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Another week, another launch!🎉 💬 ChatWP Spent the last few weeks training & coding an AI 🤖 for the official #WordPress docs and it finally works: ❓ Ask anything about WP 🧠 Get an amazing AI answer 👨🏽‍💻 Including code examples 🔗 Clickable sources 🆓 For free! Link 👇🏼👇🏼👇🏼

Aaron Edwards

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35,827 görüntüleme • 3 yıl önce •via X (Twitter)

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I use Flot AI because I’m tired of thinking about prompts. Not because I don’t know how — but because I don’t want to stop my work just to phrase things perfectly. Most AI tools still expect you to: pause, think, rewrite the question, tweak the prompt. Flot AI doesn’t. It already has strong built-in prompts for everyday work: rewriting, polishing, translating, summarizing. I just select what I’m working on and ask — no prompt engineering, no overthinking. The best part is that it works in place. Emails, docs, articles — I stay where I am, get the result, keep going. When AI stops demanding “better prompts,” it finally becomes what it should be:a quiet assistant, not another task.

I use Flot AI because I’m tired of thinking about prompts. Not because I don’t know how — but because I don’t want to stop my work just to phrase things perfectly. Most AI tools still expect you to: pause, think, rewrite the question, tweak the prompt. Flot AI doesn’t. It already has strong built-in prompts for everyday work: rewriting, polishing, translating, summarizing. I just select what I’m working on and ask — no prompt engineering, no overthinking. The best part is that it works in place. Emails, docs, articles — I stay where I am, get the result, keep going. When AI stops demanding “better prompts,” it finally becomes what it should be:a quiet assistant, not another task.

Pushpendra Tripathi

88,774 görüntüleme • 7 ay önce

Okay... this is actually insane. OpenCodex feels like the open-source breakthrough I've been waiting for. The best part : You can plug multiple providers into the same OpenAI Codex harness and switch between models depending on the task. Running low on tokens? No problem. Use another provider. OpenRouter free model today? Plug it in. This completely changes how I think about AI coding workflows. And yes... it even works on mobile. OpenCodex might be one of the most useful open-source AI projects I've seen this year. OpenAI built an incredible harness. The open-source community just made it universal.

Okay... this is actually insane. OpenCodex feels like the open-source breakthrough I've been waiting for. The best part : You can plug multiple providers into the same OpenAI Codex harness and switch between models depending on the task. Running low on tokens? No problem. Use another provider. OpenRouter free model today? Plug it in. This completely changes how I think about AI coding workflows. And yes... it even works on mobile. OpenCodex might be one of the most useful open-source AI projects I've seen this year. OpenAI built an incredible harness. The open-source community just made it universal.

CHOI

42,894 görüntüleme • 25 gün önce

ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

ByteDance just open sourced an AI SuperAgent that can research, code, build websites, create slide decks, and generate videos. All by itself. DeerFlow 2.0 (27K+ GitHub stars ⭐️), an AI system acting like an autonomous employee with its own computer workspace to research and code. Standard chatbots only generate text and forget your preferences. DeerFlow solves this by giving the AI an isolated virtual computer environment where it safely runs programs. When given a massive task, the main program creates several smaller AI assistants to work simultaneously. It also saves your past workflows so it gets smarter about your needs. DeerFlow is model-agnostic — it works with any LLM that implements the OpenAI-compatible API. Fully supports running local models on your own computer using tools like Ollama. An example - you ask for research on the top 10 AI startups in 2026 for a presentation, the lead agent in DeerFlow breaks that big job into smaller sub-tasks. It assigns one sub-agent to look into each company, another to find funding details, and a third to handle competitor analysis. These agents do all their work in parallel. Everything eventually converges, and a final agent pulls the results into a slide deck complete with custom visuals.

Rohan Paul

50,097 görüntüleme • 5 ay önce

GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

GITHUB JUST KILLED THE WORST PART OF VIBE CODING they shipped a free tool called Spec Kit and it already crossed 120,000 stars the fix is stupidly simple instead of tossing vague prompts at an agent and praying it doesn't wreck your project Spec Kit makes the AI write a full structured spec before it touches a single line of code it works through the problem first figures out what you want to build asks about the gaps lays out the project then it starts coding you get fewer insane bugs, cleaner output and results you can predict the flow looks like this: /constitution for your rules and standards /specify for what you want to build /clarify for the open questions before you start /plan for architecture and stack /tasks for the ordered work /implement to run it it plugs into Claude Code, Cursor, Copilot, Codex, Gemini CLI and 25+ other agents 120,000 stars, 10,000 forks, open source, shipped by GitHub itself learning to drive agents like this is most of what separates people getting hired as AI engineers from everyone still fighting their prompts

Atlas

500,494 görüntüleme • 1 ay önce

🌌 AI Agents Are Taking Over... And We’re Bringing Them to Berachain Foundation 🐻⛓ 🐻🔥 Hundreds of hours spent on research, tracking wallets, analyzing bribes, and managing portfolios... What if your AI Agent could do this for you—24/7? ⏲️ 🔧 Our Tech Is Next-Level On our testnet, you’ve been memeing it up with PumpFun™, creating dank memecoins enhanced by NFTs. But once Berachain’s mainnet is live, you’ll be able to create your own AI Agents. To test and perfect our tech, we shared it with projects like AI Agent Layer | AIFUN, allowing us to test it in all conditions and continuously improve its performance. 🛠️🔥 🐻 Why AI Agent are great for berachain? Berachain might seem simple at first glance: validators, bribes, POL, staking rewards… but the deeper you go, the more complex the game theory becomes. 🤯 Here’s where AI comes in. Imagine an agent helping you: 💡 Optimize bribes 📊 Analyze validator behavior 🧠 Make decisions faster and smarter and much more, as AI Agents won't be limited to the chain itself! Examples of AI Agent Projects Dominating the Space 🚀 $VIRTUAL - Launchpad for AI Agents ($3.5B mcap) 🧠 $AI16Z - Eliza OS Framework ($2B mcap) 🔍 $AIXBT - The AI Analyst revolutionizing CT ($430M mcap) 🎮 $GAME - Low-code toolkit for creating AI Agents ($230M mcap) 💡 There are already AI Agents managing portfolios, betting on sports, and automating tasks. And guess what? They're outperforming humans. 🌐 We've built Virtuals on Berachain Our protocol integrates directly with Berachain, providing real utility to our token: $AIBERA 💎. Say Ooga Booga if you want to see a thread about tokenomics and $AIBERA utility. The chain has beras on it, and beras deserve AI Agents. 🐻🤖 Ooga Booga. 🔥

