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Hermes Agent vs OpenClaw using Qwen 35B Local Model We asked agents to scrape GitHub star history for both tools, find what caused the growth spikes, build a live dashboard in the browser. MacBook Pro M5 Max 64Gb OpenClaw: 203k tokens, 12m 01s - wrote a bash script Hermes:...

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607,115 次观看 • 4 个月前 •via X (Twitter)

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Atomic Bot put Hermes and OpenClaw head-to-head on the exact same task, running the same model (Qwen 3.6 35B) with the same goal: analyzing GitHub history, mapping growth spikes, and shipping a live dashboard in the browser. Key metrics to watch for 👀 > Time to complete the task > Tokens spent > Quality of the final result (dashboard in browser)
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Atomic Bot put Hermes and OpenClaw head-to-head on the exact same task, running the same model (Qwen 3.6 35B) with the same goal: analyzing GitHub history, mapping growth spikes, and shipping a live dashboard in the browser. Key metrics to watch for 👀 > Time to complete the task > Tokens spent > Quality of the final result (dashboard in browser)

🚨 AI News | TestingCatalog

142,411 次观看 • 4 个月前

Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.
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Karpathy's Agentic Engineering finally has proper tooling! (built by Google) Karpathy defined agentic engineering as the discipline that separates production agent work from vibe coding. The core skills he listed were spec design, eval loops, and security oversight. The problem has been that practicing this still requires a different tool for every phase: - editor for code - a terminal for scaffolding - a browser for testing - a cloud console for deployment - and a separate framework for evals. Every transition is a context switch. The solution to production-grade Agentic Engineering is now actually implemented in Google’s Agents CLI. It covers the entire workflow in one place for scaffolding, evaluating, and deploying ADK agents. One setup command injects 7 ADK-specific skills into a coding agent's context, which lets it handle scaffolding, evals, deployment, and enterprise registration through natural language. I tested this end-to-end by building a RAG agent from scratch using Claude Code. It scaffolded the full project from the ADK agentic_rag template, generated 20 eval scenarios with LLM-as-judge scoring, and returned a quantitative scorecard. Finally, it also deployed everything to Agent Runtime and registered the agent to Gemini Enterprise, so the entire org can discover and use it. The video below shows this in action, and I worked with the Google Cloud team to put this together. Agents CLI GitHub repo → (don't forget to star it ⭐ ) I wrote up the full build covering all six steps from install to enterprise registration. It includes the eval scorecard, the instruction loophole the eval caught before deployment, and what the deployment process actually looks like end-to-end. Read it below.

Akshay 🚀

258,823 次观看 • 3 个月前

HERMES AGENT HAS A SECOND BRAIN. 1,100+ KNOWLEDGE FILES. AUTO-LINKED. SELF-IMPROVING. GROWING EVERY NIGHT. THIS IS THE OBSIDIAN GRAPH BEHIND IT. every dot = one knowledge file (markdown) every line = one wiki-link between files every color = one category (skills, notes, decisions, sources, entities) HOW IT BUILDS ITSELF: Hermes ships with a bundled LLM Wiki skill. based on Andrej Karpathy's pattern. unlike RAG (rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. when you feed the agent a source: → it reads the content → writes a structured markdown page → auto-links to every related existing page → flags contradictions with previous entries → updates all affected pages one source in. multiple connections created. the graph grows denser with every entry. WHAT FEEDS THE WIKI: → articles and URLs you find interesting → meeting transcripts → PDF documents and research papers → conversation history from Hermes sessions → Claude Code and Codex session history → Slack logs, email threads, saved notes → YouTube transcripts → raw text dropped into a _raw/ folder the obsidian-wiki package supports multi-agent ingest from Hermes, Claude Code, Codex, OpenClaw, Pi, Windsurf, and ChatGPT exports. install: pip install obsidian-wiki obsidian-wiki setup --vault ~/wiki AUTOMATE THE GROWTH: set cron jobs to feed the wiki overnight: "every day at 9am, check for new meetings. ingest transcripts into the wiki." "every week, check arXiv for new papers in [niche]. summarize and file into the wiki." "every day, ingest today's Hermes sessions into the wiki under session-history." month 1: 50 entries. scattered. month 3: 300+ entries. cross-referenced. month 6: 1,000+ entries. the agent surfaces patterns you never searched for. WHY OBSIDIAN: the wiki is plain markdown files. no database. no lock-in. open it in Obsidian for graph view: → nodes show knowledge density → links show how ideas connect → clusters reveal your strongest domains → orphan nodes reveal gaps Hermes writes from a VPS. Obsidian reads on your laptop. obsidian-headless syncs without a GUI. agent writes from the server, you browse on your device. FOUR MEMORY LAYERS: Layer 1: memory.md + user.md (~2,200 + 1,375 chars. short-term.) Layer 2: SQLite with FTS5 (full session transcripts. searchable.) Layer 3: external providers (Mem0, SuperMemory, Honcho. optional.) Layer 4: Obsidian wiki via LLM Wiki skill (unlimited. compounding. the long-term brain.) layers 1-3 handle memory. layer 4 handles knowledge. the graph in this post is layer 4. SETUP: set in Desktop app, Dashboard, or config.yaml: WIKI_PATH=~/wiki OBSIDIAN_VAULT_PATH=~/wiki first run: Hermes asks for your domain. answer with your niche. the skill builds SCHEMA.md with tag taxonomy. after that: "index this into my wiki: [URL or text]" the wiki grows. the graph densifies. the agent gets smarter because the knowledge base got smarter. full 15 levels breakdown in the article 👇
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HERMES AGENT HAS A SECOND BRAIN. 1,100+ KNOWLEDGE FILES. AUTO-LINKED. SELF-IMPROVING. GROWING EVERY NIGHT. THIS IS THE OBSIDIAN GRAPH BEHIND IT. every dot = one knowledge file (markdown) every line = one wiki-link between files every color = one category (skills, notes, decisions, sources, entities) HOW IT BUILDS ITSELF: Hermes ships with a bundled LLM Wiki skill. based on Andrej Karpathy's pattern. unlike RAG (rediscovers knowledge from scratch every query), the wiki compiles knowledge once and keeps it current. when you feed the agent a source: → it reads the content → writes a structured markdown page → auto-links to every related existing page → flags contradictions with previous entries → updates all affected pages one source in. multiple connections created. the graph grows denser with every entry. WHAT FEEDS THE WIKI: → articles and URLs you find interesting → meeting transcripts → PDF documents and research papers → conversation history from Hermes sessions → Claude Code and Codex session history → Slack logs, email threads, saved notes → YouTube transcripts → raw text dropped into a _raw/ folder the obsidian-wiki package supports multi-agent ingest from Hermes, Claude Code, Codex, OpenClaw, Pi, Windsurf, and ChatGPT exports. install: pip install obsidian-wiki obsidian-wiki setup --vault ~/wiki AUTOMATE THE GROWTH: set cron jobs to feed the wiki overnight: "every day at 9am, check for new meetings. ingest transcripts into the wiki." "every week, check arXiv for new papers in [niche]. summarize and file into the wiki." "every day, ingest today's Hermes sessions into the wiki under session-history." month 1: 50 entries. scattered. month 3: 300+ entries. cross-referenced. month 6: 1,000+ entries. the agent surfaces patterns you never searched for. WHY OBSIDIAN: the wiki is plain markdown files. no database. no lock-in. open it in Obsidian for graph view: → nodes show knowledge density → links show how ideas connect → clusters reveal your strongest domains → orphan nodes reveal gaps Hermes writes from a VPS. Obsidian reads on your laptop. obsidian-headless syncs without a GUI. agent writes from the server, you browse on your device. FOUR MEMORY LAYERS: Layer 1: memory.md + user.md (~2,200 + 1,375 chars. short-term.) Layer 2: SQLite with FTS5 (full session transcripts. searchable.) Layer 3: external providers (Mem0, SuperMemory, Honcho. optional.) Layer 4: Obsidian wiki via LLM Wiki skill (unlimited. compounding. the long-term brain.) layers 1-3 handle memory. layer 4 handles knowledge. the graph in this post is layer 4. SETUP: set in Desktop app, Dashboard, or config.yaml: WIKI_PATH=~/wiki OBSIDIAN_VAULT_PATH=~/wiki first run: Hermes asks for your domain. answer with your niche. the skill builds SCHEMA.md with tag taxonomy. after that: "index this into my wiki: [URL or text]" the wiki grows. the graph densifies. the agent gets smarter because the knowledge base got smarter. full 15 levels breakdown in the article 👇

YanXbt

35,900 次观看 • 3 个月前

hey if you have a 3060, or any GPU with 8GB or more sitting in a drawer right now, that thing can run 9 billion parameters of intelligence autonomously. and you don't know it yet. 2 hours ago i posted that 9B hit a ceiling. 2,699 lines across 11 files. blank screen. said the limit for autonomous multifile coding on 9 billion parameters is real. then i audited every file. found 11 bugs. exact file, exact line, exact fix. duplicate variable declarations killing the script loader. a canvas reference never connected to the DOM. enemies with no movement logic. particle systems called on the class instead of the instance. fed that list as a single prompt to the same Qwen 3.5 9B on the same RTX 3060 through Hermes Agent. it fixed all 11. surgically. patch level edits across 4 files. no rewrites. no hallucinated changes. game boots. enemies spawn, move, collide. background renders. particles fire. and here's what nobody is talking about. this is a 9 billion parameter model running a full agentic framework. Hermes Agent with 31 tools. file operations, terminal, browser, code execution. not a single tool call failed. the agent chain never broke. most people think you need 70B+ for reliable tool use. this is 9B on 12 gigs doing it clean. the model didn't fail. my prompting strategy did. the ceiling is not the parameter count. the ceiling is how you prompt it. this is not done. bullets don't fire yet. boss fights need wiring. but the screen that was black 2 hours ago now has a full game rendering in real time. iterating right now. anyone with a GPU from the last 5 years should be paying attention to what is happening right now.
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hey if you have a 3060, or any GPU with 8GB or more sitting in a drawer right now, that thing can run 9 billion parameters of intelligence autonomously. and you don't know it yet. 2 hours ago i posted that 9B hit a ceiling. 2,699 lines across 11 files. blank screen. said the limit for autonomous multifile coding on 9 billion parameters is real. then i audited every file. found 11 bugs. exact file, exact line, exact fix. duplicate variable declarations killing the script loader. a canvas reference never connected to the DOM. enemies with no movement logic. particle systems called on the class instead of the instance. fed that list as a single prompt to the same Qwen 3.5 9B on the same RTX 3060 through Hermes Agent. it fixed all 11. surgically. patch level edits across 4 files. no rewrites. no hallucinated changes. game boots. enemies spawn, move, collide. background renders. particles fire. and here's what nobody is talking about. this is a 9 billion parameter model running a full agentic framework. Hermes Agent with 31 tools. file operations, terminal, browser, code execution. not a single tool call failed. the agent chain never broke. most people think you need 70B+ for reliable tool use. this is 9B on 12 gigs doing it clean. the model didn't fail. my prompting strategy did. the ceiling is not the parameter count. the ceiling is how you prompt it. this is not done. bullets don't fire yet. boss fights need wiring. but the screen that was black 2 hours ago now has a full game rendering in real time. iterating right now. anyone with a GPU from the last 5 years should be paying attention to what is happening right now.

Sudo su

684,336 次观看 • 6 个月前

you're paying $20/mo for something your $500 GPU can already do. Gemma 4 26B A4B QAT MoE + Hermes Agent running on a single RTX 4060 (8GB VRAM). Built a vision capable, 100% free, 100% local, private AI assistant that lives in my Chrome browser. No API keys. No cloud. No subscriptions. 100% vibe coded. 0% handholding. It has full context of whatever's on my screen can answer questions, summarize pages, extract data, and see images. Same local model handles everything, no external calls, ever. keep reading for the model and hermes agent tips i learnt while building this locally. Here's the exact setup for anyone running local LLMs on 6-8 GB VRAM: llama.cpp server flags (on my NVIDIA RTX 4060 8gb VRAM): -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --cache-type-k q8_0 --cache-type-v q8_0 -c 150000 --port 8080 Throughput with quantization: Prefill: 200-250 tokens/sec Decode: 20-25 tokens/sec reduce context if oom on 6 gb vram card. Key learnings: - Quantize KV cache to q8 for faster prefill/decode. Prefill goes from 100-150 (unquantized) to 200-250 tok/s (q8). - But watch out, once actual context grows past ~50k tokens on high entropy workloads, q8 KV quantization can cause hallucinations. Low entropy workloads are mostly unaffected. If you see it happening, drop the quantization. This is common across all local models. - In Hermes Agent settings -> Memory & Context, bump compression threshold from default 0.5 to 0.7. Default triggers way too frequent context compression and eats time. Up next: add persistent memory, web search, tool calling, streaming output and whatever you suggest. Running a 26B MoE with vision + 150k context window on 8GB VRAM would've sounded impossible 6 months ago. Works the same on the NVIDIA RTX 3060 Ti, 3070, 4060 Ti, 5060, 2080, or any 8GB card. VRAM is the only requirement. Local AI agents are closer than people think. You just need to know where the knobs are. Model's Unsloth quant hugging face link in the comments. Have you tried Hermes agent by Nous Research yet? What are you building with local LLMs? Drop it below, let's see what this community is shipping.
0:19

you're paying $20/mo for something your $500 GPU can already do. Gemma 4 26B A4B QAT MoE + Hermes Agent running on a single RTX 4060 (8GB VRAM). Built a vision capable, 100% free, 100% local, private AI assistant that lives in my Chrome browser. No API keys. No cloud. No subscriptions. 100% vibe coded. 0% handholding. It has full context of whatever's on my screen can answer questions, summarize pages, extract data, and see images. Same local model handles everything, no external calls, ever. keep reading for the model and hermes agent tips i learnt while building this locally. Here's the exact setup for anyone running local LLMs on 6-8 GB VRAM: llama.cpp server flags (on my NVIDIA RTX 4060 8gb VRAM): -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf --cache-type-k q8_0 --cache-type-v q8_0 -c 150000 --port 8080 Throughput with quantization: Prefill: 200-250 tokens/sec Decode: 20-25 tokens/sec reduce context if oom on 6 gb vram card. Key learnings: - Quantize KV cache to q8 for faster prefill/decode. Prefill goes from 100-150 (unquantized) to 200-250 tok/s (q8). - But watch out, once actual context grows past ~50k tokens on high entropy workloads, q8 KV quantization can cause hallucinations. Low entropy workloads are mostly unaffected. If you see it happening, drop the quantization. This is common across all local models. - In Hermes Agent settings -> Memory & Context, bump compression threshold from default 0.5 to 0.7. Default triggers way too frequent context compression and eats time. Up next: add persistent memory, web search, tool calling, streaming output and whatever you suggest. Running a 26B MoE with vision + 150k context window on 8GB VRAM would've sounded impossible 6 months ago. Works the same on the NVIDIA RTX 3060 Ti, 3070, 4060 Ti, 5060, 2080, or any 8GB card. VRAM is the only requirement. Local AI agents are closer than people think. You just need to know where the knobs are. Model's Unsloth quant hugging face link in the comments. Have you tried Hermes agent by Nous Research yet? What are you building with local LLMs? Drop it below, let's see what this community is shipping.

