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Why automation in nano-research? FX40 Atomic Force Microscope reduces workflow bottlenecks with automatic probe exchange, automatic laser alignment, sample-view navigation, and SmartScan™ optimization. Less setup. More reproducible data.

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Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.
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Today, Box is announcing major new AI agent capabilities to let customers tap into the full value of their unstructured data. First, we’re announcing all new updates to the Box AI Studio to make it even easier to build AI agents that tap into your enterprise content for any job function, business process, or industry specific use case. We are also expanding our set of foundational agents that customers will be able to use to work with their enterprise content, including new features like search and research on unstructured data. Next, we’re announcing Box Extract to enable customers to use AI agents seamlessly for complex data extraction from any type of document or content. This makes it easier than ever to pull out data from contracts, invoices, research data, marketing assets, medical charts, and more. Finally, we’re introducing Box Automate, a new workflow automation solution within Box that lets you deploy AI agents across enterprise content-centric workflows. With Box Automate, you can design your business process in a simple drag and drop builder and then drop in AI agents at any step in the process. This ensures agents execute tasks at the right steps in a workflow every time. Best of all, our AI agents and workflow tools are designed to work across any system our customers work within, whether it’s leveraging pre-built integrations, Box APIs, or the new Box MCP Server. Ultimately, all of these capabilities come together to transform how companies can work with their enterprise content. Software has historically only been good at automating work that deals with structured data, which is why ERP, CRM, and HR systems have been mainstays of enterprise software for so long. The data in these systems fits neatly into a database, and the workflows are very ripe for automation. But it turns out most of the work in the world deals with unstructured data. It’s ideating through research documents, working with a client on contracts, reviewing details for a new product launch, looking at a patient’s healthcare record to make a diagnosis, working through due diligence documents for an M&A deal, and so on. For the first time ever, we can begin to bring all new insights and automation to this work with AI agents. At Box, we’re incredibly excited to be on this journey to help customers transform how they work with their most important data.

Aaron Levie

91,863 просмотров • 11 месяцев назад

Here's a demo on a project I've been developing and working on for the past 9 months. Called NightBeacon. Using it now in production, getting released fully this week. Our own internally trained models on our own infrastructure (no third party). Trained on our analysts knowledge and behavior (TP/FPs retrain model to be smarter with context). Handles emails (including tonality), attachments, various malicious filetypes (DLL/exe/svg/lnk/etc). Can send it full evtx exports, packet dumps, zip files, whatever. Universal log handler can parse any log from any source, EDR, SIEM, etc. Deep-Scan / sandbox detonation + shellcode emulation with IOC extraction automatically. Automatic playbook generation, full AI-based recommendations custom to the attack. Synthetic training data layer - meaning when it trains on a specific attack at a customer, generates training data based on the customers data but never has any of the actual data or information about the customer in it. No customer information. For areas its weak at, bubbles up and automatically kicks off research to become smarter on a specific topic. Supports GenAI based rulesets (to improve confidence), over 900+ YARA rules, full MITRE ATT&CK integration. Integrated into our SOAR - enriches data, creates playbooks for analysts, MTTR reduces substantially, false positives reduced, true positive escalations. Not using our MDR service? Can integrate into your EDR or SIEM for automatic enrichment and escalation of attacks. Built to help respond faster. More accurately. Be intelligent based on our analysts intelligence. Stop attackers much much faster. Coming soon.. #BinaryDefense
1:44

Here's a demo on a project I've been developing and working on for the past 9 months. Called NightBeacon. Using it now in production, getting released fully this week. Our own internally trained models on our own infrastructure (no third party). Trained on our analysts knowledge and behavior (TP/FPs retrain model to be smarter with context). Handles emails (including tonality), attachments, various malicious filetypes (DLL/exe/svg/lnk/etc). Can send it full evtx exports, packet dumps, zip files, whatever. Universal log handler can parse any log from any source, EDR, SIEM, etc. Deep-Scan / sandbox detonation + shellcode emulation with IOC extraction automatically. Automatic playbook generation, full AI-based recommendations custom to the attack. Synthetic training data layer - meaning when it trains on a specific attack at a customer, generates training data based on the customers data but never has any of the actual data or information about the customer in it. No customer information. For areas its weak at, bubbles up and automatically kicks off research to become smarter on a specific topic. Supports GenAI based rulesets (to improve confidence), over 900+ YARA rules, full MITRE ATT&CK integration. Integrated into our SOAR - enriches data, creates playbooks for analysts, MTTR reduces substantially, false positives reduced, true positive escalations. Not using our MDR service? Can integrate into your EDR or SIEM for automatic enrichment and escalation of attacks. Built to help respond faster. More accurately. Be intelligent based on our analysts intelligence. Stop attackers much much faster. Coming soon.. #BinaryDefense

Dave Kennedy

12,905 просмотров • 5 месяцев назад

Claude Code cannot read 300 files at once. So someone built a system that lets it control NotebookLM from the terminal instead. The results are wild. Here is the full workflow nobody is talking about: The Setup → Claude Code connects to NotebookLM via a command line interface → Claude searches YouTube, finds relevant videos, uploads them as sources automatically → NotebookLM processes up to 300 sources simultaneously and returns cited, grounded answers → Everything syncs back into your Obsidian vault with passage-level citations you can click to verify Why This Changes Research Forever → No more 20 browser tabs you never close → No more copy-pasting outputs into random notes → No more hallucinated answers with no sources to back them up → 60% of citations verified as strong matches in accuracy audits - answers are grounded in real data What Claude Can Do From the Terminal → Search YouTube for relevant videos on any topic and rank by relevance → Create a new NotebookLM notebook and add 20 sources in parallel automatically → Ask questions and export cited answers directly into Obsidian with wikilinks → Set custom personas per notebook - concise, no filler, no preamble → Generate audio overviews and save them as MP3 files into your vault → Build mind maps, flashcard decks, and research dashboards from your sources → Search arXiv for academic papers and feed them directly into NotebookLM → Upload competitor blog posts, podcast episodes, PDFs, and your own vault notes The Obsidian Output → Every answer arrives with clickable citations that link to the exact passage in the source video or article → Graph view shows connections between all 20 sources and the topics they share → Q&A log tracks every question asked and the grounded response received → Source dashboard shows citation frequency, topics extracted, and which questions each source answered Use Cases Worth Building Today → Academic research with arXiv papers, full citation traceability → Competitor analysis from their YouTube channels and blog posts → Company knowledge base for onboarding, new employees ask NotebookLM instead of interrupting teammates → Podcast research, feed 4-hour Lex Fridman episodes and ask what's new in AI this week → Personal second brain, 300 daily notes uploaded and queryable in one notebook Before this system existed you needed 20 tabs, hours of manual reading, and no guarantee the answers were real. Now you type one prompt in the terminal and Claude does all of it for you. The research stack of 2026 is not a browser. It is a terminal connected to everything
32:50

Claude Code cannot read 300 files at once. So someone built a system that lets it control NotebookLM from the terminal instead. The results are wild. Here is the full workflow nobody is talking about: The Setup → Claude Code connects to NotebookLM via a command line interface → Claude searches YouTube, finds relevant videos, uploads them as sources automatically → NotebookLM processes up to 300 sources simultaneously and returns cited, grounded answers → Everything syncs back into your Obsidian vault with passage-level citations you can click to verify Why This Changes Research Forever → No more 20 browser tabs you never close → No more copy-pasting outputs into random notes → No more hallucinated answers with no sources to back them up → 60% of citations verified as strong matches in accuracy audits - answers are grounded in real data What Claude Can Do From the Terminal → Search YouTube for relevant videos on any topic and rank by relevance → Create a new NotebookLM notebook and add 20 sources in parallel automatically → Ask questions and export cited answers directly into Obsidian with wikilinks → Set custom personas per notebook - concise, no filler, no preamble → Generate audio overviews and save them as MP3 files into your vault → Build mind maps, flashcard decks, and research dashboards from your sources → Search arXiv for academic papers and feed them directly into NotebookLM → Upload competitor blog posts, podcast episodes, PDFs, and your own vault notes The Obsidian Output → Every answer arrives with clickable citations that link to the exact passage in the source video or article → Graph view shows connections between all 20 sources and the topics they share → Q&A log tracks every question asked and the grounded response received → Source dashboard shows citation frequency, topics extracted, and which questions each source answered Use Cases Worth Building Today → Academic research with arXiv papers, full citation traceability → Competitor analysis from their YouTube channels and blog posts → Company knowledge base for onboarding, new employees ask NotebookLM instead of interrupting teammates → Podcast research, feed 4-hour Lex Fridman episodes and ask what's new in AI this week → Personal second brain, 300 daily notes uploaded and queryable in one notebook Before this system existed you needed 20 tabs, hours of manual reading, and no guarantee the answers were real. Now you type one prompt in the terminal and Claude does all of it for you. The research stack of 2026 is not a browser. It is a terminal connected to everything

Dami-Defi

252,693 просмотров • 2 месяцев назад

We are excited to share our latest work, "Learning on the Fly: Rapid Policy Adaptation via Differentiable Simulation", where a policy learns to adapt in the real world to unknown disturbances within 5 seconds, both with and without explicit state estimation, directly from visual features. Code released! PDF: Project Page: Starting from a simple analytical dynamics model, the system continuously learns residual dynamics from real-world data and embeds the refined model into a differentiable simulator. This enables fast, gradient-based policy updates that are far more sample-efficient than classical #ReinforcementLearning. We demonstrate rapid adaptation in <5 seconds in agile quadrotor control under challenging conditions, including added payloads, wind disturbances, and large sim-to-real gaps. In real-world experiments, our method reduces hovering error by up to 81% compared to L1-MPC and 55% compared to PPO-based adaptive methods. It also operates directly from visual features without explicit state estimation. Reference: “Learning on the Fly: Rapid Policy Adaptation via Differentiable Simulation” IEEE Robotics and Automation Letters, 2026 PDF: Video: Code: Website: Kudos to Michael Pan, Jiaxu Xing, Rudolf Reiter, Yifan Zhai, Elie Aljalbout! UZH Space Hub UZH IfI European Research Council (ERC) AUTOASSESS UZH Science University of Zurich
1:06

