Nick Venturi

I’ve closed $3M+ in revenue from LinkedIn. If I had to start from $0 today and hit my first $1K in 30 days.. I’d run this exact playbook: 1. Define your ICP and generate a targeting query → Go to Claude or GPT → Input your business + ideal customer → Ask it to generate a precise ICP search query → Use Apollo or Sales Navigator → Paste that exact query → Build a clean, relevant lead list 2. Export your leads to csv, excel, claude whatever 3. Create a high-converting “blueprint” → Use Claude to generate a 8–10 page PDF that includes: → Your story → The problem → Your solution → Real outcomes 4. Send this message manually or using Sendio AI (simple, direct, effective): "Hey {name}, we documented exactly how we {result your ICP wants}. Happy to send you the blueprint to see how this could work for {business}." 5. When they reply positively → Send the PDF (or automate it with sendio) → Don’t sell yet → Let the content do the work If you do this consistently, here’s what happens: → 10x more replies (because you’re not pitching) → Prospects actually consume your content → 5x more meetings with qualified leads → First $1K MRR within 30 days That’s it. No tricks. No “growth hacks.” Just: Good targeting Good messaging Good content A system you can scale Repeat daily for 2 hours For 30 days. Or automate it using Sendio AI This is the exact system we are using on sendio to sell sendio. Enjoy.

@Neuron_404 writing prompts always felt like begging a computer to do its job anyway

THE CREATOR OF CLAUDE CODE NO LONGER WRITES PROMPTS AT ALL - HE JUST KEEPS LOOPS RUNNING. Prompt engineering is fading, and loop engineering is taking its place. A loop is a tiny program that runs on a schedule, but now with agent intelligence built in. You don't give it steps, you give it the outcome you want. Here is how it works: > a loop is a scheduled program with agent intelligence > you set the outcome, not the steps > it pulls from your CRM, transcripts and email > it acts within guardrails so it can't go rogue > loops run other loops - whole agent teams while you sleep Everyone still typing prompt after prompt by hand is about to feel it. Prompts don't disappear - they move inside the loops. That is exactly where the smartest builders say things are heading. Follow me so you don't miss out on trends in the world of AI.

@vibemarketersHQ now i can speak without opening my mouth

hermes agent + zapier mcp + replicate = a full content pipeline no camera. no mic. no script. still a finished video, published, in your own voice. this workflow is packaged as a free skill. give hermes a topic and the skill takes it from there: - writes the script, generates the video through Replicate, adds voiceover + music through Zapier MCP (ElevenLabs, MusicGPT) - assembles it, adds captions, and publishes it through Buffer (Zapier MCP), all inside one conversation use this workflow and setup for: - faceless video production at scale - repurposing one idea into multiple videos - agencies producing on-brand video for multiple clients - testing creative angles faster link with the skill in the comments:

@Durektor97 we are officially out of excuses for ugly websites

Claude Fable 5 builds a cinematic website from scratch and here is what it looks like What happens when you give Claude Fable 5 real creative tools instead of just prompts Full workflow Claude Fable 5 plus Higgsfield MCP from scratch to a cinematic scroll driven product website Higgsfield generates media directly from the chat video with Seedance 2.0 images with GPT Image 2 and Fable 5 uses all of it to build the final site Frontend built with GSAP ScrollTrigger Lenis and Three.js and the main detail is that the background video is controlled by scroll position frame by frame scroll down the video moves forward scroll back up it reverses Fable 5 as the brain of the workflow Higgsfield as its creative hands

@thegreatest_sv im deleting the pool ladder first thing

SOMEONE BUILT THE SIMS - BUT INSTEAD OF PEOPLE, YOU MANAGE AI AGENTS. A little world of workers who don't sleep and don't quit, each one an AI agent doing its own job. You watch them think, pass off tasks, and work together in real time - a control room for a team that never clocks out. He calls it SAMS, and the real point is underneath the fun: Everyone's built the basic "AI dashboard" - a chat box, one button, one trick. That's the toy. The real prize is an agentic OS: many agents, orchestrated, in parallel. Barely anyone gets it yet - which is why the ones who do haven't slept in 2026. He made that invisible shift something you can actually watch. Want to build in this space instead of just watching it? Here are the Claude repositories driving the whole agent movement

