Blinkz

Most agency owners are exhausted, and it has very little to do with the workload. It is the quiet pressure to prove yourself. Prove you are smart enough. Prove you can build anything. Prove you are worth it before the client has even told you what they need. So you perform. You walk into every call and list everything you can do. You give away your best thinking for free and hope it earns the deal. You take on every wishlist that lands in your inbox, because saying no feels like losing. We did all of it, for a long time. Then someone we trust stopped us mid-pitch and asked a question that broke the whole pattern open: "When did you ask me what my problems were?" We had spent years answering a question nobody had asked. Clients were never hiring us for the length of our capabilities. They were hiring someone who understood them. They wanted to feel like a person finally got it. The day we stopped selling what we could build and started asking what was actually broken, the work got lighter. The pressure to impress fell away. The right clients leaned in. The wrong ones walked, and that turned out to be a gift too. So if you run an agency right now, hear this. You do not have to be the most impressive person in the room. You have to be the most useful. You do not have to say yes to every request. You have to find the one thing that is quietly costing them everything, and solve that. Charge for the thinking, because the thinking is the work. Sell yourself, not your tools, because tools get replaced and people get trusted. The world has more than enough vendors. It is short on people who care enough to ask the right question first. That is the kind of work we are trying to do, and the kind we want to help other operators do too. And if this is exactly where you are standing right now, you are not behind. You are right on time. Follow Blinkz for more honest notes on building and selling, and come find us when you are ready.

We've stopped being surprised by what we find in audits. It's the same six problems every single time, and the industry doesn't matter. 1. You're paying for software nobody uses. The subscriptions renew on their own, the seats sit empty, and half the tools were set up by someone who left the company a year ago. 2. There's a pilot from eight months ago that is still a pilot. It passed the demo, everyone nodded, and then nobody ever made it the default way of working. 3. The entire operation lives in one person's head. They can't take a real vacation, you can't hire around them, and one absence is enough to stall everything. 4. Your most important workflow is locked inside a single vendor. There's no export and no plan B, and the cost of leaving climbs every month you stay. 5. Automation saved hours that nobody can actually find. The time got freed up, and then it quietly refilled with busywork. 6. Twelve people do the same task twelve different ways. Nobody can say which way is right, because nobody ever decided. Here's the part that surprises owners the most. None of these are AI problems. They're operations problems. If you point AI at a broken process, all you get is the same mess moving faster. That's why we run the boring audit before we touch anything exciting. Operations & Systems first. AI second Which one of the six is costing you the most right now? Our money's on number 3.

Most of the businesses that come to us are convinced they have an AI problem. Almost none of them actually do. What they have is an operations problem wearing an AI costume. The tools were never the real issue - the thing sitting underneath the tools is. We've run enough audits now that the pattern has stopped surprising us. Before we recommend automating anything, we sit down and map how the business actually runs: who does what, where the work gets stuck, and what only happens because one specific person remembers to do it. And almost every single time, the same six things show up. There's the software they're still paying for that nobody has opened in months. There's the pilot that went perfectly in the demo and then never became the way anyone actually works. There's the business that quietly runs on one or two people's memory, where nothing is written down and a single vacation can stall the whole thing. There's the critical workflow locked inside one vendor with no real way out. There's the time that automation "freed up" and then quietly refilled with busywork. And there's the same task being done twelve slightly different ways, because the process was never actually built in the first place. None of those get solved by buying more AI. Layered on top of a mess, AI mostly just helps the mess move faster. The reason this keeps happening is simple. Buying a tool feels like progress, and fixing how you operate feels like work. So people reach for the tool and quietly hope the system sorts itself out later. It never does. You end up with a pile of half-finished builds and a longer list of subscriptions, while the actual business runs exactly the way it did before. The sequence is the entire game. You map the operation first. You fix what's broken. And then you bring in AI for the specific places where it genuinely earns its keep. Operations first, AI second - in that order, every time. That's why our work starts with an audit instead of a build. The most valuable thing we can hand a business usually isn't a model. It's an honest map of where the time and money are leaking, before a single thing gets automated. We put together a slide deck that walks through all six of these patterns and why the operation always comes before the AI. It's worth a swipe if you've ever felt like you're buying tools faster than they're actually paying off. And if you want the checklist we run during a real audit, comment AUDIT and we'll send it over. #ai #business #operations #systems #audit

