about 80 articles and highlights a day from Readwise, Twitter, and Kindle → Listener transcribes 4 to 6 podcasts a week through Airr and Whisper → Catcher intercepts all voice and text ideas through the Telegram bot, averaging 15 to 20 a day → Connector updates the connection graph between 4,000 notes every night, adding 25 to 30 new edges → Briefer puts a fresh brief with 3 trading ideas and the emerging thesis into the inbox at exactly 6:00 → Mobile answers any question about the vault by voice and confirms alerts right from the iPhone And only when a new note contradicts his active thesis or 1 of the ideas breaks 90% confidence does the orchestrator raise the owner with a push notification. And when the trader at that moment is driving to the gym or eating breakfast, the Mobile agent in his iPhone answers any quick question about the vault by voice: what he wrote about this ticker last week, which 3 sources support the idea of long NVDA, and what counter-thesis already sits in his notes. The trader makes the decision and sends the order before New York opens. The fresh brief from last Monday looks like this: "reader: 78 materials added over the weekend, 11 of them about semiconductors, 4 about energy, 3 about biotech. passing to connector." "connector: 27 new connections found between fresh materials and the vault, the strongest one is that the Goldman report from Wednesday matches the NVDA thesis you wrote 3 weeks ago." "briefer: 3 trading ideas for today: long NVDA (confidence 0.84), short Tesla at the close of the quarterly report (0.71), watch URI (0.62). emerging thesis of the week: the market is underpricing capex on data centers." "alert: your fresh note about long-term risk in semis contradicts the NVDA thesis. sending for review." In his work setup there is no cloud server, no team of analysts, and not even a Bloomberg subscription. At home sits a Mac Mini with a local Obsidian vault, on top run 6 N8N pipelines and a neural network analyst, and the same vault mirrors to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo trading setup on a second brain: $120 a month on the API, about $30,000 a month into the account, and between them 6 pipelines, 4,000 connected notes, and 1 iPhone in the pocket.
0:15

This Chinese guy built a Second Brain in Obsidian and every morning gets 3 trading ideas that brought him $180,000 in 6 months. Inside he runs a pipeline of 6 workflows on N8N that automatically pulls every read article, listened podcast, and voice note into a shared Obsidian vault, and a neural network analyst every morning at 6:00 finds connections between the fresh and the old and puts the 3 strongest trading ideas for the day into the inbox. No analytics desk, no Bloomberg terminal, no Telegram chats with traders. Just a Mac Mini by the wall, an iPhone in the pocket, and 1 local Obsidian vault. And traditional quant funds keep entire teams of 8 people on salary for the same flow of insights, while his expenses are only subscriptions to Readwise, Whisper API, and N8N hosting. 6 pipelines process about 200 sources a day and close the monthly API bill at about $120. The Mac Mini itself stores the entire vault and keeps the neural network analyst running 24/7, and from the iPhone the owner drops any idea he hears on the go into a Telegram bot, and it lands in the vault inbox in just 30 seconds. The starting instruction that sits in the VAULT.md file at the root of his vault looks like this: "you are the AI analyst of a solo trader. you read his vault every morning at 6:00, find connections between fresh and old notes, and deliver 3 trading ideas he can verify in the hour before the market opens. pipelines: // Reader (pulls every article and highlight from Readwise, Twitter bookmarks, and Kindle into /notes) // Listener (transcribes podcasts through Airr and voice notes through Whisper, puts them in /notes) // Catcher (accepts any message from the Telegram bot and writes it to /inbox with a timestamp) // Connector (every night reads across the entire vault and updates the connection graph between 4,000 notes) // Briefer (at 6:00 AM writes a brief: 3 trading ideas for today plus the emerging thesis of the week, puts it in /inbox) // Mobile (lives in the iPhone, answers any question about the vault by voice, and confirms alerts while the owner is on the go). you wake the owner with a push notification only when a fresh note contradicts his active thesis or when 1 of the 3 morning ideas has a confidence score above 90%." This instruction immediately sets the role for the system and the limits of its autonomy. It knows it is supposed to connect new with old on its own. It knows it is supposed to prepare 3 trading ideas every morning on its own. It knows it connects the live trader only when a thesis is contradicted or an ultra-confident idea appears. → Reader pulls about 80 articles and highlights a day from Readwise, Twitter, and Kindle → Listener transcribes 4 to 6 podcasts a week through Airr and Whisper → Catcher intercepts all voice and text ideas through the Telegram bot, averaging 15 to 20 a day → Connector updates the connection graph between 4,000 notes every night, adding 25 to 30 new edges → Briefer puts a fresh brief with 3 trading ideas and the emerging thesis into the inbox at exactly 6:00 → Mobile answers any question about the vault by voice and confirms alerts right from the iPhone And only when a new note contradicts his active thesis or 1 of the ideas breaks 90% confidence does the orchestrator raise the owner with a push notification. And when the trader at that moment is driving to the gym or eating breakfast, the Mobile agent in his iPhone answers any quick question about the vault by voice: what he wrote about this ticker last week, which 3 sources support the idea of long NVDA, and what counter-thesis already sits in his notes. The trader makes the decision and sends the order before New York opens. The fresh brief from last Monday looks like this: "reader: 78 materials added over the weekend, 11 of them about semiconductors, 4 about energy, 3 about biotech. passing to connector." "connector: 27 new connections found between fresh materials and the vault, the strongest one is that the Goldman report from Wednesday matches the NVDA thesis you wrote 3 weeks ago." "briefer: 3 trading ideas for today: long NVDA (confidence 0.84), short Tesla at the close of the quarterly report (0.71), watch URI (0.62). emerging thesis of the week: the market is underpricing capex on data centers." "alert: your fresh note about long-term risk in semis contradicts the NVDA thesis. sending for review." In his work setup there is no cloud server, no team of analysts, and not even a Bloomberg subscription. At home sits a Mac Mini with a local Obsidian vault, on top run 6 N8N pipelines and a neural network analyst, and the same vault mirrors to a secure terminal on the iPhone. Out of everything I have seen this year, this is the cleanest solo trading setup on a second brain: $120 a month on the API, about $30,000 a month into the account, and between them 6 pipelines, 4,000 connected notes, and 1 iPhone in the pocket.

