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Any content. 4 AI models. Zero blind spots. 🦾 CoreWise doesn't run one generic summarizer. It uses 4 specialized AI models (each with tuned prompts) then synthesizes everything through a final AI layer. The result: • Discovery points others miss • Consensus insights across all perspectives • Actual understanding,... show more

There's An AI For That

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16,535 次观看 • 8 个月前 •via X (Twitter)

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🚨BREAKING NEWS🚨 Adobe just activated GOD MODE for AI creation. ⚡️ Firefly Boards lets you try all the top AI models in one place: Firefly 5, Runway, Veo, FLUX, Gemini’s Nano Banana… And until December 1st, everything is 100% unlimited — even video models in Relax mode 🎬 I’ve prepared a badass thread with my favorite image models and many prompts to test the most powerful models This post is sponsored by Adobe Firefly through a paid partnership, showcasing my experiences as a Brand Ambassador for their innovative platform. All opinions are my own. 👇 #AdobeFireflyAmbassadors #Ad #FireflyPromo Adobe

Pierrick Chevallier | IA

71,266 次观看 • 10 个月前

Google is doing some great AI work in India that is actually helping farmers on the ground and honestly, almost nobody is talking about it. It’s surprising how much we focus on every new model drop, while some of the most meaningful AI work is happening quietly in the real world. Google DeepMind’s AnthroKrishi team built two AI models using satellite imagery: - Agricultural Landscape Understanding (ALU) maps farm boundaries, trees and water bodies, with historical data going back 15 years. - Agricultural Monitoring & Event Detection (AMED) uses multispectral data to monitor crops, sowing and harvesting, identifying 11 major crops. And this is already being used at scale: - 5M+ farmers in Telangana through its agricultural DPI - 140M+ hectares covered by Terrastack - 2.6M hectares of irrigated land in Karnataka - CarbonFarm is using it across 12 countries - FAO is integrating the models into its global agricultural data platform with $2.5M from Google These models are now providing agricultural insights across 6 African countries, and the Agricultural Landscape Understanding layer is one of the most popular layers on Google Earth globally.

Google is doing some great AI work in India that is actually helping farmers on the ground and honestly, almost nobody is talking about it. It’s surprising how much we focus on every new model drop, while some of the most meaningful AI work is happening quietly in the real world. Google DeepMind’s AnthroKrishi team built two AI models using satellite imagery: - Agricultural Landscape Understanding (ALU) maps farm boundaries, trees and water bodies, with historical data going back 15 years. - Agricultural Monitoring & Event Detection (AMED) uses multispectral data to monitor crops, sowing and harvesting, identifying 11 major crops. And this is already being used at scale: - 5M+ farmers in Telangana through its agricultural DPI - 140M+ hectares covered by Terrastack - 2.6M hectares of irrigated land in Karnataka - CarbonFarm is using it across 12 countries - FAO is integrating the models into its global agricultural data platform with $2.5M from Google These models are now providing agricultural insights across 6 African countries, and the Agricultural Landscape Understanding layer is one of the most popular layers on Google Earth globally.

AshutoshShrivastava

58,766 次观看 • 25 天前

I used Jev to classify 1,018 AI research papers. The result: $0.08 total cost and 256ms median end-to-end latency per paper. The pipeline was: 1. Summarize each paper with DeepSeek V4 Flash 2. Send the title + summary + 24 possible topics to Jev 3. Use Jev to classify each paper 4. Visualize everything on The summaries cost $3.99 on Together AI. The classifications cost $0.08 on TypeSafe AI. So for just over $4 of inference, I ended up with a pretty useful way to explore the top AI research papers from the past year. I think this is where things are heading: different models for different parts of the workflow, instead of using one model for everything. I’m running evals on the Jev classifications before replacing the current ones, but the site is already live:

I used Jev to classify 1,018 AI research papers. The result: $0.08 total cost and 256ms median end-to-end latency per paper. The pipeline was: 1. Summarize each paper with DeepSeek V4 Flash 2. Send the title + summary + 24 possible topics to Jev 3. Use Jev to classify each paper 4. Visualize everything on The summaries cost $3.99 on Together AI. The classifications cost $0.08 on TypeSafe AI. So for just over $4 of inference, I ended up with a pretty useful way to explore the top AI research papers from the past year. I think this is where things are heading: different models for different parts of the workflow, instead of using one model for everything. I’m running evals on the Jev classifications before replacing the current ones, but the site is already live:

Hassan

163,848 次观看 • 3 天前

recreated this exact hook with AI in 3 minutes generated first frame with nano banana -> uploaded it along with reference video to Kling motion control, done when the quality is actually there, AI UGC beats real creators every time we built the best system for this right now: - realistic visuals - realistic voice - realistic movement - all optimized for virality and conversions way cheaper than a real creator and built for scale across thousands of posts and multiple accounts this isn't generic AI slop, these are high quality systems built with real effort and ready to scale your business it works across ecom,apps, saas, coaching, DTC... everything yet people are still paying $300-500 per UGC video from creators who ghost halfway through the project

recreated this exact hook with AI in 3 minutes generated first frame with nano banana -> uploaded it along with reference video to Kling motion control, done when the quality is actually there, AI UGC beats real creators every time we built the best system for this right now: - realistic visuals - realistic voice - realistic movement - all optimized for virality and conversions way cheaper than a real creator and built for scale across thousands of posts and multiple accounts this isn't generic AI slop, these are high quality systems built with real effort and ready to scale your business it works across ecom,apps, saas, coaching, DTC... everything yet people are still paying $300-500 per UGC video from creators who ghost halfway through the project

MAX

171,662 次观看 • 6 个月前

Today we're shipping the governance layer enterprise AI was missing. Every organization running AI at scale faces the same choice: turn off AI connections to tools entirely, or let agents have full access to everything. Today we're launching a better path: precise control over connections, actions, and models across every surface. The goal is simple: IT gets visibility and control, teams keep building, no approval queue in between. 6 new capabilities in Zapier today: 1. Action restrictions at the endpoint level 2. Managed app connections with domain control 3. Bring your own model through AWS Bedrock 4. A queryable audit trail 5. Agents GA with MCP governed across every surface 6. Workspaces (GA later this quarter) One set of rules for your entire AI stack. Try it now. If you've had early access, let me know what you think.

Today we're shipping the governance layer enterprise AI was missing. Every organization running AI at scale faces the same choice: turn off AI connections to tools entirely, or let agents have full access to everything. Today we're launching a better path: precise control over connections, actions, and models across every surface. The goal is simple: IT gets visibility and control, teams keep building, no approval queue in between. 6 new capabilities in Zapier today: 1. Action restrictions at the endpoint level 2. Managed app connections with domain control 3. Bring your own model through AWS Bedrock 4. A queryable audit trail 5. Agents GA with MCP governed across every surface 6. Workspaces (GA later this quarter) One set of rules for your entire AI stack. Try it now. If you've had early access, let me know what you think.

Wade Foster

32,542 次观看 • 5 个月前

Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.

Today we’re introducing Google AI Threat Defense - a comprehensive AI-powered cybersecurity solution designed to help continuously monitor for and stop AI-powered threats before they can impact your business. Here’s how it works: 1. AI Threat Defense uses our cybersecurity platform Wiz to scan and prioritize what applications and systems have the highest security risk. 2. Gemini and other frontier AI models can then autonomously perform continual deep scanning of your applications - starting with those at the highest risk - to identify security vulnerabilities. 3. The capabilities of CodeMender - a new software repair agent - are then used to verify and accelerate the patching of vulnerabilities. 4. And our Wiz autonomous agents continuously test your systems to find unknown vulnerabilities before adversaries do so that you can remediate them before you are attacked. While other model providers focus on using AI to find and flag vulnerabilities, Google AI Threat Defense actively prioritizes your most critical real-world risks and accelerates their remediation using a variety of models since no single model finds a superset of the vulnerabilities found by all other models.

Thomas Kurian

198,394 次观看 • 3 个月前

This system generated $12K worth of market research, called 15 suppliers, and created a business plan from a single prompt. The Genspark Super Agent just made every other productivity tool obsolete. Here's what this AI workspace actually does: → Runs 169 AI models locally on your device (completely offline and free) → Generates AI podcasts from any YouTube video or content → Creates detailed documents and spreadsheets with auto-generated graphs → Uses Nano Banana for fashion try-ons and professional video generation → Builds PowerPoint presentations from video content automatically → Finds best Amazon deals with one-click price comparison → Acts as personal assistant scheduling meetings and managing emails → Connects to 1000+ tools for complete workflow automation → Makes actual phone calls to book reservations and handle real tasks While others juggle 20 different AI subscriptions, you get everything in one browser. The free browser handles basic AI locally. The Super Agent handles the advanced automations that actually run your business. Want the complete setup guide? Comment "AI" + RT + Like I'll DM you the full implementation playbook (Must be following so I can DM) Skip this and keep paying $500/month for inferior AI tools.