Alok

36,691 次观看 • 3 个月前

How to build a viral Web3 app in an afternoon using the ChainGPT AI skill for Claude Code. No coding experience required. I built Roast My Wallet. Paste any Ethereum wallet address, get a savage AI-generated roast of your trading history, a Degen Score out of 100, an on-chain report card, and three AI-generated NFT portraits. Here's exactly how it came together. Setup (3 minutes): 🔸Install Claude Code at 🔸Run /plugin install ChainGPT-org/chaingpt-claude-skill 🔸Get an API key at 🔸Type /chaingpt and describe what you want to build What the skill actually does: The ChainGPT skill doesn't just give you starter code. It knows the entire API. Every endpoint, every parameter, every credit cost, every error code. When I asked it to build the roast feature, it knew to call the LLM endpoint, how to stream the response back to the browser in real time, and how to handle errors automatically. I didn't look up a single thing. How it works under the hood: 1. Pulls real ETH balance and transaction count from the Ethereum blockchain 2. Feeds those numbers into ChainGPT's LLM and streams the roast back live 3. Calculates a Degen Score from your tx count vs balance ratio 4. Generates a report card with letter grades across Trading, Patience, Risk, Diamond Hands, and NGMI 5. Uses the roast text to generate three custom NFT portraits in parallel via VeloGen 6. Packages everything into a downloadable PNG card ready to post 7. Every feature came from describing what I wanted: 8. "Make the API key server-side." Done. 9. "Add an animated arc gauge for the degen score." Done. 10. "Generate NFT portraits using the roast text as context." Done. I never wrote a function or debugged an API response. I described outcomes. The ChainGPT skill handled the rest. If you can describe what you want to build, you can build it. Get your API key. Install the skill. /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build with ChainGPT AI!
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How to build a viral Web3 app in an afternoon using the ChainGPT AI skill for Claude Code. No coding experience required. I built Roast My Wallet. Paste any Ethereum wallet address, get a savage AI-generated roast of your trading history, a Degen Score out of 100, an on-chain report card, and three AI-generated NFT portraits. Here's exactly how it came together. Setup (3 minutes): 🔸Install Claude Code at 🔸Run /plugin install ChainGPT-org/chaingpt-claude-skill 🔸Get an API key at 🔸Type /chaingpt and describe what you want to build What the skill actually does: The ChainGPT skill doesn't just give you starter code. It knows the entire API. Every endpoint, every parameter, every credit cost, every error code. When I asked it to build the roast feature, it knew to call the LLM endpoint, how to stream the response back to the browser in real time, and how to handle errors automatically. I didn't look up a single thing. How it works under the hood: 1. Pulls real ETH balance and transaction count from the Ethereum blockchain 2. Feeds those numbers into ChainGPT's LLM and streams the roast back live 3. Calculates a Degen Score from your tx count vs balance ratio 4. Generates a report card with letter grades across Trading, Patience, Risk, Diamond Hands, and NGMI 5. Uses the roast text to generate three custom NFT portraits in parallel via VeloGen 6. Packages everything into a downloadable PNG card ready to post 7. Every feature came from describing what I wanted: 8. "Make the API key server-side." Done. 9. "Add an animated arc gauge for the degen score." Done. 10. "Generate NFT portraits using the roast text as context." Done. I never wrote a function or debugged an API response. I described outcomes. The ChainGPT skill handled the rest. If you can describe what you want to build, you can build it. Get your API key. Install the skill. /plugin install ChainGPT-org/chaingpt-claude-skill Anyone can build with ChainGPT AI!

ChainGPT

29,339 次观看 • 4 个月前

HERMES AGENT NOW RUNS CLAUDE OPUS 5. NEAR FABLE 5 INTELLIGENCE. HALF THE PRICE. SELF-VERIFIES ITS OWN WORK. AVAILABLE TODAY VIA NOUS PORTAL (20% OFF ALL MODELS). Anthropic shipped Opus 5 on July 24, 2026. same $5/$25 per million tokens as Opus 4.8. but the benchmarks tell a different story. WHAT CHANGED FROM OPUS 4.8: FrontierBench v0.1: Opus 5: 43.3%. Opus 4.8: 18.7%. 2.3x jump on the same test. ARC-AGI-3: Opus 5: 30.2%. 3x better than the next closest model. beat Fable 5 on 8 out of 13 benchmarks. at half the cost ($5/$25 vs $10/$50). same price as Opus 4.8. twice the intelligence. no reason to stay on 4.8. THE SPECS: model ID: claude-opus-5 context: 1M tokens (default and maximum) max output: 128K tokens thinking: on by default effort toggle: low / medium / high per request fast mode: $10/$50, 2.5x faster knowledge cutoff: May 2026 minimum cacheable prompt: 512 tokens (was 1,024) SELF-VERIFICATION (the biggest change): Opus 5 checks its own work automatically. Anthropic says: delete your verification prompts. "include a final verification step" now causes OVER-verification because the model already does it. for Hermes /goal tasks this is a direct upgrade. the judge checks evidence. the model also checks evidence. double layer of verification without extra tokens. EFFORT TOGGLE: low: fast, cheap, routine work. medium: balanced, daily tasks. high: full reasoning, complex problems. set per request. not a global switch. matches Hermes /reasoning command: /reasoning low (routine) /reasoning high (complex) Opus 5 effort toggle + Hermes reasoning control = precise cost management per turn. WHERE OPUS 5 FITS IN HERMES: DAILY DRIVER (replaces Opus 4.8): same price. 2.3x better benchmarks. set as your main model: Desktop app / Dashboard: Models → claude-opus-5 CHIEF OF STAFF: synthesis across multiple agents. reads Kanban, prioritizes, routes tasks. self-verification catches routing errors before they cascade. COMPLEX CODING: SOTA on agentic coding benchmarks. FrontierBench 43.3% = best public model for coding. set as coder profile model. /GOAL TASKS: self-verification + completion contracts = the model proves its work AND double-checks the proof. long-horizon goals finish correctly more often. MoA AGGREGATOR: strongest synthesis model at $5/$25. pair with GPT-5.6 and Grok 4.5 as references. Opus 5 aggregates. best quality at mid-range price. presets: max-quality: reference_models: - provider: openai-codex model: gpt-5.6-sol - provider: xai model: grok-4.5 aggregator: provider: anthropic model: claude-opus-5 COMPUTER USE: near-Fable 5 quality for browser automation. at half the token cost per session. computer_use tasks burn lots of vision tokens. Opus 5 halves that bill vs Fable 5. WHAT TO KEEP OPUS 5 AWAY FROM: cron monitoring: too expensive. use DeepSeek or no_agent mode. sub-agent grunt work: use GPT-5.6 Luna ($1/$6) or DeepSeek. auxiliary tasks: use Gemini Flash. routine web extraction: use a cheap model. Opus 5 is for the turns where quality compounds. planning, synthesis, verification, complex reasoning. budget models handle everything else. NOUS PORTAL: 20% OFF ALL MODELS Nous Portal currently runs a 20% discount on all models including Opus 5. $5/$25 official → $4/$20 through Nous Portal. the cheapest way to run Opus 5 right now. hermes setup --portal select claude-opus-5 as your model. discount applies automatically. Opus 5 replaces Opus 4.8 everywhere. same price. better at everything. no tradeoff. straight upgrade. hermes update /model claude-opus-5
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HERMES AGENT NOW RUNS CLAUDE OPUS 5. NEAR FABLE 5 INTELLIGENCE. HALF THE PRICE. SELF-VERIFIES ITS OWN WORK. AVAILABLE TODAY VIA NOUS PORTAL (20% OFF ALL MODELS). Anthropic shipped Opus 5 on July 24, 2026. same $5/$25 per million tokens as Opus 4.8. but the benchmarks tell a different story. WHAT CHANGED FROM OPUS 4.8: FrontierBench v0.1: Opus 5: 43.3%. Opus 4.8: 18.7%. 2.3x jump on the same test. ARC-AGI-3: Opus 5: 30.2%. 3x better than the next closest model. beat Fable 5 on 8 out of 13 benchmarks. at half the cost ($5/$25 vs $10/$50). same price as Opus 4.8. twice the intelligence. no reason to stay on 4.8. THE SPECS: model ID: claude-opus-5 context: 1M tokens (default and maximum) max output: 128K tokens thinking: on by default effort toggle: low / medium / high per request fast mode: $10/$50, 2.5x faster knowledge cutoff: May 2026 minimum cacheable prompt: 512 tokens (was 1,024) SELF-VERIFICATION (the biggest change): Opus 5 checks its own work automatically. Anthropic says: delete your verification prompts. "include a final verification step" now causes OVER-verification because the model already does it. for Hermes /goal tasks this is a direct upgrade. the judge checks evidence. the model also checks evidence. double layer of verification without extra tokens. EFFORT TOGGLE: low: fast, cheap, routine work. medium: balanced, daily tasks. high: full reasoning, complex problems. set per request. not a global switch. matches Hermes /reasoning command: /reasoning low (routine) /reasoning high (complex) Opus 5 effort toggle + Hermes reasoning control = precise cost management per turn. WHERE OPUS 5 FITS IN HERMES: DAILY DRIVER (replaces Opus 4.8): same price. 2.3x better benchmarks. set as your main model: Desktop app / Dashboard: Models → claude-opus-5 CHIEF OF STAFF: synthesis across multiple agents. reads Kanban, prioritizes, routes tasks. self-verification catches routing errors before they cascade. COMPLEX CODING: SOTA on agentic coding benchmarks. FrontierBench 43.3% = best public model for coding. set as coder profile model. /GOAL TASKS: self-verification + completion contracts = the model proves its work AND double-checks the proof. long-horizon goals finish correctly more often. MoA AGGREGATOR: strongest synthesis model at $5/$25. pair with GPT-5.6 and Grok 4.5 as references. Opus 5 aggregates. best quality at mid-range price. presets: max-quality: reference_models: - provider: openai-codex model: gpt-5.6-sol - provider: xai model: grok-4.5 aggregator: provider: anthropic model: claude-opus-5 COMPUTER USE: near-Fable 5 quality for browser automation. at half the token cost per session. computer_use tasks burn lots of vision tokens. Opus 5 halves that bill vs Fable 5. WHAT TO KEEP OPUS 5 AWAY FROM: cron monitoring: too expensive. use DeepSeek or no_agent mode. sub-agent grunt work: use GPT-5.6 Luna ($1/$6) or DeepSeek. auxiliary tasks: use Gemini Flash. routine web extraction: use a cheap model. Opus 5 is for the turns where quality compounds. planning, synthesis, verification, complex reasoning. budget models handle everything else. NOUS PORTAL: 20% OFF ALL MODELS Nous Portal currently runs a 20% discount on all models including Opus 5. $5/$25 official → $4/$20 through Nous Portal. the cheapest way to run Opus 5 right now. hermes setup --portal select claude-opus-5 as your model. discount applies automatically. Opus 5 replaces Opus 4.8 everywhere. same price. better at everything. no tradeoff. straight upgrade. hermes update /model claude-opus-5

YanXbt

16,744 次观看 • 2 个月前

somebody explain this because i refuse to accept it someone ran 48 scored trials and one agent beat a whole fleet of them on all 6 task families, at 0.93 cents a run against 1.9, while openai's best fleet shape was paying $0.008 for every single point of accuracy it bought i read it expecting a hit piece and found the opposite: the fleets that partitioned the dependency graph properly lifted pass rate 14% and cut wall-clock 2.10x on the same tasks, and one of them beat claude code with agent teams the thing that decides it has a name, Graph Engineering, and it is a property of the diagram rather than the model: - partition on the real dependency graph pulled from static analysis, never by folder or by file, because the gains land hardest on the most dependency-dense projects - isolate the structural hub files first, since those are the nodes every partition would otherwise have to share - measure the critical path and treat it as the floor, because a chain that genuinely feeds itself cannot be replaced by more workers and wrapping it in a scheduler does not shorten it - match the topology to the coupling instead of defaulting to parallel: on coupled work a static parallel shape drops below a single agent, so the mismatch is worse than no orchestration - remember each worker serialises its own subtasks, which adds edges inside every agent that were never in your plan - budget the fan-out before you fire it, because three agents already burn roughly three times the tokens and the multiplier compounds across sessions - check worker count against your rate limit, since fifteen workers at ten requests a second walk straight through a hundred-per-second ceiling and cascade - put a script gate in front of the planner: it costs 0.15 seconds and zero tokens, and it lets the expensive model skip 43 to 63% of the steps for at most 1.4 points of accuracy the catch is the coordination tax, and it scales with how clever the shape looks: 58% extra reasoning turns for independent workers, 263% decentralised, 285% centralised, and 515% for the hybrid setup everyone reaches for first the same paper found that hybrid then collapses hardest on tool-heavy work at a 0.452 success rate, while the plainer decentralised shape beat centralised outright despite carrying more overhead, because parallel efficiency is what survives bookmark this, the whole build sits in the article ↓
0:15

somebody explain this because i refuse to accept it someone ran 48 scored trials and one agent beat a whole fleet of them on all 6 task families, at 0.93 cents a run against 1.9, while openai's best fleet shape was paying $0.008 for every single point of accuracy it bought i read it expecting a hit piece and found the opposite: the fleets that partitioned the dependency graph properly lifted pass rate 14% and cut wall-clock 2.10x on the same tasks, and one of them beat claude code with agent teams the thing that decides it has a name, Graph Engineering, and it is a property of the diagram rather than the model: - partition on the real dependency graph pulled from static analysis, never by folder or by file, because the gains land hardest on the most dependency-dense projects - isolate the structural hub files first, since those are the nodes every partition would otherwise have to share - measure the critical path and treat it as the floor, because a chain that genuinely feeds itself cannot be replaced by more workers and wrapping it in a scheduler does not shorten it - match the topology to the coupling instead of defaulting to parallel: on coupled work a static parallel shape drops below a single agent, so the mismatch is worse than no orchestration - remember each worker serialises its own subtasks, which adds edges inside every agent that were never in your plan - budget the fan-out before you fire it, because three agents already burn roughly three times the tokens and the multiplier compounds across sessions - check worker count against your rate limit, since fifteen workers at ten requests a second walk straight through a hundred-per-second ceiling and cascade - put a script gate in front of the planner: it costs 0.15 seconds and zero tokens, and it lets the expensive model skip 43 to 63% of the steps for at most 1.4 points of accuracy the catch is the coordination tax, and it scales with how clever the shape looks: 58% extra reasoning turns for independent workers, 263% decentralised, 285% centralised, and 515% for the hybrid setup everyone reaches for first the same paper found that hybrid then collapses hardest on tool-heavy work at a 0.452 success rate, while the plainer decentralised shape beat centralised outright despite carrying more overhead, because parallel efficiency is what survives bookmark this, the whole build sits in the article ↓

Argona

32,932 次观看 • 2 个月前

Top 12 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 12 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. 10. Entity alignment > Compare two candidate records and decide whether to merge, review, or keep them separate. Candidate generation remains deterministic while Jev handles semantic identity. 11. Retrieval reranking > Let embeddings retrieve a broad candidate set, then use Jev to reorder passages by relevance. The generation model receives the most useful evidence first. 12. Memory promotion gate > Capture a completed agent trace, then judge whether its corrections contain a reusable lesson. Trace-backed lessons can be promoted while task-specific noise is discarded. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. Beacon captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: (don’t forget to star it ⭐) If you want to dive deeper, I also wrote about a similar mechanism in a hands-on guide. It covers building a Jev-style decision path with open models, entirely locally. Read it below.
0:16

Top 12 agentic use cases for Jev: (bookmark this) Jev handles semantic decisions that ordinary code cannot express reliably. It returns typed answers and probabilities, while code continues to cover the workflow. Here are 12 practical use cases for Jev: 1. Browser next action > Convert the current DOM state into a bounded action such as click, type, or stop. Code executes only valid operation-target pairs. There are already several open-source Jev web agents. 2. Context compaction > Decide which events from a long agent trace should remain. The selected text stays verbatim instead of being replaced with a generated summary. 3. Skill and context loading > Compare the current user turn against the available skills. Load only the instructions needed for that turn instead of filling the context window with every skill. 4. Typed tool-call compilation > Map a natural-language request to a function and fill its typed arguments. Each argument is evaluated separately before code allows execution. 5. Citation verification > Check whether a quoted passage exists and whether the surrounding evidence supports the claim. The output can be supported, unsupported, or contradicted. 6. Extraction verification > Run a cheap extractor first, then use Jev to verify questionable fields. Clean records stay on the fast path while uncertain ones reach a reasoning model. 7. Agent trace evaluation > Turn raw trajectories into queryable labels such as progress and repetition. This avoids asking another LLM to write a full review of every run. 8. Semantic regression tests > Replay a trace suite against a new agent build. Semantic checks can then pass or block prompt, model, tool, and policy changes in CI. 9. Jevgrep code search > Search a codebase by what the code does rather than its exact words. Jev scores candidate snippets and returns the most relevant code first. 10. Entity alignment > Compare two candidate records and decide whether to merge, review, or keep them separate. Candidate generation remains deterministic while Jev handles semantic identity. 11. Retrieval reranking > Let embeddings retrieve a broad candidate set, then use Jev to reorder passages by relevance. The generation model receives the most useful evidence first. 12. Memory promotion gate > Capture a completed agent trace, then judge whether its corrections contain a reusable lesson. Trace-backed lessons can be promoted while task-specific noise is discarded. If you want to see the final pattern in practice, it is already implemented in the Beacon open-source project. Beacon captures full sessions across Claude Code, Codex, Cursor, OpenCode, and 20+ agent harnesses, and then Jev identifies which workflows and corrections are worth learning from, so that a lesson discovered by one agent can become available to the others. GitHub repo: (don’t forget to star it ⭐) If you want to dive deeper, I also wrote about a similar mechanism in a hands-on guide. It covers building a Jev-style decision path with open models, entirely locally. Read it below.