Blaze

930,025 görüntüleme • 4 ay önce

InterLink’s Early Vision for NIST-Standardised Post-Quantum Cryptography 🔐✨ The next five years may bring a much clearer answer to a question that has long been difficult to judge: Is a digital asset truly secure? 🤔 For InterLink, the answer may increasingly depend on one critical factor: whether it is quantum-resistant and aligned with NIST standards 🧬🔒 Why this matters now ⚠️ Two fast-moving technologies are reshaping digital security: • AI is improving the ability to discover weaknesses in systems 🤖 • Quantum computing is advancing towards the point where today’s cryptographic foundations could become vulnerable ⚛️ For InterLink, this is not just a theoretical discussion. It is a reminder that blockchain networks, wallets, and custody systems must prepare now for the post-quantum era ⏳🛡️ Why NIST is central to InterLink’s approach 📘🏛️ Many in the InterLink community already know NIST. NIST, part of the U.S. Department of Commerce, plays a major role in defining and evaluating security standards, including those for post-quantum cryptography. Its work matters because it helps shape what “secure” will mean in a quantum-capable future 📊🔍 In practical terms, InterLink’s long-term security vision is closely tied to whether its cryptographic design can withstand post-quantum threats 🚀 The risk for blockchain networks and digital assets 🔎💥 Recent research and experiments from major organisations, including Google, have highlighted a growing concern: what was once considered extremely difficult, using quantum computing to threaten cryptographic systems, is no longer something that can be ignored 🧪⚠️ That does not mean current systems are broken today. It does mean that networks which fail to prepare for post-quantum threats could face serious risks later 📉 For InterLink, this is exactly why early research matters 🧠✨ If sufficiently powerful quantum computers become available, some current cryptographic methods may become vulnerable. For any network storing value, identity, NFTs, or permissions, that is a major issue 💳🖼️🧾 InterLink’s early work on post-quantum readiness 🧠🔐 InterLink Foundation has already been researching: ✅ digital signatures ✍️ ✅ cryptographic algorithms 🔣 ✅ migration mechanisms for future security upgrades 🔄 One of the most notable areas of work is the ability to generate new private keys from an existing seed phrase 🌱➡️🔑 This matters because it offers a pathway to improve security without forcing users to abandon access to their assets 🙌 Address Alias: preserving continuity during migration 🪪🔗 Another important InterLink mechanism is Address Alias. This is designed to let users: ✓ retain their existing wallet addresses 🧾 ✓ preserve associated tokens and NFTs 🖼️💰 ✓ migrate to a new cryptographic security architecture 🔐➡️🛠️ That is a practical and user-friendly design choice. Security upgrades are often hard to adopt when they break continuity. InterLink’s approach aims to solve that problem 🌉 Bringing post-quantum protection into smart contracts 🛡️📜 InterLink is also implementing SLH-DSA-SHA2-128s (FIPS 205) within IRC smart contracts. This adds another layer of protection for: • vaults 🏦 • high-value assets 💎 • long-term storage 📦 • sensitive on-chain operations ⚙️ The goal is not only to protect wallets, but also to strengthen the systems that govern custody and transaction security across the network 🧱🔒 Testing on the Taj Mahal Testnet 🧪🛰️ These experiments are currently being conducted on the InterLink Taj Mahal Testnet. According to InterLink, the experimental implementations have passed the NIST-based simulation tests carried out so far ✅📈 That is an encouraging early signal, although broader testing and real-world validation will remain important as development continues 🔍 Looking ahead to 2027 🚀🌍 InterLink’s stated goal is to fully integrate this architecture into the Open Mainnet in 2027. If achieved, that would bring InterLink closer to a future where security is defined not only by current best practice, but by resilience against quantum-era threats 🛡️⚛️ The bigger takeaway 🌍✨ The key lesson is simple: In the quantum era, security will not only mean protecting your private will mean asking whether the cryptography behind that key was built to survive the next generation of computing 🔐⏭️ For InterLink, this is a strategic direction with long-term significance 📌 Final thought 💡 Post-quantum readiness is no longer just a technical topic for specialists. For InterLink, it is becoming part of the broader conversation about long-term digital asset security 🧠🔒 NIST-aligned cryptography, practical migration paths, and user-preserving design may soon define the networks people trust most 🌟 InterLink Labs 👤 + 🌐 KV Reina | InterLink Labs InterLink Foundation #InterLink #ITLG #ITL #WeAreTheFirst10MLinkers Join me on InterLink 😁 Start mining now and use my invitation link: 💰 My code is: 111222777888 💰 Please DM me once you have used my code. 👍
0:11

InterLink’s Early Vision for NIST-Standardised Post-Quantum Cryptography 🔐✨ The next five years may bring a much clearer answer to a question that has long been difficult to judge: Is a digital asset truly secure? 🤔 For InterLink, the answer may increasingly depend on one critical factor: whether it is quantum-resistant and aligned with NIST standards 🧬🔒 Why this matters now ⚠️ Two fast-moving technologies are reshaping digital security: • AI is improving the ability to discover weaknesses in systems 🤖 • Quantum computing is advancing towards the point where today’s cryptographic foundations could become vulnerable ⚛️ For InterLink, this is not just a theoretical discussion. It is a reminder that blockchain networks, wallets, and custody systems must prepare now for the post-quantum era ⏳🛡️ Why NIST is central to InterLink’s approach 📘🏛️ Many in the InterLink community already know NIST. NIST, part of the U.S. Department of Commerce, plays a major role in defining and evaluating security standards, including those for post-quantum cryptography. Its work matters because it helps shape what “secure” will mean in a quantum-capable future 📊🔍 In practical terms, InterLink’s long-term security vision is closely tied to whether its cryptographic design can withstand post-quantum threats 🚀 The risk for blockchain networks and digital assets 🔎💥 Recent research and experiments from major organisations, including Google, have highlighted a growing concern: what was once considered extremely difficult, using quantum computing to threaten cryptographic systems, is no longer something that can be ignored 🧪⚠️ That does not mean current systems are broken today. It does mean that networks which fail to prepare for post-quantum threats could face serious risks later 📉 For InterLink, this is exactly why early research matters 🧠✨ If sufficiently powerful quantum computers become available, some current cryptographic methods may become vulnerable. For any network storing value, identity, NFTs, or permissions, that is a major issue 💳🖼️🧾 InterLink’s early work on post-quantum readiness 🧠🔐 InterLink Foundation has already been researching: ✅ digital signatures ✍️ ✅ cryptographic algorithms 🔣 ✅ migration mechanisms for future security upgrades 🔄 One of the most notable areas of work is the ability to generate new private keys from an existing seed phrase 🌱➡️🔑 This matters because it offers a pathway to improve security without forcing users to abandon access to their assets 🙌 Address Alias: preserving continuity during migration 🪪🔗 Another important InterLink mechanism is Address Alias. This is designed to let users: ✓ retain their existing wallet addresses 🧾 ✓ preserve associated tokens and NFTs 🖼️💰 ✓ migrate to a new cryptographic security architecture 🔐➡️🛠️ That is a practical and user-friendly design choice. Security upgrades are often hard to adopt when they break continuity. InterLink’s approach aims to solve that problem 🌉 Bringing post-quantum protection into smart contracts 🛡️📜 InterLink is also implementing SLH-DSA-SHA2-128s (FIPS 205) within IRC smart contracts. This adds another layer of protection for: • vaults 🏦 • high-value assets 💎 • long-term storage 📦 • sensitive on-chain operations ⚙️ The goal is not only to protect wallets, but also to strengthen the systems that govern custody and transaction security across the network 🧱🔒 Testing on the Taj Mahal Testnet 🧪🛰️ These experiments are currently being conducted on the InterLink Taj Mahal Testnet. According to InterLink, the experimental implementations have passed the NIST-based simulation tests carried out so far ✅📈 That is an encouraging early signal, although broader testing and real-world validation will remain important as development continues 🔍 Looking ahead to 2027 🚀🌍 InterLink’s stated goal is to fully integrate this architecture into the Open Mainnet in 2027. If achieved, that would bring InterLink closer to a future where security is defined not only by current best practice, but by resilience against quantum-era threats 🛡️⚛️ The bigger takeaway 🌍✨ The key lesson is simple: In the quantum era, security will not only mean protecting your private will mean asking whether the cryptography behind that key was built to survive the next generation of computing 🔐⏭️ For InterLink, this is a strategic direction with long-term significance 📌 Final thought 💡 Post-quantum readiness is no longer just a technical topic for specialists. For InterLink, it is becoming part of the broader conversation about long-term digital asset security 🧠🔒 NIST-aligned cryptography, practical migration paths, and user-preserving design may soon define the networks people trust most 🌟 InterLink Labs 👤 + 🌐 KV Reina | InterLink Labs InterLink Foundation #InterLink #ITLG #ITL #WeAreTheFirst10MLinkers Join me on InterLink 😁 Start mining now and use my invitation link: 💰 My code is: 111222777888 💰 Please DM me once you have used my code. 👍

Tekkaus® | InterLink • MOD • T2 Community Builder

22,550 görüntüleme • 1 ay önce