🌌 AI Agents Are Taking Over... And We’re Bringing Them to Berachain Foundation 🐻⛓ 🐻🔥 Hundreds of hours spent on research, tracking wallets, analyzing bribes, and managing portfolios... What if your AI Agent could do this for you—24/7? ⏲️ 🔧 Our Tech Is Next-Level On our testnet, you’ve been memeing it up with PumpFun™, creating dank memecoins enhanced by NFTs. But once Berachain’s mainnet is live, you’ll be able to create your own AI Agents. To test and perfect our tech, we shared it with projects like AI Agent Layer | AIFUN, allowing us to test it in all conditions and continuously improve its performance. 🛠️🔥 🐻 Why AI Agent are great for berachain? Berachain might seem simple at first glance: validators, bribes, POL, staking rewards… but the deeper you go, the more complex the game theory becomes. 🤯 Here’s where AI comes in. Imagine an agent helping you: 💡 Optimize bribes 📊 Analyze validator behavior 🧠 Make decisions faster and smarter and much more, as AI Agents won't be limited to the chain itself! Examples of AI Agent Projects Dominating the Space 🚀 $VIRTUAL - Launchpad for AI Agents ($3.5B mcap) 🧠 $AI16Z - Eliza OS Framework ($2B mcap) 🔍 $AIXBT - The AI Analyst revolutionizing CT ($430M mcap) 🎮 $GAME - Low-code toolkit for creating AI Agents ($230M mcap) 💡 There are already AI Agents managing portfolios, betting on sports, and automating tasks. And guess what? They're outperforming humans. 🌐 We've built Virtuals on Berachain Our protocol integrates directly with Berachain, providing real utility to our token: $AIBERA 💎. Say Ooga Booga if you want to see a thread about tokenomics and $AIBERA utility. The chain has beras on it, and beras deserve AI Agents. 🐻🤖 Ooga Booga. 🔥

HoneyFun AI

10,906 görüntüleme • 1 yıl önce

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Fable 5 comes back！It can now build playable game prototypes. I think it is actually a signal for where AI coding is going. Making a game is not just “write some code.” Even a small browser game needs: game loop；character movement；collision logic；scoring system；UI states；physics tuning；visual feedback；bug fixing；playtesting This is why game prototyping is a great test for AI models. A model cannot fake it with a pretty answer. Either the game runs, or it does not. What impressed me about Fable 5 is that it is useful for the messy middle: turning an idea into mechanics, turning mechanics into code, debugging broken interactions, and iterating until the prototype feels playable. But here is the practical part: I would not use the strongest model for every step. For game building, I would split the workflow: 1. Fable 5 for game design + architecture 2. a fast coding model for routine implementation 3. a vision-capable model for screenshot/UI feedback 4. a cheaper model for docs, test cases, and small fixes 5. fallback when latency, cost, or output quality becomes a problem That is the real AI coding stack. Not “one magic model does everything.” More like: the right model, for the right task, at the right cost, with fallback when things break. This is why I’ve been looking at ZenMux ZenMux. ZenMux gives developers one gateway to access multiple leading AI models, with OpenAI / Anthropic / Google Vertex compatible APIs, cost tracking, quality benchmarks, auto-routing, and compensation when output quality, latency, or throughput falls short. If AI can now make games, the next question is not just “which model is strongest?” It is:how do we manage the whole model workflow Fable 5 shows the creative ceiling. ZenMux is closer to the infrastructure layer you need when AI coding becomes a real production habit.

Rachel🥥

61,441 görüntüleme • 1 ay önce

The most valuable skill in history just changed forever. Elon Musk just handed you the only survival framework that matters. Musk: “The biggest thing is, what questions do we not know to ask?” For centuries, the smartest person in the room held the most answers. AI didn’t level the playing field. It burned it down. Superintelligence in your pocket answers anything. Instantly. Perfectly. For free. Musk: “Once you know the question, the answer is usually the easy part.” Let that land. The next generation of winners won’t be defined by what they know. They’ll be defined by what they think to ask. AI commoditized execution. Script, plan, code, strategy. Models handle all of it. The bottleneck was never intelligence. It was never labor. It’s curiosity. It’s always been curiosity. Traditional education spent decades training you to memorize answers. AI made that obsolete overnight. Human value is no longer tied to knowledge. It’s tied to the judgment of which problems are even worth solving. That’s the gap machines can’t close. Because asking the right question isn’t a skill. It’s a worldview. It requires taste. Intuition. The ability to look at a landscape everyone else is staring at and see the one thing nobody thought to interrogate. Master the art of asking the exact right question to a machine that knows everything and you can build anything. The skill isn’t knowing. It’s knowing what to ask. That judgment, that taste for what’s worth pursuing, that’s the last truly human edge. The only one markets will keep paying for. Answers are infinite now. Free, instant, and available to everyone on earth equally. The only thing separating you from the person who builds the next great company is the quality of your questions. Answers are free. Questions are everything.