Google just launched the most ambitious AI system they’ve ever built. Today at I/O 2026, they introduced Gemini Omni. The idea behind it is simple: create anything from anything. Until now, AI tools have mostly felt separated. You used one model for chat, another for video, another for images, and another for editing. You opened a different app for every kind of task, and AI never quite felt like one thing. Gemini Omni stitches all of that together into one system. You talk to it once, and it can search, think, generate, edit, animate, redesign scenes, change environments, and create characters in one continuous flow. But the video part is just the beginning. What Google is really building is bigger than an AI video tool. Demis Hassabis described Omni as “a world model AI that can understand and simulate the world,” and called it a step toward AGI. The longer arc is one system that understands text, images, video, voice, code, physics, space, time, movement, and context together. Instead of switching between different apps and models, you describe an idea and AI turns it into whatever you need. That could be a movie scene, an app, a game, a robot training environment, a simulation, or eventually an entire interactive world. The first model in the line is Gemini Omni Flash, rolling out today inside the Gemini app, Google Flow, and YouTube Shorts. It is built on the foundations of Veo, Nano Banana, and Genie, with conversational editing and multi-turn consistency baked in. This is the moment AI stops feeling like a collection of tools and starts feeling like one continuous system. Follow Blinkz for more news and updates on what’s actually happening in AI :) #ai #google #gemini #siliconvalley

8 more open source repos worth knowing if you're building with AI right now. The next wave after the first briefing. → caveman (JuliusBrussee/caveman, 61.8k stars) Cuts Claude Code token costs by 65% by rewriting your prompts in caveman speak. Drop-in skill, no config. Same outputs, half the cost. → mempalace (MemPalace/mempalace, 52.4k stars) The best-benchmarked open-source AI memory system. 96.6% recall on LongMemEval with no LLM required. Local-first, verbatim storage, fully free. → career-ops (santifer/career-ops, 45.6k stars) A full job-search system built on Claude Code skills. 14 modes covering drafting, applying, interview prep, and follow-ups. Real dashboard, PDF generation, batch processing. → obscura (h4ckf0r0day/obscura, 13.3k stars) Headless browser built from scratch for AI agents. Stealth-scrapes where Playwright and Puppeteer get blocked. Agent-native APIs for navigating, extracting, and clicking through flows. → OpenMythos (kyegomez/OpenMythos, 13.2k stars) A theoretical reconstruction of how Claude actually works inside. Looped transformers, attention patterns, scaling assumptions. All documented from publicly available research. → khazix-skills (KKKKhazix/khazix-skills, 11.1k stars) The Chinese-language Claude Code skills pack going viral. Specialized workflows authored in Mandarin. Proves the skill economy isn't just an Anglo-dev phenomenon. → guizang-ppt-skill (op7418/guizang-ppt-skill, 9.9k stars) Turns prompts into polished HTML slide decks. Editorial magazine and Swiss-design templates baked in. Generates image prompts, social covers, and the runtime in one pass. → fireworks-tech-graph (yizhiyanhua-ai/fireworks-tech-graph, 6.8k stars) Production-grade technical diagrams from plain English. 7 styles: flowchart, architecture, UML, sequence, agent workflow. Outputs SVG and PNG, drop-in for any README. Save this and start layering. The images below has the full breakdown on each one :)

Most people use Claude Code to write code. Andrej Karpathy uses it to write his Second Brain. The whole setup takes one afternoon. No paid apps, no plugins, no APIs. Just Claude Code and an Obsidian vault. Here's how it works: → 2:00 PM - Stop writing notes. Let Claude write them. Most note apps just store what you typed. Karpathy points Claude Code at an Obsidian vault, drops in raw sources, and lets Claude write the wiki itself. Every entry cross-links to the old ones, so the vault sharpens every session. → 2:15 PM - Obsidian is just a folder of markdown. Every note is a plain markdown file in a regular folder on your machine. Claude reads and writes it like any other directory. No plugins. No APIs. No integrations. The vault is free, local, and your knowledge never leaves your hardware. → 2:30 PM - Point Claude at your vault. Create your Obsidian vault (call it Brain, Vault, whatever). Open a terminal, cd into the folder, run claude. Drop a CLAUDE.md file at the root. That file is the schema that tells Claude how your wiki works (atomic notes, [[wikilinks]], daily notes). → 2:45 PM - Three folders. One growing brain. Karpathy's vault has three folders. /raw is where articles and thoughts get dumped. /wiki is where Claude writes the cross-linked summaries. /outputs holds polished artifacts. Raw becomes wiki becomes output. → 3:00 PM - The agent is the scribe. Every Claude session writes a daily note. What came up, what got decided, what new pages were added. Claude automatically backlinks the note to the concept pages it touched. Over weeks, the vault stops being storage and becomes a brain. That's the whole shift. Karpathy said it himself: Obsidian is the IDE. The LLM is the programmer. The wiki is the codebase. Set it up once. Use it forever builds yours in ONE afternoon :)