This system generated $12K worth of market research, called 15 suppliers, and created a business plan from a single prompt. The Genspark Super Agent just made every other productivity tool obsolete. Here's what this AI workspace actually does: → Runs 169 AI models locally on your device (completely offline and free) → Generates AI podcasts from any YouTube video or content → Creates detailed documents and spreadsheets with auto-generated graphs → Uses Nano Banana for fashion try-ons and professional video generation → Builds PowerPoint presentations from video content automatically → Finds best Amazon deals with one-click price comparison → Acts as personal assistant scheduling meetings and managing emails → Connects to 1000+ tools for complete workflow automation → Makes actual phone calls to book reservations and handle real tasks While others juggle 20 different AI subscriptions, you get everything in one browser. The free browser handles basic AI locally. The Super Agent handles the advanced automations that actually run your business. Want the complete setup guide? Comment "AI" + RT + Like I'll DM you the full implementation playbook (Must be following so I can DM) Skip this and keep paying $500/month for inferior AI tools.

Samruddhi Mokal

47,088 次观看 • 1 年前

f*ck it, I'm leaking my SECRET AI ghostwriter that's generated $200K+ in the last 4 months this AI fine-tuned agent writes better content than $5K/month ghostwriters and 99% of creators have no idea it exists while everyone's using basic ChatGPT prompts getting 200 views and 0 leads, this system flips that completely here's what you actually get: - fine-tuned AI agent trained on viral hooks + emotional copy - learns your exact tone & writing style in minutes - generates scroll-stopping hooks using viral frameworks - mimics structure from proven top-performing posts - automated posting system that improves daily with zero editing - complete setup guide (works in 20 minutes, no tech skills needed) the results speak for themselves: - 35,000+ followers - 5M+ impressions - 20,000+ leads generated - zero burnout from content creation basically everything you need to replace expensive ghostwriters with AI that sounds exactly like you reply "GHOST" + retweet and i'll send it over (must be following so i can dm)

f*ck it, I'm leaking my SECRET AI ghostwriter that's generated $200K+ in the last 4 months this AI fine-tuned agent writes better content than $5K/month ghostwriters and 99% of creators have no idea it exists while everyone's using basic ChatGPT prompts getting 200 views and 0 leads, this system flips that completely here's what you actually get: - fine-tuned AI agent trained on viral hooks + emotional copy - learns your exact tone & writing style in minutes - generates scroll-stopping hooks using viral frameworks - mimics structure from proven top-performing posts - automated posting system that improves daily with zero editing - complete setup guide (works in 20 minutes, no tech skills needed) the results speak for themselves: - 35,000+ followers - 5M+ impressions - 20,000+ leads generated - zero burnout from content creation basically everything you need to replace expensive ghostwriters with AI that sounds exactly like you reply "GHOST" + retweet and i'll send it over (must be following so i can dm)

Aryan Mahajan

27,464 次观看 • 1 年前

AI IS NO LONGER JUST WRITING CODE IT iS STARTING TO MOVE THINGS IN THE REAL WORLD. Someone just built a pizza delivery system where the drone does the driving No delivery car No traffic No driver sitting behind the wheel Just: -> Order comes in -> Drone picks up the pizza -> Flies directly to the destination -> Delivers it -> Returns And this is where the AI story gets interesting For years, the AI boom was mostly digital: > Chatbots > Coding agents > Image generation > AI music > AI video But the next phase is different AI is getting a body The same technology stack that started with models and GPUs is now moving into the physical world NVIDIA built the compute layer Researchers built the models Companies like Zoox are building autonomous vehicles And now we're seeing AI powered machines actually move through the real world The crazy part? We designed entire cities around the assumption that humans have to physically drive everything AI doesn't have that limitation Why send a pizza through 5 km of traffic when a machine can simply fly over it? The AI boom isn't just about replacing human work It's about removing constraints humans had to design around The next big AI companies might not live inside your browser They might be flying above your house Bookmark this so you wont miss the next delivery