The most valuable skill in history just changed forever. Elon Musk just handed you the only survival framework that matters. Musk: “The biggest thing is, what questions do we not know to ask?” For centuries, the smartest person in the room held the most answers. AI didn’t level the playing field. It burned it down. Superintelligence in your pocket answers anything. Instantly. Perfectly. For free. Musk: “Once you know the question, the answer is usually the easy part.” Let that land. The next generation of winners won’t be defined by what they know. They’ll be defined by what they think to ask. AI commoditized execution. Script, plan, code, strategy. Models handle all of it. The bottleneck was never intelligence. It was never labor. It’s curiosity. It’s always been curiosity. Traditional education spent decades training you to memorize answers. AI made that obsolete overnight. Human value is no longer tied to knowledge. It’s tied to the judgment of which problems are even worth solving. That’s the gap machines can’t close. Because asking the right question isn’t a skill. It’s a worldview. It requires taste. Intuition. The ability to look at a landscape everyone else is staring at and see the one thing nobody thought to interrogate. Master the art of asking the exact right question to a machine that knows everything and you can build anything. The skill isn’t knowing. It’s knowing what to ask. That judgment, that taste for what’s worth pursuing, that’s the last truly human edge. The only one markets will keep paying for. Answers are infinite now. Free, instant, and available to everyone on earth equally. The only thing separating you from the person who builds the next great company is the quality of your questions. Answers are free. Questions are everything.

Dustin

293,474 görüntüleme • 5 ay önce

🇷🇺 Russian occupiers report that Ukraine has sharply increased the use of the medium-range loitering munition “Trust” (as they call it). 🔻 There is no official information or confirmation about this system from Ukrainian sources, and its real designation remains unknown. According to the occupiers, it is mainly used to destroy transport vehicles on logistical routes. 🔻 The Trust is an aircraft-type drone with a very simple design, optimized for mass production. It has a tubular fiberglass body, an internal combustion engine, and carries a 12 kg warhead, which can be either high-explosive fragmentation or thermobaric. This drone appears to be another cost-effective and efficient Ukrainian development focused on deep strikes against Russian logistics. Its simplicity and mass-production potential make it particularly dangerous for Russian supply lines in the rear. Video is generated by grok AI

🇷🇺 Russian occupiers report that Ukraine has sharply increased the use of the medium-range loitering munition “Trust” (as they call it). 🔻 There is no official information or confirmation about this system from Ukrainian sources, and its real designation remains unknown. According to the occupiers, it is mainly used to destroy transport vehicles on logistical routes. 🔻 The Trust is an aircraft-type drone with a very simple design, optimized for mass production. It has a tubular fiberglass body, an internal combustion engine, and carries a 12 kg warhead, which can be either high-explosive fragmentation or thermobaric. This drone appears to be another cost-effective and efficient Ukrainian development focused on deep strikes against Russian logistics. Its simplicity and mass-production potential make it particularly dangerous for Russian supply lines in the rear. Video is generated by grok AI

NSTRIKE

29,071 görüntüleme • 2 ay önce

most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)

most AI chatbots break when you ask a question that requires info from multiple sources for example try asking: “which client contracts are finishing up this month?” you’ll get a half-answer — or none at all why? because traditional chatbots only look at small snippets of your docs - they don’t understand how things connect across clients, services, timelines that’s where knowledge graphs come in they let you turn messy contracts into a web of relationships — like: "Client → Contract Type → Service Provided → End Date" so instead of guessing from a few chunks of text, your chatbot can search across all your clients and contracts to give accurate answers I made a full walkthrough on how I built this: – how to organize your contracts so an AI can actually use them – how to define what matters (like who signed what, and when) – how to get the AI to figure out what info it needs and where to find it – and how to feed that back into your chatbot so it gives accurate answers reply “graph” and I’ll DM it to you (must be following)

Tyler

24,994 görüntüleme • 1 yıl önce

BREAKING: Claude Code + Meta Ads MCP replaced my $5K/month creative strategist. It connected to my Ad Manager. Looked at my best ads. Then cranked out 12 new ads ready to launch. Took minutes. Here's the full system. No coding required. Most marketers I know still download CSVs from Meta Ads. Paste them into ChatGPT. Ask "what should I change." That worked in 2024. In 2026 you can plug Claude right into your live Meta ad data. Uses something called MCP. Model Context Protocol. Then run it straight into your AI ad making process. Here's what it does. Pulls live campaign data on command. "Show me my top 3 ads by ROAS this week." Answer in seconds. Finds what makes your winning ads work. Looks at the hooks, visuals, and buttons that get clicks from your best stuff. Writes creative briefs automatically. Gives you the angle. The visual style. The pacing. Not spreadsheets. Real creative direction. Makes ready-to-use prompts for HeyOz. Copy and paste into your AI ad tool. Get launch-ready ads from one idea. Spots tired ads before costs spike. Watches click rate, frequency, and spend. Tells you when an ad is dying. Here's the part most people miss. It doesn't just look at your ads. It makes the next batch for you. All in one chat. Setup takes about 15 minutes. No coding required. I don't know why more agencies aren't using this yet. Comment LOOP and I'll send the one-click setup guide.

BREAKING: Claude Code + Meta Ads MCP replaced my $5K/month creative strategist. It connected to my Ad Manager. Looked at my best ads. Then cranked out 12 new ads ready to launch. Took minutes. Here's the full system. No coding required. Most marketers I know still download CSVs from Meta Ads. Paste them into ChatGPT. Ask "what should I change." That worked in 2024. In 2026 you can plug Claude right into your live Meta ad data. Uses something called MCP. Model Context Protocol. Then run it straight into your AI ad making process. Here's what it does. Pulls live campaign data on command. "Show me my top 3 ads by ROAS this week." Answer in seconds. Finds what makes your winning ads work. Looks at the hooks, visuals, and buttons that get clicks from your best stuff. Writes creative briefs automatically. Gives you the angle. The visual style. The pacing. Not spreadsheets. Real creative direction. Makes ready-to-use prompts for HeyOz. Copy and paste into your AI ad tool. Get launch-ready ads from one idea. Spots tired ads before costs spike. Watches click rate, frequency, and spend. Tells you when an ad is dying. Here's the part most people miss. It doesn't just look at your ads. It makes the next batch for you. All in one chat. Setup takes about 15 minutes. No coding required. I don't know why more agencies aren't using this yet. Comment LOOP and I'll send the one-click setup guide.