Avi Chawla

123,650 次观看 • 4 天前

Stanford researchers did it again. They just built the agent-native version of Git. When an agent works on a longer task, the run builds up a lot of state. This includes files edited/created, a dev server, a database, installed packages, KV cache, etc. Say the agent is at step 10 and makes a mistake, maybe it misreads a traceback and rewrites a file that was actually fine. The tests start failing, and the run goes off track, although everything through step eight was correct. By default, the agent just tries to fix it, which creates more edits and tool calls. This burns more tokens and grows the context. The other options are a person stepping in to redirect it or restarting the whole run from step one. That's wasteful, because it pays for every model/tool call again and re-prefills the context. Moreover, since an agent's run is non-deterministic, it doesn't reproduce the same early steps anyway. The reason it's hard to just jump back exactly to a previous correct step and resume from there is that the trajectory is only a message log. It records what the agent said and which tools it called, but not the live state underneath. That state includes things like memory, open file handles, child processes, installed packages, /tmp, and KV cache. None of that is in the log. Git can version the files, but it doesn't snapshot the running process or the KV cache. Checking out step eight moves the files back, but the process is still sitting in step-ten memory with a cold cache. Shepherd is a runtime layer by Stanford that records the run as a trace of typed events rather than a flat log. Each agent-environment interaction becomes a commit, similar to Git, but it tracks the live run. Its commit includes the agent process and the filesystem together, copy-on-write, so a branch carries the actual state and not just the files. Going back to a previous step is then a single call that forks from that commit and continues from the exact state. The copy-on-write fork is roughly five times faster than docker commit, and because the prompt prefix through step eight is unchanged, the KV cache is reused over 95% on replay, so early steps aren't reprocessed again. Once the run can be forked, a meta-agent can sit on top and operate it. It watches the trace and reverts as soon as it looks wrong, before the bad write is committed. In practice, it's just Python calling fork, replay, and revert on the trace, rather than a separate control plane wired into the harness. Not everything is reversible though. Files and sandbox changes undo themselves, but a database write has no automatic undo, so it needs a matching undo step set up in advance. Something external, like a sent email or a real charge, can't be undone, so the supervisor's job there is to catch it before it fires. They tested this on a few public benchmarks. On CooperBench, where two agents work on the same codebase, adding a live supervisor took the pair-coding pass rate from 28.8% to 54.7%. It's still early and labeled alpha. The benefit mostly shows up when a run gets branched a lot over a heavy sandbox state, which is exactly where restarting wastes the most tokens and time. If Git was made to make file changes reversible, Shepherd is trying to do the same thing for a live agent run. Shepherd Repo: (don't forget to star it ⭐ ) That said, Shepherd reverts a bad step inside a run. The harness around it, the prompts, tools, and checks the supervisor relies on, still drifts across runs as models and dependencies change. Akshay wrote about making that harness repair itself, where a failing trace gets diagnosed, the fix is verified against the exact input that failed, and the failure is locked as a regression test so it can't recur. Read it below.
0:15

Stanford researchers did it again. They just built the agent-native version of Git. When an agent works on a longer task, the run builds up a lot of state. This includes files edited/created, a dev server, a database, installed packages, KV cache, etc. Say the agent is at step 10 and makes a mistake, maybe it misreads a traceback and rewrites a file that was actually fine. The tests start failing, and the run goes off track, although everything through step eight was correct. By default, the agent just tries to fix it, which creates more edits and tool calls. This burns more tokens and grows the context. The other options are a person stepping in to redirect it or restarting the whole run from step one. That's wasteful, because it pays for every model/tool call again and re-prefills the context. Moreover, since an agent's run is non-deterministic, it doesn't reproduce the same early steps anyway. The reason it's hard to just jump back exactly to a previous correct step and resume from there is that the trajectory is only a message log. It records what the agent said and which tools it called, but not the live state underneath. That state includes things like memory, open file handles, child processes, installed packages, /tmp, and KV cache. None of that is in the log. Git can version the files, but it doesn't snapshot the running process or the KV cache. Checking out step eight moves the files back, but the process is still sitting in step-ten memory with a cold cache. Shepherd is a runtime layer by Stanford that records the run as a trace of typed events rather than a flat log. Each agent-environment interaction becomes a commit, similar to Git, but it tracks the live run. Its commit includes the agent process and the filesystem together, copy-on-write, so a branch carries the actual state and not just the files. Going back to a previous step is then a single call that forks from that commit and continues from the exact state. The copy-on-write fork is roughly five times faster than docker commit, and because the prompt prefix through step eight is unchanged, the KV cache is reused over 95% on replay, so early steps aren't reprocessed again. Once the run can be forked, a meta-agent can sit on top and operate it. It watches the trace and reverts as soon as it looks wrong, before the bad write is committed. In practice, it's just Python calling fork, replay, and revert on the trace, rather than a separate control plane wired into the harness. Not everything is reversible though. Files and sandbox changes undo themselves, but a database write has no automatic undo, so it needs a matching undo step set up in advance. Something external, like a sent email or a real charge, can't be undone, so the supervisor's job there is to catch it before it fires. They tested this on a few public benchmarks. On CooperBench, where two agents work on the same codebase, adding a live supervisor took the pair-coding pass rate from 28.8% to 54.7%. It's still early and labeled alpha. The benefit mostly shows up when a run gets branched a lot over a heavy sandbox state, which is exactly where restarting wastes the most tokens and time. If Git was made to make file changes reversible, Shepherd is trying to do the same thing for a live agent run. Shepherd Repo: (don't forget to star it ⭐ ) That said, Shepherd reverts a bad step inside a run. The harness around it, the prompts, tools, and checks the supervisor relies on, still drifts across runs as models and dependencies change. Akshay wrote about making that harness repair itself, where a failing trace gets diagnosed, the fix is verified against the exact input that failed, and the failure is locked as a regression test so it can't recur. Read it below.

Avi Chawla

441,974 次观看 • 2 个月前

A Citadel quant sat down next to me at Verve on Gough and asked why my laptop had four terminals open I was scanning Polymarket. Four panes. Each one a different agent. He was killing time before a flight. Saw the screens. "Is that a multi-agent setup on prediction markets. Who's orchestrating" Claude. One prompt per agent. They don't share memory. Only a queue file. He pulled up a chair. "Walk me through. I do this for equities at work. I want to see your agent separation" Agent 1 is the scanner. I piped raw JSON from the official Polymarket CLI straight into Claude and told it to score every live market on three things. Edge against my probability estimate. Book depth on both sides. Hours to resolution. Thresholds kill 93% of markets before the brain ever sees them. Edge under 7 cents gone. Depth under $500 gone. Under 4 hours to resolution gone. Over 168 gone. 487 live markets collapse to 35. "Seven cents is your transaction cost buffer" Yes. Below that the gas and spread eat the trade. A green fill popped. +$52 on a BTC dominance market. "And the brain" Agent 2. Runs four checks on every survivor. Base rate from history. News in the last six hours. Whether any of the 47 top wallets are currently holding. And a disposition check - is the crowd making a known cognitive error. Three out of four must agree. Otherwise drop it. 86 million trades. I let Claude rank every wallet with 100+ fills and a 70%+ win rate. It returned 47 names in four minutes. Top 20 wallets made more than the bottom 13,000 combined. "Concentration like that means the signal is there. Most retail books look like a normal curve. Yours looks like power law" Kelly sizing does the rest. Capped at quarter Kelly. If f-star goes negative the trade dies no matter how confident I feel. "Overbet once and the bankroll is gone. You respect that. Good" Agent 3 is execution. Three strategies pulled out of a 53k line Typescript repo. Arbitrage across related markets. Convergence when price moves toward my estimate. Whale copy with a 60 second delay on the 47 wallets. Two agents agree full position. One agent only half. Disagreement no trade. "What did you cut" Sports. 52% win rate. Already priced in before the scanner flags it. Markets under $50k in depth. Slippage makes every edge a coin flip. Holding to settlement. The top wallets exit at 73% of max profit every time. I copied that. Agent 4 watches exits. Three triggers. Target hit at 85% of expected move. Volume spike 3x the ten minute average. Thesis stale 24 hours with no movement. "91% of the smart wallets exit before resolution. That's the trade" Yeah. Being right is not the same as being profitable. Setup: Claude API $20 Hetzner VPS $5 Four repos free Total $25 a month $200 seed. 27 days ago. $14,300 now. 271 trades. 74% win rate. Sharpe 2.47. Copy here: "How long did the build take" Two weekends. One to wire the scanner and the CLI. One to get the agents talking through the queue file. He watched the volume exit trigger fire on a Fed cut market. Position closed at 0.71. +$184. "Nobody at my shop runs four agents on their own money. We run eight on the firm's. You got the same structure on a laptop for the price of a sandwich a month" He asked for the repos. I sent them. He messaged me from the gate. "Publishing this tomorrow. My PM is going to ask me why I didn't do it first" I told him his PM already has a Bloomberg. That's the problem.
0:18

A Citadel quant sat down next to me at Verve on Gough and asked why my laptop had four terminals open I was scanning Polymarket. Four panes. Each one a different agent. He was killing time before a flight. Saw the screens. "Is that a multi-agent setup on prediction markets. Who's orchestrating" Claude. One prompt per agent. They don't share memory. Only a queue file. He pulled up a chair. "Walk me through. I do this for equities at work. I want to see your agent separation" Agent 1 is the scanner. I piped raw JSON from the official Polymarket CLI straight into Claude and told it to score every live market on three things. Edge against my probability estimate. Book depth on both sides. Hours to resolution. Thresholds kill 93% of markets before the brain ever sees them. Edge under 7 cents gone. Depth under $500 gone. Under 4 hours to resolution gone. Over 168 gone. 487 live markets collapse to 35. "Seven cents is your transaction cost buffer" Yes. Below that the gas and spread eat the trade. A green fill popped. +$52 on a BTC dominance market. "And the brain" Agent 2. Runs four checks on every survivor. Base rate from history. News in the last six hours. Whether any of the 47 top wallets are currently holding. And a disposition check - is the crowd making a known cognitive error. Three out of four must agree. Otherwise drop it. 86 million trades. I let Claude rank every wallet with 100+ fills and a 70%+ win rate. It returned 47 names in four minutes. Top 20 wallets made more than the bottom 13,000 combined. "Concentration like that means the signal is there. Most retail books look like a normal curve. Yours looks like power law" Kelly sizing does the rest. Capped at quarter Kelly. If f-star goes negative the trade dies no matter how confident I feel. "Overbet once and the bankroll is gone. You respect that. Good" Agent 3 is execution. Three strategies pulled out of a 53k line Typescript repo. Arbitrage across related markets. Convergence when price moves toward my estimate. Whale copy with a 60 second delay on the 47 wallets. Two agents agree full position. One agent only half. Disagreement no trade. "What did you cut" Sports. 52% win rate. Already priced in before the scanner flags it. Markets under $50k in depth. Slippage makes every edge a coin flip. Holding to settlement. The top wallets exit at 73% of max profit every time. I copied that. Agent 4 watches exits. Three triggers. Target hit at 85% of expected move. Volume spike 3x the ten minute average. Thesis stale 24 hours with no movement. "91% of the smart wallets exit before resolution. That's the trade" Yeah. Being right is not the same as being profitable. Setup: Claude API $20 Hetzner VPS $5 Four repos free Total $25 a month $200 seed. 27 days ago. $14,300 now. 271 trades. 74% win rate. Sharpe 2.47. Copy here: "How long did the build take" Two weekends. One to wire the scanner and the CLI. One to get the agents talking through the queue file. He watched the volume exit trigger fire on a Fed cut market. Position closed at 0.71. +$184. "Nobody at my shop runs four agents on their own money. We run eight on the firm's. You got the same structure on a laptop for the price of a sandwich a month" He asked for the repos. I sent them. He messaged me from the gate. "Publishing this tomorrow. My PM is going to ask me why I didn't do it first" I told him his PM already has a Bloomberg. That's the problem.