We are excited to share our latest work, "Learning on the Fly: Rapid Policy Adaptation via Differentiable Simulation", where a policy learns to adapt in the real world to unknown disturbances within 5 seconds, both with and without explicit state estimation, directly from visual features. Code released! PDF: Project Page: Starting from a simple analytical dynamics model, the system continuously learns residual dynamics from real-world data and embeds the refined model into a differentiable simulator. This enables fast, gradient-based policy updates that are far more sample-efficient than classical #ReinforcementLearning. We demonstrate rapid adaptation in <5 seconds in agile quadrotor control under challenging conditions, including added payloads, wind disturbances, and large sim-to-real gaps. In real-world experiments, our method reduces hovering error by up to 81% compared to L1-MPC and 55% compared to PPO-based adaptive methods. It also operates directly from visual features without explicit state estimation. Reference: “Learning on the Fly: Rapid Policy Adaptation via Differentiable Simulation” IEEE Robotics and Automation Letters, 2026 PDF: Video: Code: Website: Kudos to Michael Pan, Jiaxu Xing, Rudolf Reiter, Yifan Zhai, Elie Aljalbout! UZH Space Hub UZH IfI European Research Council (ERC) AUTOASSESS UZH Science University of Zurich

Davide Scaramuzza

19,184 просмотров • 6 месяцев назад

Ex-Balyasny PM Ying Hua (Ying Hua) on why automation will increase demand for hedge fund talent, the quant/fundamental convergence, & why quant is blackjack but fundamental is poker. Ying Hua (PM @ Balyasny — built & led a quantamental team covering US insurance, capital markets & fintech | ~5 yrs @ Citadel running a long/short insurance book | Equity research @ Goldman Sachs | MS in Data Science @ UC Berkeley | Now founder & CEO of Implied Implied) "One of the best-kept secrets: fundamental investors are not good at sizing. Quant funds are really good at sizing." We cover: - The only real line between quant and fundamental: historical pattern matching vs. "how is this time different" — and the alpha neither group is looking at - Why she rebuilt her process so every model updated within 2 minutes of a print - Scraping highway patrol data from 15 states to track auto insurance losses live, every single day - The Malibu wildfire: mapping burned mansions from celebrity tweets to estimate losses before any industry consultant published a number - Her automation math: data gathering ~100% automatable, processing ~80%, judgment still 100% human - AI is quant for words — next-token prediction is pattern matching, which makes this just the next automation wave after quant and indexing - The proof differentiated views pay more: insurance stocks moved 2-3% on earnings in 2010; by the time she left, 15-20% intraday - Why "hook Claude Code up to data and let it rip" fails: BloombergGPT losing to a smaller open-source model, & why horizontal models are college grads - Quant is blackjack with card counting; multi-manager investing is poker — your hand, others' perception of it, your seat, everyone's stack - Most PMs are playing the wrong game: the positioning game hiding inside "fundamental" sectors with no new money coming in - Her hiring bar at BAM: every fundamental analyst learns Python — and the one skill she says can't be trained - The only two truly meritocratic jobs: hedge fund PM & sales Highlights: (00:00) Intro (00:40) How a quantamental PM actually puts on a position (02:05) The only real line between quant and fundamental (04:45) Why quantamental lowers the burden on your brain (06:40) Scraping 15 states of highway patrol data to nowcast insurance losses (09:25) The Malibu wildfire: estimating losses from celebrity tweets (11:55) How much of fundamental investing can be automated (13:45) Quantifying intuition: when a CFO's filler words jump 8% to 20% (16:25) The contrarian case: automation expands demand for talent (18:15) Earnings vol exploded — differentiated views pay more (20:25) Why Claude Code can't run your book (23:40) Horizontal models are college grads with no domain knowledge (30:50) Why chat is the wrong interface for investors (36:20) Will AI make markets more or less efficient? (39:10) Two things every fundamental PM should do today (41:50) The moat that expands: talent, redefined (45:50) Sometimes the game is positioning, not fundamentals (48:25) Blackjack vs. poker vs. surfing: matching the game to your horizon (52:00) Should young analysts chase the hottest sector? (57:55) Munger vs. Musk: two philosophies of wealth (1:01:05) Self-awareness in investing is bimodal (1:07:05) The only two truly meritocratic jobs: hedge funds & sales (1:08:50) The one skill for every regime: reconstruct the narrative
1:11:24

Ex-Balyasny PM Ying Hua (Ying Hua) on why automation will increase demand for hedge fund talent, the quant/fundamental convergence, & why quant is blackjack but fundamental is poker. Ying Hua (PM @ Balyasny — built & led a quantamental team covering US insurance, capital markets & fintech | ~5 yrs @ Citadel running a long/short insurance book | Equity research @ Goldman Sachs | MS in Data Science @ UC Berkeley | Now founder & CEO of Implied Implied) "One of the best-kept secrets: fundamental investors are not good at sizing. Quant funds are really good at sizing." We cover: - The only real line between quant and fundamental: historical pattern matching vs. "how is this time different" — and the alpha neither group is looking at - Why she rebuilt her process so every model updated within 2 minutes of a print - Scraping highway patrol data from 15 states to track auto insurance losses live, every single day - The Malibu wildfire: mapping burned mansions from celebrity tweets to estimate losses before any industry consultant published a number - Her automation math: data gathering ~100% automatable, processing ~80%, judgment still 100% human - AI is quant for words — next-token prediction is pattern matching, which makes this just the next automation wave after quant and indexing - The proof differentiated views pay more: insurance stocks moved 2-3% on earnings in 2010; by the time she left, 15-20% intraday - Why "hook Claude Code up to data and let it rip" fails: BloombergGPT losing to a smaller open-source model, & why horizontal models are college grads - Quant is blackjack with card counting; multi-manager investing is poker — your hand, others' perception of it, your seat, everyone's stack - Most PMs are playing the wrong game: the positioning game hiding inside "fundamental" sectors with no new money coming in - Her hiring bar at BAM: every fundamental analyst learns Python — and the one skill she says can't be trained - The only two truly meritocratic jobs: hedge fund PM & sales Highlights: (00:00) Intro (00:40) How a quantamental PM actually puts on a position (02:05) The only real line between quant and fundamental (04:45) Why quantamental lowers the burden on your brain (06:40) Scraping 15 states of highway patrol data to nowcast insurance losses (09:25) The Malibu wildfire: estimating losses from celebrity tweets (11:55) How much of fundamental investing can be automated (13:45) Quantifying intuition: when a CFO's filler words jump 8% to 20% (16:25) The contrarian case: automation expands demand for talent (18:15) Earnings vol exploded — differentiated views pay more (20:25) Why Claude Code can't run your book (23:40) Horizontal models are college grads with no domain knowledge (30:50) Why chat is the wrong interface for investors (36:20) Will AI make markets more or less efficient? (39:10) Two things every fundamental PM should do today (41:50) The moat that expands: talent, redefined (45:50) Sometimes the game is positioning, not fundamentals (48:25) Blackjack vs. poker vs. surfing: matching the game to your horizon (52:00) Should young analysts chase the hottest sector? (57:55) Munger vs. Musk: two philosophies of wealth (1:01:05) Self-awareness in investing is bimodal (1:07:05) The only two truly meritocratic jobs: hedge funds & sales (1:08:50) The one skill for every regime: reconstruct the narrative

Ethan Kho

241,298 просмотров • 18 дней назад

10 free GitHub repos that can save you hundreds every month. open-source. free to use. better than most people think. ↓ 1️⃣ OpenScreen — an alternative to Screen Studio ($29/mo) • record polished demos on macOS, Windows, and Linux • automatic cursor effects, blur, annotations, GIF + MP4 export • lightweight and perfect for product walkthroughs without extra editing — 2️⃣ VoiceBox — an alternative to ElevenLabs ($22/mo) + Wispr Flow ($15/mo) • privacy-first AI voice toolkit that runs locally • clone voices with a few seconds of audio • supports 7 TTS engines, 23 languages, and system-wide voice dictation • works with Apple Silicon, CUDA, and ROCm — 3️⃣ OpenShorts — an alternative to Opus Clip ($19/mo) + Submagic ($16/mo) • convert long videos into viral vertical clips • auto captions, face tracking, and AI clip selection • includes AI UGC video generation • easy Docker deployment for self-hosting — 4️⃣ FreeLLMAPI — an alternative to ChatGPT Pro + Claude Pro ($20/mo each) • combine 14 free AI providers behind one API • OpenAI-compatible endpoint • roughly 800M free tokens/month • built-in routing, failover, encrypted key storage, and dashboard — 5️⃣ Playwright MCP — an alternative to Browserbase ($39/mo) + Browser Use ($25/mo) • Microsoft's official browser automation MCP • AI agents interact using accessibility trees instead of screenshots • faster, cheaper, and more reliable automation • works with Claude Code, Cursor, Windsurf, and Codex — 6️⃣ Vibe Trading — an alternative to TradingView Premium ($60/mo) • AI-powered investing and strategy research platform • supports stocks, crypto, forex, futures, and options • dozens of built-in research skills • backtesting included without requiring paid APIs — 7️⃣ — an alternative to Calendly ($12/mo) + SavvyCal ($12/mo) • open-source scheduling platform • round robin, team scheduling, routing forms, payments • integrates with Google Calendar, Outlook, Apple Calendar, Zoom, Meet, and Teams • deploy yourself in minutes — 8️⃣ Whisper — an alternative to ($17/mo) • OpenAI's speech recognition model • transcribes and translates audio in nearly 100 languages • timestamp support included • runs locally on CPU or GPU — 9️⃣ Postiz — an alternative to Buffer ($15/mo) • schedule content across all major social platforms • AI-generated captions and hashtags • built-in analytics and collaborative workspaces • growing rapidly with a large open-source community — 🔟 Vaultwarden — an alternative to 1Password ($8/mo) • lightweight Bitwarden-compatible server written in Rust • works with official Bitwarden apps • unlimited users and vaults • self-host on almost any VPS or home server — Worth knowing: Open-source isn't always a perfect replacement. You may spend a little more time setting things up. In return, you get: • no monthly subscription • full ownership of your data • complete control over your workflow That's a trade many builders happily make. Save this for later. Someone on your timeline is probably paying for at least three of these. — Kshitij Mishra
0:31

