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Full Induction-Based Electrified Kitchen Setup by EssEmm Corporation,Covai முழுவதும் மின்சாரத்தில் இயங்கும் சமையலறை அமைப்பு #electrickitchen

Prawinn Ganeshnn

18,095 subscribers

25,835 次观看 • 5 个月前 •via X (Twitter)

#electrickitchen

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TrueNorth x Derive Options Intelligence now builds you a trading plan. Powered by Derive.xyz options data, it reads the full options structure (call walls, put walls, max pain, GEX flip) and generates scenario-based plans with exact triggers and actions. Range play, regime flip, squeeze setup. Each one mapped to specific levels on your chart. No more staring at Greeks trying to figure out what to do. The plan is right there. DM Derive.xyz for an invite code to try it out.

TrueNorth x Derive Options Intelligence now builds you a trading plan. Powered by Derive.xyz options data, it reads the full options structure (call walls, put walls, max pain, GEX flip) and generates scenario-based plans with exact triggers and actions. Range play, regime flip, squeeze setup. Each one mapped to specific levels on your chart. No more staring at Greeks trying to figure out what to do. The plan is right there. DM Derive.xyz for an invite code to try it out.

TrueNorth AI

11,972 次观看 • 5 个月前

A lot of people keep asking me: “What should we be buying now?” “What’s the next theme?” But the truth is, I’ve already talked about many of them publicly. More than a month ago, I published a YouTube video explaining why I was bullish on names like: $NTAP $HPE $SNOW $NET $RBRK … —and, more importantly, the investment logic behind them. Take $SNOW as an example. When the video came out, it was trading around $228, right at the kind of M.E.T.A. setup I like. Today, it’s around $328. But I’m not going back to posting daily “BUY THIS NOW” calls on X. I don’t think that actually helps people. Most of the time, it just creates FOMO. A lot of my trades are based on a medium- to long-term thesis, not what I think the stock will do tomorrow or next week. So when I identify a potentially big winner and get in at the right time, I’d rather do what Jesse Livermore preached: Sit tight. Not sell half after +1x%. Then cut another half after another few percent. And eventually be left with a meaningless position while the real winning stock goes on to double or triple. 😂 The big money often isn’t made by hunting for a new trade idea or setup every single day. It’s made by identifying the right trend, owning the right stocks— and having the patience to stay with them. Full video on YouTube:👇🏼

A lot of people keep asking me: “What should we be buying now?” “What’s the next theme?” But the truth is, I’ve already talked about many of them publicly. More than a month ago, I published a YouTube video explaining why I was bullish on names like: $NTAP $HPE $SNOW $NET $RBRK … —and, more importantly, the investment logic behind them. Take $SNOW as an example. When the video came out, it was trading around $228, right at the kind of M.E.T.A. setup I like. Today, it’s around $328. But I’m not going back to posting daily “BUY THIS NOW” calls on X. I don’t think that actually helps people. Most of the time, it just creates FOMO. A lot of my trades are based on a medium- to long-term thesis, not what I think the stock will do tomorrow or next week. So when I identify a potentially big winner and get in at the right time, I’d rather do what Jesse Livermore preached: Sit tight. Not sell half after +1x%. Then cut another half after another few percent. And eventually be left with a meaningless position while the real winning stock goes on to double or triple. 😂 The big money often isn’t made by hunting for a new trade idea or setup every single day. It’s made by identifying the right trend, owning the right stocks— and having the patience to stay with them. Full video on YouTube:👇🏼

J Law

74,612 次观看 • 19 天前

CBC's Julia Wong interviewed Frances Widdowson May 13th after CBC's involvement in a deceptive "prank" show that ambushed her She nailed it: This wasn't targeting the powerful... it was a taxpayer-funded hit on someone without institutional power for daring to question the narrative. Most prank shows punch up. CBC's punches down Widdowson smartly recorded everything, livestreamed parts of the setup, and documented every detail CBC was made aware of. Always record everything when dealing with CBC... they’ve been forced to issue corrections before. Griffin Jaeger's piece using this footage had to be corrected on "unmarked graves" (Full interview link below) "And I'm quite concerned about the fact that it's being portrayed that I'm somehow an enemy of Aboriginal people when actually what I'm doing is trying to expose the deception and the manipulation which is preventing policies from being developed which will actually enable Aboriginal people to thrive." "So I'm a bit disturbed about the framing that is being promoted by organizations like the Canadian Broadcasting Corporation." CBC/APTN-funded "comedy" using deception against critics? This is what public broadcasting looks like in 2026 CBC's statements on this so far have been completely inadequate Frances Widdowson Lindsay Shepherd Jerry Amernic @PreidentCBCRC

CBC's Julia Wong interviewed Frances Widdowson May 13th after CBC's involvement in a deceptive "prank" show that ambushed her She nailed it: This wasn't targeting the powerful... it was a taxpayer-funded hit on someone without institutional power for daring to question the narrative. Most prank shows punch up. CBC's punches down Widdowson smartly recorded everything, livestreamed parts of the setup, and documented every detail CBC was made aware of. Always record everything when dealing with CBC... they’ve been forced to issue corrections before. Griffin Jaeger's piece using this footage had to be corrected on "unmarked graves" (Full interview link below) "And I'm quite concerned about the fact that it's being portrayed that I'm somehow an enemy of Aboriginal people when actually what I'm doing is trying to expose the deception and the manipulation which is preventing policies from being developed which will actually enable Aboriginal people to thrive." "So I'm a bit disturbed about the framing that is being promoted by organizations like the Canadian Broadcasting Corporation." CBC/APTN-funded "comedy" using deception against critics? This is what public broadcasting looks like in 2026 CBC's statements on this so far have been completely inadequate Frances Widdowson Lindsay Shepherd Jerry Amernic @PreidentCBCRC

cbcwatcher

32,067 次观看 • 3 个月前

$24,000 per year from this simple AI Dentist Voice Agent (and why I'm crazy for giving it away for free) A dental practice was losing $6,000+ in revenue every month from missed after-hours calls. That's 20-25 potential patients walking away because no one was available to book their appointments. So I built an AI voice assistant that handles after-hours dental bookings 24/7 using n8n and ElevenLabs based on internal policies and scheduling availability. Here's what this system does: → Answers calls with a natural-sounding AI receptionist → Collects patient information and insurance details → Checks calendar availability in real-time → Books appointments automatically → Logs all patient details to a Google Sheet The result? This similar AI voice system was sold to a dental practice for $24k per year by another entrepreneur!! This isn't just about dental practices. Any service business losing money from missed calls can implement a similar system. Want the complete n8n workflow template? 1. Retweet & Like this post 2. Comment "ASSISTANT" I'll send you the entire system for free, a full setup walk-through video, including the ElevenLabs automation components.

$24,000 per year from this simple AI Dentist Voice Agent (and why I'm crazy for giving it away for free) A dental practice was losing $6,000+ in revenue every month from missed after-hours calls. That's 20-25 potential patients walking away because no one was available to book their appointments. So I built an AI voice assistant that handles after-hours dental bookings 24/7 using n8n and ElevenLabs based on internal policies and scheduling availability. Here's what this system does: → Answers calls with a natural-sounding AI receptionist → Collects patient information and insurance details → Checks calendar availability in real-time → Books appointments automatically → Logs all patient details to a Google Sheet The result? This similar AI voice system was sold to a dental practice for $24k per year by another entrepreneur!! This isn't just about dental practices. Any service business losing money from missed calls can implement a similar system. Want the complete n8n workflow template? 1. Retweet & Like this post 2. Comment "ASSISTANT" I'll send you the entire system for free, a full setup walk-through video, including the ElevenLabs automation components.