Quranic Textual Criticism Exposes the Myth of Perfect Preservation Muslims claim the Quran stands perfectly preserved, word for word, as the eternal speech of allah, citing Surah 15:9: “Indeed, it is We who sent down the Qur’an and indeed, We will be its guardian.” That claim collapses under historical and manuscript evidence. After Muhammad’s death in 632 C.E., the text existed in scattered oral reports and personal writings of companions. Caliph Uthman (r. 644–656 C.E.) ordered a standardized mushaf (codex), then commanded the burning of all competing versions, including those of companions such as Ibn Masud and Ubayy ibn Kab, whose collections differed in content, order, and wording. The Quran is Not A Literary Miracle. Claims that the Quran’s Arabic prose is an unmatched, divinely inspired literary masterpiece rest on subjective aesthetic judgments and a circular challenge (“produce a surah like it”) that no independent standard can fairly test. Classical Arabic already possessed sophisticated rhymed prose (saj‘), poetic meters, and rhetorical devices long before the 7th century; the Quran’s style is a recognizable development of those existing forms rather than a sudden rupture. Non-Arabic speakers, and even many native speakers, experience the text through translation or later interpretation, stripping away the alleged inimitable beauty. Literary critics have long noted uneven quality—repetitions, abrupt shifts, grammatical irregularities, and opaque passages—while later Arab poets and writers produced polished works that, by ordinary literary criteria, equal or surpass many Quranic sections. The miracle claim therefore functions more as a faith assertion than a demonstrable fact. The Quran Does Not Confirm Previous Scripture. The Quran repeatedly states that it “confirms” the Torah and the Gospel (e.g., 3:3, 5:46–48), yet it systematically contradicts the core content of those earlier texts as they existed in the 7th century and as they are preserved today. It denies the crucifixion and death of Jesus (4:157), rejects the doctrine of the Trinity and the divine sonship of Christ, alters key narratives about Abraham, Moses, and other figures, and introduces doctrines (final prophethood of Muhammad, abrogation of earlier laws) that the Hebrew Bible and New Testament do not contain. The later Islamic claim that the previous scriptures were “corrupted” is an ad-hoc solution that the Quran itself never clearly articulates in its earliest layers; it treats the Torah and Gospel as still authoritative while simultaneously contradicting them. The result is not confirmation but replacement. The Quran is Not Harmonious. Despite frequent assertions of perfect internal consistency, the text contains numerous unresolved tensions and contradictions. Abrogation (naskh) is invoked to explain conflicting commands on alcohol, warfare, inheritance, and treatment of non-believers, yet the principle itself is applied inconsistently and lacks a clear chronological or hierarchical rule within the text. Theological statements about free will versus predestination, the nature of God’s attributes, and the fate of earlier communities clash without reconciliation. Historical and narrative details—such as the identity of Mary’s relatives, the sequence of creation days, or the fate of Pharaoh—vary across passages. These discontinuities are not the product of progressive revelation in a coherent whole; they reflect the accretion of material over two decades under shifting circumstances. The Quran Is of Man, Not God. The text’s content, language, and concerns align closely with the 7th-century Arabian milieu of its human author and audience. It draws extensively on pre-existing Jewish, Christian, and Arabian oral traditions—including apocryphal stories of Jesus’ infancy, rabbinic midrash, and local legends—often in forms that were circulating in the Hijaz rather than from independent divine disclosure. Legal and social rulings mirror the evolving needs of a growing community under Muhammad’s leadership: changing attitudes toward warfare, booty, marriage, and relations with Jews and Christians track the political situation in Mecca and Medina. Linguistic features, including loanwords, dialectal peculiarities, and stylistic shifts between early and later surahs, are characteristic of a human composition process rather than timeless divine speech. The simplest and best-supported explanation is that the Quran is a product of its time, place, and prophet. The Quran is Fatally Flawed! Beyond literary and theological problems, the Quran contains demonstrable errors of fact, science, history, and ethics that undermine claims of perfect divine origin. Cosmological descriptions (sun setting in a muddy spring, heavens as solid roofs, stars as missiles against demons) and embryological sequences reflect the limited knowledge of late antiquity, not modern understanding. Historical inaccuracies—placing Mary in the family of Moses and Aaron by centuries, or mislocating events—cannot be reconciled without forced reinterpretation. Ethically, the text endorses practices (permanent slavery under certain conditions, unequal legal testimony and inheritance for women, offensive warfare and the taking of captives) that conflict with universal moral intuitions and with later human progress. These cumulative defects—literary unevenness, internal contradiction, historical dependence, and factual error—render the claim of flawless divine authorship untenable. If it’s within your capacity and you value these educational posts, we appreciate any support on our ‘Buy Me a Coffee’ link found in our profile.
0:15

Quranic Textual Criticism Exposes the Myth of Perfect Preservation Muslims claim the Quran stands perfectly preserved, word for word, as the eternal speech of allah, citing Surah 15:9: “Indeed, it is We who sent down the Qur’an and indeed, We will be its guardian.” That claim collapses under historical and manuscript evidence. After Muhammad’s death in 632 C.E., the text existed in scattered oral reports and personal writings of companions. Caliph Uthman (r. 644–656 C.E.) ordered a standardized mushaf (codex), then commanded the burning of all competing versions, including those of companions such as Ibn Masud and Ubayy ibn Kab, whose collections differed in content, order, and wording. The Quran is Not A Literary Miracle. Claims that the Quran’s Arabic prose is an unmatched, divinely inspired literary masterpiece rest on subjective aesthetic judgments and a circular challenge (“produce a surah like it”) that no independent standard can fairly test. Classical Arabic already possessed sophisticated rhymed prose (saj‘), poetic meters, and rhetorical devices long before the 7th century; the Quran’s style is a recognizable development of those existing forms rather than a sudden rupture. Non-Arabic speakers, and even many native speakers, experience the text through translation or later interpretation, stripping away the alleged inimitable beauty. Literary critics have long noted uneven quality—repetitions, abrupt shifts, grammatical irregularities, and opaque passages—while later Arab poets and writers produced polished works that, by ordinary literary criteria, equal or surpass many Quranic sections. The miracle claim therefore functions more as a faith assertion than a demonstrable fact. The Quran Does Not Confirm Previous Scripture. The Quran repeatedly states that it “confirms” the Torah and the Gospel (e.g., 3:3, 5:46–48), yet it systematically contradicts the core content of those earlier texts as they existed in the 7th century and as they are preserved today. It denies the crucifixion and death of Jesus (4:157), rejects the doctrine of the Trinity and the divine sonship of Christ, alters key narratives about Abraham, Moses, and other figures, and introduces doctrines (final prophethood of Muhammad, abrogation of earlier laws) that the Hebrew Bible and New Testament do not contain. The later Islamic claim that the previous scriptures were “corrupted” is an ad-hoc solution that the Quran itself never clearly articulates in its earliest layers; it treats the Torah and Gospel as still authoritative while simultaneously contradicting them. The result is not confirmation but replacement. The Quran is Not Harmonious. Despite frequent assertions of perfect internal consistency, the text contains numerous unresolved tensions and contradictions. Abrogation (naskh) is invoked to explain conflicting commands on alcohol, warfare, inheritance, and treatment of non-believers, yet the principle itself is applied inconsistently and lacks a clear chronological or hierarchical rule within the text. Theological statements about free will versus predestination, the nature of God’s attributes, and the fate of earlier communities clash without reconciliation. Historical and narrative details—such as the identity of Mary’s relatives, the sequence of creation days, or the fate of Pharaoh—vary across passages. These discontinuities are not the product of progressive revelation in a coherent whole; they reflect the accretion of material over two decades under shifting circumstances. The Quran Is of Man, Not God. The text’s content, language, and concerns align closely with the 7th-century Arabian milieu of its human author and audience. It draws extensively on pre-existing Jewish, Christian, and Arabian oral traditions—including apocryphal stories of Jesus’ infancy, rabbinic midrash, and local legends—often in forms that were circulating in the Hijaz rather than from independent divine disclosure. Legal and social rulings mirror the evolving needs of a growing community under Muhammad’s leadership: changing attitudes toward warfare, booty, marriage, and relations with Jews and Christians track the political situation in Mecca and Medina. Linguistic features, including loanwords, dialectal peculiarities, and stylistic shifts between early and later surahs, are characteristic of a human composition process rather than timeless divine speech. The simplest and best-supported explanation is that the Quran is a product of its time, place, and prophet. The Quran is Fatally Flawed! Beyond literary and theological problems, the Quran contains demonstrable errors of fact, science, history, and ethics that undermine claims of perfect divine origin. Cosmological descriptions (sun setting in a muddy spring, heavens as solid roofs, stars as missiles against demons) and embryological sequences reflect the limited knowledge of late antiquity, not modern understanding. Historical inaccuracies—placing Mary in the family of Moses and Aaron by centuries, or mislocating events—cannot be reconciled without forced reinterpretation. Ethically, the text endorses practices (permanent slavery under certain conditions, unequal legal testimony and inheritance for women, offensive warfare and the taking of captives) that conflict with universal moral intuitions and with later human progress. These cumulative defects—literary unevenness, internal contradiction, historical dependence, and factual error—render the claim of flawless divine authorship untenable. If it’s within your capacity and you value these educational posts, we appreciate any support on our ‘Buy Me a Coffee’ link found in our profile.