AI IS NO LONGER JUST WRITING CODE IT iS STARTING TO MOVE THINGS IN THE REAL WORLD. Someone just built a pizza delivery system where the drone does the driving No delivery car No traffic No driver sitting behind the wheel Just: -> Order comes in -> Drone picks up the pizza -> Flies directly to the destination -> Delivers it -> Returns And this is where the AI story gets interesting For years, the AI boom was mostly digital: > Chatbots > Coding agents > Image generation > AI music > AI video But the next phase is different AI is getting a body The same technology stack that started with models and GPUs is now moving into the physical world NVIDIA built the compute layer Researchers built the models Companies like Zoox are building autonomous vehicles And now we're seeing AI powered machines actually move through the real world The crazy part? We designed entire cities around the assumption that humans have to physically drive everything AI doesn't have that limitation Why send a pizza through 5 km of traffic when a machine can simply fly over it? The AI boom isn't just about replacing human work It's about removing constraints humans had to design around The next big AI companies might not live inside your browser They might be flying above your house Bookmark this so you wont miss the next delivery

0xSlyth

14,149 次观看 • 1 个月前

You can now use GPT 5.5, Gemini 3.7 Flash, Kimi K3 and 47 other AI models completely free😱 No subscription. No credit card. Even the API usage costs $0. AIHubMix just opened a free catalog with 50 AI models. Some of the available models: • Ox Alpha • Gemini 3.7 Flash • GLM 5.2 • Kimi K3 • MiniMax M3 • GPT 5.5 • 40+ more And you don’t need separate API keys for each model. Setup takes 2 minutes: > Step 1: Go to > Create an account using your email or OAuth. No card needed. Step 2: Create one API key > The same key works with every free and paid model. Step 3: Add it to any OpenAI-compatible tool Base URL: Then choose any model ending in -free, such as: coding-glm-5.2-free gpt-5.5-free That’s it. One API key. 50 AI models. $0 for both input and output. Save this. You might need a free multi-model setup later.

You can now use GPT 5.5, Gemini 3.7 Flash, Kimi K3 and 47 other AI models completely free😱 No subscription. No credit card. Even the API usage costs $0. AIHubMix just opened a free catalog with 50 AI models. Some of the available models: • Ox Alpha • Gemini 3.7 Flash • GLM 5.2 • Kimi K3 • MiniMax M3 • GPT 5.5 • 40+ more And you don’t need separate API keys for each model. Setup takes 2 minutes: > Step 1: Go to > Create an account using your email or OAuth. No card needed. Step 2: Create one API key > The same key works with every free and paid model. Step 3: Add it to any OpenAI-compatible tool Base URL: Then choose any model ending in -free, such as: coding-glm-5.2-free gpt-5.5-free That’s it. One API key. 50 AI models. $0 for both input and output. Save this. You might need a free multi-model setup later.

CDG

15,388 次观看 • 27 天前

This AI UGC workflow is f*cking nuts 🤯 One prompt -> five completely different characters + five full ad variations, all generated automatically in a single run. Perfect for DTC brands and creative agencies who need volume but don't have time to build each variation from scratch. Most people making AI UGC are doing it one video at a time. New character, new prompt, new script, new render. Repeat. It works, but it doesn't scale. By the time you've built 5 variations to test, you've burned half a day. This workflow solves it: → Enter one initial prompt with your product and angle → Auto-generates 5 unique AI characters → Builds hook scripts for each variation → Writes the bridge with your actual product image → Creates the CTA — all 5 versions in one shot No building each video manually. No copy-pasting prompts over and over. No bottleneck between idea and creative testing. What you get: > 5 unique character variations from a single prompt > Hook, bridge, and CTA scripts tailored to each character > Product image integration baked in > A repeatable system you can run every time you need fresh creative Built 100% with AI. Want a copy of the full workflow for free? > Like this post > Comment "UGC" And I'll send it over (must be following so I can DM)