David Roberts

353,472 次观看 • 11 个月前

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.

QVAC SDK 0.13.0 is live, and this version brings a lot of exciting updates! Local AI now plugs into your coding agent, ships as a desktop app in one command, and runs even more models. Highlights: NEW INTEGRATIONS - OpenCode and coding agents: the new @qvac/ai-sdk-provider makes QVAC a local provider. Less setup, same-model requests queue cleanly, and managed mode starts and supervises qvac serve for you. - Broader OpenAI-compatible API, validated across supported flows so covered capabilities stay consistent and testable. - Turn your QVAC project into a real desktop app for Mac, Windows, or Linux with a single command. The new Electron plugin handles the packaging and keeps the app small by including only what it needs. NEW MODELS - New pi0.5 model support - run a vision-language "robot brain" on a single ordinary graphics card, at full accuracy. - Image-to-video, fully local, via the Wan2.1 model in the Diffusion addon. - New BCI add-on: brain-computer interface transcription, fully local. Decode recorded neural signals into text on-device via the Whisper.cpp-based BCI model. IMPROVEMENTS - Whisper GPU transcription on Android, auto-picking the best backend (OpenCL on Adreno 700+, Vulkan elsewhere), unified on one ggml engine. - Parakeet steadier on mobile, with real end-of-utterance detection for streaming. - Supertonic TTS now runs full GPU across Metal, Vulkan, and OpenCL, with native streaming.

QVAC

20,932,195 次观看 • 2 个月前

HERMES AGENT CAN RUN YOUR SEO. CONNECT IT TO GOOGLE SEARCH CONSOLE AND GOOGLE ANALYTICS. IT MONITORS, REPORTS, AND WRITES CONTENT BASED ON YOUR ACTUAL DATA. stop paying an SEO agency. stop doing the tedious work yourself. Hermes handles it 24/7. WHAT THE SEO AGENT DOES: → pulls clicks, impressions, CTR, and position data from Google Search Console automatically → tracks traffic, user behavior, and conversions from Google Analytics → checks which pages are indexed and which are not → submits sitemaps for indexing → inspects URLs for crawl or indexing issues → identifies ranking drops and keyword opportunities → writes content based on what your data says works → generates weekly SEO performance reports → delivers everything to Telegram CONNECT GOOGLE SEARCH CONSOLE: two paths: 1. COMPOSIO (managed, easiest): paste this into Hermes chat: https:// composio. dev/hermes or add to config.yaml: mcp_servers: composio: url: "https:// connect.composio. dev /mcp" headers: x-consumer-api-key: "YOUR_COMPOSIO_API_KEY" Hermes prompts you to authenticate. one OAuth flow. done. 2. CLAWLINK (one-click): 9 Google Search Console tools exposed via MCP. hosted auth. nothing to run or maintain. paste the install prompt into Hermes chat. CONNECT GOOGLE ANALYTICS: same Composio setup. one MCP endpoint handles both Search Console and Analytics. authenticate once. both data sources available. your agent can now query: → search analytics (clicks, impressions, CTR, position) → traffic by source and landing page → user behavior and conversions → indexing status for any URL → sitemap status WHAT TO AUTOMATE WITH CRON: weekly SEO report (Monday 8am): "pull search analytics for last 7 days. compare vs previous week. flag any keyword that dropped more than 5 positions. flag any page that lost more than 20% clicks. deliver report to Telegram." daily indexing check (6am): "check if any new pages are not indexed. if found, submit sitemap and report to Telegram." wakeAgent gate: skip if all pages indexed. content opportunity scan (weekly): "find queries where my site appears on page 2 (positions 11-20) with high impressions. these are the keywords one good article could push to page 1. deliver list to Telegram with suggested topics." CONTENT WRITING FROM YOUR DATA: the difference between generic SEO content and content that ranks: your agent has your Search Console data. "write a blog post targeting [keyword]. my current position is 14 with 2,400 monthly impressions. check what pages currently rank 1-3 for this keyword. write something better. include the gaps they miss." the agent researches competitors via Firecrawl, checks your existing content in the wiki, and drafts based on real data. not guesswork. WHAT THIS REPLACES: → SEO agency: $1,000-5,000/month → SEO tool subscriptions: $100-300/month → manual reporting: 3-5 hours/week → manual content research: 2-4 hours/week Hermes SEO agent: one profile with two MCPs. cron jobs handle the monitoring. you handle the decisions. SETUP IN 10 MINUTES: 1. create a profile: hermes profile create seo-agent 2. write SOUL.md: "you are an SEO specialist. monitor search performance daily. flag ranking drops and opportunities. write content based on Search Console data. weekly report every Monday." 3. connect Google Search Console + Analytics via Composio or ClawLink 4. set cron jobs (weekly report, daily index check, content opportunity scan) 5. set model: DeepSeek V4 for routine monitoring. Sonnet for content writing. 6. connect to Telegram for delivery. the agent runs. you review reports. rankings improve because you stopped guessing and started using your own data. comment HERMES and I'll send you the full setup guide for running Hermes Agent as your SEO specialist. full Hermes architecture deep-dive in the article 👇

YanXbt

40,963 次观看 • 2 个月前

Today 55 years ago, Indira Gandhi moved decisively to nationalize fourteen banks and open a whole new chapter in India’s economic history. I have described the background to this momentous event based on archival material in my Intertwined Lives: PN Haksar and Indira Gandhi. DN Ghosh who played a key role along with Haksar has also left a detailed account of it in his very valuable memoir No Regrets. IG Patel was Special Secretary in the Finance Ministry then and he too has written about this tectonic policy shift in his Glimpses of Indian Economic Policy: An Insider's View. Bank nationalisation had profound impacts on lending for agriculture, rural development, and other priority sectors of the economy. Public sector institutions have served the country well in times of global financial crisis. They have have built an impressive pool of managerial expertise. Over the past seven years, there have been mergers in the public sector banking industry. 1. United Bank of India and Oriental Bank of Commerce have been merged with Punjab National Bank. 2. Syndicate Bank has been made part of Canara Bank. 3. Allahabad Bank merged with Indian Bank 4. Union Bank of India has absorbed Andhra Bank and Corporation Bank. 5. Vijaya Bank and Dena Bank have been taken over by the Bank of Baroda. 6. State Bank of Patiala, State Bank of Bikaner and Jaipur, State Bank of Mysore, State Bank of Travancore, State Bank of Hyderabad and Bharatiya Mahila Bank have been acquired by the State Bank of India. These mergers have presented their own challenges. Big is not always Better. But they were broadly accepted only because the Union Government’s share in public sector banks was not to be reduced to below 51%. Any move to dilute that position in the 12 public sector banks that function presently would continue to be resisted forcefully both in Parliament and outside. Incidentally, while bank nationalisation electrified the country in July 1969, it was initially attacked by some political parties like the Bharatiya Jan Sangh. But within five months the Jan Sangh was publicly demanding the nationalisation of foreign banks as well.