AMERICAN VALUES ALLIANCE

11,216 görüntüleme • 1 ay önce

🚨 Protocol Update #9 It's incredible how time flies when you’re laser-focused on building and delivering the essential products that form the backbone of decentralized finance. Hatom has now been live on the Mainnet for over a year, and we're proud to say that this entire period has been free of issues or downtime. Our platform has been battle-tested during volatile market conditions, and each of our products has performed exactly as expected—solidifying our place as a cornerstone in the #MultiversX ecosystem. Describing last year as “incredible” feels like an understatement. We’ve witnessed unprecedented growth across the entire #MultiversX ecosystem, particularly in terms of TVL and yield opportunities. The day before Hatom launched its Lending Protocol and Liquid Staking on Mainnet, #MultiversX had a total TVL of $95 million. Within two weeks, the ecosystem surpassed $200 million in TVL, with Hatom driving over 50% of that growth. At its peak, Hatom reached over $280 million in TVL, accounting for more than 70% of the chain’s total TVL. What's even more remarkable is that, after initially using Treasury funds to incentivize users, Hatom has shifted to distributing rewards solely from protocol revenue. This marks the start of a fully sustainable, real-yield model, proving our products' rapid product-market fit and long-term viability. A Recap of the Past Year Here’s a quick overview of what we’ve accomplished in the past year: • Launched the first Lending Protocol in the #MultiversX ecosystem, along with the Liquid Staking Protocol on Mainnet. • Surpassed $100 million in TVL within just five days of the launch. • Deployed the HTM Booster Module and Accumulator. • Launched the Tao Bridge and Tao Liquid Staking, bringing over 33k $TAO into the #MultiversX ecosystem in just two weeks. • Implemented multiple upgrades to core infrastructure. • $HTM became the second-largest ESDT token after $EGLD. • Distributed over $3.85 million in rewards to our users. We are happy to announce that Hatom V2 is now live! After an incredible year of growth, we’re excited to take the next step toward becoming the leading liquidity hub across multiple chains. We invite you to explore our newly rebranded website at marking the beginning of our omni-chain journey. This rebranding reflects our bold vision and sets the stage for a full overhaul of our dApps, delivering a fresh and enhanced experience for all users. Achieving self-sustainability in such a short time, we now focus on research and development. Instead of pursuing many ideas, we’re committed to building high-impact products that create perfect synergies within our ecosystem. With that said, let’s dive into the key topics of this update: USH and Booster V2. Hatom USD (USH) We’ve highlighted USH in several updates, and it’s great to see the community recognizing its potential. USH is set to be one of the most impactful products on #MultiversX, providing a key revenue stream for Hatom while helping us maintain competitive rates and long-term sustainability. USH is the result of extensive research and careful development, designed to seamlessly fit into the Hatom ecosystem. While many DeFi projects are raising millions for new stablecoins, USH stands as another powerful product within our hub. The time has finally come for USH to be unveiled to the public, and we are excited to announce that USH will officially launch on Devnet on 28th October. While we’ve thoroughly tested for bugs internally, we’re excited to engage the community in this critical phase. To encourage participation, we’ll offer incentives for those testing USH on the Devnet, with more details to be shared at launch. Understanding USH's architecture is key to how it functions within our ecosystem. Let’s break it down step by step, starting with an explanation of each component. Facilitators USH’s minting process is driven by Facilitators—smart contracts responsible for the controlled minting and burning of USH. At launch, two primary facilitators will handle these tasks, each with distinct functionality: 1. Lending Protocol Facilitator The Lending Protocol Facilitator allows users to mint USH using a variety of supported collateral assets directly into the Hatom Lending Protocol. Unlike traditional lending mechanisms, where interest rates fluctuate based on the utilization rate, the minting of USH has fixed interest rates, thanks to Hatom's unique role as the entity managing the minting process. In a scenario where a user is minting USH through this facilitator using multiple assets as collateral, the protocol automatically prioritizes collateral with the lowest Minting APY. Let’s consider an example where a user deposits: - $1,000 in USDC (with a collateral factor of 80% and a 2% Minting APY) - $1,000 in BTC (with a collateral factor of 75% and a 3% Minting APY) - $1,000 in HTM (with a collateral factor of 70% and a 4% Minting APY) Based on these parameters, the user can mint a maximum of $2,250 worth of USH, distributed as follows: - $800 from $USDC (80% of $1,000) at 2% Minting APY - $750 from $BTC (75% of $1,000) at 3% Minting APY - $700 from $HTM (70% of $1,000) at 4% Minting APY The overall Minting APY will be a weighted average of these individual APYs, calculated based on the proportion of USH minted from each collateral type. Now, if the user decides to borrow only $1,000 worth of USH, the APY is determined as follows: - The first $800 will be borrowed from $USDC at 2% APY - The remaining $200 will be borrowed from $BTC at 3% APY This results in an effective Minting APY of 2.2%, reflecting a weighted average of the APYs across the borrowed amounts. It’s important to note that EGLD and wTAO, along with their liquid staking derivatives such as sEGLD and swTAO, can only be used as collateral in the Isolated Pools (which will be explained in the next section), not in the Lending Protocol 2. Isolated Pools Facilitator The Isolated Pools Facilitator allows users to mint $USH at zero interest using $EGLD, $wTAO, or their liquid staking derivatives ( $sEGLD or $swTAO) as collateral. Here’s how it works: When depositing EGLD or wTAO • These assets are staked through the Hatom Liquid Staking Protocol, generating the staking APY. • The staked assets are then deposited into the Lending Protocol, earning a supply APY, but are not activated as collateral. When depositing sEGLD or swTAO • When users deposit staking derivatives into the Isolated Pools, the protocol holds the staking derivatives, but the user's exposure is immediately shifted to the underlying asset ( $EGLD or $wTAO). This means the user no longer benefits from the staking rewards of the derivative, and instead, their exposure is entirely tied to the value and price movements of the underlying asset. • The staked assets are deposited into the Hatom Lending Protocol, earning the supply APY, but again not being activated as collateral. Since the protocol generates revenue from staking and supplying assets in the Lending Protocol, this income is used to incentivize the USH Staking Module. The protocol buys HTM tokens from the open market and distributes them, along with all fees generated by other facilitators, as rewards to stakers. We believe that the Isolated Pools Facilitator is one of the most important pieces of the USH ecosystem. Its potential impact on the TVL within both the Hatom ecosystem and the broader #MultiversX blockchain is immense and the revenue generated by this facilitator through fees will significantly bolster the overall growth of the protocol. To illustrate the potential of Isolated Pools, let’s use the following example: • $50 million worth of $EGLD is deposited into the Isolated Pools, generating a 6% staking APY • $50 million worth of $wTAO is also deposited, earning a 15% staking APY The total staking rewards generated from these assets would be: • $EGLD staking rewards: $50 million × 6% = $3 million annually • $wTAO staking rewards: $50 million × 15% = $7.5 million annually In total, the protocol generates $10.5 million in staking rewards annually. These rewards are then used to buy back HTM tokens from the open market, driving significant buying pressure on the HTM token itself. The purchased HTM tokens are distributed to USH LP stakers in the USH Staking Module, alongside the revenue generated by the Lending Protocol Facilitator. TVL and Yield Impact As we explore the broader impact of USH and the Isolated Pools, it becomes evident how these mechanisms contribute to the overall growth of the Hatom ecosystem, particularly in terms of TVL and potential yield generation. Based on the above numbers, if $50 million worth of $EGLD and $50 million worth of $wTAO are deposited into the Isolated Pools with a 75% collateral factor, we could mint up to $75 million worth of $USH. However, to prioritize safety, we’ll mint only 50% of the maximum, resulting in $37.5 million worth of $USH. In an ideal scenario, but also very unlikely, the $37.5 million $USH would be deposited in the Staking Module to generate rewards. In order for $USH to be deposited in the Staking Module, it is paired with another token (e.g., $USDC or $EGLD) to form Liquidity Pool (LP) position, contributing $75 million to the USH Staking Module. Additionally, the $100 million deposited in the Isolated Pools cycles through Liquid Staking and into the Lending Protocol, contributing a total of $300 million in TVL. Total TVL Breakdown: • $300 million from assets flowing through Isolated Pools ($100m) → Liquid Staking ($100m) → Lending Protocol ($100m) • $75 million from LP positions in the USH Staking Module Total TVL = $375 million As mentioned above, the $100 million deposited in Isolated Pools generates approximately $10.5 million annually in staking rewards (6% APY from $sEGLD and 15% APY from $swTAO). If all minted $USH is deposited into the Staking Module, the $75 million staked would benefit from these rewards, resulting in a 14% APY for USH LP stakers. On top of the protocol’s rewards, liquidity providers earn additional fees from their LP positions on decentralized exchanges, creating the perfect opportunity for all the participants in the USH Staking Module looking for attractive yields. USH Stability: The Peg Mechanism Ensuring the stability of USH is paramount, and to maintain its value close to $1 under all market conditions, we’ve implemented a robust dual peg mechanism. This system consists of two key layers of protection—Soft Peg and Hard Peg—designed to keep USH stable through both market-driven incentives and other mechanisms for scenarios where the Soft Peg mechanism can’t reclaim the peg. 1. Soft Peg Mechanism The Soft Peg Mechanism helps keep USH stable around its $1 value by encouraging market participants to act when USH trades above or below $1. When USH trades below $1 Users can buy USH at a discount, on a DEX, and repay their USH loans on Hatom, as USH is always valued at $1 on the protocol. This action removes $USH from circulation, helping to restore its price. When USH trades above $1 Users can borrow USH from the protocol at $1 and sell it on the open market at the higher price, increasing the circulating supply of USH and pushing its price back down to $1. 