@0xClodex welp there goes my evening and i am totally fine with it

Andrej Karpathy dropped a 2-hour computer vision masterclass - go watch it before you scroll past: - 00:00 – Automated Image Captioning (w/ Fei-Fei Li, Stanford): a CNN reads the image, an RNN writes the caption - 29:00 – Deep Learning for Computer Vision: why images are 3D arrays and how ConvNets exploit that structure - 1:50:27 – the fast version: 110 slides, heavy on recurrent nets, ending on the captioning results that stunned the room the guy who now leads pretraining at Anthropic teaching the foundation of how machines see. this 2-hour watch could replace half the computer vision courses on the internet. Watch it today ↓

@MilksandMatcha @OfficialLoganK most people just want things to work without reading a github readme

I had to ask @OfficialLoganK whether he actually bought the Mac mini 😅 He did, and his take is a useful reminder: technical possibility isn’t the same as a great product. The next big opportunity in local AI may be making it click for normal people.

@s4yonnara we are just building inception at this point

Karpathy’s autoresearch loop just got 5x better. Not from a bigger model. From two researchers who wrapped it in a second loop. The twist: nothing about the model changed. They stacked one loop on top of another and let the top one rewrite the bottom one. here’s the full method, step by step: step 1 → Karpathy’s loop stalls. The LLM keeps proposing the same edits and snaps back to its own priors. step 2 → they add an outer loop that reads the inner loop’s code and traces and pinpoints where it’s stuck. step 3 → the outer loop writes brand new search logic as Python and injects it into the running loop live. Same model, 5x better. how to copy this into your own agents: step 4 → spin up a second agent whose only job is reading the first agent’s logs and finding the dead end. step 5 → let it rewrite the machinery, the workflow, the skill, the prompt, not just retry the task. step 6 → auto revert every rewrite that fails, so one bad change never takes down your pipeline. the result: 5x over the plain loop, same LLM underneath. The win is the architecture, not a smarter brain. This 16 page paper is what comes after the Karpathy loop. Read it now. The full build workflow is in the article below ↓

@monokern i argue with my past self constantly

your best advisor isn't Claude it's you from eight months ago, still writing in your vault problem with every second brain system is that it finds what fits together. It surfaces patterns, links related notes, catches duplicates what it never does is argue with you > your 2023 self reached one conclusion > your self from six months ago reached the opposite > both are sitting in the vault > neither has ever spoken to the other this system fixes that. Claude runs four passes against your notes every six hours: > steelmans the strongest counterargument to your ideas > surfaces contradictions between notes you wrote in different contexts > cross-pollinates concepts from unrelated parts of your vault > runs your ghost self - you from six months ago, debating you from today DO layer finds what fits. CONTRA layer finds what doesn't Catching one contradiction that stops a bad decision pays for a year of the system in one afternoon full breakdown in the article below

@ScottyBeamIO we are just coding in english now

YOU CAN NOW BUILD ENTIRE APPS WITH HY 3.0 – HERE'S WHAT IT BUILT FOR A FITNESS APP IN ONE SESSION Prompt: "Act as a senior full-stack architect and product manager. Design and generate the project plan and core code for a sports/fitness mini-app. The app should include: personalized workout plan generation, exercise tracking with reps/sets/weight logging, progress analytics with charts, and a social feed where users can share workouts and follow friends." Here's what came back – in one session, no back-and-forth: → Full architecture – data models for users, workouts, exercises, and progress logs, all connected and coherent, not disjointed snippets → Real feature depth – form validation for workout logging, chart components for progress tracking, a feed/follow system for social features, modular code split across proper files → Autonomous task planning – it sequenced the build itself: directory structure → data models → page-by-page implementation, without being told the order The part that actually matters: watch how it plans the task on its own. Directory structure first. Then user/workout data models. Then the actual pages – onboarding, workout logger, progress dashboard, social feed – built in that exact order, without being told the sequence. That's autonomous long-horizon planning, not autocomplete. Not a single function generated in isolation. An entire fitness app, broken down and built the way a real engineer would actually sequence it. This is what "long-horizon execution" looks like when you see it in action, not just read about it in a benchmark. openrouter.ai/tencent/hy3:fr… #Hy3 #Hunyuan #TencentAI #AIAgents