This AI UGC workflow is f*cking nuts 🤯 One prompt -> five completely different characters + five full ad variations, all generated automatically in a single run. Perfect for DTC brands and creative agencies who need volume but don't have time to build each variation from scratch. Most people making AI UGC are doing it one video at a time. New character, new prompt, new script, new render. Repeat. It works, but it doesn't scale. By the time you've built 5 variations to test, you've burned half a day. This workflow solves it: → Enter one initial prompt with your product and angle → Auto-generates 5 unique AI characters → Builds hook scripts for each variation → Writes the bridge with your actual product image → Creates the CTA — all 5 versions in one shot No building each video manually. No copy-pasting prompts over and over. No bottleneck between idea and creative testing. What you get: > 5 unique character variations from a single prompt > Hook, bridge, and CTA scripts tailored to each character > Product image integration baked in > A repeatable system you can run every time you need fresh creative Built 100% with AI. Want a copy of the full workflow for free? > Like this post > Comment "UGC" And I'll send it over (must be following so I can DM)

Mike Futia

15,247 次观看 • 7 个月前

Kling 3.0 for AI UGC videos is absolutely insane 🤯 I spent 25,000+ credits in Kling perfecting the ultimate prompting framework to get the best AI UGC outputs possible. And the 3.0 update just made everything even better. Perfect for DTC brands and agencies who want high-quality AI UGC without paying $500/video for actual UGC. Here's what Kling 3.0 unlocks for AI UGCL → "AI Director" system that understands full scripts and auto-schedules camera angles (shot/reverse shot) in one generation → 3 to 15 second cinematic clips with full temporal coherence → Improved character and element locking so your subject stays consistent across shots and angles → Native 4K output for both video and stills — actually usable for professional ad creative And best of all: perfect character consistency ACROSS different shots. What this means for AI UGC: - Multi-shot storytelling in a single generation cycle - Longer clips that actually hold together - Consistent characters across your entire ad - Output quality that's ready for paid media This is the closest AI video has gotten to replacing a real shoot for performance creative. I recorded a full breakdown of the prompting framework I built after burning through 25,000 credits. Want access to all the prompts I use to create AI UGC with Kling? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

Kling 3.0 for AI UGC videos is absolutely insane 🤯 I spent 25,000+ credits in Kling perfecting the ultimate prompting framework to get the best AI UGC outputs possible. And the 3.0 update just made everything even better. Perfect for DTC brands and agencies who want high-quality AI UGC without paying $500/video for actual UGC. Here's what Kling 3.0 unlocks for AI UGCL → "AI Director" system that understands full scripts and auto-schedules camera angles (shot/reverse shot) in one generation → 3 to 15 second cinematic clips with full temporal coherence → Improved character and element locking so your subject stays consistent across shots and angles → Native 4K output for both video and stills — actually usable for professional ad creative And best of all: perfect character consistency ACROSS different shots. What this means for AI UGC: - Multi-shot storytelling in a single generation cycle - Longer clips that actually hold together - Consistent characters across your entire ad - Output quality that's ready for paid media This is the closest AI video has gotten to replacing a real shoot for performance creative. I recorded a full breakdown of the prompting framework I built after burning through 25,000 credits. Want access to all the prompts I use to create AI UGC with Kling? > Like this post > Comment "KLING" And I'll send it over (must be following so I can DM)

Mike Futia

25,607 次观看 • 7 个月前

What happens when you stop guessing and let machine learning read the market for you? $2.2M in 4 months. ilovecircle built something different on Polymarket. Not a speed bot. Not a spread farmer. An AI system that actually thinks. 1,347 predictions. 74% win rate. Biggest single hit: $258.4K. Current positions: basically zero he extracted everything. The setup: 10 machine learning models running in parallel, each trained on news feeds and social media data. They don't predict events they predict when the crowd is wrong about probabilities. Market prices an outcome at 50 cents. His ensemble says the real odds are 60%. That gap is the trade. Every week the models retrain themselves on fresh data. The edge evolves because the system never stops learning. Most traders react to headlines. This wallet front runs the market's understanding of what headlines actually mean. 51K people watching now. Most still think AI trading is a scam until they see a curve like this. → Following wallets that run AI-powered probability models is simpler with PMX.

What happens when you stop guessing and let machine learning read the market for you? $2.2M in 4 months. ilovecircle built something different on Polymarket. Not a speed bot. Not a spread farmer. An AI system that actually thinks. 1,347 predictions. 74% win rate. Biggest single hit: $258.4K. Current positions: basically zero he extracted everything. The setup: 10 machine learning models running in parallel, each trained on news feeds and social media data. They don't predict events they predict when the crowd is wrong about probabilities. Market prices an outcome at 50 cents. His ensemble says the real odds are 60%. That gap is the trade. Every week the models retrain themselves on fresh data. The edge evolves because the system never stops learning. Most traders react to headlines. This wallet front runs the market's understanding of what headlines actually mean. 51K people watching now. Most still think AI trading is a scam until they see a curve like this. → Following wallets that run AI-powered probability models is simpler with PMX.