2. Hard Peg Mechanism (Redemption Mode) In cases where the Soft Peg alone cannot restore USH to $1 and its price drops significantly below the peg, the Hard Peg Mechanism is triggered through Redemption Mode. This mechanism allows any market participant to step in and help restore the peg by repaying USH loans for other borrowers, seizing their collateral at the full $1 value. It's important to note that Redemption Mode is only activated in the Isolated Pools and does not impact users minting USH through the Lending Protocol. Here’s how Redemption Mode works: When USH trades below $1 and the Redemption Mode is activated, redeemers can buy USH at the lower market price (e.g., $0.95), and use it to repay borrowers' debts at the full $1 value within the protocol. The redeemer receives collateral in the form of liquid staked tokens(such as $sEGLD or $swTAO) equivalent to the USH they repaid at its full $1 value, profiting from the difference between the discounted purchase price and the redemption value. The borrower being redeemed also benefits by receiving a redemption bonus, which allows them to keep a portion of their collateral after part of it is seized after loan was repaid. This system ensures that borrowers are not penalized during redemption, creating a balanced mechanism where both the redeemer and the borrower have something to gain. Redemption Mode differs from Liquidation in several ways: Redemption is triggered by USH falling below $1 and involves repaying borrower accounts to restore the peg. Both the redeemer and the borrower benefit, with the redeemer profiting from the price difference, and the borrower receiving a bonus from their collateral. Liquidation occurs when a borrower’s collateral falls below a certain threshold, making them risky. During liquidation, a portion of the borrower’s loan is repaid, and the collateral is seized, while also incurring a liquidation penalty. Redemption Mode uses a data structure known as a Red-Black Tree to efficiently monitor and rank all borrower positions within the protocol smart contract itself. This structure dynamically tracks borrowers based on their Borrow Limit Used, which is the percentage of collateral they have utilized relative to their borrowing capacity. The system prioritizes borrowers with the highest Borrow Limit Used, meaning those who have borrowed the most relative to their collateral are considered first for redemption. USH Airdrop Regarding the USH Airdrop, we would like to inform you that snapshots will end once USH is deployed on the Public Mainnet. The airdrop will be concluded shortly after, once all liquidity pools are stable and we determine the optimal moment to distribute the rewards to the community. USH Staking Module & Booster V2 The USH Staking Module will play a critical role in maintaining deep liquidity for USH while offering users high-yield opportunities. By staking USH LP tokens, such as USH/USDC and USH/EGLD, users can earn rewards generated by USH facilitators. This approach strengthens USH’s liquidity pools, making them robust enough to handle significant trades without destabilizing its price, thus reinforcing USH’s peg and overall stability. Beyond creating robust liquidity, the USH Staking Module serves as the key utility module within the USH ecosystem, designed to provide users with an opportunity to earn high yields on their USH holdings in a sustainable and organic way. All rewards distributed through the module are generated by various products across the Hatom ecosystem, ensuring long-term sustainability. For users seeking a more stable yield, the USH/USDC LP provides lower risk and steady returns. Those looking to leverage their EGLD holdings can opt for the USH/EGLD LP, which can be staked in the USH Staking Module. A key advantage of staking in the USH Staking Module is that rewards are based on the full value of the LP, not just the USH portion, maximizing your yield potential. As we continue to grow, we’ll be adding more LPs, providing users with even greater flexibility and options for staking their USH in the module. While our current focus is on LP tokens, we’re also exploring the possibility of allowing direct USH staking in the future, expanding the staking opportunities across the ecosystem. The Integration of Booster V2 with the Staking Module Booster V2 will be available for testing with the USH Devnet release, and with its introduction, we’ve strengthened the relationship between the HTM token and USH. Our ecosystem now features two independent boosters: one for the Lending Protocol and one for the USH Staking Module, each operating with the goal of maximizing yields for users. Key Improvements in Booster V2 Booster V2 brings several enhancements that elevate the functionality and user experience: Support for Multiple Token Types: Users will be able to deposit Pool Tokens, Farm Tokens, Dual Farm Tokens, or Staked HTM Tokens (via xExchange). Only the HTM portion will be considered for boosting. Unlimited Staking: The cap on HTM deposits will be removed, allowing users to stake without limits. This will foster a competitive environment where the more HTM you stake, the higher your potential APY. Integrated xExchange Management: Users will be able to manage their xExchange positions directly from the Booster dashboard. This will include creating pools, farming, dual farming, and staking HTM tokens, all from one convenient dashboard. Energy Management Integration: Booster V2 will allow users to manage their xExchange Energy directly from the dashboard, providing an additional way to boost rewards even further. Seamless Migration: Users will be able to migrate HTM between the Lending Protocol Booster and the USH Staking Module Booster without any cooldown periods, making it easier to optimize strategies across both modules. How the Yields Work Booster V2 will introduce a more structured and competitive approach to yield distribution across both the Lending Protocol and the Staking Module. HTM Booster in the Lending Protocol Base APY (First Batch): This is available to all users who stake a specific percentage of HTM relative to their collateral value. Any user can achieve this Base APY by staking the required amount of HTM. Boosted APY (Second Batch): After achieving the base level, users can boost their returns further by staking additional HTM, competing for the second batch of rewards. The more HTM staked beyond the base threshold, the higher the potential yield. USH Staking Module Yields Staking APY: Users who deposit USH-related LP tokens without boosting through the HTM Booster will still receive a Staking APY. This ensures that even passive participants which are not looking to stake their HTM in the Booster can take advantage of the USH Ecosystem to generate yields. Booster APY: Similar to the system in the Lending Protocol, users can stake HTM to unlock a Base APY. Beyond this threshold, any additional HTM staked will increase their APY in a competitive manner, allowing users to maximize their returns based on the amount of HTM they commit to boosting their positions. Rollout Plan for USH USH will be deployed in a phased rollout to ensure smooth implementation: Public Devnet: Open for testing, with incentives for participants to explore and stress-test the platform. Private Mainnet: A limited launch with partners to mint USH, bootstrap USH liquidity and generate initial protocol revenue. Public Mainnet: A full-scale launch, enabling all users to mint, stake, and trade USH. We know DeFi can be complex, which is why we’re committed to providing the tools and resources needed to navigate our ecosystem. With the USH Public Devnet launch, we’ll release updated documentation offering clear guidance on Hatom’s products. Developer documentation is also in the works, and we’re exploring the idea of a Hatom Academy for educational resources. Plus, we’ll soon roll out content focused on USH, helping users fully tap into its potential within Hatom and the MultiversX ecosystem. What’s Next? Hatom Pulse As Hatom grows, our focus remains on pushing DeFi boundaries while expanding across multiple ecosystems. Although this update doesn’t include a full roadmap—that will come later—our priority is clear: expanding Hatom across chains. To stand out in the competitive DeFi landscape, we’re committed to developing standout products. With that in mind, we’re excited to give you an exclusive preview of one of our most innovative products in development: Hatom Pulse. Over-collateralized