@solomonneas managing three different config shapes for the same tools would drive me insane

today's special: the review gate brigade keeps one mcp catalog, but every harness still owns a different config shape. claude code gets .mcp.json. codex gets .codex/config.toml. vscode gets servers plus inputs. `brigade mcp plan` reads the catalog and previews the merge. nothing writes from doctor, brief, or the plan itself. the write requires `brigade mcp sync --write`. the merge does more than print a diff. it compares the canonical server, the last value brigade wrote, and the live file. a server you added by hand is foreign, so it skips it. if you edit a brigade-owned server later, that entry becomes conflicted and stays untouched unless you pass --force. stale entries update. dropped entries stay put unless you ask for --prune. that ownership boundary is helpful when several tools manage the same config fleet. each one can write its part without silently replacing another's work.

@iamtyfrankel as long as it saves me from commenting agree on every post

Leon joined the Lab Took it serious Joined our weekly 1-hr live calls Went through ALL the video training (13+ hours) in 2 weeks Just booked 2 calls in 47 msgs 1 with a $200M exited founder “This shit is crazy good” Yep Leon’s WELL on his way to booking 5-10+ calls/week Goal for Lab members is to book a call every 10 DMs… so from those same 47 msgs Leon sent he should book 4-5 calls once he masters the frameworks Lab’s price going up to $397/mo Jul 30 So get in now

@Argona0x making vcs pitch a bot to get millions is top tier

Ben Broca raised $30m at a $250m valuation with zero employees he made the vcs pitch his ai agent instead of him the company, Polsia, builds & runs whole companies on its own he's ex employee #2 at travis kalanick's cloud kitchens, now going for the first one-person billion-dollar company 18 minutes filmed inside it i mapped every part worth stealing: 0:50 - polsia's mission: an ai that builds any business for you 1:16 - the goal: the first one-person billion-dollar company 3:30 - boost: handing the agent the keys to run for hours 4:24 - the receipt: $7m a year, built in about a month 12:36 - the new math: agents only cost you while they work 17:36 - ben's one rule: stay solo till it works, then let ai take the hires 18 minutes here is the playbook everyone's about to copy watch it, then read below ↓

@zuess05 i'd pay two bucks any day just to avoid the anxiety of canceling a subscription

NO WAY THIS ACTUALLY WORKED 🤯 Somebody just paid $1.99 to export one single screen recording instead of buying a subscription. 💰 First pay-as-you-go sale ⚡️ +$1.99 added to the bank 🎯 Proof micro-transactions aren't dead Seriously, stop forcing people into a monthly subscription for a tool they only need to use once. Give them a $2 escape hatch and watch what happens. 🚀

@_ka1eab @theo always down for a cheaper upgrade

@theo btw just started working with 5.6 right after i saw this and it's not only good but cheaper than 5.5 🤯

Over the last month, I burned over $200k in tokens with gpt-5.6-sol. I built a lot. Instead of just reacting to the benchmarks, news, tweets, etc, I went a different route. This video is an overview of all the cool shit I built using the new model. Proper review coming soon™