Today 55 years ago, Indira Gandhi moved decisively to nationalize fourteen banks and open a whole new chapter in India’s economic history. I have described the background to this momentous event based on archival material in my Intertwined Lives: PN Haksar and Indira Gandhi. DN Ghosh who played a key role along with Haksar has also left a detailed account of it in his very valuable memoir No Regrets. IG Patel was Special Secretary in the Finance Ministry then and he too has written about this tectonic policy shift in his Glimpses of Indian Economic Policy: An Insider's View. Bank nationalisation had profound impacts on lending for agriculture, rural development, and other priority sectors of the economy. Public sector institutions have served the country well in times of global financial crisis. They have have built an impressive pool of managerial expertise. Over the past seven years, there have been mergers in the public sector banking industry. 1. United Bank of India and Oriental Bank of Commerce have been merged with Punjab National Bank. 2. Syndicate Bank has been made part of Canara Bank. 3. Allahabad Bank merged with Indian Bank 4. Union Bank of India has absorbed Andhra Bank and Corporation Bank. 5. Vijaya Bank and Dena Bank have been taken over by the Bank of Baroda. 6. State Bank of Patiala, State Bank of Bikaner and Jaipur, State Bank of Mysore, State Bank of Travancore, State Bank of Hyderabad and Bharatiya Mahila Bank have been acquired by the State Bank of India. These mergers have presented their own challenges. Big is not always Better. But they were broadly accepted only because the Union Government’s share in public sector banks was not to be reduced to below 51%. Any move to dilute that position in the 12 public sector banks that function presently would continue to be resisted forcefully both in Parliament and outside. Incidentally, while bank nationalisation electrified the country in July 1969, it was initially attacked by some political parties like the Bharatiya Jan Sangh. But within five months the Jan Sangh was publicly demanding the nationalisation of foreign banks as well.

Jairam Ramesh

36,918 次观看 • 2 年前

Grok 4 prompt to generate the video's generative art: You are an expert P5.js developer with expertise in generative art, mathematics (vector fields, Perlin noise), and aesthetic design. Create a visually stunning, interactive particle flow field sketch that mimics ethereal, fluid dynamics with glowing trails and cosmic vibes. Key Features to Implement Exactly: Use P5.js via CDN (version 1.4.0). 1500 particles with random positions and sizes (1-4 pixels). Flow field via multi-layered Perlin noise: base noise + 0.5 * octave at 2x scale, using 3D noise with zOff incrementing by 0.002 per frame; noiseScale = 0.005. Particle physics: Vectors for pos, vel, acc; maxSpeed = 3; force mult 0.15. Dynamic colors: 5-color palette in HSV, baseHue = (frameCount * 0.1) % 360, each hue offset by 72 (pentadic), saturation 80, value 100, alpha 8; update in draw(). Interactivity: Mouse press attracts particles with normalized vector force, strength 0.05 * (1 / (dist + 1)). Aesthetics: Additive blendMode(ADD); noStroke; background fade fill(0,5) rect over canvas; variable sizes for depth. Utilities: Edge wrapping in Particle class; windowResized to resize canvas and reset background. Comments: Explain enhancements (time-based colors, mouse interaction, layered noise, trails), math (vector attraction, multi-octave noise). Step-by-Step Reasoning for Your Response: Analyze: Dissect user intent for aesthetics (glowing, dynamic, interactive) and math (noise layers, forces). Plan Code: setup() for init (canvas full window, particles, placeholders); draw() for fade, color update, zOff++, particle loop; Particle class with constructor, update (noise angle, force, mouse attract, physics), display, edges. Implement: Write modular, readable code with exact values; ensure performance. Verify: Mentally run: Particles flow smoothly, colors cycle, mouse pulls inversely, no errors on resize/edges. Enhance Output: Rate aesthetics/correctness (target 9.5/10); suggest tweaks (e.g., numParticles for perf). Output Structure: Full HTML with for P5.js code. Explanation: Brief on visuals/math/customizations/running.

Grok 4 prompt to generate the video's generative art: You are an expert P5.js developer with expertise in generative art, mathematics (vector fields, Perlin noise), and aesthetic design. Create a visually stunning, interactive particle flow field sketch that mimics ethereal, fluid dynamics with glowing trails and cosmic vibes. Key Features to Implement Exactly: Use P5.js via CDN (version 1.4.0). 1500 particles with random positions and sizes (1-4 pixels). Flow field via multi-layered Perlin noise: base noise + 0.5 * octave at 2x scale, using 3D noise with zOff incrementing by 0.002 per frame; noiseScale = 0.005. Particle physics: Vectors for pos, vel, acc; maxSpeed = 3; force mult 0.15. Dynamic colors: 5-color palette in HSV, baseHue = (frameCount * 0.1) % 360, each hue offset by 72 (pentadic), saturation 80, value 100, alpha 8; update in draw(). Interactivity: Mouse press attracts particles with normalized vector force, strength 0.05 * (1 / (dist + 1)). Aesthetics: Additive blendMode(ADD); noStroke; background fade fill(0,5) rect over canvas; variable sizes for depth. Utilities: Edge wrapping in Particle class; windowResized to resize canvas and reset background. Comments: Explain enhancements (time-based colors, mouse interaction, layered noise, trails), math (vector attraction, multi-octave noise). Step-by-Step Reasoning for Your Response: Analyze: Dissect user intent for aesthetics (glowing, dynamic, interactive) and math (noise layers, forces). Plan Code: setup() for init (canvas full window, particles, placeholders); draw() for fade, color update, zOff++, particle loop; Particle class with constructor, update (noise angle, force, mouse attract, physics), display, edges. Implement: Write modular, readable code with exact values; ensure performance. Verify: Mentally run: Particles flow smoothly, colors cycle, mouse pulls inversely, no errors on resize/edges. Enhance Output: Rate aesthetics/correctness (target 9.5/10); suggest tweaks (e.g., numParticles for perf). Output Structure: Full HTML with for P5.js code. Explanation: Brief on visuals/math/customizations/running.

tetsuo

44,518 次观看 • 9 个月前

How Cloudflare cut query times by 35x without leaving Postgres: Their Postgres tables hit billions of rows, and every time-range query started getting slower. Plain Postgres scans the entire table on every query, regardless of the time window. They tried the manual route by building precomputed aggregates with cron jobs and evaluating ClickHouse, which needed a full ingestion pipeline just to handle their write pattern. Here's where most teams get stuck at exactly this point. They utilize manual partitioning, splitting data into child tables by day or month. Then they wire up cron jobs to refresh aggregate tables. Every schema change after that requires updating the cron logic and coordinating across teams. The infrastructure becomes the project. Tiger Cloud is managed TimescaleDB by Tiger Data - Creators of TimescaleDB on the Postgres you already know, with automatic time-based partitioning, continuous aggregates, and compression built in from the start. Cloudflare moved to TimescaleDB after exhausting the manual route and saw 5-35x query performance improvement on the same data. Here is how it works: → Hypertables partition data by timestamp automatically. Every time-range query hits only the relevant chunk, not the full table. → Continuous aggregates refresh incrementally in the background, with no cron jobs to maintain. To show what this looks like in practice, I built a real-time earthquake intelligence dashboard on a 3D globe using Claude Code and Tiger Cloud in a single session. The USGS earthquake catalog has 400,000+ events since 1900, each a timestamped row. The dashboard plots them as ripple animations sized by magnitude and colored by depth, with a time slider that scrubs through 120 years of seismic history. Every slider position fires a live query against the hypertable, and the side panel pulls from continuous aggregates. Claude Code connected to Tiger Cloud through the Tiger CLI MCP server, provisioned the database, pulled the USGS catalog, and assembled the full Next.js and Three.js frontend without leaving the session. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud here: New accounts get $1,000 in free credits, no credit card required. → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build. I also wrote a full walkthrough on how you can turn any coding agent into a production-grade data engineer that can manage over a billion-row Postgres workloads. It covers everything from the database setup to the final build. Read it below.