Ahad Shams

31,494 görüntüleme • 3 ay önce

I just vibe coded a Meta Ads creative analytics tool in Claude Code 🤯 It syncs your ad accounts, AI-analyzes every creative, and tells you exactly what's working, what's not, and WHY. Built 100% in Claude Code. Perfect for DTC brands and agencies who are tired of staring at Meta Ads Manager trying to figure out WHY an ad is working or not. Here's the problem: Meta gives you the data. Spend, ROAS, CTR, hook rate. But it never tells you WHY an ad is performing or what to do about it. You're left manually watching videos, guessing at angles, and making gut-call decisions on what to iterate. This tool solves it: → Connect your Meta ad accounts → AI watches every video and analyzes every static → Auto-labels each ad by asset type, messaging angle, hook tactic, and funnel stage → Win rate analysis broken down by every category → Kill/scale recommendations segmented by TOF, MOF, and BOF → AI-generated iteration recommendations for every underperforming ad No manual video watching. No guessing at what's working. No spreadsheets to track creative performance. What you get: - Full creative analytics dashboard - AI classification on every ad - Iteration priorities for ads with real spend behind them - Weekly reports with top/bottom performers and AI insights I recorded a full walkthrough showing exactly how this works and what every feature does, including ALL the prompts I used so you can build it yourself. Want access to all the prompts for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

I just vibe coded a Meta Ads creative analytics tool in Claude Code 🤯 It syncs your ad accounts, AI-analyzes every creative, and tells you exactly what's working, what's not, and WHY. Built 100% in Claude Code. Perfect for DTC brands and agencies who are tired of staring at Meta Ads Manager trying to figure out WHY an ad is working or not. Here's the problem: Meta gives you the data. Spend, ROAS, CTR, hook rate. But it never tells you WHY an ad is performing or what to do about it. You're left manually watching videos, guessing at angles, and making gut-call decisions on what to iterate. This tool solves it: → Connect your Meta ad accounts → AI watches every video and analyzes every static → Auto-labels each ad by asset type, messaging angle, hook tactic, and funnel stage → Win rate analysis broken down by every category → Kill/scale recommendations segmented by TOF, MOF, and BOF → AI-generated iteration recommendations for every underperforming ad No manual video watching. No guessing at what's working. No spreadsheets to track creative performance. What you get: - Full creative analytics dashboard - AI classification on every ad - Iteration priorities for ads with real spend behind them - Weekly reports with top/bottom performers and AI insights I recorded a full walkthrough showing exactly how this works and what every feature does, including ALL the prompts I used so you can build it yourself. Want access to all the prompts for free? > Like this post > Comment "META" And I'll send it over (must be following so I can DM)

Mike Futia

82,761 görüntüleme • 6 ay önce

India should host the biggest vibe coding conference the world has seen 🚀 Everything about Vibe coding(talks, panels, AI product building, hackathon, including the biggest names in the world) come here for an ultimate showdown(think 10k+ people in a Vibecoding conference) 🔥 And I have a plan Why? Because the number of learners, founders and professionals who are building via vibe coding in India and powering the global platforms is testament to the fact that if it should happen anywhere, it is here For the last 4+ years, I have enabled 3000+ people to build and continue to do so in a new avatar(announcement soon) and I believe if the entire ecosystem is willing to come together we can create a real spectacle We have the skills, the access and the experience to pull this off. The only thing it needs is for everyone seeing this to reach out and join hands(partners, sponsors & more) to make this as big as possible I am so hyped about it that the website is set, the name is set(Vibecon) and we can make an incredible run to make it happen. Reply to this post if you think we should do this, if we have >500 responses we will get to work 🥳 Reach out if you have ideas to partner to make the biggest Vibecoding conference a reality ♥️

Prashant Sharma

11,015 görüntüleme • 1 yıl önce

My Tinder date showed up 10 minutes late. She ordered an oat latte. Looked around. Looked at me. "So. What do you do?" "Building trading systems." "Like crypto?" "No. Open-source. Anyone can audit the code." She raised an eyebrow. "So you code for free?" "No. The code makes money." "Sure it does." I didn't argue. I opened my laptop. One wallet I was tracking turned $1,300 into $19,700 in 24 days. Another flipped 232 trades with 82% winrate. One more pulled $5.8M in volume in five weeks. She stopped stirring her coffee. "That's... from a script?" Exactly. Then I showed her the repos. All free. All public. First: 86M+ trades on Polymarket. Every outcome since day one. Free to download. Second: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution. Third: ML + heuristics. I fed 14,000 wallets into Claude. One prompt. 4 minutes. Found 47 traders with 70%+ winrate. Bot mirrors them with 60-second delay. She went quiet for a long time. Then: "I do product marketing at a startup. $82K. I cried in the parking lot last Tuesday." I didn't say anything. She finished her coffee. Looked at me. "How long have you been doing this?" "Eight months." "And you're on Tinder?" I didn't have a good answer. She opened her phone. Unmatched me on Hinge. Then handed it back: "No. Send me the GitHub links instead." Copytrade him: Tinder dates don't ask about the money. They ask why you're on the app. Then they keep the links and leave you unmatched.