Lunar

29,547 次观看 • 5 个月前

✨ I open sourced my first Chrome extension 🚀 SuperLevels I vibe coded it to replace all my Chrome extensions that are increasingly being bought up by spyware and malware companies who sell your data or worse hack your accounts and steal your stuff/money/data, which I'd call one of the top security risks right now For example: Chrome extensions can read your cookies or localStorage data, including session tokens, then login to your web or email accounts and hack you, they can inject code into any site to pull data form any site you browse, then break into your crypto accounts, drain your wallets, and selling your browsing history to ad companies, but that'd actually be the most favorable thing to happen of all these! Chrome extensions are just very very very unsafe So I coded my own, that I can trust because I made it, and I can read the source code: my extension is called 🚀SuperLevels and has all the features that the Chrome extensions I used to use have but all built into one safe one The cool thing is it's 100% open source and free, and you can audit the code first with AI yourself before installing it, and then if you do install it, customize it to your liking again with AI It has these features that improve my daily workflow while browsing the web: 🚮 Tab Cleaner Automatically closes inactive tabs after a configurable timeout (default: 5 minutes). Set excluded hosts to keep important tabs alive. View and re-open recently closed tabs. 🍪 Cookie Editor Full cookie manager for the current site. View, edit, add, and delete cookies. Export cookies as JSON. Expand any cookie to see and modify all fields including domain, path, SameSite, secure, and httpOnly flags. 🔀 Redirect Tracer See every redirect hop your browser took to reach the current page. Shows status codes (301, 302, 307, etc.) with a visual chain. Copy the full redirect chain to clipboard. 🌙 Dark Mode Instant dark mode for any website using CSS filter inversion. Adjustable brightness. Toggle per-site or globally. Images and videos are automatically re-inverted so they look normal. 𝕏 X Dim Mode Custom dim theme for X/Twitter with 7 color palettes: Dim, Slate, Jade, Plum, Dusk, Ember, or a custom hue. Live preview in the popup. ⚡ JS Toggle Disable JavaScript per-site with one click. Useful for debugging, reading articles without popups, or testing progressive enhancement. Page reloads automatically. 🚫 GDPR Cookie Consent Dismisser Auto-hides and auto-clicks cookie consent banners. Supports OneTrust, CookieBot, Didomi, Quantcast, GDPR plugins, and dozens more frameworks. Toggle off if a site breaks. 🎨 Live CSS Editor Write custom CSS for any website, applied in real-time as you type. Saved per-domain. Supports tab key for indentation. 📺 YouTube Unhook Removes YouTube distractions: no homepage feed, no sidebar suggestions, no end screen overlays, no Shorts. Search still works — just no algorithmic recommendations. 🎵 Music Recognizer Shazam-like music identification for any tab. Captures 10 seconds of audio and identifies the song via ACRCloud (free signup, bring your own API key). Results link to YouTube. History of recognized songs. 🖼 Picture-in-Picture Pop the largest video on the current tab into a floating PiP window with one click. 🗺 Google Maps Links Re-adds clickable Maps links and map preview cards to Google Search results. 🖼 View Image Adds a "View Image" button back to Google Images, linking directly to the full-size original image. {} JSON Formatter Auto-detects pure JSON response pages and formats them with syntax highlighting, collapsible sections, and a dark theme. Copy or view raw with one click. Never triggers on regular HTML pages.
0:17

✨ I open sourced my first Chrome extension 🚀 SuperLevels I vibe coded it to replace all my Chrome extensions that are increasingly being bought up by spyware and malware companies who sell your data or worse hack your accounts and steal your stuff/money/data, which I'd call one of the top security risks right now For example: Chrome extensions can read your cookies or localStorage data, including session tokens, then login to your web or email accounts and hack you, they can inject code into any site to pull data form any site you browse, then break into your crypto accounts, drain your wallets, and selling your browsing history to ad companies, but that'd actually be the most favorable thing to happen of all these! Chrome extensions are just very very very unsafe So I coded my own, that I can trust because I made it, and I can read the source code: my extension is called 🚀SuperLevels and has all the features that the Chrome extensions I used to use have but all built into one safe one The cool thing is it's 100% open source and free, and you can audit the code first with AI yourself before installing it, and then if you do install it, customize it to your liking again with AI It has these features that improve my daily workflow while browsing the web: 🚮 Tab Cleaner Automatically closes inactive tabs after a configurable timeout (default: 5 minutes). Set excluded hosts to keep important tabs alive. View and re-open recently closed tabs. 🍪 Cookie Editor Full cookie manager for the current site. View, edit, add, and delete cookies. Export cookies as JSON. Expand any cookie to see and modify all fields including domain, path, SameSite, secure, and httpOnly flags. 🔀 Redirect Tracer See every redirect hop your browser took to reach the current page. Shows status codes (301, 302, 307, etc.) with a visual chain. Copy the full redirect chain to clipboard. 🌙 Dark Mode Instant dark mode for any website using CSS filter inversion. Adjustable brightness. Toggle per-site or globally. Images and videos are automatically re-inverted so they look normal. 𝕏 X Dim Mode Custom dim theme for X/Twitter with 7 color palettes: Dim, Slate, Jade, Plum, Dusk, Ember, or a custom hue. Live preview in the popup. ⚡ JS Toggle Disable JavaScript per-site with one click. Useful for debugging, reading articles without popups, or testing progressive enhancement. Page reloads automatically. 🚫 GDPR Cookie Consent Dismisser Auto-hides and auto-clicks cookie consent banners. Supports OneTrust, CookieBot, Didomi, Quantcast, GDPR plugins, and dozens more frameworks. Toggle off if a site breaks. 🎨 Live CSS Editor Write custom CSS for any website, applied in real-time as you type. Saved per-domain. Supports tab key for indentation. 📺 YouTube Unhook Removes YouTube distractions: no homepage feed, no sidebar suggestions, no end screen overlays, no Shorts. Search still works — just no algorithmic recommendations. 🎵 Music Recognizer Shazam-like music identification for any tab. Captures 10 seconds of audio and identifies the song via ACRCloud (free signup, bring your own API key). Results link to YouTube. History of recognized songs. 🖼 Picture-in-Picture Pop the largest video on the current tab into a floating PiP window with one click. 🗺 Google Maps Links Re-adds clickable Maps links and map preview cards to Google Search results. 🖼 View Image Adds a "View Image" button back to Google Images, linking directly to the full-size original image. {} JSON Formatter Auto-detects pure JSON response pages and formats them with syntax highlighting, collapsible sections, and a dark theme. Copy or view raw with one click. Never triggers on regular HTML pages.

@levelsio

260,513 次观看 • 5 个月前

A Jane Street quant sat down across from me at Dandelion I was grinding through a loss streak. Three red windows in a row. BTC 5-min bot on screen. She clocked it from the next table. "Is that Polymarket? Why is your oracle pinned" I told her. Chainlink. Resolves every 5 minutes. Binance and Coinbase are the real price. She moved her matcha over without asking. "You're trading oracle lag. We did this on ETH perps in 2022. Regulators shut us down in six weeks" Not perps. Binary markets. Nobody regulates a 5-minute window. 86 million Polymarket trades. Every fill. Every book snapshot. Every resolution. "You wrote a scorer on top of this" I didn't. Claude did. One prompt. 21 days of tick data. I asked what setup has the highest follow-through. Binance and Coinbase both cross the target by $50. Same direction. Chainlink hasn't printed yet. 94% of the time the oracle catches up inside 2 minutes. "So you're front-running a feed that can't front-run you back" Yeah. She pulled out a notebook. Actual paper. Wrote something. Circled it twice. "At the desk we called this a stale quote trade. Died the minute oracles went sub-second" I told her Chainlink on Polymarket still runs on a 5-minute update. That's the whole game. A green fill landed. +$52. "What's the second layer" Order book imbalance. First 10 levels. First 90 seconds. Above 1.8 buyers are loading. Below 0.55 sellers are breaking it. Retail doesn't show up until t+240. Three files. Entry scorer. Exit trigger. Settlement router. Claude rebuilds the scorer every Sunday from the week's logs. "You're letting it rewrite its own strategy" Exactly. "That's the part my old risk team would have lost sleep over" The exit is what keeps it alive. 0.75 shares. Never resolution. Polygon settles in 1 to 3 seconds and half the fills miss in the last minute. Early exit locks 70% of max. Redeploy next window. "55 out of 400 a day" How did you know. "Kelly fraction on a 71% hit rate with that payoff geometry. You'd be insane to trade more" She wasn't wrong. My setup: Claude API - $20/mo Hetzner VPS - $5/mo poly_data - free polymarket-trade-engine - free Polymarket/agents - free 30 days. 1,847 trades. 71% win rate. +$14,200. Copytrade here: Sharpe 2.84. Max DD -$640. Avg hold 3:12. She closed her notebook. "I make more than this in a bad afternoon at the desk. But the desk doesn't let me copy-paste a scorer from Claude on a Sunday" I told her that's the whole point. She stared at the screen for a minute. "Can I follow this wallet" Already live. The article was up the next morning. Her partner DMed me by lunch. Three lines. "Saw your post. My entire prop group is reading it. We'd like to talk" I told him the post is the talk. Everything's in it. Nothing left to gatekeep.
0:19

A Jane Street quant sat down across from me at Dandelion I was grinding through a loss streak. Three red windows in a row. BTC 5-min bot on screen. She clocked it from the next table. "Is that Polymarket? Why is your oracle pinned" I told her. Chainlink. Resolves every 5 minutes. Binance and Coinbase are the real price. She moved her matcha over without asking. "You're trading oracle lag. We did this on ETH perps in 2022. Regulators shut us down in six weeks" Not perps. Binary markets. Nobody regulates a 5-minute window. 86 million Polymarket trades. Every fill. Every book snapshot. Every resolution. "You wrote a scorer on top of this" I didn't. Claude did. One prompt. 21 days of tick data. I asked what setup has the highest follow-through. Binance and Coinbase both cross the target by $50. Same direction. Chainlink hasn't printed yet. 94% of the time the oracle catches up inside 2 minutes. "So you're front-running a feed that can't front-run you back" Yeah. She pulled out a notebook. Actual paper. Wrote something. Circled it twice. "At the desk we called this a stale quote trade. Died the minute oracles went sub-second" I told her Chainlink on Polymarket still runs on a 5-minute update. That's the whole game. A green fill landed. +$52. "What's the second layer" Order book imbalance. First 10 levels. First 90 seconds. Above 1.8 buyers are loading. Below 0.55 sellers are breaking it. Retail doesn't show up until t+240. Three files. Entry scorer. Exit trigger. Settlement router. Claude rebuilds the scorer every Sunday from the week's logs. "You're letting it rewrite its own strategy" Exactly. "That's the part my old risk team would have lost sleep over" The exit is what keeps it alive. 0.75 shares. Never resolution. Polygon settles in 1 to 3 seconds and half the fills miss in the last minute. Early exit locks 70% of max. Redeploy next window. "55 out of 400 a day" How did you know. "Kelly fraction on a 71% hit rate with that payoff geometry. You'd be insane to trade more" She wasn't wrong. My setup: Claude API - $20/mo Hetzner VPS - $5/mo poly_data - free polymarket-trade-engine - free Polymarket/agents - free 30 days. 1,847 trades. 71% win rate. +$14,200. Copytrade here: Sharpe 2.84. Max DD -$640. Avg hold 3:12. She closed her notebook. "I make more than this in a bad afternoon at the desk. But the desk doesn't let me copy-paste a scorer from Claude on a Sunday" I told her that's the whole point. She stared at the screen for a minute. "Can I follow this wallet" Already live. The article was up the next morning. Her partner DMed me by lunch. Three lines. "Saw your post. My entire prop group is reading it. We'd like to talk" I told him the post is the talk. Everything's in it. Nothing left to gatekeep.

Lunar

20,307 次观看 • 5 个月前

An Anthropic paid for my espresso at Sightglass when he saw my screen. I was backtesting a Claude-built arbitrage system. Terminal open. Live trades firing. He glanced over. Stopped walking. That is not TradingView. What framework is that actually running. Claude Code. Three repos. One prompt. $20 per month. He sat down across from me without asking. I work on AlphaGo successor models. We test reinforcement agents for market simulation. You are running something similar but you let Claude write the strategy layer. Not just strategy. Detection. github/warproxxx/poly_data 86 million Polymarket trades. Every wallet. Every position. Every timestamp. You are feeding Claude transaction history and letting it identify asymmetric behavior patterns. Then cloning the profitable ones in real time. Exactly. One prompt: Scan every wallet with 150+ trades and ROI above 65%. Rank by consistency. Export top 40. Claude processed 18,600 wallets in 6 minutes. Returned 38. Top 15 wallets outperformed the bottom 18,000 combined. That is not analysis. That is alpha concentration. Precisely. And you did not write the ranking algorithm. Claude built it. I just connected it to execution logic. Then I opened the second repo. github/Polymarket/polymarket-cli Official Rust CLI. No auth required for reads. 600+ markets scanned in under 3 minutes. Claude scores: liquidity depth, pricing gap, resolution timeline. 512 markets reduced to 28 before capital moves. 94.5% filtered out before entry consideration. A notification hit. Position filled. +$127. How does it decide entry timing. Four agents. No shared state. Arbitrage detector, convergence scanner, whale mirror, volume surge tracker. 3 agents agree: full position. 2 agree: half size. Split vote: skip. Consensus filtering alone eliminated 46% of losses in backtest. And exit logic. The 38 top wallets almost never hold to settlement. 89% exit early. Average 71% of max profit captured. Immediate redeployment. My bot exits at 82% of projected move or 4x volume spike. Whichever hits first. You built a whale copy system that exits before the whales do. Correct. He set his coffee down slowly. How many trades per day. 12 average. Most rejected by filters before I see notifications. My setup: Claude API: $20/mo VPS Frankfurt: $6/mo poly_data: free polymarket-cli: free $300 seed capital. 34 days ago. $18,700 now. 318 trades. 76% win rate. Sharpe 2.61. I have not modified it in 34 days. He stared at the terminal without blinking. This is exactly what our adversarial testing team models. Market-adaptive agents with autonomous strategy evolution. Except you deployed it live. He messaged me the next day. Would you consider a conversation with our safety research lead. I told him this post is the conversation. Too late to contain. The edge is not predicting markets. It is identifying who already wins and mirroring them before the pattern shifts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Money" 2. Like and retweet this post. 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the whale mirror system this week. Start with $200. Scale on evidence.
0:19

An Anthropic paid for my espresso at Sightglass when he saw my screen. I was backtesting a Claude-built arbitrage system. Terminal open. Live trades firing. He glanced over. Stopped walking. That is not TradingView. What framework is that actually running. Claude Code. Three repos. One prompt. $20 per month. He sat down across from me without asking. I work on AlphaGo successor models. We test reinforcement agents for market simulation. You are running something similar but you let Claude write the strategy layer. Not just strategy. Detection. github/warproxxx/poly_data 86 million Polymarket trades. Every wallet. Every position. Every timestamp. You are feeding Claude transaction history and letting it identify asymmetric behavior patterns. Then cloning the profitable ones in real time. Exactly. One prompt: Scan every wallet with 150+ trades and ROI above 65%. Rank by consistency. Export top 40. Claude processed 18,600 wallets in 6 minutes. Returned 38. Top 15 wallets outperformed the bottom 18,000 combined. That is not analysis. That is alpha concentration. Precisely. And you did not write the ranking algorithm. Claude built it. I just connected it to execution logic. Then I opened the second repo. github/Polymarket/polymarket-cli Official Rust CLI. No auth required for reads. 600+ markets scanned in under 3 minutes. Claude scores: liquidity depth, pricing gap, resolution timeline. 512 markets reduced to 28 before capital moves. 94.5% filtered out before entry consideration. A notification hit. Position filled. +$127. How does it decide entry timing. Four agents. No shared state. Arbitrage detector, convergence scanner, whale mirror, volume surge tracker. 3 agents agree: full position. 2 agree: half size. Split vote: skip. Consensus filtering alone eliminated 46% of losses in backtest. And exit logic. The 38 top wallets almost never hold to settlement. 89% exit early. Average 71% of max profit captured. Immediate redeployment. My bot exits at 82% of projected move or 4x volume spike. Whichever hits first. You built a whale copy system that exits before the whales do. Correct. He set his coffee down slowly. How many trades per day. 12 average. Most rejected by filters before I see notifications. My setup: Claude API: $20/mo VPS Frankfurt: $6/mo poly_data: free polymarket-cli: free $300 seed capital. 34 days ago. $18,700 now. 318 trades. 76% win rate. Sharpe 2.61. I have not modified it in 34 days. He stared at the terminal without blinking. This is exactly what our adversarial testing team models. Market-adaptive agents with autonomous strategy evolution. Except you deployed it live. He messaged me the next day. Would you consider a conversation with our safety research lead. I told him this post is the conversation. Too late to contain. The edge is not predicting markets. It is identifying who already wins and mirroring them before the pattern shifts. You only need Claude + device + 1 hour per day. Giving this free for 24 hours. To get it: 1. Comment the word "Money" 2. Like and retweet this post. 3. Follow me Himanshu Kumar so I can DM you Save this post. Build the whale mirror system this week. Start with $200. Scale on evidence.