non-custodial lending protocols, liquid staking, and over-collateralized stablecoins already exist on #Ethereum. What sets us apart is the synergy between these components within a unified ecosystem. By integrating these pillars, we tackle capital inefficiencies, allowing one protocol to enhance strategies that benefit the others, maximizing returns across the board. For example, when USH is minted, it means that EGLD is deposited, liquid-staked, and supplied in the lending protocol—all three protocols working in harmony. Hatom Pulse will elevate this synergy to another level, solving key issues faced by Aave, Compound Labs , and other leading protocols. We believe this innovation will be pivotal as we work to gain market share while expanding cross-chain. Our proof of concept will be deployed and battle-tested on #MultiversX, but the real growth will come when we scale this to markets that are thousands of times larger. This will be a turning point for Hatom. So, what is Hatom Pulse? On Hatom, like on Aave and other leading lending protocols, the largest assets used as collateral are often not borrowed, leading to substantial revenue loss for the protocol. This also results in very low income on the supply side, as borrowing fees depend on utilization rates, which only increase when borrowing activity rises. Generally, lending protocols are used to provide assets for borrowing stablecoins or for leveraging liquid staking strategies. This inefficiency locks up billions of dollars in dormant assets, and users earn very low supply rates on their collateral, which doesn’t help offset their loan interest. Hatom Pulse is designed to address these inefficiencies by leveraging the synergy between our existing products. It creates sophisticated vaults that activate dormant assets, unlocking advanced yield opportunities through a delta-neutral strategy. By utilizing assets like $EGLD, $sEGLD, $wTAO, and $swTAO, Hatom Pulse enables users to engage in delta-neutral strategies, where we long and short these assets on (CEXs), earning funding rates and staking rewards while keeping their assets intact. (The exact strategy, along with all the details, will be shared once USH is fully established). Initially, these vaults will operate on CEXs, where liquidity is highest, and will be managed through custodians like Copper.co to mitigate counterparty risks. Later, we plan to extend this to DEXs where all operations will be governed by smart contracts, ensuring full decentralization. serves as a strong proof of concept for us in this regard. However, our strategy will differ, as our focus will be on protecting the unit value, rather than the dollar value. Although Hatom Pulse is still in its research phase, early estimates suggest that this product alone could generate over 18% annual returns on $EGLD and more than 35% on $wTAO, with what we believe to be minimal risk. It’s important to note that these figures reflect current metrics based on internal calculations and may slightly differ upon product launch. But imagine reaching this on #Ethereum, while allowing users to borrow using their assets—this could be a disruptive protocol. We believe Hatom Pulse has the potential to become a cornerstone product as we transition into an omni-chain future. In a competitive DeFi landscape, it could give us a significant edge by offering something truly groundbreaking, capable of competing with well-established protocols across various chains. This strategy represents immense untapped potential. Hatom Pulse is being developed for risk-averse users who seek higher returns without excessive risk. By addressing inefficiencies in current DeFi strategies, we aim to offer a secure, robust option for yield generation that could rival established protocols. It's been an intense year for our team, and we sincerely thank the community for their patience, trust, and unwavering support as we've worked hard to build and deliver these groundbreaking products. As Hatom's omni-chain expansion nears, we remain focused on improving our existing products and researching new innovations to stay ahead in this competitive market. Our goal is to build a comprehensive DeFi ecosystem, accessible across all blockchains. With USH approaching its Mainnet release, we're proud of how our products have reshaped the DeFi landscape on MultiversX. By filling key gaps in the on-chain economy, we've created opportunities for users to generate yield, unlock the potential of decentralized finance, and provide strong utility for EGLD. In just over a year, we’ve built a strong ecosystem, but this is only the beginning. We’re ready to go even further, developing better products and unlocking new opportunities for our users. We’ll share more about our expansion plans in a dedicated post, staying focused on what matters most. Rest assured, what’s coming will be truly impressive for Hatom and our growing community!
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🚨 Protocol Update #9 It's incredible how time flies when you’re laser-focused on building and delivering the essential products that form the backbone of decentralized finance. Hatom has now been live on the Mainnet for over a year, and we're proud to say that this entire period has been free of issues or downtime. Our platform has been battle-tested during volatile market conditions, and each of our products has performed exactly as expected—solidifying our place as a cornerstone in the #MultiversX ecosystem. Describing last year as “incredible” feels like an understatement. We’ve witnessed unprecedented growth across the entire #MultiversX ecosystem, particularly in terms of TVL and yield opportunities. The day before Hatom launched its Lending Protocol and Liquid Staking on Mainnet, #MultiversX had a total TVL of $95 million. Within two weeks, the ecosystem surpassed $200 million in TVL, with Hatom driving over 50% of that growth. At its peak, Hatom reached over $280 million in TVL, accounting for more than 70% of the chain’s total TVL. What's even more remarkable is that, after initially using Treasury funds to incentivize users, Hatom has shifted to distributing rewards solely from protocol revenue. This marks the start of a fully sustainable, real-yield model, proving our products' rapid product-market fit and long-term viability. A Recap of the Past Year Here’s a quick overview of what we’ve accomplished in the past year: • Launched the first Lending Protocol in the #MultiversX ecosystem, along with the Liquid Staking Protocol on Mainnet. • Surpassed $100 million in TVL within just five days of the launch. • Deployed the HTM Booster Module and Accumulator. • Launched the Tao Bridge and Tao Liquid Staking, bringing over 33k $TAO into the #MultiversX ecosystem in just two weeks. • Implemented multiple upgrades to core infrastructure. • $HTM became the second-largest ESDT token after $EGLD. • Distributed over $3.85 million in rewards to our users. We are happy to announce that Hatom V2 is now live! After an incredible year of growth, we’re excited to take the next step toward becoming the leading liquidity hub across multiple chains. We invite you to explore our newly rebranded website at marking the beginning of our omni-chain journey. This rebranding reflects our bold vision and sets the stage for a full overhaul of our dApps, delivering a fresh and enhanced experience for all users. Achieving self-sustainability in such a short time, we now focus on research and development. Instead of pursuing many ideas, we’re committed to building high-impact products that create perfect synergies within our ecosystem. With that said, let’s dive into the key topics of this update: USH and Booster V2. Hatom USD (USH) We’ve highlighted USH in several updates, and it’s great to see the community recognizing its potential. USH is set to be one of the most impactful products on #MultiversX, providing a key revenue stream for Hatom while helping us maintain competitive rates and long-term sustainability. USH is the result of extensive research and careful development, designed to seamlessly fit into the Hatom ecosystem. While many DeFi projects are raising millions for new stablecoins, USH stands as another powerful product within our hub. The time has finally come for USH to be unveiled to the public, and we are excited to announce that USH will officially launch on Devnet on 28th October. While we’ve thoroughly tested for bugs internally, we’re excited to engage the community in this critical phase. To encourage participation, we’ll offer incentives for those testing USH on the Devnet, with more details to be shared at launch. Understanding USH's architecture is key to how it functions within our ecosystem. Let’s break it down step by step, starting with an explanation of each component. Facilitators USH’s minting process is driven by Facilitators—smart contracts responsible for the controlled minting and burning of USH. At launch, two primary facilitators will handle these tasks, each with distinct functionality: 1. Lending Protocol Facilitator The Lending Protocol Facilitator allows users to mint USH using a variety of supported collateral assets directly into the Hatom Lending Protocol. Unlike traditional lending mechanisms, where interest rates fluctuate based on the utilization rate, the minting of USH has fixed interest rates, thanks to Hatom's unique role as the entity managing the minting process. In a scenario where a user is minting USH through this facilitator using multiple assets as collateral, the protocol automatically prioritizes collateral with the lowest Minting APY. Let’s consider an example where a user deposits: - $1,000 in USDC (with a collateral factor of 80% and a 2% Minting APY) - $1,000 in BTC (with a collateral factor of 75% and a 3% Minting APY) - $1,000 in HTM (with a collateral factor of 70% and a 4% Minting APY) Based on these