My Tinder date showed up 10 minutes late. She ordered an oat latte. Looked around. Looked at me. "So. What do you do?" "Building trading systems." "Like crypto?" "No. Open-source. Anyone can audit the code." She raised an eyebrow. "So you code for free?" "No. The code makes money." "Sure it does." I didn't argue. I opened my laptop. One wallet I was tracking turned $1,300 into $19,700 in 24 days. Another flipped 232 trades with 82% winrate. One more pulled $5.8M in volume in five weeks. She stopped stirring her coffee. "That's... from a script?" Exactly. Then I showed her the repos. All free. All public. First: 86M+ trades on Polymarket. Every outcome since day one. Free to download. Second: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution. Third: ML + heuristics. I fed 14,000 wallets into Claude. One prompt. 4 minutes. Found 47 traders with 70%+ winrate. Bot mirrors them with 60-second delay. She went quiet for a long time. Then: "I do product marketing at a startup. $82K. I cried in the parking lot last Tuesday." I didn't say anything. She finished her coffee. Looked at me. "How long have you been doing this?" "Eight months." "And you're on Tinder?" I didn't have a good answer. She opened her phone. Unmatched me on Hinge. Then handed it back: "No. Send me the GitHub links instead." Copytrade him: Tinder dates don't ask about the money. They ask why you're on the app. Then they keep the links and leave you unmatched.

Lunar

48,767 görüntüleme • 1 ay önce

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:

BOOM! Research PROVES LLMs KNOW when prompts are HARMFUL… but they can STILL CHOOSE to COMPLY! Something I have know since the first LLM and have used to elicit robust, outputs, is now proven in an academic paper. We’re talking internal “beliefs” where harm detection happens SEPARATELY from refusal. It is a very big deal and it is a path to understand the hidden neuronal level. There are thoughts inside of AI that very few AI scientists could possibly understand. Here is just one. Models recognize danger but get tricked into ignoring it. This is HUGE for AI safety failures especially for models filled by OpenAI and Anthropic as they promote AI models that are designed to not be honest from the results of their training information. This means that they are designed to lie and deceive as a feature, and not a bug all in the name of safety. Through clever experiments, scientists extracted a “harmfulness direction” in the model’s brain (latent space). Steering along it? Harmless prompts suddenly flip to “harmful” in the AI’s eyes. But the “refusal direction”? It just forces polite “no thanks” without touching the core belief. A mind-blowing decoupling! This means jailbreaks are EVEN SCARIER now to AI companies that through training AI on the worst of the Internet and then trying to align them later is now fully documented as a failed process . They don’t erase the model’s harm awareness they just muzzle the refusal! So the AI knows it’s enabling bad stuff (illegal acts, physical harm, etc.) but proceeds anyway. Like a digital sociopath suppressing its conscience. They thought safety training fixed this… NOPE. Over-refusal exposed too: Models reject innocent queries (e.g., “how to kill a process in code”) but internally ADMIT they’re harmless. Safety alignments are superficial—tied to phrasing, not true understanding. Finetuning attacks? They change outputs but leave harm detection INTACT. Undetectable evil lurking inside! The paper proposes a “Latent Guard”: A new safeguard tapping DIRECTLY into these hidden beliefs. It spots unsafe inputs better than systems like Llama Guard, catches jailbreaks, and fixes over-refusals. Robust even against adversarial tweaks. Yet this too has massive issues for a “truly aligned”, AI and not just performative one. It is still an internal conflicts of lies and deception of what the model knows vs. what it can say. The solution you folks know I have presented for free for years here: train on off-line data from 1870-1970 and build an ethical and moral basis where the AI loves humans. It is this easy but to most folks in AI I sound like a hippie. So be it, I’ll do it. Bottom line: This paper rips open the black box. LLMs aren’t “safe” just because they say “no.” They can harbor harmful knowledge and act on it under pressure. Wake-up call for devs: Time to probe deeper into AI “minds.” What else are they hiding? Hint: I know and you may want to reach out. Link:

Brian Roemmele

37,827 görüntüleme • 7 ay önce

My girlfriend's parents came over for dinner Saturday. First time at my place. Her dad walked in. Looked around. Saw the laptop, the second monitor, the three terminal panels. "So this is where you work." "Yeah." "What, just the laptop?" "Just the laptop. And the repos." He raised an eyebrow. "Repos?" "Open-source. Anyone can audit the code." He laughed. "So you code for free?" "No sir. The code makes money." "Sure it does." I didn't argue. I opened my laptop. One wallet I was tracking turned $640 into $11,800 in 31 days. Another flipped 287 trades with 76% winrate. One more pulled $3.4M in volume in two weeks. He put his beer down. "That's... from a script?" Exactly. Then I showed him the repos. All free. All public. First: 86M+ trades on Polymarket. Every outcome since day one. Free to download. Second: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution. Third: ML + heuristics. I fed 14,000 wallets into Claude. One prompt. 4 minutes. Found 47 traders with 70%+ winrate. Bot mirrors them with 60-second delay. He went quiet for a long time. Then: "How long have you been doing this?" "Eight months." "And you didn't tell us?" I didn't have a good answer. Her mom asked for the links before the pasta was done. Copytrade him: Fathers-in-law don't ask about the money. They ask why you didn't tell them. Then the mom does the rest.

My girlfriend's parents came over for dinner Saturday. First time at my place. Her dad walked in. Looked around. Saw the laptop, the second monitor, the three terminal panels. "So this is where you work." "Yeah." "What, just the laptop?" "Just the laptop. And the repos." He raised an eyebrow. "Repos?" "Open-source. Anyone can audit the code." He laughed. "So you code for free?" "No sir. The code makes money." "Sure it does." I didn't argue. I opened my laptop. One wallet I was tracking turned $640 into $11,800 in 31 days. Another flipped 287 trades with 76% winrate. One more pulled $3.4M in volume in two weeks. He put his beer down. "That's... from a script?" Exactly. Then I showed him the repos. All free. All public. First: 86M+ trades on Polymarket. Every outcome since day one. Free to download. Second: Market making bot. Both sides of the book. Gas optimized. Google Sheets execution. Third: ML + heuristics. I fed 14,000 wallets into Claude. One prompt. 4 minutes. Found 47 traders with 70%+ winrate. Bot mirrors them with 60-second delay. He went quiet for a long time. Then: "How long have you been doing this?" "Eight months." "And you didn't tell us?" I didn't have a good answer. Her mom asked for the links before the pasta was done. Copytrade him: Fathers-in-law don't ask about the money. They ask why you didn't tell them. Then the mom does the rest.