How Cloudflare cut query times by 35x without leaving Postgres: Their Postgres tables hit billions of rows, and every time-range query started getting slower. Plain Postgres scans the entire table on every query, regardless of the time window. They tried the manual route by building precomputed aggregates with cron jobs and evaluating ClickHouse, which needed a full ingestion pipeline just to handle their write pattern. Here's where most teams get stuck at exactly this point. They utilize manual partitioning, splitting data into child tables by day or month. Then they wire up cron jobs to refresh aggregate tables. Every schema change after that requires updating the cron logic and coordinating across teams. The infrastructure becomes the project. Tiger Cloud is managed TimescaleDB by Tiger Data - Creators of TimescaleDB on the Postgres you already know, with automatic time-based partitioning, continuous aggregates, and compression built in from the start. Cloudflare moved to TimescaleDB after exhausting the manual route and saw 5-35x query performance improvement on the same data. Here is how it works: → Hypertables partition data by timestamp automatically. Every time-range query hits only the relevant chunk, not the full table. → Continuous aggregates refresh incrementally in the background, with no cron jobs to maintain. To show what this looks like in practice, I built a real-time earthquake intelligence dashboard on a 3D globe using Claude Code and Tiger Cloud in a single session. The USGS earthquake catalog has 400,000+ events since 1900, each a timestamped row. The dashboard plots them as ripple animations sized by magnitude and colored by depth, with a time slider that scrubs through 120 years of seismic history. Every slider position fires a live query against the hypertable, and the side panel pulls from continuous aggregates. Claude Code connected to Tiger Cloud through the Tiger CLI MCP server, provisioned the database, pulled the USGS catalog, and assembled the full Next.js and Three.js frontend without leaving the session. The video below shows the final build in action, and I worked with the Tiger Data team to put this together. Tiger CLI is open-source (Apache 2.0) and works with Claude Code, Cursor, Codex, Gemini CLI, and VS Code. To try this yourself: → Sign up for Tiger Cloud here: New accounts get $1,000 in free credits, no credit card required. → Install Tiger CLI: curl -fsSL https(:)//cli(.)tigerdata(.)com | sh → Run tiger mcp install claude-code → Give Claude Code a prompt and let it build. I also wrote a full walkthrough on how you can turn any coding agent into a production-grade data engineer that can manage over a billion-row Postgres workloads. It covers everything from the database setup to the final build. Read it below.

Avi Chawla

22,094 次观看 • 18 天前

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

We’re excited to launch OpenMemory MCP, a private memory for MCP-compatible clients powered by mem0 Today, most AI assistants and dev tools operate without memory. You plan your roadmap in Claude, implement tasks in Cursor, but none of them know what the other did. Each tool operates in isolation, and your context disappears as soon as the session ends. OpenMemory MCP solves this. Built on the open Model Context Protocol (MCP), OpenMemory MCP runs 100% locally and provides a persistent, portable memory layer for all your AI tools. It enables agents and assistants to read from and write to a shared memory, securely and privately. Key capabilities: ✅Works across MCP clients like Cursor, Claude Desktop, Windsurf, Cline and more ✅Provides standardized memory operations (add_memories, search_memory, list_memories, delete_all_memories) ✅Stores data on your machine, fully private ✅Offers a centralized dashboard for visibility and control ✅Simple Docker-based setup with no vendor lock-in If you're building on MCP, OpenMemory MCP is the easiest way to add persistent, private memory to your clients with zero cloud dependencies and full local control. We’ve put together a short demo to show how it works Check it out in the links below 👇

Taranjeet

472,126 次观看 • 1 年前

Checklists are where processes go to be remembered, not done. Mine had 10-12 steps for onboarding a new partner and still cost me 90+ minutes every close, plus one partner who slipped through anyway. I run a creator-led media business. A signed deal triggers invisible work: folder, kickoff doc, tracker entry (really important), reporting schedule, welcome email, intake questions. We built a beautiful checklist in Google Drive. 10-12 steps based on our first meeting. The checklist remembered everything. It did nothing. And the one time we rushed it, a new partner never made it into my tracker, so they never got their first report. Now when a deal closes, we send Viktor one message: run the new-partner setup from our onboarding checklist in Notion. Folder from template, kickoff doc, tracker entry, reporting schedule, welcome email drafted with intake questions, held for my approval. 10-12 minutes later it's done. We review just one email, then send. 90+ minutes of admin became one message. Nothing skipped since, because nothing depends on memory. By the way, writing down your processes was never the hard part. Running them every time, especially on your best days, is. Your checklist can't do that. An AI employee can. Tell me I'm wrong. Hire Viktor for your team. $100 in credits included, no card. Full link in first comment.

Checklists are where processes go to be remembered, not done. Mine had 10-12 steps for onboarding a new partner and still cost me 90+ minutes every close, plus one partner who slipped through anyway. I run a creator-led media business. A signed deal triggers invisible work: folder, kickoff doc, tracker entry (really important), reporting schedule, welcome email, intake questions. We built a beautiful checklist in Google Drive. 10-12 steps based on our first meeting. The checklist remembered everything. It did nothing. And the one time we rushed it, a new partner never made it into my tracker, so they never got their first report. Now when a deal closes, we send Viktor one message: run the new-partner setup from our onboarding checklist in Notion. Folder from template, kickoff doc, tracker entry, reporting schedule, welcome email drafted with intake questions, held for my approval. 10-12 minutes later it's done. We review just one email, then send. 90+ minutes of admin became one message. Nothing skipped since, because nothing depends on memory. By the way, writing down your processes was never the hard part. Running them every time, especially on your best days, is. Your checklist can't do that. An AI employee can. Tell me I'm wrong. Hire Viktor for your team. $100 in credits included, no card. Full link in first comment.

Sufyan Maan, M.Eng

41,983 次观看 • 1 个月前

VIDEO | Balasore: Priyadarshini Acharya, wife of Pahalgam terrorist cttack victim Prashant Kumar Satpathy, said she is still awaiting the promised government job. She said, “It has been one year, and today completes one year since his passing. Since that day, life is just going on somehow, days are passing but it does not feel normal. After he left, everything is gone, the happiness of our home and all the happiness of my life has ended. At that time, many promises were made by the government, such as giving me a government job, support for my children’s education, and financial help. Financially, I have received an amount of Rs 20 lakh, but the government job has still not been given. In other states like Maharashtra, such jobs have already been provided. I was told that I would be given a government job according to my qualifications, which would be good because I have seen that in Maharashtra a person was given a Group B post, like an Administrative Officer in the Municipal Corporation, based on their qualifications. I have completed my graduation in Physics and also an MBA, so I would like to say that the government should fulfill the promises soon, as it has already been one year. Everything of mine is gone, and I still cannot forget it; whenever I close my eyes, those moments come back.” #Pahalgamterrorattack (Full video available on PTI Videos -

VIDEO | Balasore: Priyadarshini Acharya, wife of Pahalgam terrorist cttack victim Prashant Kumar Satpathy, said she is still awaiting the promised government job. She said, “It has been one year, and today completes one year since his passing. Since that day, life is just going on somehow, days are passing but it does not feel normal. After he left, everything is gone, the happiness of our home and all the happiness of my life has ended. At that time, many promises were made by the government, such as giving me a government job, support for my children’s education, and financial help. Financially, I have received an amount of Rs 20 lakh, but the government job has still not been given. In other states like Maharashtra, such jobs have already been provided. I was told that I would be given a government job according to my qualifications, which would be good because I have seen that in Maharashtra a person was given a Group B post, like an Administrative Officer in the Municipal Corporation, based on their qualifications. I have completed my graduation in Physics and also an MBA, so I would like to say that the government should fulfill the promises soon, as it has already been one year. Everything of mine is gone, and I still cannot forget it; whenever I close my eyes, those moments come back.” #Pahalgamterrorattack (Full video available on PTI Videos -