10 free GitHub repos that can save you hundreds every month. open-source. free to use. better than most people think. ↓ 1️⃣ OpenScreen — an alternative to Screen Studio ($29/mo) • record polished demos on macOS, Windows, and Linux • automatic cursor effects, blur, annotations, GIF + MP4 export • lightweight and perfect for product walkthroughs without extra editing — 2️⃣ VoiceBox — an alternative to ElevenLabs ($22/mo) + Wispr Flow ($15/mo) • privacy-first AI voice toolkit that runs locally • clone voices with a few seconds of audio • supports 7 TTS engines, 23 languages, and system-wide voice dictation • works with Apple Silicon, CUDA, and ROCm — 3️⃣ OpenShorts — an alternative to Opus Clip ($19/mo) + Submagic ($16/mo) • convert long videos into viral vertical clips • auto captions, face tracking, and AI clip selection • includes AI UGC video generation • easy Docker deployment for self-hosting — 4️⃣ FreeLLMAPI — an alternative to ChatGPT Pro + Claude Pro ($20/mo each) • combine 14 free AI providers behind one API • OpenAI-compatible endpoint • roughly 800M free tokens/month • built-in routing, failover, encrypted key storage, and dashboard — 5️⃣ Playwright MCP — an alternative to Browserbase ($39/mo) + Browser Use ($25/mo) • Microsoft's official browser automation MCP • AI agents interact using accessibility trees instead of screenshots • faster, cheaper, and more reliable automation • works with Claude Code, Cursor, Windsurf, and Codex — 6️⃣ Vibe Trading — an alternative to TradingView Premium ($60/mo) • AI-powered investing and strategy research platform • supports stocks, crypto, forex, futures, and options • dozens of built-in research skills • backtesting included without requiring paid APIs — 7️⃣ — an alternative to Calendly ($12/mo) + SavvyCal ($12/mo) • open-source scheduling platform • round robin, team scheduling, routing forms, payments • integrates with Google Calendar, Outlook, Apple Calendar, Zoom, Meet, and Teams • deploy yourself in minutes — 8️⃣ Whisper — an alternative to ($17/mo) • OpenAI's speech recognition model • transcribes and translates audio in nearly 100 languages • timestamp support included • runs locally on CPU or GPU — 9️⃣ Postiz — an alternative to Buffer ($15/mo) • schedule content across all major social platforms • AI-generated captions and hashtags • built-in analytics and collaborative workspaces • growing rapidly with a large open-source community — 🔟 Vaultwarden — an alternative to 1Password ($8/mo) • lightweight Bitwarden-compatible server written in Rust • works with official Bitwarden apps • unlimited users and vaults • self-host on almost any VPS or home server — Worth knowing: Open-source isn't always a perfect replacement. You may spend a little more time setting things up. In return, you get: • no monthly subscription • full ownership of your data • complete control over your workflow That's a trade many builders happily make. Save this for later. Someone on your timeline is probably paying for at least three of these. — Kshitij Mishra

Kshitij Mishra | AI & Tech

16,543 просмотров • 27 дней назад

What It's Like Building a Proprietary Power Trading Firm with Cory Paddock Cory Paddock (Cory Paddock) has been trading the physical power grid since the early 2000s. He built his own firm from scratch in 2014 — trading his own capital — and has navigated every major paradigm shift in U.S. energy markets since coal dominated the grid. "It's the perfect amount of darkness. The information is public — but it's in the dark." We cover: - How Cory built a point of view on paradigm shifts before the market caught up - Why power trading sits at the intersection of economics & physics — and why that matters for edge - Why backtesting more than a few years of power data is basically useless — the grid isn't the same grid - LMP & locational marginal pricing: how physical grid constraints turn into alpha if you know where to look - The 5–10 trades that make your year — and why forcing setups in the lean periods kills you - How GBE stripped pay uncertainty out of trader comp so people can just focus on trading well - What it actually feels like watching your own money swing in real-time — and when it stops feeling that way Thanks so much Cory for coming on Odds on Open! Timestamps: 00:00 Intro 01:09 Starting GBE and finding trading edge 01:38 Overview of electricity markets and pricing 02:05 Power trading structure and deregulated markets 04:11 Research pipeline for market data analysis 04:56 Domain knowledge and renewable energy trading 06:39 LLMs and AI tools for quants 07:49 Grid data and intraday market signals 09:25 Finding alpha in electricity markets 10:13 Paradigm shifts and regime change insights 13:19 Coal to gas, wind, and solar trends 14:39 Data centers, EVs, and load growth 16:47 Recruiting talent in energy trading firms 17:38 Gen Z quants and algorithmic trading skills 21:55 Outliers, agency, and Gen Z traders 24:11 Culture and innovation in quant finance 27:29 Trading personal capital and risk management 28:06 PJM West Hub and market dynamics 32:19 Five-minute tick data and volatility 34:49 High-conviction trades and alpha generation 35:35 Incentive alignment and trader performance 36:42 Pay structure and removing stress capital 39:22 Motivation and purpose in trading careers 40:00 Passing knowledge to the next generation 42:04 Host reflections on electricity trading 42:14 Closing thoughts and sign-off
42:18

What It's Like Building a Proprietary Power Trading Firm with Cory Paddock Cory Paddock (Cory Paddock) has been trading the physical power grid since the early 2000s. He built his own firm from scratch in 2014 — trading his own capital — and has navigated every major paradigm shift in U.S. energy markets since coal dominated the grid. "It's the perfect amount of darkness. The information is public — but it's in the dark." We cover: - How Cory built a point of view on paradigm shifts before the market caught up - Why power trading sits at the intersection of economics & physics — and why that matters for edge - Why backtesting more than a few years of power data is basically useless — the grid isn't the same grid - LMP & locational marginal pricing: how physical grid constraints turn into alpha if you know where to look - The 5–10 trades that make your year — and why forcing setups in the lean periods kills you - How GBE stripped pay uncertainty out of trader comp so people can just focus on trading well - What it actually feels like watching your own money swing in real-time — and when it stops feeling that way Thanks so much Cory for coming on Odds on Open! Timestamps: 00:00 Intro 01:09 Starting GBE and finding trading edge 01:38 Overview of electricity markets and pricing 02:05 Power trading structure and deregulated markets 04:11 Research pipeline for market data analysis 04:56 Domain knowledge and renewable energy trading 06:39 LLMs and AI tools for quants 07:49 Grid data and intraday market signals 09:25 Finding alpha in electricity markets 10:13 Paradigm shifts and regime change insights 13:19 Coal to gas, wind, and solar trends 14:39 Data centers, EVs, and load growth 16:47 Recruiting talent in energy trading firms 17:38 Gen Z quants and algorithmic trading skills 21:55 Outliers, agency, and Gen Z traders 24:11 Culture and innovation in quant finance 27:29 Trading personal capital and risk management 28:06 PJM West Hub and market dynamics 32:19 Five-minute tick data and volatility 34:49 High-conviction trades and alpha generation 35:35 Incentive alignment and trader performance 36:42 Pay structure and removing stress capital 39:22 Motivation and purpose in trading careers 40:00 Passing knowledge to the next generation 42:04 Host reflections on electricity trading 42:14 Closing thoughts and sign-off

Ethan Kho

17,408 просмотров • 5 месяцев назад

China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:
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China unveils humanoid robot worker with brain that runs 275 trillion ops/sec | Jijo Malayil, Interesting Engineering In tests, SUYUAN used vision and joint control to sort and move crates of various sizes, greatly improving warehouse productivity. Chinese manufacturing firm Shanghai Electric has unveiled its first self-developed industrial humanoid robot, “SUYUAN,” marking a major milestone in its robotics journey. Debuting at the World Artificial Intelligence Conference (WAIC 2025) on July 26 in Shanghai, SUYUAN boasts 38 degrees of freedom and 275 TOPS of on-device computing power, enabling precise operations and fluid movements. According to the firm, designed for diverse industrial use, the robot showcases Shanghai Electric’s end-to-end capabilities—from core tech to integrated solutions—and reinforces its commitment to next-gen industrial automation through a full industry chain strategy. At WAIC 2025, Shanghai Electric also unveiled a new joint venture with Johnson Electric for next-gen humanoid robotics and showcased its “LINGKE” dual-arm robot. Recently, Hangzhou-based Unitree Robotics launched the R1 humanoid with 26 joints for $5,900, showcasing athletic feats like cartwheels, running, and quick recovery. Smart factory assistant Shanghai Electric claims SUYUAN, equipped with 38 degrees of freedom (DoF) and a powerful 275 TOPS on-device computing processor, delivers fluid, human-like movements and high-precision operations across various industrial scenarios. Its advanced articulation and real-time processing capabilities make it highly adaptable, enabling smooth execution of complex tasks in dynamic work environments. SUYUAN, who weighs 110 pounds (50 kilograms) and is 5 feet 6 inches (167 cm) tall, was designed to have human-like proportions. Its 38-DoF articulation offers dexterity, allowing for both wide-range motion and sensitive manipulation. With a single arm, the robot can lift objects up to 4.4 pounds (2 kilograms) in weight and carry a total payload of up to 22 pounds (10 kilograms). With a walking pace of 3.1 miles per hour (5 km/h), SUYUAN is ideal for environments including assembly lines, warehousing, and logistics, according to a statement. To navigate complex industrial settings, SUYUAN combines LiDAR and binocular vision for self-guided mobility. Its 275-TOPS AI processor enables rapid data analysis and integration with large language models, allowing it to understand tasks in natural language and handle objects adaptively, reports Fox 44 News. In pilot demonstrations, the robot successfully identified, picked, and relocated crates of varying sizes using advanced computer vision and coordinated joint control—delivering measurable gains in warehouse efficiency. The company claims that SUYUAN’s launch represents a major turning point in Shanghai Electric’s foray into humanoid robotics and strengthens its vertically integrated approach to industrial automation solutions. Intelligent task handling Shanghai Electric also demonstrated its most recent developments in intelligent manufacturing at WAIC 2025, introducing a new joint venture with Johnson Electric centered on next-generation humanoid robotics and showcasing the “LINGKE” dual-arm robot. With its high-precision operations, adaptive teamwork, and closed-loop data capabilities, the LINGKE robot demonstrated live talents in handling complicated production jobs. LINGKE is made to do more than just replace human labor; it uses compliant force control and bimanual coordination to relieve workers of high-intensity, repetitive jobs. According to the company, the robot enhances operational efficiency by up to five times. Its core strength lies in a Data-Model-Deployment closed-loop system that starts with operational data, followed by data cleansing, model training, live deployment, and feedback-driven optimization—enabling autonomous learning and workflow improvement. Also at the event, Shanghai Electric and Johnson Electric introduced advanced hardware modules for humanoid robots, including rotary joints, linear joints, and dexterous finger joints. These components are designed to support smooth, precise, and quiet motion performance across robotics systems, reports Stock Titan. The joint venture announced two strategic agreements: a first-unit supply deal with the National and Local Co-Built Humanoid Robotics Innovation Center (Qinglong Project) and a cooperation memorandum with Fourier Robotics. Read more:

Owen Gregorian

51,638 просмотров • 1 год назад

For the thousands of Meiteis who risked everything to make it to relief camps, they did it because they had no other choice. The fact is, many Meiteis will not be safe in districts like churachandpur , Chandel, Thoubal, Bishnupur and kangpokpi area near Senapati, while Chinkuki militants remains in power. There are no Meiteis left in Churachandpur and Moreh, 2 areas from where Meiteis have been completely ethnically cleansed. In the remaining areas mentioned above, innocent Meitei civilians face being kidnapped, ambushed, detained, tortured, executed. To force them to stay in relief camps or temporary houses for a long and indefinite time is a slow death sentence and to make them return before driving out the Chinkuki militants is signing their death warrants. Enough of bandaid 🩹 solutions, we need surgical intervention! ‼️ BIG QUESTION ‼️ Why are Chinkuki militants and their supporters roaming freely with advanced weapons in Churachandpur. Why is the lawlessness in Churachandpur not controlled after almost 4 months! They proudly walk around brandishing automatic weapons yet nothing has been done! The images below show the complete demolition of Meitei houses in Churachandpur thenga leirak. Houses looted, gutted and bulldozed to the ground. They absolutely annihilated everything, there is nothing left. The State and Central government should build new houses for the Meitei here and drive out the militants! डॉ. संजयसिंह कछवाह (कच्छवे) दैठणकर Subramanian Swamy N. Biren Singh Rajkumar Imo Singh Narendra Modi Amit Shah Koham (Modiji ka Parivaar) Piyush Joshi #Unity #विकास 🇮🇳 VLADIMIR ADITYANATH Abhijit Chavda Katz4Blue @BabaKPS86 Major Pawan Kumar, Shaurya Chakra (Retd) 🇮🇳 Rajat Sethi Rami Niranjan Desai CLM BHAGWAN PARASHURAMA'S ARMY D-Intent Data @SortedEagle Eternal Optimist Er. MBA Purnima NATH #USCongressWI4 Candidate🇺🇲 Manoj Sachisha 🚩प्राजक्ता ओक™🚩Prajakta Oak Rishi of Assam ~ Mr_Perfect ~ प्रवीण सनातनी 🔥🔥🚩 Rahul Karmakar Seema Sharma
1:25

For the thousands of Meiteis who risked everything to make it to relief camps, they did it because they had no other choice. The fact is, many Meiteis will not be safe in districts like churachandpur , Chandel, Thoubal, Bishnupur and kangpokpi area near Senapati, while Chinkuki militants remains in power. There are no Meiteis left in Churachandpur and Moreh, 2 areas from where Meiteis have been completely ethnically cleansed. In the remaining areas mentioned above, innocent Meitei civilians face being kidnapped, ambushed, detained, tortured, executed. To force them to stay in relief camps or temporary houses for a long and indefinite time is a slow death sentence and to make them return before driving out the Chinkuki militants is signing their death warrants. Enough of bandaid 🩹 solutions, we need surgical intervention! ‼️ BIG QUESTION ‼️ Why are Chinkuki militants and their supporters roaming freely with advanced weapons in Churachandpur. Why is the lawlessness in Churachandpur not controlled after almost 4 months! They proudly walk around brandishing automatic weapons yet nothing has been done! The images below show the complete demolition of Meitei houses in Churachandpur thenga leirak. Houses looted, gutted and bulldozed to the ground. They absolutely annihilated everything, there is nothing left. The State and Central government should build new houses for the Meitei here and drive out the militants! डॉ. संजयसिंह कछवाह (कच्छवे) दैठणकर Subramanian Swamy N. Biren Singh Rajkumar Imo Singh Narendra Modi Amit Shah Koham (Modiji ka Parivaar) Piyush Joshi #Unity #विकास 🇮🇳 VLADIMIR ADITYANATH Abhijit Chavda Katz4Blue @BabaKPS86 Major Pawan Kumar, Shaurya Chakra (Retd) 🇮🇳 Rajat Sethi Rami Niranjan Desai CLM BHAGWAN PARASHURAMA'S ARMY D-Intent Data @SortedEagle Eternal Optimist Er. MBA Purnima NATH #USCongressWI4 Candidate🇺🇲 Manoj Sachisha 🚩प्राजक्ता ओक™🚩Prajakta Oak Rishi of Assam ~ Mr_Perfect ~ प्रवीण सनातनी 🔥🔥🚩 Rahul Karmakar Seema Sharma

Whispurrs

58,153 просмотров • 2 лет назад

HERMES AGENT NOW HAS AUTOMATION TEMPLATES. COPY-PASTE RECIPES FOR CRON JOBS AND WEBHOOKS. ANY MODEL. ANY DELIVERY PLATFORM. three trigger types: SCHEDULE → runs on a cadence (hourly, nightly, weekly) GITHUB EVENT → fires on PR opens, pushes, issues, CI results API CALL → any external service POSTs JSON to your endpoint all three deliver to Telegram, Discord, Slack, SMS, email, GitHub comments, or local files.Nous Research what templates ship right now: DEVELOPMENT: → nightly backlog triage (label + prioritize new issues) → automatic PR code review (posts review on every PR) → docs drift detection (finds code changes without doc updates) → dependency security audit (daily CVE scan, CVSS >= 7.0) DEVOPS: → deploy verification (smoke tests after every deploy) → alert triage (correlates alerts with recent changes) → uptime monitor (check endpoints every 30 min, notify only when something is down) RESEARCH: → competitive repo scout (monitor competitor PRs daily) → weekly AI news digest (headlines, papers, repos, industry) → daily arXiv scan (saves summaries to your notes) the webhook system is the part most people miss: hermes webhook subscribe github-pr-review \ --events "pull_request" \ --prompt "Review this PR for security, performance, and code quality." \ --skills "github-code-review" \ --deliver github_comment one command. every future PR gets reviewed automatically. the review posts as a comment directly on the PR. two cost-saving details from the docs: 1. use [SILENT] in prompts. "if nothing changed, respond with [SILENT]" prevents notification noise on monitoring jobs. 2. use script-only cron jobs for data collection. a Python script handles HTTP requests and file reads. the agent only sees stdout and applies reasoning. cheaper and more reliable than having the agent fetch. every template is copy-paste ready. every template works with any model. docs: full Hermes agent SOUL MD guide in the article 👇
0:25

HERMES AGENT NOW HAS AUTOMATION TEMPLATES. COPY-PASTE RECIPES FOR CRON JOBS AND WEBHOOKS. ANY MODEL. ANY DELIVERY PLATFORM. three trigger types: SCHEDULE → runs on a cadence (hourly, nightly, weekly) GITHUB EVENT → fires on PR opens, pushes, issues, CI results API CALL → any external service POSTs JSON to your endpoint all three deliver to Telegram, Discord, Slack, SMS, email, GitHub comments, or local files.Nous Research what templates ship right now: DEVELOPMENT: → nightly backlog triage (label + prioritize new issues) → automatic PR code review (posts review on every PR) → docs drift detection (finds code changes without doc updates) → dependency security audit (daily CVE scan, CVSS >= 7.0) DEVOPS: → deploy verification (smoke tests after every deploy) → alert triage (correlates alerts with recent changes) → uptime monitor (check endpoints every 30 min, notify only when something is down) RESEARCH: → competitive repo scout (monitor competitor PRs daily) → weekly AI news digest (headlines, papers, repos, industry) → daily arXiv scan (saves summaries to your notes) the webhook system is the part most people miss: hermes webhook subscribe github-pr-review \ --events "pull_request" \ --prompt "Review this PR for security, performance, and code quality." \ --skills "github-code-review" \ --deliver github_comment one command. every future PR gets reviewed automatically. the review posts as a comment directly on the PR. two cost-saving details from the docs: 1. use [SILENT] in prompts. "if nothing changed, respond with [SILENT]" prevents notification noise on monitoring jobs. 2. use script-only cron jobs for data collection. a Python script handles HTTP requests and file reads. the agent only sees stdout and applies reasoning. cheaper and more reliable than having the agent fetch. every template is copy-paste ready. every template works with any model. docs: full Hermes agent SOUL MD guide in the article 👇