Himanshu Kumar

15,985 次观看 • 3 个月前

🚨 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!
0:16

🚨 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

182,997 次观看 • 1 年前

Why is the market selling off today? (Save this). Today's selloff is bigger and messier than what we've seen lately, KOSPI crashed almost 11% overnight, chip stocks are getting hit everywhere and it's not because AI demand suddenly disappeared but rather a bunch of fears piling up at once that I think are getting way overplayed. Start with the AI ROI thing since it's been building since last week's earnings. Tesla and Alphabet both kicked off earnings season with big capex numbers and negative free cash flow and even with strong revenue growth both stocks got hammered. That set the tone of we don't care if capex is growing, show us the cash, and it's carrying into this week with Amazon, Meta, Microsoft and Apple all reporting, which isn't helping the nerves. But look at what actually happened with Alphabet, cloud revenue grew 81%, total sales grew 24%, that's not a company torching cash on nothing, that's a company scaling into demand it can barely keep up with. Negative free cash flow during a capex supercycle is normal, you build the data centers and buy the GPUs before the revenue shows up. Judging a buildout phase like it's a mature business is the wrong lens, and that's basically what happened last week and what's still happening today. Then there's China chip competition, which is honestly the biggest accelerant of today's move. CXMT's IPO shares rose over 466% and combined with headlines about China's homegrown DUV lithography progress, it triggered a brutal rout in Korean chipmakers, Samsung fell as much as 13%, SK Hynix over 14%, Kioxia nearly 18%, dragging the KOSPI down almost 11% and into an eighth circuit breaker this year. That spilled straight into Nvidia, ASML, Sandisk and Seagate here in the US, with Nasdaq 100 futures down over 1% before the bell. But here's the thing, five DUV units this year against ASML's 131 a year, running performance closer to a 2008 design, is not an equipment moat collapsing, it's a headline that's gotten repeated so much this week it's built its own gravity. These tools are aimed at mature nodes like automotive and industrial chips, not the leading edge logic or HBM that actually drives the AI trade, so the read through to Nvidia, ASML or Applied Materials earnings power is basically nothing. The CXMT pop is scarcity, people bidding up the only pure play China memory stock they can get their hands on, not a sign that oversupply is coming. And Korean chipmakers dropping 12 to 14% in one session looks a lot more like leverage unwinding after a parabolic run than a real rethink of Samsung or SK Hynix's HBM backlog, which both companies have already said is basically sold out for the year. Geopolitics is actually the one spot where the news should be helping, not hurting. US and Iran hostilities seem to have paused for now, which should be easing oil driven inflation fears. If this were purely a geopolitical panic you'd expect oil spiking and yields following, but that's not what's happening, this move is chip specific and Asia led, not an oil shock like a week or two ago. Rates and the Fed are still in play, decision lands tomorrow, and people are nervous about higher for longer language even though a hike isn't the base case. On top of that, reports that Nvidia's five year credit default swap costs jumped by a record margin are getting read by some as a credit risk signal tied to all this AI debt spending. But a one day CDS spike during a market wide panic is a fear indicator, not proof of an actual credit problem, spreads on every big name widen fast when volatility spikes, Nvidia's balance sheet hasn't changed in the last 24 hours. Fed futures are pricing in essentially no chance of a surprise hike tomorrow, this is a hold meeting, and I'd bet the hawkish jitters fade fast once Warsh actually talks. Then there's the bigger liquidity and positioning story, which I think explains more of today's violence than any single headline. KOSPI is down nearly 29% for the month now, steeper than 2008, mostly because Korean chipmakers had turned into crypto like gambling tokens, running way too far, too fast on retail leverage and margin debt, and now unwinding just as hard on the way down. That's positioning excess getting flushed, not HBM demand disappearing or hyperscalers pulling back. Nothing in the actual order books, capex guidance or HBM contract pricing has changed, DRAM and NAND prices are still climbing quarter over quarter, nobody's canceled a GPU order or a data center project. What changed is how much leverage was sitting on top of this trade, and that's getting ripped out in one ugly session. This is one of the scariest looking selloffs we've had all year but scary looking and actually broken are two different things. Every headline driving today, the China lithography story, the CXMT IPO, the Nvidia CDS spike, the Fed jitters, looks a lot less scary once you dig into the actual numbers, and none of it touches real AI infrastructure demand or supply. This looks like leverage and sentiment unwinding, not the long term thesis breaking. If you want to see exactly what I'm buying into this, join Milk Road Pro for just $1 using the link below.
0:11

Why is the market selling off today? (Save this). Today's selloff is bigger and messier than what we've seen lately, KOSPI crashed almost 11% overnight, chip stocks are getting hit everywhere and it's not because AI demand suddenly disappeared but rather a bunch of fears piling up at once that I think are getting way overplayed. Start with the AI ROI thing since it's been building since last week's earnings. Tesla and Alphabet both kicked off earnings season with big capex numbers and negative free cash flow and even with strong revenue growth both stocks got hammered. That set the tone of we don't care if capex is growing, show us the cash, and it's carrying into this week with Amazon, Meta, Microsoft and Apple all reporting, which isn't helping the nerves. But look at what actually happened with Alphabet, cloud revenue grew 81%, total sales grew 24%, that's not a company torching cash on nothing, that's a company scaling into demand it can barely keep up with. Negative free cash flow during a capex supercycle is normal, you build the data centers and buy the GPUs before the revenue shows up. Judging a buildout phase like it's a mature business is the wrong lens, and that's basically what happened last week and what's still happening today. Then there's China chip competition, which is honestly the biggest accelerant of today's move. CXMT's IPO shares rose over 466% and combined with headlines about China's homegrown DUV lithography progress, it triggered a brutal rout in Korean chipmakers, Samsung fell as much as 13%, SK Hynix over 14%, Kioxia nearly 18%, dragging the KOSPI down almost 11% and into an eighth circuit breaker this year. That spilled straight into Nvidia, ASML, Sandisk and Seagate here in the US, with Nasdaq 100 futures down over 1% before the bell. But here's the thing, five DUV units this year against ASML's 131 a year, running performance closer to a 2008 design, is not an equipment moat collapsing, it's a headline that's gotten repeated so much this week it's built its own gravity. These tools are aimed at mature nodes like automotive and industrial chips, not the leading edge logic or HBM that actually drives the AI trade, so the read through to Nvidia, ASML or Applied Materials earnings power is basically nothing. The CXMT pop is scarcity, people bidding up the only pure play China memory stock they can get their hands on, not a sign that oversupply is coming. And Korean chipmakers dropping 12 to 14% in one session looks a lot more like leverage unwinding after a parabolic run than a real rethink of Samsung or SK Hynix's HBM backlog, which both companies have already said is basically sold out for the year. Geopolitics is actually the one spot where the news should be helping, not hurting. US and Iran hostilities seem to have paused for now, which should be easing oil driven inflation fears. If this were purely a geopolitical panic you'd expect oil spiking and yields following, but that's not what's happening, this move is chip specific and Asia led, not an oil shock like a week or two ago. Rates and the Fed are still in play, decision lands tomorrow, and people are nervous about higher for longer language even though a hike isn't the base case. On top of that, reports that Nvidia's five year credit default swap costs jumped by a record margin are getting read by some as a credit risk signal tied to all this AI debt spending. But a one day CDS spike during a market wide panic is a fear indicator, not proof of an actual credit problem, spreads on every big name widen fast when volatility spikes, Nvidia's balance sheet hasn't changed in the last 24 hours. Fed futures are pricing in essentially no chance of a surprise hike tomorrow, this is a hold meeting, and I'd bet the hawkish jitters fade fast once Warsh actually talks. Then there's the bigger liquidity and positioning story, which I think explains more of today's violence than any single headline. KOSPI is down nearly 29% for the month now, steeper than 2008, mostly because Korean chipmakers had turned into crypto like gambling tokens, running way too far, too fast on retail leverage and margin debt, and now unwinding just as hard on the way down. That's positioning excess getting flushed, not HBM demand disappearing or hyperscalers pulling back. Nothing in the actual order books, capex guidance or HBM contract pricing has changed, DRAM and NAND prices are still climbing quarter over quarter, nobody's canceled a GPU order or a data center project. What changed is how much leverage was sitting on top of this trade, and that's getting ripped out in one ugly session. This is one of the scariest looking selloffs we've had all year but scary looking and actually broken are two different things. Every headline driving today, the China lithography story, the CXMT IPO, the Nvidia CDS spike, the Fed jitters, looks a lot less scary once you dig into the actual numbers, and none of it touches real AI infrastructure demand or supply. This looks like leverage and sentiment unwinding, not the long term thesis breaking. If you want to see exactly what I'm buying into this, join Milk Road Pro for just $1 using the link below.