Carver

13,072 次观看 • 7 个月前

Perplexity Computer in 60 seconds: 1. It's a cloud-based AI employee that runs tasks in the background. 2. 19 models working together. Claude for reasoning, GPT-5.2 for research, Grok for speed tasks. You don't pick. It routes automatically. 3. 400+ connectors. Gmail, Slack, Notion, Salesforce, HubSpot. One click to enable each. 4. Credits, not tokens. Simple tasks cost ~30. Complex builds cost 1,000+. Vague prompts waste them. Specific prompts save them. 5. Spaces = persistent project folders. Upload context once, every task inherits it. 6. Scheduled tasks run on autopilot. "Every Monday, prep my calendar." Set it and forget it. The PRD hack alone (in the article) will save you hundreds in credits. Full breakdown in the article below.

Perplexity Computer in 60 seconds: 1. It's a cloud-based AI employee that runs tasks in the background. 2. 19 models working together. Claude for reasoning, GPT-5.2 for research, Grok for speed tasks. You don't pick. It routes automatically. 3. 400+ connectors. Gmail, Slack, Notion, Salesforce, HubSpot. One click to enable each. 4. Credits, not tokens. Simple tasks cost ~30. Complex builds cost 1,000+. Vague prompts waste them. Specific prompts save them. 5. Spaces = persistent project folders. Upload context once, every task inherits it. 6. Scheduled tasks run on autopilot. "Every Monday, prep my calendar." Set it and forget it. The PRD hack alone (in the article) will save you hundreds in credits. Full breakdown in the article below.

Corey Ganim

106,105 次观看 • 5 个月前

The recent Massachusetts Institute of Technology (MIT) CSAIL paper published on Recursive Language Models is a fascinating look into how AI systems reason in 2026. You can check it out here ➡️ → The paper notes that even frontier models suffer from “context rot” as inputs grow longer. More tokens don’t mean more understanding. Instead of compressing or summarising, RLMs “treat long prompts as part of an external environment” …and let the model programmatically inspect, decompose, and recursively requery itself over precise sections. This matters in Web3. Why? Smart contracts are long, stateful, and brittle. One missed assumption = unnecessary hassle. In the video below, we applied RLM principles to improve the prompt shown. The old prompt would try to force multi-step pauses. Therefore breaking the AI's workflow. Now it decomposes requirements, scores confidence for each component, verifies the logic, and then synthesises. Result = Production-ready contracts with flagged risks, not blind single-pass outputs. Try it out with our Smart Contract Generator today!

The recent Massachusetts Institute of Technology (MIT) CSAIL paper published on Recursive Language Models is a fascinating look into how AI systems reason in 2026. You can check it out here ➡️ → The paper notes that even frontier models suffer from “context rot” as inputs grow longer. More tokens don’t mean more understanding. Instead of compressing or summarising, RLMs “treat long prompts as part of an external environment” …and let the model programmatically inspect, decompose, and recursively requery itself over precise sections. This matters in Web3. Why? Smart contracts are long, stateful, and brittle. One missed assumption = unnecessary hassle. In the video below, we applied RLM principles to improve the prompt shown. The old prompt would try to force multi-step pauses. Therefore breaking the AI's workflow. Now it decomposes requirements, scores confidence for each component, verifies the logic, and then synthesises. Result = Production-ready contracts with flagged risks, not blind single-pass outputs. Try it out with our Smart Contract Generator today!

ChainGPT

82,385 次观看 • 7 个月前

This AI Agent generates 1000s of winning static ads automatically. → Auto-generates 100+ unique image variations → AI writes custom prompts for each ad angle → OpenAI Image creates on-brand visuals in seconds → Links each image to specific ad copy automatically → Bulk generation with one checkbox → Editable prompts before image creation → Hosted images ready for immediate upload → Built in n8n + Airtable (zero coding) Perfect for ecomm operators, creative strategists, and Meta ad agencies. The workflow: 1. Drop in product URL 2. AI researches brand & audience 3. Generates 100+ ad copy variations 4. Creates 100+ custom image prompts 5. Produces 100+ static ad images All in under 5 minutes. >> No more creative bottlenecks. >> No more waiting on designers. Just static ads, at scale, on demand. Want access to the n8n template + Airtable database? Like + Comment "ADS" and I'll send it over.