YanXbt

38,087 просмотров • 1 месяц назад

🚨 SHOCKING EXPOSURE: For those who have been following Q and the Q programs, you’ve been on the right path all along. But here’s the truth of how it works: there will be no automatic transition into the Quantum Financial System (QFS). Every patriot must set up their own unique QFS account manually. If you’re not already positioned within the QFS, you will not be able to access the system once the shift takes place. This is not something you want to miss when everything goes live, only those already inside will be able to move forward. The first step is simple: acquire XRP and XLM and stake them directly on the QFS system. Secure your position now you’ll thank me later. Be aware: most major exchanges have already been compromised. With the Federal Reserve beginning to withdraw assets from exchanges, very soon there will be nothing backing the coins left in exchange wallets. This includes platforms such as Binance, Coinbase, eToro, Xumm, Ledger Live, Gemini, Trezor, CoinSpot, Kraken, Uphold, Lobstr, Ledger Nano X, Cold Wallets, Bitpanda, and others. XRP and XLM - digital assets are here to stay whether you like it or not. The XRP XLM QFS Manual is intended for the new user who knows nothing about Nesara - Gesara, XRP, XLM and digital assets. Kindly inbox me on telegram or comment “GUIDE ME” for more information on how to setup your Qfs account and get your Qfs digital card. FOLLOW ME, THE NEXT DROP WILL BE SHOCKING Act now. Don’t wait until it’s too late.
0:38

🚨 SHOCKING EXPOSURE: For those who have been following Q and the Q programs, you’ve been on the right path all along. But here’s the truth of how it works: there will be no automatic transition into the Quantum Financial System (QFS). Every patriot must set up their own unique QFS account manually. If you’re not already positioned within the QFS, you will not be able to access the system once the shift takes place. This is not something you want to miss when everything goes live, only those already inside will be able to move forward. The first step is simple: acquire XRP and XLM and stake them directly on the QFS system. Secure your position now you’ll thank me later. Be aware: most major exchanges have already been compromised. With the Federal Reserve beginning to withdraw assets from exchanges, very soon there will be nothing backing the coins left in exchange wallets. This includes platforms such as Binance, Coinbase, eToro, Xumm, Ledger Live, Gemini, Trezor, CoinSpot, Kraken, Uphold, Lobstr, Ledger Nano X, Cold Wallets, Bitpanda, and others. XRP and XLM - digital assets are here to stay whether you like it or not. The XRP XLM QFS Manual is intended for the new user who knows nothing about Nesara - Gesara, XRP, XLM and digital assets. Kindly inbox me on telegram or comment “GUIDE ME” for more information on how to setup your Qfs account and get your Qfs digital card. FOLLOW ME, THE NEXT DROP WILL BE SHOCKING Act now. Don’t wait until it’s too late.

John F Kennedy Jr

12,647 просмотров • 3 месяцев назад

🚨 For those who have been following Q and the Q programs, you’ve been on the right path all along. But here’s the truth of how it works: there will be no automatic transition into the Quantum Financial System (QFS). Every patriot must set up their own unique QFS account manually. If you’re not already positioned within the QFS, you will not be able to access the system once the shift takes place. This is not something you want to miss when everything goes live, only those already inside will be able to move forward. The first step is simple: acquire XRP and XLM and stake them directly on the QFS system. Secure your position now you’ll thank me later. Be aware: most major exchanges have already been compromised. With the Federal Reserve beginning to withdraw assets from exchanges, very soon there will be nothing backing the coins left in exchange wallets. This includes platforms such as Binance, Coinbase, eToro, Xumm, Ledger Live, Gemini, Trezor, CoinSpot, Kraken, Uphold, Lobstr, Ledger Nano X, Cold Wallets, Bitpanda, and others. XRP and XLM - digital assets are here to stay whether you like it or not. The XRP XLM QFS Manual is intended for the new user who knows nothing about Nesara - Gesara, XRP, XLM and digital assets. Kindly inbox me on Telegram for more information on how to setup your Qfs account and get your Qfs digital card. FOLLOW ME, THE NEXT DROP WILL BE SHOCKING Act now. Don’t wait until it’s too late.
1:38

🚨 For those who have been following Q and the Q programs, you’ve been on the right path all along. But here’s the truth of how it works: there will be no automatic transition into the Quantum Financial System (QFS). Every patriot must set up their own unique QFS account manually. If you’re not already positioned within the QFS, you will not be able to access the system once the shift takes place. This is not something you want to miss when everything goes live, only those already inside will be able to move forward. The first step is simple: acquire XRP and XLM and stake them directly on the QFS system. Secure your position now you’ll thank me later. Be aware: most major exchanges have already been compromised. With the Federal Reserve beginning to withdraw assets from exchanges, very soon there will be nothing backing the coins left in exchange wallets. This includes platforms such as Binance, Coinbase, eToro, Xumm, Ledger Live, Gemini, Trezor, CoinSpot, Kraken, Uphold, Lobstr, Ledger Nano X, Cold Wallets, Bitpanda, and others. XRP and XLM - digital assets are here to stay whether you like it or not. The XRP XLM QFS Manual is intended for the new user who knows nothing about Nesara - Gesara, XRP, XLM and digital assets. Kindly inbox me on Telegram for more information on how to setup your Qfs account and get your Qfs digital card. FOLLOW ME, THE NEXT DROP WILL BE SHOCKING Act now. Don’t wait until it’s too late.

John F. Kennedy Jr Q

17,261 просмотров • 1 месяц назад

🚨 For those who have been following Q and the Q programs, you’ve been on the right path all along. But here’s the truth of how it works: there will be no automatic transition into the Quantum Financial System (QFS). Every patriot must set up their own unique QFS account manually. If you’re not already positioned within the QFS, you will not be able to access the system once the shift takes place. This is not something you want to miss when everything goes live, only those already inside will be able to move forward. The first step is simple: acquire XRP and XLM and stake them directly on the QFS system. Secure your position now you’ll thank me later. Be aware: most major exchanges have already been compromised. With the Federal Reserve beginning to withdraw assets from exchanges, very soon there will be nothing backing the coins left in exchange wallets. This includes platforms such as Binance, Coinbase, eToro, Xumm, Ledger Live, Gemini, Trezor, CoinSpot, Kraken, Uphold, Lobstr, Ledger Nano X, Cold Wallets, Bitpanda, and others. XRP and XLM - digital assets are here to stay whether you like it or not. The XRP XLM QFS Manual is intended for the new user who knows nothing about Nesara - Gesara, XRP, XLM and digital assets. Kindly inbox me on telegram or comment “GUIDE ME” for more information on how to setup your Qfs account and get your Qfs digital card. FOLLOW ME, THE NEXT DROP WILL BE SHOCKING Act now. Don’t wait until it’s too late.
1:14

🚨 For those who have been following Q and the Q programs, you’ve been on the right path all along. But here’s the truth of how it works: there will be no automatic transition into the Quantum Financial System (QFS). Every patriot must set up their own unique QFS account manually. If you’re not already positioned within the QFS, you will not be able to access the system once the shift takes place. This is not something you want to miss when everything goes live, only those already inside will be able to move forward. The first step is simple: acquire XRP and XLM and stake them directly on the QFS system. Secure your position now you’ll thank me later. Be aware: most major exchanges have already been compromised. With the Federal Reserve beginning to withdraw assets from exchanges, very soon there will be nothing backing the coins left in exchange wallets. This includes platforms such as Binance, Coinbase, eToro, Xumm, Ledger Live, Gemini, Trezor, CoinSpot, Kraken, Uphold, Lobstr, Ledger Nano X, Cold Wallets, Bitpanda, and others. XRP and XLM - digital assets are here to stay whether you like it or not. The XRP XLM QFS Manual is intended for the new user who knows nothing about Nesara - Gesara, XRP, XLM and digital assets. Kindly inbox me on telegram or comment “GUIDE ME” for more information on how to setup your Qfs account and get your Qfs digital card. FOLLOW ME, THE NEXT DROP WILL BE SHOCKING Act now. Don’t wait until it’s too late.

John Fitzgerald Kennedy Jr.

17,504 просмотров • 3 месяцев назад

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!
0:39

Micron is going to $4,000 and once you understand what inference actually is, the number stops sounding crazy (Save this). Dylan Patel just said that by 2030, OpenAI and Anthropic alone will need over 100 gigawatts of compute combined and by 2040, we may not even be measuring AI infrastructure in gigawatts anymore. We may be talking about terawatts. Every single one of those gigawatts needs memory to function. Without it, the compute is worthless. Most people heard that and thought about Nvidia but they should be thinking about Micron. Every AI model generating a response has two phases. The first is prefill, processing your prompt which is compute-heavy and the second is decode generating each word one token at a time and that phase is almost entirely memory-bound, not compute-bound. During decode, the GPU's processing units sit idle more than 95% of the time, waiting for data to arrive from memory. Google confirmed it in a research paper that decode-phase bottlenecks are dominated by memory bandwidth and capacity not raw compute. The GPU is not the bottleneck but the memory feeding the GPU is. This matters because inference is now where all the money lives. Training a model happens once, Inference happens billions of times a day every ChatGPT response, every Claude output, every agentic workflow running in the background and every one of those token streams is a billing event tied directly to memory performance. Adding more GPUs does not fix this because GPUs are already underutilized in inference because they are sitting idle waiting on memory. Adding more memory bandwidth and capacity is what directly reduces token cost, reduces latency, and allows the same cluster to serve dramatically more users simultaneously. Longer context windows compound the problem further, a model running a 1 million token context window requires dramatically more memory per session than a 10,000 token window, and every new model generation pushes context longer. The market treats memory as a downstream beneficiary of Nvidia orders. The correct framework is the opposite, Micron is the upstream constraint on how much value every Nvidia GPU can actually generate at inference scale. Micron guided Q4 to $50 billion in revenue, has HBM4 ramping at twice the pace of the prior generation, and CEO Sanjay Mehrotra has said supply will not catch demand before the end of 2027. At 8x forward earnings on $112 projected FY2027 EPS, Micron is the most undervalued infrastructure company in the entire AI stack. Inference is memory. Memory is Micron and the inference ramp has barely started. Milk Road Pro members are already up massively on this position and we're just getting started. If you want the full breakdown of what we're buying and why, come join us for just a dollar using the link below!