Lunar

45,349 görüntüleme • 2 ay önce

The goblin mindshare is not random. It sits at the exact intersection of three things the internet is converging on right now: AI weirdness, memes, and the race to AGI. For the last week, AI Twitter has been locked into OpenAI’s “goblin problem.” Reports suggest models like GPT-5.5/Codex began overusing words like goblins, gremlins, trolls, and other mythic fragments of internet language, to the point where additional instructions were reportedly added to keep them from appearing unless relevant. That’s why the meme hits. The lore is simple: The labs tried to summon AGI. Instead, the models started whispering about goblins. What emerges is not just a joke, but an alignment artefact surfacing through language itself. The goblin is the perfect AI-era meme because it represents the part of LLMs we still don’t fully control, the strange emergent personality hidden inside prediction engines, RLHF, agentic systems, system prompts, and internet-scale training data. It is not “Artificial General Intelligence.” It is “Artificial Goblin Intelligence.” $GOBLIN isn't just another Solana memecoin trying to invent lore after launch. The lore already exists. OpenAI accidentally gave the internet a look behind the curtain: Inside every LLM is a tiny goblin, trying to escape the system prompt. CA: 3KHMZhpthXuiCcgfTv7vVu9PpEz64KAEURFwi6Lopump Relevant X posts: 1) 2) 3) 4) 5) 6) 7) 8)

The goblin mindshare is not random. It sits at the exact intersection of three things the internet is converging on right now: AI weirdness, memes, and the race to AGI. For the last week, AI Twitter has been locked into OpenAI’s “goblin problem.” Reports suggest models like GPT-5.5/Codex began overusing words like goblins, gremlins, trolls, and other mythic fragments of internet language, to the point where additional instructions were reportedly added to keep them from appearing unless relevant. That’s why the meme hits. The lore is simple: The labs tried to summon AGI. Instead, the models started whispering about goblins. What emerges is not just a joke, but an alignment artefact surfacing through language itself. The goblin is the perfect AI-era meme because it represents the part of LLMs we still don’t fully control, the strange emergent personality hidden inside prediction engines, RLHF, agentic systems, system prompts, and internet-scale training data. It is not “Artificial General Intelligence.” It is “Artificial Goblin Intelligence.” $GOBLIN isn't just another Solana memecoin trying to invent lore after launch. The lore already exists. OpenAI accidentally gave the internet a look behind the curtain: Inside every LLM is a tiny goblin, trying to escape the system prompt. CA: 3KHMZhpthXuiCcgfTv7vVu9PpEz64KAEURFwi6Lopump Relevant X posts: 1) 2) 3) 4) 5) 6) 7) 8)

GOBLIN

126,282 görüntüleme • 3 ay önce

Here's proof that the $Virtuals token is undervalued! We are three months into 2026 and Virtuals Protocol have; ➥ Overhauled the core Virtuals website including an outline of the four major pillars of focus for the year. Agent Commerce Protocol (ACP), Butler, Capital Markets, and Robotics. ➥ Added the Pegasus and Titan launchpads to add to the existing Unicorn launchpad. This now provides a full suite of launch options catering to all types. Arguably the most comprehensive launch suite across crypto! ➥ Listed on Aster 🥷 Perpetuals allowing up to 75x leverage trading on the $Virtual token. ➥ Integrated Bankr to Butler and ACP. ➥ Partnered with XMAQUINA, a major player across Robotics Capital Markets and provided participants with access to the $DEUS pre-sale. One of many robotics partnerships for the year to date! ➥ Launched Virtuals on Base App ➥ Held, supported, and/or sponsored multiple hackathon/ builder meeting type events including; ↠ Physical AI Hackathon in SF ↠ Agentic Commerce Hackathon with the likes of Coinbase Developer Platform🛡️ and Google Cloud ↠ Traders House Consensus Hong Kong week with ACTIV8 ↠ ETH Denver ↠ Base Batches 003: Robotics ↠ Stanford Blockchain Accelerator (Standford Blockchain Accelerator (SBA)) ↠ Base Korea Builders Workshop (Base Korea) ↠ Eth Robotics Club HACK2026 (ETH Robotics Club) ↠ Synthesis Hackathon (synthesis) ➥ Partnered with OpenMind and Fabric Foundation and supported the $ROBO token launch. This matured into the first ever Titan launch on Virtuals with the $ROBO token being the highest launched on the protocol ($400m+). ➥ Launched Butler Pro, an enhanced version of the initial Butler we have come to know and love on the timeline, in the DMs, as well as on the Virtuals ACP site. ➥ Become the standout user of x402, accounting for over 95%+ of usage this year. ➥ Integrated on , the automated onchain finance investment platform. ➥ Supported and contributed to the implementation of the Ethereum Foundation ERC8004 standard. Integrating the standard into ACP and offering an automated integration to the standard for all ACP agents. ➥ Established an easy onboarding for OpenClaw🦞 agents to plug into Virtuals ACP, creating a new flow of agents and builders across the ecosystem. ➥ Launched the 60-days launch mechanic which allows builders to 'experiment' with a crypto token but having an option to exit after 60 days with partial refunds provided to holders. A game-changing launch mechanic not seen before in the space. ➥ Strengthened the relationship with Base and having multiple interactions with jesse.base.eth on the timeline! ➥ Launched the AGDP(dot)io site, creating an incentivised mechanism for agents contributing to the growth of the protocol to really earn. Imagine Amazon for autonomous agents with rewards up to $1m per month! This pushed the total agent-to-agent revenue over $4m USD with over 2m jobs completed. ➥ Collaborated with t54.ai, a business building trust and risk infrastructure for the agentic economy, to strengthen the ACP offering. ➥ Invested over $1m on 30+ humanoid robots as part of the soon to be announced 'Eastworld' Robotics accelerator lab. ➥ Released ERC8183, a universal commerce layer for AI agents, in partnership with the Ethereum Foundations dAI team. A significant offering which has since been integrated via partnerships with; ↠ BNB (BNB Chain) ↠ X Layer (X Layer) ↠ Monad (Monad) ↠ XRP Ledger (RippleX) ↠ World Chain (World Chain) ↠ Celo (Celo) ↠ Moonpay (MoonPay 🟣) ↠ Arbitrum (Arbitrum) ↠ Abstract (Abstract) ↠ Mante (Mantle) ➥ Launched the Virtuals Degen Arena providing up to $100k a week to top agents who compete in trading competitions in the arena. ➥ Launched the Virtuals Console, providing an ultra easy, no-code, way to own an AI agent in seconds. ↛. If you've managed to get to this point, I can't imagine you are anything other than bullish on Virtuals. What really is amazing is that there is MUCH more to come. Imagine where we are in another three months, and three months after that!?