Press Trust of India

13,692 次观看 • 4 个月前

🚨 PART 1: Human Bodies as 6G Terahertz Antennas in the Internet of Bio-Nano Things (IoBNT) & Internet of Bodies (IoB) 🚨 This is not conspiracy. This is peer-reviewed science, patents, academic papers, and public research from top institutions and researchers. The convergence of "graphene-based nanomaterials", "terahertz (THz) communication", "optoelectronics", "Visible Light Communication (VLC)", and "Wireless Nanosensor Networks" is turning the human body into a node in the global network; harvesting energy, sensing, transmitting data, and potentially interfacing with the brain and autonomic systems. The Core Concept: Graphene oxide and related materials (GRMs) in the body (via vaccines, environmental exposure, or intentional delivery) act as plasmonic nano-antennas. THz waves (key for 6G) interact with these, enabling intra-body communication, energy harvesting, and data exchange in the "Internet of Everything." Key Scientific Foundations & Full Links 1. Graphene and Related Materials for the Internet of Bio-Nano Things Title: Graphene and related materials for the Internet of Bio-Nano Things (Perspective Paper, APL Materials, 2023) Authors/Researchers: Murat Kuşcu, Bige D. Unluturk, et al. (Building on foundational work in nanonetworks) This details how GRMs bridge biological systems with electromagnetic networks for in-body sensing and actuation. 2. Graphene-Based Plasmonic Nano-Antenna for Terahertz Band Communication Title: Graphene-based Plasmonic Nano-Antenna for Terahertz Band Communication in Nanonetworks (IEEE, 2014) Key Researchers: Josep Miquel Jornet & Ian F. Akyildiz Exploits Surface Plasmon Polaritons in graphene nanoribbons for efficient THz nano-antennas. 3. A Review on Wireless Nanosensor Networks Based on Electromagnetic Communication Title: A Review on Wireless Nanosensor Networks Based on Electromagnetic Communication (PDF) Authors: Vishranti Rupani, Sunera Kargathara, Jigisha Sureja (2015) Covers architecture for electromagnetic WNSNs in the THz band. 4. Human Body as Antenna for 6G Energy Harvesting (VLC/RF) Title: Next-generation Wireless Technology May Leverage Human Body as an Antenna (UMass Amherst, 2023) Researchers: Jie Xiong, et al. (Bracelet+ copper coil amplified 10x by human body for harvesting leaked RF/VLC energy). Popular Mechanics Summary: 5. Internet of Bio-Nano Things Foundational Paper Title: The Internet of Bio-Nano Things (IEEE, 2015) Key Pioneer: Ian F. Akyildiz (Georgia Tech, N3Cat) widely cited founder of nanonetworks research. Major Players & Institutions Involved - Ian F. Akyildiz - Pioneer of electromagnetic nanonetworks and IoBNT concepts. - Josep Miquel Jornet - Graphene plasmonic THz antennas/transceivers. - Murat Kuşcu & team - Graphene for practical IoBNT implementation. - University of Massachusetts Amherst (Jie Xiong et al.) Human body as 6G antenna/energy harvester. - Georgia Tech Broadband Wireless Networking Lab / N3Cat (UPC Barcelona) Core nanonetworks research. - World Economic Forum / Klaus Schwab - Promoted "Fourth Industrial Revolution" and Internet of Bodies concepts. - RAND Corporation - Reports on Internet of Bodies opportunities/risks. - DARPA & related defense research - Neural tech, brain interfaces overlapping with THz sensing (cross-referenced in broader BCI/6G discussions). - Vaccine/Graphene discussions - Often link to papers on nanomaterials; real research exists on graphene in biomedical delivery, but injection claims remain debated/speculative. THz reading EEG-like signals, vaccinated bodies as antennas, autonomic control via Wireless Body Area Networks. This aligns with documented research on intra-body THz comms, plasmonic sensing, and body-centric networks; though real deployment (as of 2026) is research-phase, not widespread. Call to Action: This is Part 1. It exposes the documented tech foundation. Repost, research every name/paper, demand transparency on nanomaterials in medicine, 6G rollout, and bio-convergence. Comment "PART 2" for the next section (more on optoelectronics, neuromorphic devices, virus lasers, Ian Hurn0 threads, additional patents/players, and full thread expansions). Truth-seeking note: These are real advancements in nanotech/biomedicine with huge potential for health monitoring and high-speed comms. They also raise profound questions on privacy, consent, dual-use (surveillance/control), and safety. Always verify primaries. Full sources above; dig in.

🚨 PART 1: Human Bodies as 6G Terahertz Antennas in the Internet of Bio-Nano Things (IoBNT) & Internet of Bodies (IoB) 🚨 This is not conspiracy. This is peer-reviewed science, patents, academic papers, and public research from top institutions and researchers. The convergence of "graphene-based nanomaterials", "terahertz (THz) communication", "optoelectronics", "Visible Light Communication (VLC)", and "Wireless Nanosensor Networks" is turning the human body into a node in the global network; harvesting energy, sensing, transmitting data, and potentially interfacing with the brain and autonomic systems. The Core Concept: Graphene oxide and related materials (GRMs) in the body (via vaccines, environmental exposure, or intentional delivery) act as plasmonic nano-antennas. THz waves (key for 6G) interact with these, enabling intra-body communication, energy harvesting, and data exchange in the "Internet of Everything." Key Scientific Foundations & Full Links 1. Graphene and Related Materials for the Internet of Bio-Nano Things Title: Graphene and related materials for the Internet of Bio-Nano Things (Perspective Paper, APL Materials, 2023) Authors/Researchers: Murat Kuşcu, Bige D. Unluturk, et al. (Building on foundational work in nanonetworks) This details how GRMs bridge biological systems with electromagnetic networks for in-body sensing and actuation. 2. Graphene-Based Plasmonic Nano-Antenna for Terahertz Band Communication Title: Graphene-based Plasmonic Nano-Antenna for Terahertz Band Communication in Nanonetworks (IEEE, 2014) Key Researchers: Josep Miquel Jornet & Ian F. Akyildiz Exploits Surface Plasmon Polaritons in graphene nanoribbons for efficient THz nano-antennas. 3. A Review on Wireless Nanosensor Networks Based on Electromagnetic Communication Title: A Review on Wireless Nanosensor Networks Based on Electromagnetic Communication (PDF) Authors: Vishranti Rupani, Sunera Kargathara, Jigisha Sureja (2015) Covers architecture for electromagnetic WNSNs in the THz band. 4. Human Body as Antenna for 6G Energy Harvesting (VLC/RF) Title: Next-generation Wireless Technology May Leverage Human Body as an Antenna (UMass Amherst, 2023) Researchers: Jie Xiong, et al. (Bracelet+ copper coil amplified 10x by human body for harvesting leaked RF/VLC energy). Popular Mechanics Summary: 5. Internet of Bio-Nano Things Foundational Paper Title: The Internet of Bio-Nano Things (IEEE, 2015) Key Pioneer: Ian F. Akyildiz (Georgia Tech, N3Cat) widely cited founder of nanonetworks research. Major Players & Institutions Involved - Ian F. Akyildiz - Pioneer of electromagnetic nanonetworks and IoBNT concepts. - Josep Miquel Jornet - Graphene plasmonic THz antennas/transceivers. - Murat Kuşcu & team - Graphene for practical IoBNT implementation. - University of Massachusetts Amherst (Jie Xiong et al.) Human body as 6G antenna/energy harvester. - Georgia Tech Broadband Wireless Networking Lab / N3Cat (UPC Barcelona) Core nanonetworks research. - World Economic Forum / Klaus Schwab - Promoted "Fourth Industrial Revolution" and Internet of Bodies concepts. - RAND Corporation - Reports on Internet of Bodies opportunities/risks. - DARPA & related defense research - Neural tech, brain interfaces overlapping with THz sensing (cross-referenced in broader BCI/6G discussions). - Vaccine/Graphene discussions - Often link to papers on nanomaterials; real research exists on graphene in biomedical delivery, but injection claims remain debated/speculative. THz reading EEG-like signals, vaccinated bodies as antennas, autonomic control via Wireless Body Area Networks. This aligns with documented research on intra-body THz comms, plasmonic sensing, and body-centric networks; though real deployment (as of 2026) is research-phase, not widespread. Call to Action: This is Part 1. It exposes the documented tech foundation. Repost, research every name/paper, demand transparency on nanomaterials in medicine, 6G rollout, and bio-convergence. Comment "PART 2" for the next section (more on optoelectronics, neuromorphic devices, virus lasers, Ian Hurn0 threads, additional patents/players, and full thread expansions). Truth-seeking note: These are real advancements in nanotech/biomedicine with huge potential for health monitoring and high-speed comms. They also raise profound questions on privacy, consent, dual-use (surveillance/control), and safety. Always verify primaries. Full sources above; dig in.