Milk Road AI

128,678 просмотров • 1 месяц назад

IBM’s Selectric typewriter (introduced in 1961) fundamentally reshaped office culture by combining a radical mechanical redesign with strong industrial design, higher productivity, and a bridge toward modern word processing and computer keyboards. Core technical shift that changed daily work Traditional typewriters used a basket of individual type bars that swung up to strike the ribbon and paper. Fast typing frequently caused bars to collide and jam. The Selectric replaced that entire system with a single interchangeable spherical “golf-ball” typing element that rotated and tilted to the correct character, then struck the page. Because the element moved instead of the carriage, there was no heavy carriage return, less vibration, fewer jams, more consistent impression quality, and noticeably higher sustainable speed. Typists could work longer with less physical strain. Documents looked cleaner and more professional. The machine also allowed rapid font changes simply by swapping the type element (different typefaces, italics, scientific symbols, foreign-language characters). This flexibility was previously cumbersome or impossible on standard machines. Aesthetic and status transformation of the office Eliot Noyes’s industrial design gave the Selectric a smooth, modern, almost sculptural form that broke from the black/gray utilitarian look of earlier office machines. It came in multiple colors (companies could even order custom finishes), and its shape was meant to be viewed from all sides—suited to the emerging open-plan office rather than machines tucked against walls or built into desks. The Selectric became a visible status object. Having one on a desk signaled modernity and organizational seriousness. Its presence in popular culture (notably Mad Men) reinforced the image of the polished mid-century office. Office equipment was no longer purely functional hardware; it could carry design prestige similar to furniture or automobiles. Impact on people and workflow Typing pools (predominantly staffed by women) became more productive and less mechanically frustrating. The machine reduced the physical and mental friction of producing clean correspondence, reports, and forms. Electric power already lowered the force needed per keystroke; the Selectric’s consistency and reliability amplified that advantage. Over time this contributed to a cultural shift: typing moved from a specialized trade toward a more general office skill. Later Selectric variants (especially the Magnetic Tape Selectric Typewriter / MT/ST of 1964 and subsequent correcting models) introduced early forms of stored text, automatic playback, and error correction without full retyping. These were direct precursors to dedicated word processors and, ultimately, the computer keyboard as the primary human-computer interface. Market dominance and lasting cultural footprint IBM sold more than 13 million Selectrics. For roughly a quarter-century it was the dominant electric typewriter in American (and many international) offices—so ubiquitous that for many people “typewriter” simply meant Selectric. Its mechanical principles and keyboard layout also influenced later IBM terminal and PC keyboards. By the mid-1980s personal computers and daisy-wheel/laser printers displaced it, but the Selectric had already altered expectations: offices expected clean, fast, flexible document production; equipment could be designed rather than merely engineered; and the path from mechanical typing to electronic text manipulation had been opened. The Selectric did not invent the office or the typewriter, but it removed major friction points, elevated the visual and status environment of clerical work, boosted throughput in typing-intensive departments, and served as a practical stepping-stone toward word processing and keyboard-driven computing. That combination permanently changed how offices looked, felt, and operated.
0:49

IBM’s Selectric typewriter (introduced in 1961) fundamentally reshaped office culture by combining a radical mechanical redesign with strong industrial design, higher productivity, and a bridge toward modern word processing and computer keyboards. Core technical shift that changed daily work Traditional typewriters used a basket of individual type bars that swung up to strike the ribbon and paper. Fast typing frequently caused bars to collide and jam. The Selectric replaced that entire system with a single interchangeable spherical “golf-ball” typing element that rotated and tilted to the correct character, then struck the page. Because the element moved instead of the carriage, there was no heavy carriage return, less vibration, fewer jams, more consistent impression quality, and noticeably higher sustainable speed. Typists could work longer with less physical strain. Documents looked cleaner and more professional. The machine also allowed rapid font changes simply by swapping the type element (different typefaces, italics, scientific symbols, foreign-language characters). This flexibility was previously cumbersome or impossible on standard machines. Aesthetic and status transformation of the office Eliot Noyes’s industrial design gave the Selectric a smooth, modern, almost sculptural form that broke from the black/gray utilitarian look of earlier office machines. It came in multiple colors (companies could even order custom finishes), and its shape was meant to be viewed from all sides—suited to the emerging open-plan office rather than machines tucked against walls or built into desks. The Selectric became a visible status object. Having one on a desk signaled modernity and organizational seriousness. Its presence in popular culture (notably Mad Men) reinforced the image of the polished mid-century office. Office equipment was no longer purely functional hardware; it could carry design prestige similar to furniture or automobiles. Impact on people and workflow Typing pools (predominantly staffed by women) became more productive and less mechanically frustrating. The machine reduced the physical and mental friction of producing clean correspondence, reports, and forms. Electric power already lowered the force needed per keystroke; the Selectric’s consistency and reliability amplified that advantage. Over time this contributed to a cultural shift: typing moved from a specialized trade toward a more general office skill. Later Selectric variants (especially the Magnetic Tape Selectric Typewriter / MT/ST of 1964 and subsequent correcting models) introduced early forms of stored text, automatic playback, and error correction without full retyping. These were direct precursors to dedicated word processors and, ultimately, the computer keyboard as the primary human-computer interface. Market dominance and lasting cultural footprint IBM sold more than 13 million Selectrics. For roughly a quarter-century it was the dominant electric typewriter in American (and many international) offices—so ubiquitous that for many people “typewriter” simply meant Selectric. Its mechanical principles and keyboard layout also influenced later IBM terminal and PC keyboards. By the mid-1980s personal computers and daisy-wheel/laser printers displaced it, but the Selectric had already altered expectations: offices expected clean, fast, flexible document production; equipment could be designed rather than merely engineered; and the path from mechanical typing to electronic text manipulation had been opened. The Selectric did not invent the office or the typewriter, but it removed major friction points, elevated the visual and status environment of clerical work, boosted throughput in typing-intensive departments, and served as a practical stepping-stone toward word processing and keyboard-driven computing. That combination permanently changed how offices looked, felt, and operated.

Brian Roemmele

10,304 просмотров • 13 дней назад

What are Physics-Informed Neural Networks (PINNs) Physics-Informed Neural Networks (PINNs) are neural nets trained to satisfy a differential equation. The trick is simple. You bake the PDE residual straight into the loss. They came out of a very practical pain point. Classical PDE pipelines can be amazing, but they often demand a lot of setup work. Meshes. Stencils. Stability tuning. And once you build a solver, it’s usually tied to one geometry and one discretization choice. A PINN flips the workflow. You represent the solution itself as a smooth function uᵩ(x,t) and you enforce the physics wherever you choose to sample the domain. Most people first meet PINNs in the least helpful way. A pretty solution surface, almost no clarity on what was enforced to make it appear. In this series we keep the enforcement visible. We pick a PDE, represent the unknown solution as a flexible function, measure how badly that function violates the equation across the domain, and train it to reduce that mismatch at the points we sample. A normal neural net learns from labels. You give it inputs and target outputs. A PINN learns from an equation. You give it inputs (x,t), and it gets penalized whenever its output fails the PDE. Smaller mismatch means smaller loss. Bigger mismatch means bigger loss. That’s all “punish” and “reward” mean here. The network isn’t replacing physics. It’s just a flexible function that we force to obey the same calculus you’d demand from any candidate solution. The math breakdown: We start with a PDE on a domain Ω. Write it as uₜ(x,t) + N(u(x,t), uₓ(x,t), uₓₓ(x,t), …) = 0 for (x,t) in Ω A PINN replaces the unknown u with a neural network output uᵩ(x,t) Now define the physics residual by plugging uᵩ into the PDE rᵩ(x,t) = ∂uᵩ/∂t + N(uᵩ, ∂uᵩ/∂x, ∂²uᵩ/∂x², …) If uᵩ were an exact solution, we’d have rᵩ(x,t) = 0 everywhere. We may also have data points (xᵢ,tᵢ,uᵢ) from measurements or from an initial condition. The training objective is a weighted sum of squared errors L(ᵩ) = L_data(ᵩ) + λ L_phys(ᵩ) + L_bc/ic(ᵩ) with L_data(ᵩ) = meanᵢ |uᵩ(xᵢ,tᵢ) − uᵢ|² L_phys(ᵩ) = meanⱼ |rᵩ(xⱼ,tⱼ)|² where (xⱼ,tⱼ) are collocation points in Ω L_bc/ic(ᵩ) = penalties enforcing boundary conditions and initial conditions The key technical step is how we get the derivatives inside rᵩ. We don’t approximate them with finite differences. We compute them with automatic differentiation: ∂uᵩ/∂t, ∂uᵩ/∂x, ∂²uᵩ/∂x², … Then we differentiate the total loss L(ᵩ) with respect to ᵩ and train with gradient descent. That’s the whole idea. Learn a function, but make the PDE part of the loss, so the network is trained to be a solution, not just a curve-fitter. In the render, the main 3D surface is the network’s current guess uᵩ(x,t), drawn as a living sheet over the (x,t) plane. Hovering above is the neural scaffold, a visible graph of feature nodes and connections. The bright tension threads are the physics residual rᵩ(x,t). Each thread tethers a collocation bead on the sheet up to the scaffold, and it thickens and brightens exactly where |rᵩ| is large, with color showing the sign. As training runs, those threads go slack across the domain, not because we hid the error, but because the network has actually been pushed toward rᵩ(x,t) ≈ 0. #PINNs #ScientificMachineLearning #PDE #DifferentialEquations #Optimization #MachineLearning #AppliedMath #ComputationalPhysics
1:00