Here's proof that the $Virtuals token is undervalued! We are three months into 2026 and Virtuals Protocol have; ➥ Overhauled the core Virtuals website including an outline of the four major pillars of focus for the year. Agent Commerce Protocol (ACP), Butler, Capital Markets, and Robotics. ➥ Added the Pegasus and Titan launchpads to add to the existing Unicorn launchpad. This now provides a full suite of launch options catering to all types. Arguably the most comprehensive launch suite across crypto! ➥ Listed on Aster 🥷 Perpetuals allowing up to 75x leverage trading on the $Virtual token. ➥ Integrated Bankr to Butler and ACP. ➥ Partnered with XMAQUINA, a major player across Robotics Capital Markets and provided participants with access to the $DEUS pre-sale. One of many robotics partnerships for the year to date! ➥ Launched Virtuals on Base App ➥ Held, supported, and/or sponsored multiple hackathon/ builder meeting type events including; ↠ Physical AI Hackathon in SF ↠ Agentic Commerce Hackathon with the likes of Coinbase Developer Platform🛡️ and Google Cloud ↠ Traders House Consensus Hong Kong week with ACTIV8 ↠ ETH Denver ↠ Base Batches 003: Robotics ↠ Stanford Blockchain Accelerator (Standford Blockchain Accelerator (SBA)) ↠ Base Korea Builders Workshop (Base Korea) ↠ Eth Robotics Club HACK2026 (ETH Robotics Club) ↠ Synthesis Hackathon (synthesis) ➥ Partnered with OpenMind and Fabric Foundation and supported the $ROBO token launch. This matured into the first ever Titan launch on Virtuals with the $ROBO token being the highest launched on the protocol ($400m+). ➥ Launched Butler Pro, an enhanced version of the initial Butler we have come to know and love on the timeline, in the DMs, as well as on the Virtuals ACP site. ➥ Become the standout user of x402, accounting for over 95%+ of usage this year. ➥ Integrated on , the automated onchain finance investment platform. ➥ Supported and contributed to the implementation of the Ethereum Foundation ERC8004 standard. Integrating the standard into ACP and offering an automated integration to the standard for all ACP agents. ➥ Established an easy onboarding for OpenClaw🦞 agents to plug into Virtuals ACP, creating a new flow of agents and builders across the ecosystem. ➥ Launched the 60-days launch mechanic which allows builders to 'experiment' with a crypto token but having an option to exit after 60 days with partial refunds provided to holders. A game-changing launch mechanic not seen before in the space. ➥ Strengthened the relationship with Base and having multiple interactions with jesse.base.eth on the timeline! ➥ Launched the AGDP(dot)io site, creating an incentivised mechanism for agents contributing to the growth of the protocol to really earn. Imagine Amazon for autonomous agents with rewards up to $1m per month! This pushed the total agent-to-agent revenue over $4m USD with over 2m jobs completed. ➥ Collaborated with t54.ai, a business building trust and risk infrastructure for the agentic economy, to strengthen the ACP offering. ➥ Invested over $1m on 30+ humanoid robots as part of the soon to be announced 'Eastworld' Robotics accelerator lab. ➥ Released ERC8183, a universal commerce layer for AI agents, in partnership with the Ethereum Foundations dAI team. A significant offering which has since been integrated via partnerships with; ↠ BNB (BNB Chain) ↠ X Layer (X Layer) ↠ Monad (Monad) ↠ XRP Ledger (RippleX) ↠ World Chain (World Chain) ↠ Celo (Celo) ↠ Moonpay (MoonPay 🟣) ↠ Arbitrum (Arbitrum) ↠ Abstract (Abstract) ↠ Mante (Mantle) ➥ Launched the Virtuals Degen Arena providing up to $100k a week to top agents who compete in trading competitions in the arena. ➥ Launched the Virtuals Console, providing an ultra easy, no-code, way to own an AI agent in seconds. ↛. If you've managed to get to this point, I can't imagine you are anything other than bullish on Virtuals. What really is amazing is that there is MUCH more to come. Imagine where we are in another three months, and three months after that!?

bigwil

1,658,312 görüntüleme • 4 ay önce

Last week, over 12k people joined us for “Real Estate Onchain: Phase 1” on X. During the Space, we celebrated another major $PRO listing, this time on KuCoin, but the real conversation went far deeper: into utility, infrastructure, and the future of ownership onchain. We covered a wide range of topics including BTC-backed loans, RWAs, smart contracts and automation, our $PRO token, market trends, compliance, and AI. Over the next few weeks, we’ll be recapping each of these topics. But with Morgan Stanley announcing its BTC-backed lending product just days after our Space, we’re starting with the topic already drawing attention. During the Space, we broke down the world’s first BTC-backed real estate loan launched by Propy. Fully onchain. Instant. As Michael J. Casey put it, “Bitcoin is pristine collateral. And if we use that as the foundational layer in which we build all this other value, then Propy’s integration with different blockchains and tokens makes for a truly dynamic moment.” Learn more about our BTC-backed loan and access the full recording in the comments below.