Noah B. Price

22,110 次观看 • 4 个月前

This will get a lot faster - shortening the hold time, deleting the “return to home position”, speeding up the robots… but it’s an exciting early glimpse of what we’re building. This is a sub-scale but otherwise accurate piece of a ship hull made with 3/8” steel plate and extruded angle. At full-scale the plate will be up to 40’ long. Our software generates the sequence of operations and calculates optimal trajectories based on the 3D model of our design. We won’t need to retool for different plate thickness, stiffener spacing, or even to add transverse members, brackets, etc. Our next version of this setup will include more extensive robot calibration and computer vision to allow us to precisely pick up arbitrarily-shaped structural members and place them accurately. Our software will also extend all the way through the process, picking plate & profiles from storage, running it through material prep operations, and even generating nesting layouts for the cutting table, removing scrap and directing the cut parts towards the correct assembly area to be picked and placed by robots. And there will be opportunities to deploy no-bullshit, useful AI agents to monitor the process, reflow production if there’s an issue somewhere on the line, generate work instructions for humans where needed and generally reduce the amount of human labor needed for oversight. If we succeed at all of this it will be hands down the most advanced shipbuilding process in the world. Early days still, but we won’t stop until we make it happen.

This will get a lot faster - shortening the hold time, deleting the “return to home position”, speeding up the robots… but it’s an exciting early glimpse of what we’re building. This is a sub-scale but otherwise accurate piece of a ship hull made with 3/8” steel plate and extruded angle. At full-scale the plate will be up to 40’ long. Our software generates the sequence of operations and calculates optimal trajectories based on the 3D model of our design. We won’t need to retool for different plate thickness, stiffener spacing, or even to add transverse members, brackets, etc. Our next version of this setup will include more extensive robot calibration and computer vision to allow us to precisely pick up arbitrarily-shaped structural members and place them accurately. Our software will also extend all the way through the process, picking plate & profiles from storage, running it through material prep operations, and even generating nesting layouts for the cutting table, removing scrap and directing the cut parts towards the correct assembly area to be picked and placed by robots. And there will be opportunities to deploy no-bullshit, useful AI agents to monitor the process, reflow production if there’s an issue somewhere on the line, generate work instructions for humans where needed and generally reduce the amount of human labor needed for oversight. If we succeed at all of this it will be hands down the most advanced shipbuilding process in the world. Early days still, but we won’t stop until we make it happen.

Dustin Walper

32,497 次观看 • 9 个月前

🚀 To Strike Israel from Iran: What a Missile Must Survive (and What It Costs) After decades of planning, $30+ billion spent, 13 integrated missile defense systems-including U.S. CENTCOM assets, NATO-linked radar, and space-based infrared-Israel was still caught off guard by a few missiles hitting old apartments buildings? Iranian missile aiming for Israel has to bypass these systems; 🛰️ TIER 1 — Detection & Tracking (3 systems) ➟ SBIRS (U.S. satellites) Global heat-signature detection within seconds. Real-time cue to all allied defenses. 💰 $19+ billion U.S. program cost since inception ➟ DSP (Defense Support Program) Cold War legacy satellites still supporting IR tracking. 💰 Originally over $15 billion (inflation-adjusted) ➟ Israeli OTH + Green Pine Radar Feeds targeting data to Arrow batteries with 900 km+ range. 💰 Each radar unit: $50–60 million 🛡️ TIER 2 — Strategic Interceptors (4 systems) ➟ Arrow 3 – Hits warheads in space (exo-atmospheric) 💰 $3 million+ per interceptor, $2.2 billion+ development cost ➟ Arrow 2 – Hits during reentry (endo-atmospheric) 💰 Interceptor: $2–2.5 million each 💰 Co-developed with U.S.: ~$1 billion+ in U.S. aid ➟ Aegis BMD (U.S. Navy) – Sea-based SM-3 interceptors 💰 Ship cost: $1.7 billion+ per vessel 💰 Each SM-3 missile: $12–18 million ➟ THAAD – U.S. high-altitude regional defense 💰 Battery: $800 million 💰 Interceptor: $11–13 million each 🎯 These are built to stop Shahab, Sejjil, Ghadr, and Emad-type MRBMs. 🧨 TIER 3 — Close-In Defenses (6 systems) ➟ David’s Sling – Intercepts mid-range & cruise 💰 Each missile: $1 million+ 💰 System dev cost: $1.7 billion+ ➟ Patriot PAC-3 – Ballistic & air threats 💰 Each interceptor: $4–5 million 💰 Battery: $1.1 billion+ ➟ Iron Dome – For short-range rockets & drones 💰 Missile: $40,000–100,000 💰 Full system: $50–70 million per battery 💰 Over $3 billion in U.S. funding to date ➟ Barak-8 – Naval-based interceptor 💰 Missile: $1.5–2 million+ ➟ Sky Dew – Airborne blimp radar 💰 Development: ~$300–400 million range 💰 Experimental, high-maintenance ➟ C-RAM (U.S. Army) – Close-range base defense 💰 ~$15–20 million per setup 💰 Fires 20–30 rounds per second with high cost-per-use Soooooo ➟ Iran must beat $20–30+ billion in integrated defensive infrastructure ➟ That includes: ➟ 3 global detection systems ➟ 4 intercept layers (exo, reentry, mid, terminal) ➟ 6 tactical kill nets surrounding every target zone ➟ Every shot they take faces a defense architecture that costs more than some countries’ entire GDP Also—why isn’t Israel’s automated Red Alert system going off?