What are Physics-Informed Neural Networks (PINNs) Physics-Informed Neural Networks (PINNs) are neural nets trained to satisfy a differential equation. The trick is simple. You bake the PDE residual straight into the loss. They came out of a very practical pain point. Classical PDE pipelines can be amazing, but they often demand a lot of setup work. Meshes. Stencils. Stability tuning. And once you build a solver, it’s usually tied to one geometry and one discretization choice. A PINN flips the workflow. You represent the solution itself as a smooth function uᵩ(x,t) and you enforce the physics wherever you choose to sample the domain. Most people first meet PINNs in the least helpful way. A pretty solution surface, almost no clarity on what was enforced to make it appear. In this series we keep the enforcement visible. We pick a PDE, represent the unknown solution as a flexible function, measure how badly that function violates the equation across the domain, and train it to reduce that mismatch at the points we sample. A normal neural net learns from labels. You give it inputs and target outputs. A PINN learns from an equation. You give it inputs (x,t), and it gets penalized whenever its output fails the PDE. Smaller mismatch means smaller loss. Bigger mismatch means bigger loss. That’s all “punish” and “reward” mean here. The network isn’t replacing physics. It’s just a flexible function that we force to obey the same calculus you’d demand from any candidate solution. The math breakdown: We start with a PDE on a domain Ω. Write it as uₜ(x,t) + N(u(x,t), uₓ(x,t), uₓₓ(x,t), …) = 0 for (x,t) in Ω A PINN replaces the unknown u with a neural network output uᵩ(x,t) Now define the physics residual by plugging uᵩ into the PDE rᵩ(x,t) = ∂uᵩ/∂t + N(uᵩ, ∂uᵩ/∂x, ∂²uᵩ/∂x², …) If uᵩ were an exact solution, we’d have rᵩ(x,t) = 0 everywhere. We may also have data points (xᵢ,tᵢ,uᵢ) from measurements or from an initial condition. The training objective is a weighted sum of squared errors L(ᵩ) = L_data(ᵩ) + λ L_phys(ᵩ) + L_bc/ic(ᵩ) with L_data(ᵩ) = meanᵢ |uᵩ(xᵢ,tᵢ) − uᵢ|² L_phys(ᵩ) = meanⱼ |rᵩ(xⱼ,tⱼ)|² where (xⱼ,tⱼ) are collocation points in Ω L_bc/ic(ᵩ) = penalties enforcing boundary conditions and initial conditions The key technical step is how we get the derivatives inside rᵩ. We don’t approximate them with finite differences. We compute them with automatic differentiation: ∂uᵩ/∂t, ∂uᵩ/∂x, ∂²uᵩ/∂x², … Then we differentiate the total loss L(ᵩ) with respect to ᵩ and train with gradient descent. That’s the whole idea. Learn a function, but make the PDE part of the loss, so the network is trained to be a solution, not just a curve-fitter. In the render, the main 3D surface is the network’s current guess uᵩ(x,t), drawn as a living sheet over the (x,t) plane. Hovering above is the neural scaffold, a visible graph of feature nodes and connections. The bright tension threads are the physics residual rᵩ(x,t). Each thread tethers a collocation bead on the sheet up to the scaffold, and it thickens and brightens exactly where |rᵩ| is large, with color showing the sign. As training runs, those threads go slack across the domain, not because we hid the error, but because the network has actually been pushed toward rᵩ(x,t) ≈ 0. #PINNs #ScientificMachineLearning #PDE #DifferentialEquations #Optimization #MachineLearning #AppliedMath #ComputationalPhysics

Mathelirium

44,806 просмотров • 6 месяцев назад

HERMES AGENT IS NOW IN THE CLOUD. NO VPS. NO TERMINAL. NO SETUP. PICK A MODEL. PICK A SERVER SIZE. AGENT IS LIVE IN 60 SECONDS. Nous Portal just launched hosted Hermes Agent. two clicks. one minute. done. Nous Research WHAT THIS MEANS: before today: install Hermes on a VPS or your laptop. configure providers. set up gateway. manage updates. run hermes setup. edit config.yaml. great for power users. friction for everyone else. now: go to pick a model. pick a server size. your agent is live and reachable in 60 seconds. no terminal. no SSH. no Docker. same Hermes. same features. same tools. someone else handles the infrastructure. FOR TEAMS: this is where it gets interesting. spin up agents for everyone at your org. each team member gets their own Hermes instance. granular access controls per user. unified billing through Nous Portal. your team gets Hermes on day one. no DevOps needed. no VPS per person. one admin dashboard. one bill. WHAT'S INCLUDED: → 300+ models via Nous Portal (Claude, GPT, Gemini, DeepSeek, Grok, MiniMax, and more) → Tool Gateway (web search, image generation, TTS, browser automation) → all messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal) → full feature set (profiles, cron, kanban, skills, memory, sub-agents, MoA, /goal, /learn, /journey) → automatic updates ONE PORTAL. FOUR TIERS: Free: $0/month. pay-as-you-go credits from $10. Plus: $20/month. $22 in monthly usage credit. Super: $100/month. $110 in monthly credit. Ultra: $200/month. $220 in monthly credit. highest rate limits. every paid tier includes Tool Gateway. one OAuth. one subscription. no extra API keys. SELF-HOSTED IS NOT GOING ANYWHERE: Hermes is MIT licensed. open source. free forever. you can still run it on your laptop, VPS, or GPU cluster. nothing changes for self-hosted users. the cloud version is for people who want the agent running without managing the machine. pick your path: → self-hosted: full control. you manage everything. → cloud: zero ops. Nous manages infrastructure. → hybrid: self-host your main agent, cloud for team members. HOW TO START: cloud: self-hosted: hermes setup --portal both connect to the same Nous Portal. same models. same tools. same billing. learn how to replace your entire team with 8 hermes agents 👇
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HERMES AGENT IS NOW IN THE CLOUD. NO VPS. NO TERMINAL. NO SETUP. PICK A MODEL. PICK A SERVER SIZE. AGENT IS LIVE IN 60 SECONDS. Nous Portal just launched hosted Hermes Agent. two clicks. one minute. done. Nous Research WHAT THIS MEANS: before today: install Hermes on a VPS or your laptop. configure providers. set up gateway. manage updates. run hermes setup. edit config.yaml. great for power users. friction for everyone else. now: go to pick a model. pick a server size. your agent is live and reachable in 60 seconds. no terminal. no SSH. no Docker. same Hermes. same features. same tools. someone else handles the infrastructure. FOR TEAMS: this is where it gets interesting. spin up agents for everyone at your org. each team member gets their own Hermes instance. granular access controls per user. unified billing through Nous Portal. your team gets Hermes on day one. no DevOps needed. no VPS per person. one admin dashboard. one bill. WHAT'S INCLUDED: → 300+ models via Nous Portal (Claude, GPT, Gemini, DeepSeek, Grok, MiniMax, and more) → Tool Gateway (web search, image generation, TTS, browser automation) → all messaging platforms (Telegram, Discord, Slack, WhatsApp, Signal) → full feature set (profiles, cron, kanban, skills, memory, sub-agents, MoA, /goal, /learn, /journey) → automatic updates ONE PORTAL. FOUR TIERS: Free: $0/month. pay-as-you-go credits from $10. Plus: $20/month. $22 in monthly usage credit. Super: $100/month. $110 in monthly credit. Ultra: $200/month. $220 in monthly credit. highest rate limits. every paid tier includes Tool Gateway. one OAuth. one subscription. no extra API keys. SELF-HOSTED IS NOT GOING ANYWHERE: Hermes is MIT licensed. open source. free forever. you can still run it on your laptop, VPS, or GPU cluster. nothing changes for self-hosted users. the cloud version is for people who want the agent running without managing the machine. pick your path: → self-hosted: full control. you manage everything. → cloud: zero ops. Nous manages infrastructure. → hybrid: self-host your main agent, cloud for team members. HOW TO START: cloud: self-hosted: hermes setup --portal both connect to the same Nous Portal. same models. same tools. same billing. learn how to replace your entire team with 8 hermes agents 👇

YanXbt

45,446 просмотров • 1 месяц назад

“He (Oscar) missed out in the end, but only his 3rd season in Formula 1 and and his rate of development in between each season as well has been so notable. How have you looked on Oscar Piastri's first three years with McLaren, this fight, and do you expect him to bounce back quite strong next year?” Andrea Stella: “Oscar will be definitely stronger next year. Oscar will win championships at McLaren in the future. He has all it takes. I've always said that the rate of development tells me why he won championship at the first year in the junior categories. His capacity to adapt, his capacity to analyze what is needed, got it, I'll go out and do it and he does it. He’s no short than exceptional. And I think this year Oscar for two third of the season has been the most consistent. We then went to tracks that challenged Oscar from a technical point of view. The low grip circuits required a driving style that came so natural for Lando and so much to think for Oscar like ‘ah, that's what the car needs, it doesn't come natural to me. I have to think how I do it in an automatic way.’ And with the level of drivers we have at the moment, as soon as you have to think too much in the way you drive, you lose several positions. But I think really the only event in which Oscar had some hesitations this year is just Baku. I think all the rest in my view, it has some technical explanations. It has to do with the level overall of drivers that we have there for the impact of not being perfect, not being to the last 1%. And also Oscar showed maturity once again in the way he withstood the difficulties we had as a team. Which once again are quite impressive for somebody who is at the third season in Formula 1 and gives me the chance to thank Oscar and Lando for how they stayed supportive of the team. And like I say, if we made it, I think we made it not in the events in which we gained the points but in the way we dealt with the events in which we lost the points. And ultimately we did it. We were discussing this morning with the team by less than alpha percent, two points over more than 400. This shows that how marginal this game, how competitive this game is, which is obviously also a message to - at least for us, we said it's a message for us to do better because we would like to be slightly more comfortable in the future.”
3:01