parameters, the user can mint a maximum of $2,250 worth of USH, distributed as follows: - $800 from $USDC (80% of $1,000) at 2% Minting APY - $750 from $BTC (75% of $1,000) at 3% Minting APY - $700 from $HTM (70% of $1,000) at 4% Minting APY The overall Minting APY will be a weighted average of these individual APYs, calculated based on the proportion of USH minted from each collateral type. Now, if the user decides to borrow only $1,000 worth of USH, the APY is determined as follows: - The first $800 will be borrowed from $USDC at 2% APY - The remaining $200 will be borrowed from $BTC at 3% APY This results in an effective Minting APY of 2.2%, reflecting a weighted average of the APYs across the borrowed amounts. It’s important to note that EGLD and wTAO, along with their liquid staking derivatives such as sEGLD and swTAO, can only be used as collateral in the Isolated Pools (which will be explained in the next section), not in the Lending Protocol 2. Isolated Pools Facilitator The Isolated Pools Facilitator allows users to mint $USH at zero interest using $EGLD, $wTAO, or their liquid staking derivatives ( $sEGLD or $swTAO) as collateral. Here’s how it works: When depositing EGLD or wTAO • These assets are staked through the Hatom Liquid Staking Protocol, generating the staking APY. • The staked assets are then deposited into the Lending Protocol, earning a supply APY, but are not activated as collateral. When depositing sEGLD or swTAO • When users deposit staking derivatives into the Isolated Pools, the protocol holds the staking derivatives, but the user's exposure is immediately shifted to the underlying asset ( $EGLD or $wTAO). This means the user no longer benefits from the staking rewards of the derivative, and instead, their exposure is entirely tied to the value and price movements of the underlying asset. • The staked assets are deposited into the Hatom Lending Protocol, earning the supply APY, but again not being activated as collateral. Since the protocol generates revenue from staking and supplying assets in the Lending Protocol, this income is used to incentivize the USH Staking Module. The protocol buys HTM tokens from the open market and distributes them, along with all fees generated by other facilitators, as rewards to stakers. We believe that the Isolated Pools Facilitator is one of the most important pieces of the USH ecosystem. Its potential impact on the TVL within both the Hatom ecosystem and the broader #MultiversX blockchain is immense and the revenue generated by this facilitator through fees will significantly bolster the overall growth of the protocol. To illustrate the potential of Isolated Pools, let’s use the following example: • $50 million worth of $EGLD is deposited into the Isolated Pools, generating a 6% staking APY • $50 million worth of $wTAO is also deposited, earning a 15% staking APY The total staking rewards generated from these assets would be: • $EGLD staking rewards: $50 million × 6% = $3 million annually • $wTAO staking rewards: $50 million × 15% = $7.5 million annually In total, the protocol generates $10.5 million in staking rewards annually. These rewards are then used to buy back HTM tokens from the open market, driving significant buying pressure on the HTM token itself. The purchased HTM tokens are distributed to USH LP stakers in the USH Staking Module, alongside the revenue generated by the Lending Protocol Facilitator. TVL and Yield Impact As we explore the broader impact of USH and the Isolated Pools, it becomes evident how these mechanisms contribute to the overall growth of the Hatom ecosystem, particularly in terms of TVL and potential yield generation. Based on the above numbers, if $50 million worth of $EGLD and $50 million worth of $wTAO are deposited into the Isolated Pools with a 75% collateral factor, we could mint up to $75 million worth of $USH. However, to prioritize safety, we’ll mint only 50% of the maximum, resulting in $37.5 million worth of $USH. In an ideal scenario, but also very unlikely, the $37.5 million $USH would be deposited in the Staking Module to generate rewards. In order for $USH to be deposited in the Staking Module, it is paired with another token (e.g., $USDC or $EGLD) to form Liquidity Pool (LP) position, contributing $75 million to the USH Staking Module. Additionally, the $100 million deposited in the Isolated Pools cycles through Liquid Staking and into the Lending Protocol, contributing a total of $300 million in TVL. Total TVL Breakdown: • $300 million from assets flowing through Isolated Pools ($100m) → Liquid Staking ($100m) → Lending Protocol ($100m) • $75 million from LP positions in the USH Staking Module Total TVL = $375 million As mentioned above, the $100 million deposited in Isolated Pools generates approximately $10.5 million annually in staking rewards (6% APY from $sEGLD and 15% APY from $swTAO). If all minted $USH is deposited into the Staking Module, the $75 million staked would benefit from these rewards, resulting in a 14% APY for USH LP stakers. On top of the protocol’s rewards, liquidity providers earn additional fees from their LP positions on decentralized exchanges, creating the perfect opportunity for all the participants in the USH Staking Module looking for attractive yields. USH Stability: The Peg Mechanism Ensuring the stability of USH is paramount, and to maintain its value close to $1 under all market conditions, we’ve implemented a robust dual peg mechanism. This system consists of two key layers of protection—Soft Peg and Hard Peg—designed to keep USH stable through both market-driven incentives and other mechanisms for scenarios where the Soft Peg mechanism can’t reclaim the peg. 1. Soft Peg Mechanism The Soft Peg Mechanism helps keep USH stable around its $1 value by encouraging market participants to act when USH trades above or below $1. When USH trades below $1 Users can buy USH at a discount, on a DEX, and repay their USH loans on Hatom, as USH is always valued at $1 on the protocol. This action removes $USH from circulation, helping to restore its price. When USH trades above $1 Users can borrow USH from the protocol at $1 and sell it on the open market at the higher price, increasing the circulating supply of USH and pushing its price back down to $1. 2. Hard Peg Mechanism (Redemption Mode) In cases where the Soft Peg alone cannot restore USH to $1 and its price drops significantly below the peg, the Hard Peg Mechanism is triggered through Redemption Mode. This mechanism allows any market participant to step in and help restore the peg by repaying USH loans for other borrowers, seizing their collateral at the full $1 value. It's important to note that Redemption Mode is only activated in the Isolated Pools and does not impact users minting USH through the Lending Protocol. Here’s how Redemption Mode works: When USH trades below $1 and the Redemption Mode is activated, redeemers can buy USH at the lower market price (e.g., $0.95), and use it to repay borrowers' debts at the full $1 value within the protocol. The redeemer receives collateral in the form of liquid staked tokens(such as $sEGLD or $swTAO) equivalent to the USH they repaid at its full $1 value, profiting from the difference between the discounted purchase price and the redemption value. The borrower being redeemed also benefits by receiving a redemption bonus, which allows them to keep a portion of their collateral after part of it is seized after loan was repaid. This system ensures that borrowers are not penalized during redemption, creating a balanced mechanism where both the redeemer and the borrower have something to gain. Redemption Mode differs from Liquidation in several ways: Redemption is triggered by USH falling below $1 and involves repaying borrower accounts to restore the peg. Both the redeemer and the borrower benefit, with the redeemer profiting from the price difference, and the borrower receiving a bonus from their collateral. Liquidation occurs when a borrower’s collateral falls below a certain threshold, making them risky. During liquidation, a portion of the borrower’s loan is repaid, and the collateral is seized, while also incurring a liquidation penalty. Redemption Mode uses a data structure known as a Red-Black Tree to efficiently monitor and rank all borrower positions within the protocol smart contract itself. This structure dynamically tracks borrowers based on their Borrow Limit Used, which is the percentage of collateral they have utilized relative to their borrowing capacity. The system prioritizes borrowers with the highest Borrow Limit Used, meaning those who have borrowed the most relative to their collateral are considered first for redemption. USH Airdrop Regarding the USH Airdrop, we would like to inform you that snapshots will end once USH is deployed on the Public Mainnet. The airdrop will be concluded shortly after, once all liquidity pools are stable and we determine the optimal moment to distribute the rewards to the community. USH Staking Module & Booster V2 The USH Staking Module will play a critical role in maintaining deep liquidity for USH while offering users high-yield opportunities. By staking USH LP tokens, such as USH/USDC and USH/EGLD, users can earn rewards generated by USH facilitators. This approach strengthens USH’s liquidity pools, making them robust enough to handle significant trades without destabilizing its price, thus reinforcing USH’s peg and overall stability. Beyond creating robust liquidity, the USH Staking Module serves as the key utility module within the USH ecosystem, designed to provide users with an opportunity to earn high yields on their USH holdings in a sustainable and organic way. All rewards distributed through the module are generated by various products across the Hatom ecosystem, ensuring long-term sustainability. For users seeking a more stable yield, the USH/USDC