Mike Futia

33,063 次观看 • 1 年前

JENSEN HUANG UNVEILED A BOARD THAT RUNS 1 TRILLION PARAMETER AI MODELS. THE $249 NVIDIA BOX UNDER YOUR DESK KILLS A $200/MONTH AI BILL FOR $5 IN ELECTRICITY jensen held it up on stage with one hand and called it the architecture that runs the future of ai. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

JENSEN HUANG UNVEILED A BOARD THAT RUNS 1 TRILLION PARAMETER AI MODELS. THE $249 NVIDIA BOX UNDER YOUR DESK KILLS A $200/MONTH AI BILL FOR $5 IN ELECTRICITY jensen held it up on stage with one hand and called it the architecture that runs the future of ai. that same technology now ships in a $249 box smaller than your wallet the jetson orin nano super pulls 7-25 watts and does 67 trillion ai operations per second. llama 3, mistral and deepseek run locally with no api fees and no data leaving your machine most developers pay $2,400 a year across chatgpt, openai api, claude pro and cursor. the jetson costs $314 in year one and $60 a year after. 2 year savings hit $4,431 install ollama with one command, change one line of code to point at localhost, and every tool built for openai works identically. zero rewrites, zero rate limits cloud subscriptions keep getting more expensive and rate limits keep getting tighter. the people who own the box in 2026 are going to look very far ahead in 2028 bookmark this and read the article below

starmex

54,492 次观看 • 3 个月前

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.

This guy built a mini AI farm out of 4 Nvidia boxes It does not look like a data center. It looks like a stack of small machines sitting next to a laptop. But each box is a DGX Spark with Grace Blackwell inside, 128GB unified memory, and enough room to run models normal gaming GPUs cannot even open. Using the launch price from the article, 4 of them is almost $12,000 of local AI compute on one desk. That sounds expensive until you compare it to cloud GPUs. A serious AI builder can burn $1,500 to $3,000 a month renting A100s and H100s for client work, fine-tunes, agents and 70B models. He basically moved that bill from the cloud into hardware he owns. 4 Nvidia boxes. 512GB unified memory. No hourly meter running in the background. No rented GPUs eating the margin every time an agent runs too long. The funny part is most people still think local AI means a slow laptop running a toy model. Meanwhile guys like this are stacking compute at home. Save this, local AI is turning into the new mining farm.

Gipp 🦅

591,405 次观看 • 3 个月前

most of you don't know how hard hermes agent is optimized for local AI at the system level. watch the full setup flow on screen. you paste an openai-compatible v1 endpoint, hermes auto-detects every model running behind it. doesn't matter if it's llama.cpp or vllm or any compatible server, all your models surface and become selectable in seconds. no config gymnastics, no manual model list. then it goes deeper. hermes ships with per model parsers, prompt template auto-handling, tool call format detection per model architecture, thinking mode awareness, all the small friction points other harnesses leak on. these were not built for cloud apis with one canonical model. they were built for builders running 10 different local models across 10 different stacks. cloud first harnesses bolt local support on top. hermes agent is local first from the architecture out. that's the system level gap. if you're getting started on local AI, this is the harness you start with. try for yourself and find out. anyone serious about local AI lands here eventually.

most of you don't know how hard hermes agent is optimized for local AI at the system level. watch the full setup flow on screen. you paste an openai-compatible v1 endpoint, hermes auto-detects every model running behind it. doesn't matter if it's llama.cpp or vllm or any compatible server, all your models surface and become selectable in seconds. no config gymnastics, no manual model list. then it goes deeper. hermes ships with per model parsers, prompt template auto-handling, tool call format detection per model architecture, thinking mode awareness, all the small friction points other harnesses leak on. these were not built for cloud apis with one canonical model. they were built for builders running 10 different local models across 10 different stacks. cloud first harnesses bolt local support on top. hermes agent is local first from the architecture out. that's the system level gap. if you're getting started on local AI, this is the harness you start with. try for yourself and find out. anyone serious about local AI lands here eventually.

Sudo su

14,407 次观看 • 4 个月前