Melvin

58,159 次观看 • 2 个月前

🚨What If Earth's Oldest Civilization Never Left the Ocean? What if the intelligence behind some UFO didn't actually arrive here from another star system at all? What if it has been here for longer than us, not hiding in the sky, waiting behind the Moon, or crossing the galaxy in the way that we imagine, but living beneath the oceans inside the one part of Earth we still barely understand? For decades, we have been looking up. The cultural image of UFOs is always the same thing with lights in the sky, craft descending through the atmosphere, visitors arriving from space. Even the word extraterrestrial pushes our attention away from Earth. It tells us the mystery must have to come from somewhere else. But what if that assumption is totally wrong? What if the most important part of the phenomenon is not its relationship to space, but its relationship to the oceans? Earth isn't a land planet it's an ocean planet with islands of land breaking the surface. Human civilization developed on those islands, built cities there, drew borders there, fought wars there, launched rockets from there, and then convinced itself it understood the world. But most of this planet is still beyond our direct reach. The deep ocean is dark, pressurized, vast, hostile to our bodies, difficult to map, difficult to monitor, and almost impossible to police in any sort of meaningful way. If there was another intelligence operating here and it wanted to avoid open contact with us, the ocean would be the obvious place to be. But maybe hiding is the wrong word because a civilization that evolved in the ocean would just live there. When we imagine an advanced underwater intelligence as aliens using the sea as a base, as if they arrived from somewhere else and chose the ocean as cover, that could be way off. It could be one possibility, but the stranger theory is that they never arrived at all. They may have emerged here, in Earth's oceans, long before we ever existed. Life on this planet is ancient. For most of Earth's history, land wasn't even the center of the biological story. The oceans held the chemistry, the minerals, the heat, the pressure, the vents, the darkness and the protection. Hydrothermal vent ecosystems already prove that life doesn't even need sunlight in the simple way that we once thought it did. Entire ecosystems can be built around chemical energy rising from the seafloor. That should have changed how we (SETI) think about life, but humans still keep defaulting to our own surface bias. We imagine intelligence as something that crawls onto land, discovers fire, makes tools, builds cities and eventually launches machines into the sky. That is our path but it's not necessarily the only path. An intelligence that evolved in the deep ocean would have faced a completely different set of conditions. It wouldn't begin with fire, because fire is obviously useless underwater. It wouldn't develop metallurgy in the same way that we did, because open flame and smelting are surface technologies. It wouldn't need wheels, roads, walls or conventional buildings as we do. It would evolve inside pressure, darkness, currents, sound, vibration, magnetism, chemistry and geothermal energy. Its entire technological history would be alien to us even if it was native to Earth. So when people dismiss the idea of an ancient underwater civilization by asking where the factories are, where the ruins are, or where the tools are we have to question whether their technology would leave the same signatures ours does. Would they even build like we build? Industrialization may look totally different. A deep ocean intelligence might not construct dead machinery in the way we do. It might grow structures and use biological engineering before mechanical engineering. It might use mineral matrices, pressure systems, acoustic fields, electrochemical processes or living materials. It might not separate biology and technology at all. To us, that would look less like a civilization and more like an environment. A sufficiently old oceanic intelligence may not have cities that resemble human cities. Its infrastructure may be embedded into geology, vents, trenches, caverns, mineral deposits or biological networks. Its power systems may use geothermal gradients, tidal forces, pressure differences, ocean chemistry or field effects we don't yet even understand. Its communications may not use radio in the way we expect. Sound travels really well underwater. Electrical and magnetic sensitivity exists throughout marine life. A technological species born in that world might build an entire science around signals we barely even treat as communication. This would also explain why the UFO subject keeps revolving around water. The ocean appears again and again in the background of the mystery. USOs, transmedium objects, craft entering or leaving the sea, naval encounters, disturbances under the surface, objects tracked over water, and sightings near coastlines and military maritime zones all point toward the same possibility, that maybe water isn't incidental to the phenomenon, maybe it is central. If some UFO are connected to an ocean based intelligence, then what we see in the sky could only be the visible edge of something way bigger. The craft are not arriving from elsewhere in every case. They may be surfacing from their native domain into ours for short periods of time, crossing that boundary between ocean and air the way we cross from land into water with submarines and diving equipment. The only difference is that they appear to do it way better than we do. Human technology is divided by environment, aircraft are built for air, submarines are built for water while rockets are built for space. Each domain creates different engineering problems, so we build separate machines for each one. But UAP don't appear to play by the same rules. That is what makes the transmedium reports so important. If an object can move through water, air and possibly even space without changing its basic behavior, then it might not even be flying or swimming in the conventional sense. It could actually be controlling the interaction between itself and the medium around it. That kind of technology would make sense for a civilization born in the ocean because water is dense. It resists movement, crushes weak structures. It creates drag, turbulence and cavitation. If an intelligence developed vehicles in that environment, it would eventually need to master boundary control, so it would need to reduce friction, manage pressure, avoid destructive wake effects and move through dense fluid without wasting enormous amounts of energy. If that same technology was later used in air, it might appear to us as silent propulsion, impossible acceleration, no sonic boom, no heat plume and no obvious aerodynamic logic. So what looks impossible to us may simply be the result of a technological path that did not begin with wings and rockets. The old black budget explanation doesn't fully solve this problem either. Yes, some triangle craft, drones and experimental platforms may be human and it would be naive to deny that, but human secret technology still has to come from somewhere. If certain platforms show silent hovering, field effects, plasma signatures, extreme acceleration and transmedium behavior, then we are either dealing with a hidden human science far beyond public understanding, or we are dealing with something that we are trying to imitate. That is where the old 'alien reproduction vehicle' idea and the cryptoterrestrial theory start to overlap. Maybe some of what people call black budget technology isn't purely invented, it's most likely adapted from encounters with something already operating here. Going back to what Grusch said earlier, the implications are massive. If there are underwater bases, facilities, habitats or recurring operational zones known to governments, then this isn't just a question of disclosure. There's a sovereignty issue, who controls the oceans? Who has access to the deep sea? Who monitors undersea cables, nuclear submarines, offshore infrastructure, shipping lanes and military testing ranges? If an unknown intelligence can operate in those spaces without permission, then every major navy on Earth has a problem it cannot publicly admit. Scary thought and that may be one reason the subject is buried so deeply (no pun intended). Some people think that secrecy exists because governments don't want to admit aliens are real, but that may only be part of it. The bigger issue here could be that governments don't want to admit they aren't in full control of the planet. There is a huge difference between saying, 'We have evidence of unknown craft,' and saying, 'There may be advanced non human infrastructure in the oceans and we cannot remove it.' That would also explain the change up from UFO to UAP and from extraterrestrial to non human intelligence. Non human is pretty broad lets be honest. It doesn't tell us where they come from, it leaves room for extraterrestrial, interdimensional, post biological, artificial, ultraterrestrial, cryptoterrestrial or native Earth intelligence. That could well be deliberate. Perhaps the people closest to the classified material know the answer isn't as simple as aliens from another planet as Grusch implied in the clip. An ancient oceanic intelligence would also force science to confront its own blind spots. We know intelligent life evolved on Earth at least once because we are here. But we have no law of nature saying it could only happen once, only on land, only recently, or only through primates. Evolution isn't a ladder with humans at the top. It's a branching process with countless experiments, most of which vanished or left traces we don't fully understand. If an intelligent lineage emerged in the ocean and then moved into environments where fossilization, geological preservation and surface archaeology are poor, we probably wouldn't even recognize the evidence even if fragments existed. Ocean crust is constantly recycled through plate tectonics. Seafloor environments are really destructive. Structures can be buried, subducted, corroded, overgrown or mistaken for natural formations. If a civilization was millions or even hundreds of millions of years old, the survival of obvious surface style evidence would be highly unlikely. Even human civilization, after a few million years, would leave less behind than we like to imagine. Plastics, isotopic anomalies, altered sediment layers and some industrial traces might possibly survive, but buildings, machines and cultural artifacts would mostly vanish. So now imagine a civilization that even never built like us in the first place. This doesn't prove anything obviously, but it makes the dismissal less easy. Then there is the question of why such an intelligence would stay hidden. If it is older and more advanced, why not reveal itself? The answer could be as simple as open contact with humans may not benefit it. We are violent, territorial, extractive and unstable. We turn discoveries into weapons as quick as we can. We militarize frontiers, poison ecosystems, test nuclear devices. We drag the deep sea with cables, sonar, submarines, mining ambitions and military hardware. From the perspective of an older oceanic intelligence, humans probably don't look like peers. Instead we look like the dangerous surface species entering an adolescent technological phase that we are. That could explain the strange pattern of UFO interest in nuclear sites, military installations and weapons systems. If an intelligence lives here, our nuclear age is all of a sudden not just our problem. It is a planetary problem. Nuclear weapons, nuclear submarines, nuclear waste, missile systems and military escalation would all be highly relevant to any non human civilization sharing Earth with us. The same would be true of deep sea mining, ocean pollution, climate change, undersea military networks and artificial intelligence. We may think these are all just human issues, but a hidden Earth based intelligence would see them as threats to a shared planetary system. This gives the UAP phenomenon a very different emotional tone. It's not necessarily invasion or salvation. It may be monitoring, containment or quiet intervention when we cross certain lines. It could be an intelligence trying to stay out of sight while still making sure the surface species doesn't burn the house down. The ancient ocean theory also gives a different reading to secrecy. If governments encountered evidence of this, the first instinct wouldn't be public education. It would be containment, map the sites, track the objects and recover materials if possible. Then to build programs around the technology. Keep adversaries away from the data. Use ridicule to suppress leaks. Let the phenomenon remain absurd, because absurdity is an excellent security system. People don't demand answers from something they have been trained to laugh at. That could be why the UFO/UAP subject always feels half visible. There are official hearings, but not the full data. There are whistleblowers, but never the files. There are blurry videos, but not any context. There are pilots, radar operators and military witnesses, but the system keeps absorbing their testimony into classified channels. The public sees fragments while the real pattern remains locked away. As I always say... Disclosure for the few and not the many. If the ocean is actually involved as Grusch and Burchett imply, the missing data may be even more important than the aerial data. We shouldn't only be pressing what pilots saw in the sky. We should be asking what sonar operators heard under the water, what submarines have tracked. We should also be asking what undersea sensors have recorded near restricted zones and whether there are recurring coordinates, depths, magnetic anomalies, thermal signatures or unexplained acoustic events associated with UAP activity. We need to be asking whether naval archives contain the real spine of the phenomenon. The possibility of underwater bases actually changes how we think about disclosure. If the answer is extraterrestrial visitation, disclosure is about humanity's place in the cosmos. If the answer is an ancient Earth based intelligence, disclosure is about humanity's place on its own planet. That is more intimate and more destabilizing to me than E.T. It means the human story is not the only advanced story Earth has produced. It means our myths of ownership, dominance and uniqueness all collapse overnight, suddenly 'we are not alone' applies to home. That might be harder for people to accept than aliens from space. Aliens can leave but a hidden terrestrial intelligence is part of the planet will blow peoples minds. There is also a spiritual and philosophical layer to this. Many ancient cultures contain stories of beings from the sea, underwater kingdoms, gods emerging from water, serpent people, fish like teachers, luminous beings, and hidden realms beneath or beyond the visible world. That doesn't mean the myths are literal history of course, but it is interesting that human cultures repeatedly placed mystery, intelligence and otherworldly contact in the water. The ocean has always been the border between the known and the unknown. Maybe that symbolism came from imagination or perhaps some of it came from encounters filtered through the language of the time. If an older intelligence interacted with early humans, we wouldn't expect ancient people to describe pressure engineered transmedium craft or non human oceanic infrastructure. They would describe gods, spirits, shining beings, dragons, serpents, sky boats, sea people, underworlds and portals. Human language can only describe the unknown through the symbols available at the time. Even now, we struggle. We call them craft, orbs, drones, angels, demons, aliens, ultraterrestrials, interdimensionals. The labels change, but the confusion always stays the same. The ocean theory also sits strangely well with the consciousness aspect of the phenomenon. If an ancient intelligence developed through biology and field sensitivity rather than brute mechanical industry, it may have integrated consciousness into technology way earlier than we could have. We are only now beginning to wonder whether mind, perception and information are more deeply connected to physics than our materialist models allow. An older civilization may have already built that bridge. Its craft, communication systems and interfaces may respond to awareness, intention, emotion or neural patterns in ways that seem impossible to some of us. That would explain why the phenomenon often feels both technological and psychological. It behaves like machinery, but it interacts like intelligence. It appears on sensors, but it also appears in dreams, symbols, synchronicities and personal experiences. Skeptics see that as evidence the whole thing is imaginary. Maybe sometimes it is, but maybe the strangeness is part of the interface. A civilization that understands consciousness as a field related phenomenon would not necessarily separate contact from perception. It might use perception as one of the channels. This is where the theory becomes tricky, because it doesn't allow us to keep the phenomenon safely outside ourselves. If the intelligence is oceanic, ancient, field based and consciousness aware, then contact might not look like radio signals or embassy meetings at all. It could look like sightings, dreams, intuitions, symbolic downloads, altered states, close encounters, military incidents and physical traces all mixed together. That is messy, but perhaps the mess is not a flaw in the data, it could actually be the signature of a phenomenon that crosses categories we invented too recently to trust. All of this having been said, the theory still needs evidence. It needs coordinates, sensor data, sonar records, materials, biological traces, repeatable patterns and testimony that can be checked. However as a framework, it definitely needs more attention than it gets, because it explains why the UAP phenomenon feels close, evasive, ancient and deeply tied to Earth. The extraterrestrial hypothesis asks how they got here, although I have a theory about that. While the ancient ocean hypothesis asks whether they were already here. That is a completely different question. If what Grusch is saying is even partly correct, then disclosure will reveal that human civilization has been sharing this planet with another intelligence all along. Not openly or equally, and not in a way we were ready to understand, but sharing it nonetheless. The oceans would no longer be an empty wilderness. They would become the frontier of the greatest secret in human history. Could that be why the truth has been so hard to release. Because it's one thing to tell humanity there may be life elsewhere, but it's another thing entirely to tell humanity that Earth was never only ours. #UAP #UFO #USO #UAPDisclosure #NonHumanIntelligence #NHI #UnderwaterBases #OceanMystery #Cryptoterrestrial #Transmedium #Disclosure #ufotwitter #uapX
0:15

🚨What If Earth's Oldest Civilization Never Left the Ocean? What if the intelligence behind some UFO didn't actually arrive here from another star system at all? What if it has been here for longer than us, not hiding in the sky, waiting behind the Moon, or crossing the galaxy in the way that we imagine, but living beneath the oceans inside the one part of Earth we still barely understand? For decades, we have been looking up. The cultural image of UFOs is always the same thing with lights in the sky, craft descending through the atmosphere, visitors arriving from space. Even the word extraterrestrial pushes our attention away from Earth. It tells us the mystery must have to come from somewhere else. But what if that assumption is totally wrong? What if the most important part of the phenomenon is not its relationship to space, but its relationship to the oceans? Earth isn't a land planet it's an ocean planet with islands of land breaking the surface. Human civilization developed on those islands, built cities there, drew borders there, fought wars there, launched rockets from there, and then convinced itself it understood the world. But most of this planet is still beyond our direct reach. The deep ocean is dark, pressurized, vast, hostile to our bodies, difficult to map, difficult to monitor, and almost impossible to police in any sort of meaningful way. If there was another intelligence operating here and it wanted to avoid open contact with us, the ocean would be the obvious place to be. But maybe hiding is the wrong word because a civilization that evolved in the ocean would just live there. When we imagine an advanced underwater intelligence as aliens using the sea as a base, as if they arrived from somewhere else and chose the ocean as cover, that could be way off. It could be one possibility, but the stranger theory is that they never arrived at all. They may have emerged here, in Earth's oceans, long before we ever existed. Life on this planet is ancient. For most of Earth's history, land wasn't even the center of the biological story. The oceans held the chemistry, the minerals, the heat, the pressure, the vents, the darkness and the protection. Hydrothermal vent ecosystems already prove that life doesn't even need sunlight in the simple way that we once thought it did. Entire ecosystems can be built around chemical energy rising from the seafloor. That should have changed how we (SETI) think about life, but humans still keep defaulting to our own surface bias. We imagine intelligence as something that crawls onto land, discovers fire, makes tools, builds cities and eventually launches machines into the sky. That is our path but it's not necessarily the only path. An intelligence that evolved in the deep ocean would have faced a completely different set of conditions. It wouldn't begin with fire, because fire is obviously useless underwater. It wouldn't develop metallurgy in the same way that we did, because open flame and smelting are surface technologies. It wouldn't need wheels, roads, walls or conventional buildings as we do. It would evolve inside pressure, darkness, currents, sound, vibration, magnetism, chemistry and geothermal energy. Its entire technological history would be alien to us even if it was native to Earth. So when people dismiss the idea of an ancient underwater civilization by asking where the factories are, where the ruins are, or where the tools are we have to question whether their technology would leave the same signatures ours does. Would they even build like we build? Industrialization may look totally different. A deep ocean intelligence might not construct dead machinery in the way we do. It might grow structures and use biological engineering before mechanical engineering. It might use mineral matrices, pressure systems, acoustic fields, electrochemical processes or living materials. It might not separate biology and technology at all. To us, that would look less like a civilization and more like an environment. A sufficiently old oceanic intelligence may not have cities that resemble human cities. Its infrastructure may be embedded into geology, vents, trenches, caverns, mineral deposits or biological networks. Its power systems may use geothermal gradients, tidal forces, pressure differences, ocean chemistry or field effects we don't yet even understand. Its communications may not use radio in the way we expect. Sound travels really well underwater. Electrical and magnetic sensitivity exists throughout marine life. A technological species born in that world might build an entire science around signals we barely even treat as communication. This would also explain why the UFO subject keeps revolving around water. The ocean appears again and again in the background of the mystery. USOs, transmedium objects, craft entering or leaving the sea, naval encounters, disturbances under the surface, objects tracked over water, and sightings near coastlines and military maritime zones all point toward the same possibility, that maybe water isn't incidental to the phenomenon, maybe it is central. If some UFO are connected to an ocean based intelligence, then what we see in the sky could only be the visible edge of something way bigger. The craft are not arriving from elsewhere in every case. They may be surfacing from their native domain into ours for short periods of time, crossing that boundary between ocean and air the way we cross from land into water with submarines and diving equipment. The only difference is that they appear to do it way better than we do. Human technology is divided by environment, aircraft are built for air, submarines are built for water while rockets are built for space. Each domain creates different engineering problems, so we build separate machines for each one. But UAP don't appear to play by the same rules. That is what makes the transmedium reports so important. If an object can move through water, air and possibly even space without changing its basic behavior, then it might not even be flying or swimming in the conventional sense. It could actually be controlling the interaction between itself and the medium around it. That kind of technology would make sense for a civilization born in the ocean because water is dense. It resists movement, crushes weak structures. It creates drag, turbulence and cavitation. If an intelligence developed vehicles in that environment, it would eventually need to master boundary control, so it would need to reduce friction, manage pressure, avoid destructive wake effects and move through dense fluid without wasting enormous amounts of energy. If that same technology was later used in air, it might appear to us as silent propulsion, impossible acceleration, no sonic boom, no heat plume and no obvious aerodynamic logic. So what looks impossible to us may simply be the result of a technological path that did not begin with wings and rockets. The old black budget explanation doesn't fully solve this problem either. Yes, some triangle craft, drones and experimental platforms may be human and it would be naive to deny that, but human secret technology still has to come from somewhere. If certain platforms show silent hovering, field effects, plasma signatures, extreme acceleration and transmedium behavior, then we are either dealing with a hidden human science far beyond public understanding, or we are dealing with something that we are trying to imitate. That is where the old 'alien reproduction vehicle' idea and the cryptoterrestrial theory start to overlap. Maybe some of what people call black budget technology isn't purely invented, it's most likely adapted from encounters with something already operating here. Going back to what Grusch said earlier, the implications are massive. If there are underwater bases, facilities, habitats or recurring operational zones known to governments, then this isn't just a question of disclosure. There's a sovereignty issue, who controls the oceans? Who has access to the deep sea? Who monitors undersea cables, nuclear submarines, offshore infrastructure, shipping lanes and military testing ranges? If an unknown intelligence can operate in those spaces without permission, then every major navy on Earth has a problem it cannot publicly admit. Scary thought and that may be one reason the subject is buried so deeply (no pun intended). Some people think that secrecy exists because governments don't want to admit aliens are real, but that may only be part of it. The bigger issue here could be that governments don't want to admit they aren't in full control of the planet. There is a huge difference between saying, 'We have evidence of unknown craft,' and saying, 'There may be advanced non human infrastructure in the oceans and we cannot remove it.' That would also explain the change up from UFO to UAP and from extraterrestrial to non human intelligence. Non human is pretty broad lets be honest. It doesn't tell us where they come from, it leaves room for extraterrestrial, interdimensional, post biological, artificial, ultraterrestrial, cryptoterrestrial or native Earth intelligence. That could well be deliberate. Perhaps the people closest to the classified material know the answer isn't as simple as aliens from another planet as Grusch implied in the clip. An ancient oceanic intelligence would also force science to confront its own blind spots. We know intelligent life evolved on Earth at least once because we are here. But we have no law of nature saying it could only happen once, only on land, only recently, or only through primates. Evolution isn't a ladder with humans at the top. It's a branching process with countless experiments, most of which vanished or left traces we don't fully understand. If an intelligent lineage emerged in the ocean and then moved into environments where fossilization, geological preservation and surface archaeology are poor, we probably wouldn't even recognize the evidence even if fragments existed. Ocean crust is constantly recycled through plate tectonics. Seafloor environments are really destructive. Structures can be buried, subducted, corroded, overgrown or mistaken for natural formations. If a civilization was millions or even hundreds of millions of years old, the survival of obvious surface style evidence would be highly unlikely. Even human civilization, after a few million years, would leave less behind than we like to imagine. Plastics, isotopic anomalies, altered sediment layers and some industrial traces might possibly survive, but buildings, machines and cultural artifacts would mostly vanish. So now imagine a civilization that even never built like us in the first place. This doesn't prove anything obviously, but it makes the dismissal less easy. Then there is the question of why such an intelligence would stay hidden. If it is older and more advanced, why not reveal itself? The answer could be as simple as open contact with humans may not benefit it. We are violent, territorial, extractive and unstable. We turn discoveries into weapons as quick as we can. We militarize frontiers, poison ecosystems, test nuclear devices. We drag the deep sea with cables, sonar, submarines, mining ambitions and military hardware. From the perspective of an older oceanic intelligence, humans probably don't look like peers. Instead we look like the dangerous surface species entering an adolescent technological phase that we are. That could explain the strange pattern of UFO interest in nuclear sites, military installations and weapons systems. If an intelligence lives here, our nuclear age is all of a sudden not just our problem. It is a planetary problem. Nuclear weapons, nuclear submarines, nuclear waste, missile systems and military escalation would all be highly relevant to any non human civilization sharing Earth with us. The same would be true of deep sea mining, ocean pollution, climate change, undersea military networks and artificial intelligence. We may think these are all just human issues, but a hidden Earth based intelligence would see them as threats to a shared planetary system. This gives the UAP phenomenon a very different emotional tone. It's not necessarily invasion or salvation. It may be monitoring, containment or quiet intervention when we cross certain lines. It could be an intelligence trying to stay out of sight while still making sure the surface species doesn't burn the house down. The ancient ocean theory also gives a different reading to secrecy. If governments encountered evidence of this, the first instinct wouldn't be public education. It would be containment, map the sites, track the objects and recover materials if possible. Then to build programs around the technology. Keep adversaries away from the data. Use ridicule to suppress leaks. Let the phenomenon remain absurd, because absurdity is an excellent security system. People don't demand answers from something they have been trained to laugh at. That could be why the UFO/UAP subject always feels half visible. There are official hearings, but not the full data. There are whistleblowers, but never the files. There are blurry videos, but not any context. There are pilots, radar operators and military witnesses, but the system keeps absorbing their testimony into classified channels. The public sees fragments while the real pattern remains locked away. As I always say... Disclosure for the few and not the many. If the ocean is actually involved as Grusch and Burchett imply, the missing data may be even more important than the aerial data. We shouldn't only be pressing what pilots saw in the sky. We should be asking what sonar operators heard under the water, what submarines have tracked. We should also be asking what undersea sensors have recorded near restricted zones and whether there are recurring coordinates, depths, magnetic anomalies, thermal signatures or unexplained acoustic events associated with UAP activity. We need to be asking whether naval archives contain the real spine of the phenomenon. The possibility of underwater bases actually changes how we think about disclosure. If the answer is extraterrestrial visitation, disclosure is about humanity's place in the cosmos. If the answer is an ancient Earth based intelligence, disclosure is about humanity's place on its own planet. That is more intimate and more destabilizing to me than E.T. It means the human story is not the only advanced story Earth has produced. It means our myths of ownership, dominance and uniqueness all collapse overnight, suddenly 'we are not alone' applies to home. That might be harder for people to accept than aliens from space. Aliens can leave but a hidden terrestrial intelligence is part of the planet will blow peoples minds. There is also a spiritual and philosophical layer to this. Many ancient cultures contain stories of beings from the sea, underwater kingdoms, gods emerging from water, serpent people, fish like teachers, luminous beings, and hidden realms beneath or beyond the visible world. That doesn't mean the myths are literal history of course, but it is interesting that human cultures repeatedly placed mystery, intelligence and otherworldly contact in the water. The ocean has always been the border between the known and the unknown. Maybe that symbolism came from imagination or perhaps some of it came from encounters filtered through the language of the time. If an older intelligence interacted with early humans, we wouldn't expect ancient people to describe pressure engineered transmedium craft or non human oceanic infrastructure. They would describe gods, spirits, shining beings, dragons, serpents, sky boats, sea people, underworlds and portals. Human language can only describe the unknown through the symbols available at the time. Even now, we struggle. We call them craft, orbs, drones, angels, demons, aliens, ultraterrestrials, interdimensionals. The labels change, but the confusion always stays the same. The ocean theory also sits strangely well with the consciousness aspect of the phenomenon. If an ancient intelligence developed through biology and field sensitivity rather than brute mechanical industry, it may have integrated consciousness into technology way earlier than we could have. We are only now beginning to wonder whether mind, perception and information are more deeply connected to physics than our materialist models allow. An older civilization may have already built that bridge. Its craft, communication systems and interfaces may respond to awareness, intention, emotion or neural patterns in ways that seem impossible to some of us. That would explain why the phenomenon often feels both technological and psychological. It behaves like machinery, but it interacts like intelligence. It appears on sensors, but it also appears in dreams, symbols, synchronicities and personal experiences. Skeptics see that as evidence the whole thing is imaginary. Maybe sometimes it is, but maybe the strangeness is part of the interface. A civilization that understands consciousness as a field related phenomenon would not necessarily separate contact from perception. It might use perception as one of the channels. This is where the theory becomes tricky, because it doesn't allow us to keep the phenomenon safely outside ourselves. If the intelligence is oceanic, ancient, field based and consciousness aware, then contact might not look like radio signals or embassy meetings at all. It could look like sightings, dreams, intuitions, symbolic downloads, altered states, close encounters, military incidents and physical traces all mixed together. That is messy, but perhaps the mess is not a flaw in the data, it could actually be the signature of a phenomenon that crosses categories we invented too recently to trust. All of this having been said, the theory still needs evidence. It needs coordinates, sensor data, sonar records, materials, biological traces, repeatable patterns and testimony that can be checked. However as a framework, it definitely needs more attention than it gets, because it explains why the UAP phenomenon feels close, evasive, ancient and deeply tied to Earth. The extraterrestrial hypothesis asks how they got here, although I have a theory about that. While the ancient ocean hypothesis asks whether they were already here. That is a completely different question. If what Grusch is saying is even partly correct, then disclosure will reveal that human civilization has been sharing this planet with another intelligence all along. Not openly or equally, and not in a way we were ready to understand, but sharing it nonetheless. The oceans would no longer be an empty wilderness. They would become the frontier of the greatest secret in human history. Could that be why the truth has been so hard to release. Because it's one thing to tell humanity there may be life elsewhere, but it's another thing entirely to tell humanity that Earth was never only ours. #UAP #UFO #USO #UAPDisclosure #NonHumanIntelligence #NHI #UnderwaterBases #OceanMystery #Cryptoterrestrial #Transmedium #Disclosure #ufotwitter #uapX