🚀 To Strike Israel from Iran: What a Missile Must Survive (and What It Costs) After decades of planning, $30+ billion spent, 13 integrated missile defense systems-including U.S. CENTCOM assets, NATO-linked radar, and space-based infrared-Israel was still caught off guard by a few missiles hitting old apartments buildings? Iranian missile aiming for Israel has to bypass these systems; 🛰️ TIER 1 — Detection & Tracking (3 systems) ➟ SBIRS (U.S. satellites) Global heat-signature detection within seconds. Real-time cue to all allied defenses. 💰 $19+ billion U.S. program cost since inception ➟ DSP (Defense Support Program) Cold War legacy satellites still supporting IR tracking. 💰 Originally over $15 billion (inflation-adjusted) ➟ Israeli OTH + Green Pine Radar Feeds targeting data to Arrow batteries with 900 km+ range. 💰 Each radar unit: $50–60 million 🛡️ TIER 2 — Strategic Interceptors (4 systems) ➟ Arrow 3 – Hits warheads in space (exo-atmospheric) 💰 $3 million+ per interceptor, $2.2 billion+ development cost ➟ Arrow 2 – Hits during reentry (endo-atmospheric) 💰 Interceptor: $2–2.5 million each 💰 Co-developed with U.S.: ~$1 billion+ in U.S. aid ➟ Aegis BMD (U.S. Navy) – Sea-based SM-3 interceptors 💰 Ship cost: $1.7 billion+ per vessel 💰 Each SM-3 missile: $12–18 million ➟ THAAD – U.S. high-altitude regional defense 💰 Battery: $800 million 💰 Interceptor: $11–13 million each 🎯 These are built to stop Shahab, Sejjil, Ghadr, and Emad-type MRBMs. 🧨 TIER 3 — Close-In Defenses (6 systems) ➟ David’s Sling – Intercepts mid-range & cruise 💰 Each missile: $1 million+ 💰 System dev cost: $1.7 billion+ ➟ Patriot PAC-3 – Ballistic & air threats 💰 Each interceptor: $4–5 million 💰 Battery: $1.1 billion+ ➟ Iron Dome – For short-range rockets & drones 💰 Missile: $40,000–100,000 💰 Full system: $50–70 million per battery 💰 Over $3 billion in U.S. funding to date ➟ Barak-8 – Naval-based interceptor 💰 Missile: $1.5–2 million+ ➟ Sky Dew – Airborne blimp radar 💰 Development: ~$300–400 million range 💰 Experimental, high-maintenance ➟ C-RAM (U.S. Army) – Close-range base defense 💰 ~$15–20 million per setup 💰 Fires 20–30 rounds per second with high cost-per-use Soooooo ➟ Iran must beat $20–30+ billion in integrated defensive infrastructure ➟ That includes: ➟ 3 global detection systems ➟ 4 intercept layers (exo, reentry, mid, terminal) ➟ 6 tactical kill nets surrounding every target zone ➟ Every shot they take faces a defense architecture that costs more than some countries’ entire GDP Also—why isn’t Israel’s automated Red Alert system going off?

Morgan

14,342 次观看 • 1 年前

If you think AI is "not there yet" - You are not informed Here is what I did on maxfusion Full prompt: Handheld digital camcorder aesthetic, landscape framing. A gorgeous fitness and food influencer films herself by hand in selfie-cam and first-person style while cooking a late-night high-protein meal after training and a shower. Keep realistic hand shake, slightly crooked framing, autofocus briefly hunting between her face and the food, awkward zoom-ins onto the pan, occasional motion blur, brief lens steam-up over the hot pan that clears naturally, and small framing mistakes where her face or the pan edge slips out of frame. Mix handheld selfie footage with a few fixed external shots. Important: never show her placing, adjusting, or setting up a camera. When switching from handheld footage to a fixed external angle, use a clean jump cut as if the camera had already been positioned before the shot began. The camera itself must never appear on screen or in the window reflection. LOOK: Warm late-night kitchen light. A single warm pendant over the counter, cool blue night visible through the window behind her, under-cabinet strip light glowing on the backsplash. Slight camcorder softness, faint noise in the shadows, mild bloom on the pendant. Food realism is the core of the look: real sizzle with fine oil spatter, steam rising and curling from the pan, honest food textures with sear marks and glistening surfaces, knife cuts that separate cleanly, droplets on rinsed vegetables. Realistic skin texture, fresh-from-the-shower glow, clean dewy skin, realistic skin tones. STYLE: A realistic late-night what-I-eat-after-training vlog. The tone is relaxed, hungry, funny, quietly confident, and natural, talking to the lens like a friend while her hands keep working. She is clearly attractive and charismatic, but the video must feel like a believable self-recorded kitchen vlog, not a food commercial. Fast clean jump cuts, strong continuity, natural body language, real kitchen sounds, sizzle, chopping, no awkward dead moments. CHARACTER: The woman in the reference image. Her face, features, skin tone, body, and clothing must match the reference image exactly in every shot — she wears exactly what she is wearing in the reference image. Do not restyle, beautify, or alter her in any way beyond the reference. The one addition: a soft white towel wrapped around her head like a turban, as if she just stepped out of the shower. The towel stays neatly wrapped for the whole video. A few small damp strands of hair may peek out at the hairline and neck. Minimal or no makeup, fresh clean post-shower look. A small kitchen towel over one shoulder for most of the video. SETTING: A small warm real apartment kitchen at night. Wooden counter with knife marks, a gas hob with one cast-iron pan, a cutting board with chicken breast and vegetables, a bowl of cooked rice, a bottle of olive oil, salt in a small dish, a fridge with magnets and photos, dishes drying on a rack, dark window over the sink reflecting the warm kitchen. Lived-in and slightly cluttered. No other people, no pets. IMPORTANT CONTINUITY RULES: The same woman from the reference image must remain fully consistent in every shot. No face changes, no outfit changes, no body changes. The head towel stays wrapped in the same position in every shot — it never unwraps, falls, changes color, or disappears. The meal progresses in one direction only: raw ingredients, then chopping, then searing, then plating, then eating, and never reverses or regenerates. Food already cooked never becomes raw again. Hands and knife are the top priority: five fingers always, correct knife grip with curled guiding fingers, clean cuts, the knife never bends and never passes through her hand. The pan, board, oil bottle and rice bowl stay in the same positions. Steam and sizzle must behave with real physics. No extra people, including in the dark window reflection, and the reflection never shows a camera. No duplicated limbs. No camera visible. No camera setup shown. Keep her matching the reference image, warm, and photogenic in every shot. STORYBOARD: 30 seconds total, 10 cuts. (~3s, arm's-length selfie) She leans on the counter, towel wrapped on her head, clearly fresh from the shower after training, and grins tiredly at the lens. Dialogue: "Trained late. Showered. Starving. Let's cook." (~3s, handheld pan across the counter and back to her) The camera drifts across the board with raw chicken and vegetables, the pan, the rice bowl, then back up to her face. Dialogue: "Ten minute meal. Watch." (~3s, fixed external medium shot from across the counter) Jump cut. She is already chopping a red pepper with quick confident cuts, guiding fingers curled, pieces falling evenly. No camera setup shown. No dialogue, just the knife on the board. (~3s, same fixed shot) She slices the chicken breast into strips, seasons it from the salt dish with a high pinch, and rubs it in with her fingertips. Dialogue: "Protein first. Always." (~3s, tight handheld first-person shot over the pan) Oil shimmering, she lays the chicken strips in one by one and they hit with a loud real sizzle, fine spatter, steam rising into the lens which fogs for a beat and clears. No dialogue, just the sizzle. (~3s, handheld selfie while the pan sizzles behind her) She turns the camera on herself, fanning steam away from her face, laughing, one hand briefly steadying the head towel. Dialogue: "The smell. You have no idea." (~3s, tight handheld close-up of the pan) She flips the strips with tongs, showing deep golden sear marks, tosses in the peppers, and shakes the pan once so everything jumps and resettles. Dialogue: "That colour? That's the whole point." (~3s, fixed external shot) Jump cut. She plates it: rice pressed from the bowl, chicken and peppers over the top, a last drizzle of olive oil in a thin ribbon, and she wipes the plate rim with the towel like a chef, then smirks at her own seriousness. Dialogue: "Yes, I wiped the rim. Let me live." (~3s, tight handheld close-up) First fork bite, steam still coming off it, she chews, closes her eyes for a beat and nods slowly. Dialogue: "Ten minutes. That's it. Ridiculous." (~3s, arm's-length selfie ending) Plate in one hand, camera in the other, she backs out of the kitchen toward the sofa, flicking the kitchen light off with her elbow. Dialogue: "Okay. Eating. Good night." FINAL INSTRUCTION: The result must feel like a real self-recorded late-night cooking vlog by an athlete who actually cooks, filmed right after her shower. The highest priorities are matching the reference image exactly, correct hands and knife work at close range, one-directional cooking progression that never reverses, real sizzle, steam and food texture, the head towel staying consistently wrapped, honest warm kitchen light against the dark window, and subtle imperfection. Keep it warm, hungry, and real. Not a food commercial, not overhead recipe content, not stiff.