“He (Oscar) missed out in the end, but only his 3rd season in Formula 1 and and his rate of development in between each season as well has been so notable. How have you looked on Oscar Piastri's first three years with McLaren, this fight, and do you expect him to bounce back quite strong next year?” Andrea Stella: “Oscar will be definitely stronger next year. Oscar will win championships at McLaren in the future. He has all it takes. I've always said that the rate of development tells me why he won championship at the first year in the junior categories. His capacity to adapt, his capacity to analyze what is needed, got it, I'll go out and do it and he does it. He’s no short than exceptional. And I think this year Oscar for two third of the season has been the most consistent. We then went to tracks that challenged Oscar from a technical point of view. The low grip circuits required a driving style that came so natural for Lando and so much to think for Oscar like ‘ah, that's what the car needs, it doesn't come natural to me. I have to think how I do it in an automatic way.’ And with the level of drivers we have at the moment, as soon as you have to think too much in the way you drive, you lose several positions. But I think really the only event in which Oscar had some hesitations this year is just Baku. I think all the rest in my view, it has some technical explanations. It has to do with the level overall of drivers that we have there for the impact of not being perfect, not being to the last 1%. And also Oscar showed maturity once again in the way he withstood the difficulties we had as a team. Which once again are quite impressive for somebody who is at the third season in Formula 1 and gives me the chance to thank Oscar and Lando for how they stayed supportive of the team. And like I say, if we made it, I think we made it not in the events in which we gained the points but in the way we dealt with the events in which we lost the points. And ultimately we did it. We were discussing this morning with the team by less than alpha percent, two points over more than 400. This shows that how marginal this game, how competitive this game is, which is obviously also a message to - at least for us, we said it's a message for us to do better because we would like to be slightly more comfortable in the future.”

naenia ¹ ⁶³

43,589 просмотров • 7 месяцев назад

Great question! 🤔 How do you simulate *multiple* layers of glass/refraction in video games? In the last breakdown, I discussed how to create a glass shader in Unity URP. In essence, we were taking the render of the scene from the camera without any transparent objects. This is available in URP as the global _CameraOpaqueTexture. This is good enough for most use-cases, and more or less the classic way of doing it. 🔍 What is _CameraOpaqueTexture? As the name implies, there are no transparent objects rendered into this texture, so it's not possible by default to have something like a transparent-type ocean material/shader rendered through a refractive glass shader (which samples and distorts this texture to render on its surface, as if it's transparent). ⚠️ Why it’s tricky: It's much easier to sort without much further setup if you don't have refraction, and only a transparent material, because in that case you're not simulating the transparency yourself via sampling the rendered scene texture. But for refraction, it's required-- unless you want to go down the ray/path tracing route. You could simulate accurate, real dispersion... and that's about as expensive as it sounds, and it requires a rework of your entire rendering. --> 🚫 It's not a viable suggestion to offer. 📚 There are well-known terms regarding transparency sorting you can search up, but as you've specifically asked for refractive boxes, I'll discuss briefly about that. 🧱 Simulating layers of refraction: For this kind of rendering, you need some way to render the backfaces before rendering the front. And the backfaces that are rendered may contain whatever data you'd like for additional processing in the layer front-facing mesh render. 🧪 Examples: You could render the back face as a glass shader of its own, as an intermediate step after _CameraOpaqueTexture. Then you sample this texture instead and you end up with multi-layered refraction, "just like that". You can also render the back normals only, via a fully opaque shader, and use that to manually account for that during the front render. You could even bake in data needed for thickness in realtime. 🛠️ Without making it complicated for yourself, the most straightforward method is via render textures, and you can easily set some fractional resolution. Cameras in Unity have an open slot for target textures to render to. You can use custom render textures to process _SelfTexture2D. ⏱️ It's great to do low-resolution processing for more complex tasks, like blurring and caustics. You can get massive performance boosts, considering the square law and number of pixels/fragments that need calculations (quadratic scaling). 🚧 I've not fully exploited the possibilities myself, but research/development with PRISM is ongoing!
0:56

Great question! 🤔 How do you simulate *multiple* layers of glass/refraction in video games? In the last breakdown, I discussed how to create a glass shader in Unity URP. In essence, we were taking the render of the scene from the camera without any transparent objects. This is available in URP as the global _CameraOpaqueTexture. This is good enough for most use-cases, and more or less the classic way of doing it. 🔍 What is _CameraOpaqueTexture? As the name implies, there are no transparent objects rendered into this texture, so it's not possible by default to have something like a transparent-type ocean material/shader rendered through a refractive glass shader (which samples and distorts this texture to render on its surface, as if it's transparent). ⚠️ Why it’s tricky: It's much easier to sort without much further setup if you don't have refraction, and only a transparent material, because in that case you're not simulating the transparency yourself via sampling the rendered scene texture. But for refraction, it's required-- unless you want to go down the ray/path tracing route. You could simulate accurate, real dispersion... and that's about as expensive as it sounds, and it requires a rework of your entire rendering. --> 🚫 It's not a viable suggestion to offer. 📚 There are well-known terms regarding transparency sorting you can search up, but as you've specifically asked for refractive boxes, I'll discuss briefly about that. 🧱 Simulating layers of refraction: For this kind of rendering, you need some way to render the backfaces before rendering the front. And the backfaces that are rendered may contain whatever data you'd like for additional processing in the layer front-facing mesh render. 🧪 Examples: You could render the back face as a glass shader of its own, as an intermediate step after _CameraOpaqueTexture. Then you sample this texture instead and you end up with multi-layered refraction, "just like that". You can also render the back normals only, via a fully opaque shader, and use that to manually account for that during the front render. You could even bake in data needed for thickness in realtime. 🛠️ Without making it complicated for yourself, the most straightforward method is via render textures, and you can easily set some fractional resolution. Cameras in Unity have an open slot for target textures to render to. You can use custom render textures to process _SelfTexture2D. ⏱️ It's great to do low-resolution processing for more complex tasks, like blurring and caustics. You can get massive performance boosts, considering the square law and number of pixels/fragments that need calculations (quadratic scaling). 🚧 I've not fully exploited the possibilities myself, but research/development with PRISM is ongoing!

Mirza Beig

61,468 просмотров • 1 год назад

🚨 I think I just found one of the smartest AI Agents for real-world investing. No prompt engineering. No complicated workflows. Just pick an expert and start chatting. I tested it on one of the hottest AI semiconductor stocks right now: Micron ($MU). Here’s all I did: → Opened EasyClaw → Added the Stock Master Agent → Installed the Serenity skill → Typed ONE sentence: “Use Serenity’s framework to research $MU and give me an investment recommendation.” A few minutes later, I had a research report that looked like something from a professional analyst. It automatically covered: ✅ Market outlook ✅ Supply chain trends ✅ Fundamental analysis ✅ Technical analysis ✅ Valuation ✅ Risk assessment ✅ Clear investment recommendation The result? Surprisingly… it said DON’T chase $MU at current prices. Why? • AI-driven HBM demand is still exploding 📈 • Micron’s fundamentals remain incredibly strong 📈 • Memory market sentiment is bullish 📈 But… The stock has run much faster than the business has improved. Its recommendations were refreshingly practical: 📌 Already holding? → Consider scaling out gradually and protect gains with trailing stops. 📌 Waiting to buy? → Stay patient. Let the market come to you. 📌 Risk-averse investor? → Skip the volatility until a better setup appears. What impressed me most wasn’t the conclusion… It was how the AI reached it. It highlighted the 3 metrics that actually matter going forward: 1️⃣ DRAM & HBM pricing trends 2️⃣ HBM4 production ramp for next-gen AI chips 3️⃣ CapEx plans from major memory manufacturers That’s the kind of analysis that separates signal from noise. The entire workflow felt like watching a veteran analyst think in real time: Hot trend → Data → Industry insights → Risk analysis → Decision. No prompts. No setup. No building AI agents from scratch. Just choose an expert. Ask your question. Get a structured investment report in minutes. If you’re into AI, semiconductors, or US stocks, this is absolutely worth trying. Learn More Here :- #AIAgent #StockMarket #Micron #MU #Investing #Semiconductors #HBM #Claude #EasyClaw #AIInvesting
0:31

🚨 I think I just found one of the smartest AI Agents for real-world investing. No prompt engineering. No complicated workflows. Just pick an expert and start chatting. I tested it on one of the hottest AI semiconductor stocks right now: Micron ($MU). Here’s all I did: → Opened EasyClaw → Added the Stock Master Agent → Installed the Serenity skill → Typed ONE sentence: “Use Serenity’s framework to research $MU and give me an investment recommendation.” A few minutes later, I had a research report that looked like something from a professional analyst. It automatically covered: ✅ Market outlook ✅ Supply chain trends ✅ Fundamental analysis ✅ Technical analysis ✅ Valuation ✅ Risk assessment ✅ Clear investment recommendation The result? Surprisingly… it said DON’T chase $MU at current prices. Why? • AI-driven HBM demand is still exploding 📈 • Micron’s fundamentals remain incredibly strong 📈 • Memory market sentiment is bullish 📈 But… The stock has run much faster than the business has improved. Its recommendations were refreshingly practical: 📌 Already holding? → Consider scaling out gradually and protect gains with trailing stops. 📌 Waiting to buy? → Stay patient. Let the market come to you. 📌 Risk-averse investor? → Skip the volatility until a better setup appears. What impressed me most wasn’t the conclusion… It was how the AI reached it. It highlighted the 3 metrics that actually matter going forward: 1️⃣ DRAM & HBM pricing trends 2️⃣ HBM4 production ramp for next-gen AI chips 3️⃣ CapEx plans from major memory manufacturers That’s the kind of analysis that separates signal from noise. The entire workflow felt like watching a veteran analyst think in real time: Hot trend → Data → Industry insights → Risk analysis → Decision. No prompts. No setup. No building AI agents from scratch. Just choose an expert. Ask your question. Get a structured investment report in minutes. If you’re into AI, semiconductors, or US stocks, this is absolutely worth trying. Learn More Here :- #AIAgent #StockMarket #Micron #MU #Investing #Semiconductors #HBM #Claude #EasyClaw #AIInvesting

Marry Evan

17,629 просмотров • 1 месяц назад