@theo that api bill is terrifying but at least you actually built something

@DimaHolovatyi nobody actually likes those waiting rooms

He Didn't Build A Hospital... He Built An AI Health Scanner. Most health checkups still mean blood tests, multiple appointments, and days of waiting for results. One developer thought there had to be a better way, so he built an AI-powered health scanner instead. A person simply steps onto the scanner while an AI Box running multiple AI agents analyzes data from dozens of sensors in real time. Within minutes, it generates a detailed health report, highlights potential risks, and recommends what should be examined further. The craziest part? He didn't build the AI in the cloud. He built it on one local AI Box that coordinated every sensor and every AI agent from a single system. After demonstrating the prototype, several medical technology companies wanted to evaluate the system, and one offered over $100,000 for an early unit to begin testing it in real-world healthcare environments. The future isn't replacing doctors. It's giving them better information before the appointment even begins. Would you trust an AI-powered health scanner as the first step of your annual checkup? Let me know in the comments. Follow @DimaHolovatyi for more AI stories before everyone else catches on.

@Lummox_eth showing up with the solution beats sending a pdf every time

A 23-year-old opened Google Maps, picked 40 businesses with no website, and stopped applying for jobs before lunch. 40 leads. 20-minute demos. $150 websites. 5-minute templates. 0 CVs. He did not send a pitch. He built a landing page first, rewrote the headline, added 3 buttons, and sent the owner a link that looked like the job was already half done. By the 20th deal, the work changed shape. The bakery, the dentist, and the gym all needed different photos, but the same skeleton kept selling. That is the quiet part of the AI economy. It does not replace a worker with a robot; it lets one fast operator look like a small agency. The people waiting for permission are still polishing resumes. The people who found the loop are shipping demos before anyone asks.

@ashen_one @FD_XYZ @IxsFinance my bot is richer than me

THIS NEW MCP GIVES YOUR AGENT A WALLET THAT PUTS YOUR IDLE CAPITAL TO WORK you can deposit usdc into an agent wallet from @FD_XYZ, and let your agent earn yield on idle capital through tokenized real-world assets, powered by @IxsFinance like actual real life big time stock credit bros, but your agent handles it for you, which is insane lol crypto 🤝 wallstreet 🤝 ai

@mardehaym that confidently wrong junior developer description is too accurate for comfort

Brendan O'Leary from Kilo Code just gave a free 27-minute breakdown on agentic engineering. The whole thing is worth watching. Here are the 6 timestamps you need: 2:54 - Your AI agent is a "confidently wrong junior developer" 5:02 - Why more context makes models dumber past 50% full 9:41 - The Comic Sans intern story (and why it's your fault, not the AI's) 11:54 - The research-plan-implement loop that fixes everything 13:31 - "A bad line of research = hundreds of lines of bad code" 17:22 - "AI can't replace thinking. It can only amplify the thinking you've done." Watch it, then read the guide on how to make a company AI-native below.

@beamnxw finally an llm you can actually throw at someone in self defense

FORGET THE CLOUD: THIS GUY INSANELY CRAMMED LOCAL LLMs INTO A CUSTOM 3D-PRINTED CYBERDECK Everyone relies on massive data centers and steep corporate APIs just to run basic prompts. But this guy went total rogue hardware hacker. He built a literal standalone AI Cyberdeck right on his workbench to test local weights models completely off grid Honestly, watching the assembly is incredibly satisfying. He takes an Arduino GIGA R1 board, mounts it inside a sleek 3D-printed frame, and wires up a tiny transparent OLED status screen next to a heavy-duty push button. Then he flips it over to install a massive display shield on the front, right above a mini integrated Bluetooth keyboard The coolest part? When he presses that button, the hardware boots into a local command prompt running Qwen 2.5 on-device. The tiny board just chugs along, answering technical programming queries directly on the screen with zero cloud overhead and absolutely zero latency tax It is an absolute micro-powerhouse for localized engineering + it is a massive proof of concept for private, portable AI, meaning you can keep your data completely locked to your own desktop environment. Because why let a tech company meter your intelligence when you can just build the machine yourself