If you think AI is "not there yet" - You are not informed Here is what I did on maxfusion Full prompt: Handheld digital camcorder aesthetic, landscape framing. A gorgeous fitness and food influencer films herself by hand in selfie-cam and first-person style while cooking a late-night high-protein meal after training and a shower. Keep realistic hand shake, slightly crooked framing, autofocus briefly hunting between her face and the food, awkward zoom-ins onto the pan, occasional motion blur, brief lens steam-up over the hot pan that clears naturally, and small framing mistakes where her face or the pan edge slips out of frame. Mix handheld selfie footage with a few fixed external shots. Important: never show her placing, adjusting, or setting up a camera. When switching from handheld footage to a fixed external angle, use a clean jump cut as if the camera had already been positioned before the shot began. The camera itself must never appear on screen or in the window reflection. LOOK: Warm late-night kitchen light. A single warm pendant over the counter, cool blue night visible through the window behind her, under-cabinet strip light glowing on the backsplash. Slight camcorder softness, faint noise in the shadows, mild bloom on the pendant. Food realism is the core of the look: real sizzle with fine oil spatter, steam rising and curling from the pan, honest food textures with sear marks and glistening surfaces, knife cuts that separate cleanly, droplets on rinsed vegetables. Realistic skin texture, fresh-from-the-shower glow, clean dewy skin, realistic skin tones. STYLE: A realistic late-night what-I-eat-after-training vlog. The tone is relaxed, hungry, funny, quietly confident, and natural, talking to the lens like a friend while her hands keep working. She is clearly attractive and charismatic, but the video must feel like a believable self-recorded kitchen vlog, not a food commercial. Fast clean jump cuts, strong continuity, natural body language, real kitchen sounds, sizzle, chopping, no awkward dead moments. CHARACTER: The woman in the reference image. Her face, features, skin tone, body, and clothing must match the reference image exactly in every shot — she wears exactly what she is wearing in the reference image. Do not restyle, beautify, or alter her in any way beyond the reference. The one addition: a soft white towel wrapped around her head like a turban, as if she just stepped out of the shower. The towel stays neatly wrapped for the whole video. A few small damp strands of hair may peek out at the hairline and neck. Minimal or no makeup, fresh clean post-shower look. A small kitchen towel over one shoulder for most of the video. SETTING: A small warm real apartment kitchen at night. Wooden counter with knife marks, a gas hob with one cast-iron pan, a cutting board with chicken breast and vegetables, a bowl of cooked rice, a bottle of olive oil, salt in a small dish, a fridge with magnets and photos, dishes drying on a rack, dark window over the sink reflecting the warm kitchen. Lived-in and slightly cluttered. No other people, no pets. IMPORTANT CONTINUITY RULES: The same woman from the reference image must remain fully consistent in every shot. No face changes, no outfit changes, no body changes. The head towel stays wrapped in the same position in every shot — it never unwraps, falls, changes color, or disappears. The meal progresses in one direction only: raw ingredients, then chopping, then searing, then plating, then eating, and never reverses or regenerates. Food already cooked never becomes raw again. Hands and knife are the top priority: five fingers always, correct knife grip with curled guiding fingers, clean cuts, the knife never bends and never passes through her hand. The pan, board, oil bottle and rice bowl stay in the same positions. Steam and sizzle must behave with real physics. No extra people, including in the dark window reflection, and the reflection never shows a camera. No duplicated limbs. No camera visible. No camera setup shown. Keep her matching the reference image, warm, and photogenic in every shot. STORYBOARD: 30 seconds total, 10 cuts. (~3s, arm's-length selfie) She leans on the counter, towel wrapped on her head, clearly fresh from the shower after training, and grins tiredly at the lens. Dialogue: "Trained late. Showered. Starving. Let's cook." (~3s, handheld pan across the counter and back to her) The camera drifts across the board with raw chicken and vegetables, the pan, the rice bowl, then back up to her face. Dialogue: "Ten minute meal. Watch." (~3s, fixed external medium shot from across the counter) Jump cut. She is already chopping a red pepper with quick confident cuts, guiding fingers curled, pieces falling evenly. No camera setup shown. No dialogue, just the knife on the board. (~3s, same fixed shot) She slices the chicken breast into strips, seasons it from the salt dish with a high pinch, and rubs it in with her fingertips. Dialogue: "Protein first. Always." (~3s, tight handheld first-person shot over the pan) Oil shimmering, she lays the chicken strips in one by one and they hit with a loud real sizzle, fine spatter, steam rising into the lens which fogs for a beat and clears. No dialogue, just the sizzle. (~3s, handheld selfie while the pan sizzles behind her) She turns the camera on herself, fanning steam away from her face, laughing, one hand briefly steadying the head towel. Dialogue: "The smell. You have no idea." (~3s, tight handheld close-up of the pan) She flips the strips with tongs, showing deep golden sear marks, tosses in the peppers, and shakes the pan once so everything jumps and resettles. Dialogue: "That colour? That's the whole point." (~3s, fixed external shot) Jump cut. She plates it: rice pressed from the bowl, chicken and peppers over the top, a last drizzle of olive oil in a thin ribbon, and she wipes the plate rim with the towel like a chef, then smirks at her own seriousness. Dialogue: "Yes, I wiped the rim. Let me live." (~3s, tight handheld close-up) First fork bite, steam still coming off it, she chews, closes her eyes for a beat and nods slowly. Dialogue: "Ten minutes. That's it. Ridiculous." (~3s, arm's-length selfie ending) Plate in one hand, camera in the other, she backs out of the kitchen toward the sofa, flicking the kitchen light off with her elbow. Dialogue: "Okay. Eating. Good night." FINAL INSTRUCTION: The result must feel like a real self-recorded late-night cooking vlog by an athlete who actually cooks, filmed right after her shower. The highest priorities are matching the reference image exactly, correct hands and knife work at close range, one-directional cooking progression that never reverses, real sizzle, steam and food texture, the head towel staying consistently wrapped, honest warm kitchen light against the dark window, and subtle imperfection. Keep it warm, hungry, and real. Not a food commercial, not overhead recipe content, not stiff.

Stav Zilbershtein

86,429 次观看 • 22 天前