LP provides lower risk and steady returns. Those looking to leverage their EGLD holdings can opt for the USH/EGLD LP, which can be staked in the USH Staking Module. A key advantage of staking in the USH Staking Module is that rewards are based on the full value of the LP, not just the USH portion, maximizing your yield potential. As we continue to grow, we’ll be adding more LPs, providing users with even greater flexibility and options for staking their USH in the module. While our current focus is on LP tokens, we’re also exploring the possibility of allowing direct USH staking in the future, expanding the staking opportunities across the ecosystem. The Integration of Booster V2 with the Staking Module Booster V2 will be available for testing with the USH Devnet release, and with its introduction, we’ve strengthened the relationship between the HTM token and USH. Our ecosystem now features two independent boosters: one for the Lending Protocol and one for the USH Staking Module, each operating with the goal of maximizing yields for users. Key Improvements in Booster V2 Booster V2 brings several enhancements that elevate the functionality and user experience: Support for Multiple Token Types: Users will be able to deposit Pool Tokens, Farm Tokens, Dual Farm Tokens, or Staked HTM Tokens (via xExchange). Only the HTM portion will be considered for boosting. Unlimited Staking: The cap on HTM deposits will be removed, allowing users to stake without limits. This will foster a competitive environment where the more HTM you stake, the higher your potential APY. Integrated xExchange Management: Users will be able to manage their xExchange positions directly from the Booster dashboard. This will include creating pools, farming, dual farming, and staking HTM tokens, all from one convenient dashboard. Energy Management Integration: Booster V2 will allow users to manage their xExchange Energy directly from the dashboard, providing an additional way to boost rewards even further. Seamless Migration: Users will be able to migrate HTM between the Lending Protocol Booster and the USH Staking Module Booster without any cooldown periods, making it easier to optimize strategies across both modules. How the Yields Work Booster V2 will introduce a more structured and competitive approach to yield distribution across both the Lending Protocol and the Staking Module. HTM Booster in the Lending Protocol Base APY (First Batch): This is available to all users who stake a specific percentage of HTM relative to their collateral value. Any user can achieve this Base APY by staking the required amount of HTM. Boosted APY (Second Batch): After achieving the base level, users can boost their returns further by staking additional HTM, competing for the second batch of rewards. The more HTM staked beyond the base threshold, the higher the potential yield. USH Staking Module Yields Staking APY: Users who deposit USH-related LP tokens without boosting through the HTM Booster will still receive a Staking APY. This ensures that even passive participants which are not looking to stake their HTM in the Booster can take advantage of the USH Ecosystem to generate yields. Booster APY: Similar to the system in the Lending Protocol, users can stake HTM to unlock a Base APY. Beyond this threshold, any additional HTM staked will increase their APY in a competitive manner, allowing users to maximize their returns based on the amount of HTM they commit to boosting their positions. Rollout Plan for USH USH will be deployed in a phased rollout to ensure smooth implementation: Public Devnet: Open for testing, with incentives for participants to explore and stress-test the platform. Private Mainnet: A limited launch with partners to mint USH, bootstrap USH liquidity and generate initial protocol revenue. Public Mainnet: A full-scale launch, enabling all users to mint, stake, and trade USH. We know DeFi can be complex, which is why we’re committed to providing the tools and resources needed to navigate our ecosystem. With the USH Public Devnet launch, we’ll release updated documentation offering clear guidance on Hatom’s products. Developer documentation is also in the works, and we’re exploring the idea of a Hatom Academy for educational resources. Plus, we’ll soon roll out content focused on USH, helping users fully tap into its potential within Hatom and the MultiversX ecosystem. What’s Next? Hatom Pulse As Hatom grows, our focus remains on pushing DeFi boundaries while expanding across multiple ecosystems. Although this update doesn’t include a full roadmap—that will come later—our priority is clear: expanding Hatom across chains. To stand out in the competitive DeFi landscape, we’re committed to developing standout products. With that in mind, we’re excited to give you an exclusive preview of one of our most innovative products in development: Hatom Pulse. Over-collateralized non-custodial lending protocols, liquid staking, and over-collateralized stablecoins already exist on #Ethereum. What sets us apart is the synergy between these components within a unified ecosystem. By integrating these pillars, we tackle capital inefficiencies, allowing one protocol to enhance strategies that benefit the others, maximizing returns across the board. For example, when USH is minted, it means that EGLD is deposited, liquid-staked, and supplied in the lending protocol—all three protocols working in harmony. Hatom Pulse will elevate this synergy to another level, solving key issues faced by Aave, Compound Labs , and other leading protocols. We believe this innovation will be pivotal as we work to gain market share while expanding cross-chain. Our proof of concept will be deployed and battle-tested on #MultiversX, but the real growth will come when we scale this to markets that are thousands of times larger. This will be a turning point for Hatom. So, what is Hatom Pulse? On Hatom, like on Aave and other leading lending protocols, the largest assets used as collateral are often not borrowed, leading to substantial revenue loss for the protocol. This also results in very low income on the supply side, as borrowing fees depend on utilization rates, which only increase when borrowing activity rises. Generally, lending protocols are used to provide assets for borrowing stablecoins or for leveraging liquid staking strategies. This inefficiency locks up billions of dollars in dormant assets, and users earn very low supply rates on their collateral, which doesn’t help offset their loan interest. Hatom Pulse is designed to address these inefficiencies by leveraging the synergy between our existing products. It creates sophisticated vaults that activate dormant assets, unlocking advanced yield opportunities through a delta-neutral strategy. By utilizing assets like $EGLD, $sEGLD, $wTAO, and $swTAO, Hatom Pulse enables users to engage in delta-neutral strategies, where we long and short these assets on (CEXs), earning funding rates and staking rewards while keeping their assets intact. (The exact strategy, along with all the details, will be shared once USH is fully established). Initially, these vaults will operate on CEXs, where liquidity is highest, and will be managed through custodians like Copper.co to mitigate counterparty risks. Later, we plan to extend this to DEXs where all operations will be governed by smart contracts, ensuring full decentralization. serves as a strong proof of concept for us in this regard. However, our strategy will differ, as our focus will be on protecting the unit value, rather than the dollar value. Although Hatom Pulse is still in its research phase, early estimates suggest that this product alone could generate over 18% annual returns on $EGLD and more than 35% on $wTAO, with what we believe to be minimal risk. It’s important to note that these figures reflect current metrics based on internal calculations and may slightly differ upon product launch. But imagine reaching this on #Ethereum, while allowing users to borrow using their assets—this could be a disruptive protocol. We believe Hatom Pulse has the potential to become a cornerstone product as we transition into an omni-chain future. In a competitive DeFi landscape, it could give us a significant edge by offering something truly groundbreaking, capable of competing with well-established protocols across various chains. This strategy represents immense untapped potential. Hatom Pulse is being developed for risk-averse users who seek higher returns without excessive risk. By addressing inefficiencies in current DeFi strategies, we aim to offer a secure, robust option for yield generation that could rival established protocols. It's been an intense year for our team, and we sincerely thank the community for their patience, trust, and unwavering support as we've worked hard to build and deliver these groundbreaking products. As Hatom's omni-chain expansion nears, we remain focused on improving our existing products and researching new innovations to stay ahead in this competitive market. Our goal is to build a comprehensive DeFi ecosystem, accessible across all blockchains. With USH approaching its Mainnet release, we're proud of how our products have reshaped the DeFi landscape on MultiversX. By filling key gaps in the on-chain economy, we've created opportunities for users to generate yield, unlock the potential of decentralized finance, and provide strong utility for EGLD. In just over a year, we’ve built a strong ecosystem, but this is only the beginning. We’re ready to go even further, developing better products and unlocking new opportunities for our users. We’ll share more about our expansion plans in a dedicated post, staying focused on what matters most. Rest assured, what’s coming will be truly impressive for Hatom and our growing community!

Hatom Labs

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