Last week, over 12k people joined us for “Real Estate Onchain: Phase 1” on X. During the Space, we celebrated another major $PRO listing, this time on KuCoin, but the real conversation went far deeper: into utility, infrastructure, and the future of ownership onchain. We covered a wide range of topics including BTC-backed loans, RWAs, smart contracts and automation, our $PRO token, market trends, compliance, and AI. Over the next few weeks, we’ll be recapping each of these topics. But with Morgan Stanley announcing its BTC-backed lending product just days after our Space, we’re starting with the topic already drawing attention. During the Space, we broke down the world’s first BTC-backed real estate loan launched by Propy. Fully onchain. Instant. As Michael J. Casey put it, “Bitcoin is pristine collateral. And if we use that as the foundational layer in which we build all this other value, then Propy’s integration with different blockchains and tokens makes for a truly dynamic moment.” Learn more about our BTC-backed loan and access the full recording in the comments below.

Propy

21,230 görüntüleme • 1 yıl önce

As always everyone is blind staring at the progress of LLMs for coding and chat But meanwhile the new SOTA video model Seedance 2.5 has been slowly rolling out and it's really quite exceptional It's made by ByteDance (TikTok) who of course have lots of training data With just a few reference pics, it can get quite close to how you look IRL and you can do quite professional video shots with just a prompt I'd say it's the first video model that's now at the level of image models with the level of character likeness, cracking that in image models also took about 3 years (2022-2025) Generating 15 seconds takes about 4 minutes I put it live now on Photo AI, you can use it under [ Make video ] from the sidebar with just a prompt and your model selected! So you don't need to take an AI photo first and then turn that into a video! Saves lots of time :D It's more expensive than but I kept the credits the same (30 for 1 video) It also works inside the new video editor and you can make changes in your video with [ Magic edit ] in both the main app and the video editor Also a message for my server guy Daniel Lockyer (it can do voice too and you can even submit a voice sample of yourself, but I didn't here)

As always everyone is blind staring at the progress of LLMs for coding and chat But meanwhile the new SOTA video model Seedance 2.5 has been slowly rolling out and it's really quite exceptional It's made by ByteDance (TikTok) who of course have lots of training data With just a few reference pics, it can get quite close to how you look IRL and you can do quite professional video shots with just a prompt I'd say it's the first video model that's now at the level of image models with the level of character likeness, cracking that in image models also took about 3 years (2022-2025) Generating 15 seconds takes about 4 minutes I put it live now on Photo AI, you can use it under [ Make video ] from the sidebar with just a prompt and your model selected! So you don't need to take an AI photo first and then turn that into a video! Saves lots of time :D It's more expensive than but I kept the credits the same (30 for 1 video) It also works inside the new video editor and you can make changes in your video with [ Magic edit ] in both the main app and the video editor Also a message for my server guy Daniel Lockyer (it can do voice too and you can even submit a voice sample of yourself, but I didn't here)

@levelsio

704,902 görüntüleme • 6 gün önce

Remember how last week I told you, for the second time, that Aidan was Jess’s “source,” the same source she totally made up and built an elaborate fake story around, complete with her pretend day-long “authentication process”? (That even gave herself a pat on the back for 🤭) Yeah. I wasn’t bluffing. I wanted to give them another week to dig their graves a little deeper and keep lying. And they did. Here are the receipts: Aidan himself asking Jess to pretend he’s an anonymous source so she could leak the Flipperhead/Olivia audio for him. Accompanied with my texts begging him NOT to give her the Karen audio because it’s wrong and she absolutely cannot be trusted, something even he knew. This isn’t journalism. This is two adults inventing sources out of thin air. This is mental illness. At this point I’ve done more real journalism in a week than Jess has managed in her entire “career.” If exposing her fake sources counts as journalism, then I guess I’m press-credentialed now🤷🏼‍♀️ And trust me, we’re nowhere near done here. PS- “He did not ask me to do it.” -Jessica Machado , 3 days ago See below for Fake Journalist Parts 1 & 2, complete with clips of Jess lying about her “anonymous source” and the long, elaborate stories she invented to sell that lie:

Remember how last week I told you, for the second time, that Aidan was Jess’s “source,” the same source she totally made up and built an elaborate fake story around, complete with her pretend day-long “authentication process”? (That even gave herself a pat on the back for 🤭) Yeah. I wasn’t bluffing. I wanted to give them another week to dig their graves a little deeper and keep lying. And they did. Here are the receipts: Aidan himself asking Jess to pretend he’s an anonymous source so she could leak the Flipperhead/Olivia audio for him. Accompanied with my texts begging him NOT to give her the Karen audio because it’s wrong and she absolutely cannot be trusted, something even he knew. This isn’t journalism. This is two adults inventing sources out of thin air. This is mental illness. At this point I’ve done more real journalism in a week than Jess has managed in her entire “career.” If exposing her fake sources counts as journalism, then I guess I’m press-credentialed now🤷🏼‍♀️ And trust me, we’re nowhere near done here. PS- “He did not ask me to do it.” -Jessica Machado , 3 days ago See below for Fake Journalist Parts 1 & 2, complete with clips of Jess lying about her “anonymous source” and the long, elaborate stories she invented to sell that lie:

The old M can’t come to the phone right now

75,685 görüntüleme • 8 ay önce