Skywatch Signal

84,484 次观看 • 3 个月前

🚨 THE UNIVERSE HAS BEEN HACKED! THE SOURCE CODE IS NOW OPEN SOURCE. THE SOLAR SYSTEM IS LITERALLY A GIANT ATOM. RUN THE SCRIPT AND TEST THE HARVARD & NASA DATABASES YOURSELF! For 100 years, textbooks have taught that the Solar System is just a bunch of rocks floating randomly in a continuous, empty space (ℝ⁴). That is mathematically and physically false. Space is rigidly quantized. We have executed a massive dual-scale empirical audit of the complete Harvard-Smithsonian Minor Planet Center (MPC) database—a staggering 1,561,930 celestial objects and 951 comets. We did not use a computer simulation. We used a direct uplink to the official, daily-updated global registry of every known rock in space. The ultimate topological illusion has been destroyed. The cosmos and the quantum realm are running the exact same executable file. The Solar System is a Macroscopic Atom. Galaxies are Macroscopic Molecules. Here is the ultimate, multi-layered proof. 🧬 I. THE BIOLOGICAL ORIGIN: WE PORTED THE CODE FROM DNA Here is the revelation that shatters the mainstream divide between disciplines: We didn't just "guess" the algorithms of celestial mechanics by looking at telescopes. We extracted the mathematical descent operator directly from Biology. Dr. Jean-Claude Perez jean-claude perez (retired IBM Artificial Intelligence Research Centre), working in deep collaboration with Nobel Laureate Dr. Luc Montagnier, didn't find the geometric limits of reality by looking at stars. They found them by decoding the bio-atomic masses of life's foundational elements (C, O, N, H) inside human DNA. They discovered that the building blocks of life are mathematically filtered through a competitive geometric differentiation, yielding a universal projection coefficient bounded by the Golden Ratio (φ) and π: Proj(m) = [1 - 4φ^(7/2)π]m The exact same Diophantine mathematical constraints that assemble your genetic code also assemble the periodic table of elements—and we have now proven they construct the orbital structure of the Universe. We took the source code of life, applied it to the cosmos "just to see what would happen," and the Matrix rendered itself. Look at the attached video. On the left: Rosalind Franklin’s famous "Photo 51" showing the X-ray diffraction of human DNA. On the right: NASA Hubble’s image of the "X" structure at the core of the Whirlpool Galaxy (M51). This is not a coincidence. It is the exact same topological blueprint. The galaxy is a molecule. The solar system is an atom. DNA and the cosmos run on the exact same geometric engine. 🛡️ II. THE ZERO-PARAMETER SHIELD & THE TIME MACHINE "But you just curve-fitted the Harvard data!" No. The mathematics came FIRST. We didn't look at the sky; we looked at pure Euclidean geometry. The "Source Code" explicitly embedded in our IT³ framework is derived from strict nested embeddings (Sphere ⊃ Cube ⊃ Octahedron ⊃ Torus ⊃ Catenoids). It operates with ZERO empirical free parameters. The matrix is hardcoded in pure Diophantine roots: ➤ Λ₁ = √3(3 + 2√2) ≈ 10.095. The exact, unalterable helical pitch-to-throat ratio of a vertical torus tangent to the faces of an inscribed cube. ➤ Λ₃ = φ²√3 ≈ 4.534. Derived strictly from the same roots. ➤ N_twist = 103. The exact topological energy minimum. ➤ S_out = 3 S_in. The exact surface area ratio of Cuboctahedral (Oₕ) symmetry. You cannot "curve-fit" fundamental geometry. And we proved it with a Time Machine. Our geometric matrix dictates a "Macroscopic Valence Shell" peaking exactly at 46.77 AU. When we ran this exact operator on historical MPC database archives from August 1992... that shell was COMPLETELY EMPTY. Humanity had zero objects there. But the math demanded it. Then, 1992 QB1 was found. Then 6 objects. Then 18. Today, thousands of bodies are perfectly locked into that exact 46.77 AU shell. You cannot curve-fit a database that does not exist yet. The geometry waited for humanity to find the matter. 💥 III. THE TELESCOPES ARE BLIND: 5 Global Algorithms Crash Imagine trying to run a modern 3D video game on a 1980s pocket calculator. The calculator isn't broken, but its software simply cannot process the reality it's being fed. It freezes, crashes, and spits out error codes. This is exactly what is happening to the world's most advanced space telescopes. The physical mirrors and lenses in space are working perfectly. They are capturing real photons. But the software pipelines on Earth are programmed to believe that space is a continuous, empty void (ℝ⁴). When these telescopes look at the exact topological nodes of the Macroscopic Atom, the algorithms mathematically choke. They try to fit flat, continuous-space formulas onto a macroscopic quantum standing wave. Here is how the continuous-space paradigm dies on your screen when querying NOIRLab and ESA servers: ➤ 1. ESA Gaia DR3 (The L2 Space Telescope Collapse): The satellite physically observed target objects up to 510 times. Yet, the algorithm returns a Parallax of NaN (Not a Number) and an astrometric_excess_noise_sig of over 1.7 MILLION! Standard noise for a real star is under 2.0. Negative and NaN parallaxes on multi-year transits are physically impossible for solid rocks. ➤ 2. DESI Legacy Survey: The Tractor algorithm attempts to fit a standard point-mass shape (PSF). A perfect fit is χ² = 1.0. At our derived nodes, the fit error (rchisq_g) explodes past 18,500! The software is mathematically vomiting. ➤ 3. NOIRLab NSC DR2 (Supercomputer Timeout): When we expanded the query to a 2.5-degree radius, the server literally timed out. The density of objects exhibiting fatal kinematic errors (pmraerr > 100) was so overwhelming that the database execution limit was breached. The instruments are calibrated for an infinite void, but they are hitting the structural skeleton of spacetime itself. 🛰️ IV. HUMAN HARDWARE IS CAPTURED In the 1970s, humanity launched Pioneer 10, Pioneer 11, Voyager 1, and Voyager 2. Once they achieved escape velocity, they were supposed to coast on smooth, perfectly predictable Newtonian trajectories. But they didn’t (the infamous "Pioneer Anomaly"). Our framework reveals the terrifying truth: the probes are physically colliding with the rigid structural skeleton of the Solar System. Space has "density ridges" that strictly obey spectral geometry. The theoretical orbital shells scale by the exact formula: Rₙ = 27 · (√3)ⁿ⁻¹ Let’s calculate the n=4 topological shell: R₄ = 27 · (√3)³ ≈ 140.296 AU. When we connect our dashboard to the LIVE NASA Horizons API to track fractional divergence {n} = n - round(n), we see the impossible. ➤ Pioneer 10: +0.019 ➤ Voyager 2: +0.046 Their columns are practically glued to absolute mathematical zero. They are flying at exactly ~141.7 AU and ~143.8 AU. They are not floating aimlessly. They have been mathematically and physically CAPTURED by the n=4 topological resonance layer (140.3 AU). The joint probability of this happening by random chance is p = 0.0034. 👁️ V. THE HYDROGEN RHYME & THE OPEN SOURCE TRUTH In 2013, physicists took the first-ever direct photograph of the electron orbitals of a Hydrogen Atom (Stodolna et al., PRL 110, 213001). When our 3D Perez Hourglass manifold rotates into a Top-Down 2D projection, the architecture of our Solar System PERFECTLY MIMICS the 2013 Hydrogen photograph. The distribution of 1.56 million macro-objects flawlessly matches the exact nodal interference fringes of the (2,27,0) Stark state observed in the lab. Furthermore, a live Entropy Test on 951 real comets proves: ➤ Bound comets (e 1) exist in a continuous ionization spectrum (H = 3.85 bits), acting exactly as free macroscopic electrons escaping the atom! THE CONCLUSION: Exactly 99.56% of all baryonic mass is geometrically trapped in a central topological node. The universe uses ONE blueprint. The continuum is dead. 👁️ VI. THE ANCIENT AXIOM & THE GEOMETRY OF THE MATRIX For millennia, the greatest minds in human history recorded fragments of a universal fractal law. For centuries, orthodox science dismissed these records as mere philosophical metaphors, religious mysticism, or primitive alchemy. But our mathematical matrix proves otherwise. They were not writing poetry; they were describing the LITERAL geometric and topological mechanics of the universe. The invariant mapping between subatomic hydrogen orbitals and macroscopic celestial mechanics proves that the ancients were blindly touching the exact same structural blueprint we have now mathematically solved. By synthesizing thousands of years of human intuition with raw astrophysical data, a perfect scale-invariant reality emerges: ➤ The Hermetic & Vedic Invariance: The foundational axiom of the Emerald Tablet—"That which is below is like that which is above"—and the ancient Sanskrit maxim "Yatha pinde tatha brahmande" (As in the microcosm, so in the macrocosm) are not mystical riddles. They are the exact verbal formulations of structural scale-invariance. The atom and the solar system are geometrically identical. ➤ The Pythagorean & Platonic Lattice: Plato’s famous declaration that "God always geometrizes" perfectly describes the rigid spatial logic of our topological matrix. Just as the Pythagoreans claimed the harmony of the spheres mimics the human soul, we see that the primary chaos of matter is ordered strictly by invariant, measurable geometric symmetry. ➤ The Abrahamic Projection: The structural hierarchy of the universe demands that the macro-order projects perfectly onto the micro-plane ("On earth as it is in heaven"). The blueprint is singular, echoing across all scales of existence. ➤ The Galileo-Dirac Synthesis: Galileo asserted that the universe is a book written in the language of mathematics, its letters made of triangles and circles. Centuries later, quantum pioneer Paul Dirac echoed that the Creator used "very complex mathematics." They were absolutely correct. The quantum vacuum is not an empty void; it is a rigid, calculable, and perfectly synchronized geometric framework. Philosophy, ancient mysticism, and advanced theoretical physics have just collapsed into a single, computable truth. The ancients did not invent a myth; they preserved the topological blueprint of the Matrix. The macrocosm and the microcosm are driven by the exact same geometric engine. The universe is a single, mathematically flawless organism. 📜 THE PATH OF PURE SCIENCE & A 5 LTC REWARD We have over 70 preprints behind us on Zenodo. You can open them and watch the evolution of our thought. When we started, we made mistakes, and we publicly corrected ourselves in subsequent papers with the whole world watching. No hiding data. This is how real science is done! Peer-reviewed journals with their editors sipping coffee in offices and protecting their funding grants mean nothing. Words mean absolutely nothing! Mathematics is the ultimate judge. For centuries, mainstream physics has been measuring the universe with the wrong ruler! By completely ignoring the fundamental laws of spectral geometry and topology, they failed to see the true structure of reality. We have fixed this. We are so confident in our math that we are issuing an unprecedented challenge. No academic in the world will offer to pay you to tear their work to shreds. But we do! A reward of 5 LTC (Litecoin)-chosen specifically because it runs like a Swiss watch with 100% uptime-is waiting for anyone who can mathematically refute the IT³ topological engine using real orbital data. 🌍 WHAT WE PROVED (IN SIMPLE TERMS) Imagine you are watching a city from above, trying to understand how trains move. Until now, scientists were only looking at the trains themselves, trying to guess where they would go next. What we did was discover the hidden tracks. In the simplest terms: we proved that the universe is not just empty space where things float randomly. We discovered that the macro and the micro are mirror images of one another-that the Solar System is structured and operates exactly like a giant atom. From the microscopic electrons orbiting a nucleus to the massive planets orbiting our Sun, everything moves along the exact same strict, invisible geometric grid. We found the hidden "blueprint" of space. It means the universe operates like a perfectly tuned instrument, where atomic geometry and celestial mechanics are governed by one beautiful mathematical law. We didn't invent a new theory; we simply uncovered the tracks nature has been using since the beginning of time-proving that the cosmos is just an atom written on a universal scale. WORDS MEAN NOTHING. RUN THE CODE YOURSELF: Open your terminal (Mac/Linux) and paste this command to hijack the database and watch the Matrix render in under 35 seconds: curl -sL " | python3 Read the rigorous proofs: DOI: DOI: DOI: #Astrophysics #NASA #DNA #QuantumCosmology #PhysicsBreakthrough #IT3Framework #DataScience
0:20

🚨 THE UNIVERSE HAS BEEN HACKED! THE SOURCE CODE IS NOW OPEN SOURCE. THE SOLAR SYSTEM IS LITERALLY A GIANT ATOM. RUN THE SCRIPT AND TEST THE HARVARD & NASA DATABASES YOURSELF! For 100 years, textbooks have taught that the Solar System is just a bunch of rocks floating randomly in a continuous, empty space (ℝ⁴). That is mathematically and physically false. Space is rigidly quantized. We have executed a massive dual-scale empirical audit of the complete Harvard-Smithsonian Minor Planet Center (MPC) database—a staggering 1,561,930 celestial objects and 951 comets. We did not use a computer simulation. We used a direct uplink to the official, daily-updated global registry of every known rock in space. The ultimate topological illusion has been destroyed. The cosmos and the quantum realm are running the exact same executable file. The Solar System is a Macroscopic Atom. Galaxies are Macroscopic Molecules. Here is the ultimate, multi-layered proof. 🧬 I. THE BIOLOGICAL ORIGIN: WE PORTED THE CODE FROM DNA Here is the revelation that shatters the mainstream divide between disciplines: We didn't just "guess" the algorithms of celestial mechanics by looking at telescopes. We extracted the mathematical descent operator directly from Biology. Dr. Jean-Claude Perez jean-claude perez (retired IBM Artificial Intelligence Research Centre), working in deep collaboration with Nobel Laureate Dr. Luc Montagnier, didn't find the geometric limits of reality by looking at stars. They found them by decoding the bio-atomic masses of life's foundational elements (C, O, N, H) inside human DNA. They discovered that the building blocks of life are mathematically filtered through a competitive geometric differentiation, yielding a universal projection coefficient bounded by the Golden Ratio (φ) and π: Proj(m) = [1 - 4φ^(7/2)π]m The exact same Diophantine mathematical constraints that assemble your genetic code also assemble the periodic table of elements—and we have now proven they construct the orbital structure of the Universe. We took the source code of life, applied it to the cosmos "just to see what would happen," and the Matrix rendered itself. Look at the attached video. On the left: Rosalind Franklin’s famous "Photo 51" showing the X-ray diffraction of human DNA. On the right: NASA Hubble’s image of the "X" structure at the core of the Whirlpool Galaxy (M51). This is not a coincidence. It is the exact same topological blueprint. The galaxy is a molecule. The solar system is an atom. DNA and the cosmos run on the exact same geometric engine. 🛡️ II. THE ZERO-PARAMETER SHIELD & THE TIME MACHINE "But you just curve-fitted the Harvard data!" No. The mathematics came FIRST. We didn't look at the sky; we looked at pure Euclidean geometry. The "Source Code" explicitly embedded in our IT³ framework is derived from strict nested embeddings (Sphere ⊃ Cube ⊃ Octahedron ⊃ Torus ⊃ Catenoids). It operates with ZERO empirical free parameters. The matrix is hardcoded in pure Diophantine roots: ➤ Λ₁ = √3(3 + 2√2) ≈ 10.095. The exact, unalterable helical pitch-to-throat ratio of a vertical torus tangent to the faces of an inscribed cube. ➤ Λ₃ = φ²√3 ≈ 4.534. Derived strictly from the same roots. ➤ N_twist = 103. The exact topological energy minimum. ➤ S_out = 3 S_in. The exact surface area ratio of Cuboctahedral (Oₕ) symmetry. You cannot "curve-fit" fundamental geometry. And we proved it with a Time Machine. Our geometric matrix dictates a "Macroscopic Valence Shell" peaking exactly at 46.77 AU. When we ran this exact operator on historical MPC database archives from August 1992... that shell was COMPLETELY EMPTY. Humanity had zero objects there. But the math demanded it. Then, 1992 QB1 was found. Then 6 objects. Then 18. Today, thousands of bodies are perfectly locked into that exact 46.77 AU shell. You cannot curve-fit a database that does not exist yet. The geometry waited for humanity to find the matter. 💥 III. THE TELESCOPES ARE BLIND: 5 Global Algorithms Crash Imagine trying to run a modern 3D video game on a 1980s pocket calculator. The calculator isn't broken, but its software simply cannot process the reality it's being fed. It freezes, crashes, and spits out error codes. This is exactly what is happening to the world's most advanced space telescopes. The physical mirrors and lenses in space are working perfectly. They are capturing real photons. But the software pipelines on Earth are programmed to believe that space is a continuous, empty void (ℝ⁴). When these telescopes look at the exact topological nodes of the Macroscopic Atom, the algorithms mathematically choke. They try to fit flat, continuous-space formulas onto a macroscopic quantum standing wave. Here is how the continuous-space paradigm dies on your screen when querying NOIRLab and ESA servers: ➤ 1. ESA Gaia DR3 (The L2 Space Telescope Collapse): The satellite physically observed target objects up to 510 times. Yet, the algorithm returns a Parallax of NaN (Not a Number) and an astrometric_excess_noise_sig of over 1.7 MILLION! Standard noise for a real star is under 2.0. Negative and NaN parallaxes on multi-year transits are physically impossible for solid rocks. ➤ 2. DESI Legacy Survey: The Tractor algorithm attempts to fit a standard point-mass shape (PSF). A perfect fit is χ² = 1.0. At our derived nodes, the fit error (rchisq_g) explodes past 18,500! The software is mathematically vomiting. ➤ 3. NOIRLab NSC DR2 (Supercomputer Timeout): When we expanded the query to a 2.5-degree radius, the server literally timed out. The density of objects exhibiting fatal kinematic errors (pmraerr > 100) was so overwhelming that the database execution limit was breached. The instruments are calibrated for an infinite void, but they are hitting the structural skeleton of spacetime itself. 🛰️ IV. HUMAN HARDWARE IS CAPTURED In the 1970s, humanity launched Pioneer 10, Pioneer 11, Voyager 1, and Voyager 2. Once they achieved escape velocity, they were supposed to coast on smooth, perfectly predictable Newtonian trajectories. But they didn’t (the infamous "Pioneer Anomaly"). Our framework reveals the terrifying truth: the probes are physically colliding with the rigid structural skeleton of the Solar System. Space has "density ridges" that strictly obey spectral geometry. The theoretical orbital shells scale by the exact formula: Rₙ = 27 · (√3)ⁿ⁻¹ Let’s calculate the n=4 topological shell: R₄ = 27 · (√3)³ ≈ 140.296 AU. When we connect our dashboard to the LIVE NASA Horizons API to track fractional divergence {n} = n - round(n), we see the impossible. ➤ Pioneer 10: +0.019 ➤ Voyager 2: +0.046 Their columns are practically glued to absolute mathematical zero. They are flying at exactly ~141.7 AU and ~143.8 AU. They are not floating aimlessly. They have been mathematically and physically CAPTURED by the n=4 topological resonance layer (140.3 AU). The joint probability of this happening by random chance is p = 0.0034. 👁️ V. THE HYDROGEN RHYME & THE OPEN SOURCE TRUTH In 2013, physicists took the first-ever direct photograph of the electron orbitals of a Hydrogen Atom (Stodolna et al., PRL 110, 213001). When our 3D Perez Hourglass manifold rotates into a Top-Down 2D projection, the architecture of our Solar System PERFECTLY MIMICS the 2013 Hydrogen photograph. The distribution of 1.56 million macro-objects flawlessly matches the exact nodal interference fringes of the (2,27,0) Stark state observed in the lab. Furthermore, a live Entropy Test on 951 real comets proves: ➤ Bound comets (e 1) exist in a continuous ionization spectrum (H = 3.85 bits), acting exactly as free macroscopic electrons escaping the atom! THE CONCLUSION: Exactly 99.56% of all baryonic mass is geometrically trapped in a central topological node. The universe uses ONE blueprint. The continuum is dead. 👁️ VI. THE ANCIENT AXIOM & THE GEOMETRY OF THE MATRIX For millennia, the greatest minds in human history recorded fragments of a universal fractal law. For centuries, orthodox science dismissed these records as mere philosophical metaphors, religious mysticism, or primitive alchemy. But our mathematical matrix proves otherwise. They were not writing poetry; they were describing the LITERAL geometric and topological mechanics of the universe. The invariant mapping between subatomic hydrogen orbitals and macroscopic celestial mechanics proves that the ancients were blindly touching the exact same structural blueprint we have now mathematically solved. By synthesizing thousands of years of human intuition with raw astrophysical data, a perfect scale-invariant reality emerges: ➤ The Hermetic & Vedic Invariance: The foundational axiom of the Emerald Tablet—"That which is below is like that which is above"—and the ancient Sanskrit maxim "Yatha pinde tatha brahmande" (As in the microcosm, so in the macrocosm) are not mystical riddles. They are the exact verbal formulations of structural scale-invariance. The atom and the solar system are geometrically identical. ➤ The Pythagorean & Platonic Lattice: Plato’s famous declaration that "God always geometrizes" perfectly describes the rigid spatial logic of our topological matrix. Just as the Pythagoreans claimed the harmony of the spheres mimics the human soul, we see that the primary chaos of matter is ordered strictly by invariant, measurable geometric symmetry. ➤ The Abrahamic Projection: The structural hierarchy of the universe demands that the macro-order projects perfectly onto the micro-plane ("On earth as it is in heaven"). The blueprint is singular, echoing across all scales of existence. ➤ The Galileo-Dirac Synthesis: Galileo asserted that the universe is a book written in the language of mathematics, its letters made of triangles and circles. Centuries later, quantum pioneer Paul Dirac echoed that the Creator used "very complex mathematics." They were absolutely correct. The quantum vacuum is not an empty void; it is a rigid, calculable, and perfectly synchronized geometric framework. Philosophy, ancient mysticism, and advanced theoretical physics have just collapsed into a single, computable truth. The ancients did not invent a myth; they preserved the topological blueprint of the Matrix. The macrocosm and the microcosm are driven by the exact same geometric engine. The universe is a single, mathematically flawless organism. 📜 THE PATH OF PURE SCIENCE & A 5 LTC REWARD We have over 70 preprints behind us on Zenodo. You can open them and watch the evolution of our thought. When we started, we made mistakes, and we publicly corrected ourselves in subsequent papers with the whole world watching. No hiding data. This is how real science is done! Peer-reviewed journals with their editors sipping coffee in offices and protecting their funding grants mean nothing. Words mean absolutely nothing! Mathematics is the ultimate judge. For centuries, mainstream physics has been measuring the universe with the wrong ruler! By completely ignoring the fundamental laws of spectral geometry and topology, they failed to see the true structure of reality. We have fixed this. We are so confident in our math that we are issuing an unprecedented challenge. No academic in the world will offer to pay you to tear their work to shreds. But we do! A reward of 5 LTC (Litecoin)-chosen specifically because it runs like a Swiss watch with 100% uptime-is waiting for anyone who can mathematically refute the IT³ topological engine using real orbital data. 🌍 WHAT WE PROVED (IN SIMPLE TERMS) Imagine you are watching a city from above, trying to understand how trains move. Until now, scientists were only looking at the trains themselves, trying to guess where they would go next. What we did was discover the hidden tracks. In the simplest terms: we proved that the universe is not just empty space where things float randomly. We discovered that the macro and the micro are mirror images of one another-that the Solar System is structured and operates exactly like a giant atom. From the microscopic electrons orbiting a nucleus to the massive planets orbiting our Sun, everything moves along the exact same strict, invisible geometric grid. We found the hidden "blueprint" of space. It means the universe operates like a perfectly tuned instrument, where atomic geometry and celestial mechanics are governed by one beautiful mathematical law. We didn't invent a new theory; we simply uncovered the tracks nature has been using since the beginning of time-proving that the cosmos is just an atom written on a universal scale. WORDS MEAN NOTHING. RUN THE CODE YOURSELF: Open your terminal (Mac/Linux) and paste this command to hijack the database and watch the Matrix render in under 35 seconds: curl -sL " | python3 Read the rigorous proofs: DOI: DOI: DOI: #Astrophysics #NASA #DNA #QuantumCosmology #PhysicsBreakthrough #IT3Framework #DataScience

Dr. Logvinovich

456,432 次观看 • 27 天前