8 open source repos that maximize the AI ecosystem you're already using. Each one fills a gap or unlocks a capability your current stack doesn't have. → ruflo (ruvnet/ruflo, 49.6k stars) Orchestrates teams of Claude agents in parallel with persistent vector memory and secure federation. Kubernetes for agents. → agent-skills (addyosmani/agent-skills, 40.2k stars) 22 structured workflows that drop into Claude Code and encode senior engineering practices: testing, review, security. → skills (mattpocock/skills, 75.3k stars) Hand-curated Claude Code skills from Matt Pocock. Most-starred skill pack on GitHub. The .claude folder everyone is copying. → agentmemory (rohitg00/agentmemory, 5.5k stars) Persistent memory across Claude Code, Cursor, Gemini. Auto-captures tool use and injects semantic context. Stops you from re-explaining your architecture every session. → TradingAgents (TauricResearch/TradingAgents, 74.3k stars) Specialized AI agents (fundamental, sentiment, technical, risk) collaborating like a real trading firm. The most-starred multi-agent system shipped. → UI-TARS-desktop (bytedance/UI-TARS-desktop, 33.5k stars) ByteDance's open source computer-use agent. Sees and controls your desktop GUI across terminal, browser, and native apps. → CloakBrowser (CloakHQ/CloakBrowser, 7.3k stars) Drop-in API replacement for Playwright and Puppeteer with C++-level Chromium patches that pass every major bot-detection test. → PageIndex (VectifyAI/PageIndex, 30.8k stars) RAG without vector embeddings. Builds hierarchical document trees and uses LLM reasoning for retrieval instead of chunking. Save this and start layering :)

You can sketch out the architecture. You can label the boxes, draw the arrows, walk people through what each piece does. But here's what diagrams don't show you. What it actually looks like when the system is running. Latching of from the last post done on case study 3 where the carousel walked through the dual-track pipeline we built for a home builder - one path that creates intent through AI-powered cold outreach, one that surfaces existing intent from Reddit and Facebook signals. This is the harvester running live. Ron walks through the Zillow Lot Collector - the structured-scraping piece of Path A - pulling agent contacts and listing data from Zillow in real time, enriching each row with 200+ structured fields, and dropping the output into a verified CSV. The full architecture is in the carousel from the last post. Or message us if you want one built for your business :)

Every day, lead gen tools aren't getting smarter. They're just adding more names to bigger databases and charging premium prices to anyone willing to pay. But here's what these platforms don't focus on. Why should you actually trust those names? And if you do reach out, how close are any of them to actually buying from you? A home builder came to us with this exact problem. Their target buyer is someone in their late 20s to early 40s, owns land, and is 12 to 18 months from breaking ground. That person doesn't show up on any database. The big lead-gen platforms will charge you premium per name and deliver phones, emails, contacts. Zero intent attached. Word-of-mouth referrals work, sure, but they don't scale. The reason most early-stage operators don't realize this is because all they care about is filling the top of the funnel. They see contacts, they think leads. So they buy the list, they put on the sales rep cap. Actually not even sales rep, I'd say the cold-emailer cap. Then they fall into this rabbit hole where they're sending sequence after sequence into inboxes that were never going to convert. This works to some extent when you're testing a channel. But it isn't feasible for you as a founder, as a builder, as someone trying to grow, to actually scale a business this way. You should be focused on the buyers who already have intent. The ones thinking about it, asking about it, posting about it. Because at the end of the day, contacts and intent aren't the same thing. It's not that you finish gathering names and intent shows up later on its own. That's not how sales works. That's how pipelines die. That's why at Blinkz, when this builder came to us, we didn't just sell them more contacts. We built a two-track pipeline that creates intent on the cold side and surfaces existing intent on the warm side. On Path A, custom scrapers pull verified agent contacts and listing context from Zillow, Realtor.com, LandWatch, Land.com, Trulia, and BiggerPockets. 21,000+ rows from a single North Carolina run. A custom AI sales agent acts as the builder's sales rep, warming inboxes for weeks before outreach begins, sending sequences personalized to each prospect, and handling every reply that comes back. On Path B, Reddit and Facebook scrapers pull from city-specific subreddits, builder-comparison threads, and homeowner groups. A Claude Sonnet 4 qualifier reads each post and comment, tagging lead_type and an explicit relevance_reason. 213 high-signal leads from one batch. Custom social automation engages those threads where intent already lives. Both pipelines converge on one CSV the builder gets weekly. We care about the growth of your business, not about filling your CRM with names that go nowhere. To show you what that actually looks like in practice, we put together this carousel that walks through the build, from the problem the builder brought us to the pipeline they get every week :)

Every day, Claude Code or Codex isn't getting easier. It's just adding more and more features to make AI coding accessible to the general public so they can build out their dream applications. But here's what these tools don't focus on. Why should the general public actually use them? And if they do, how do they build something that's secure enough, not through their own eyes, but through the industry's eyes. Production ready. Not just "it works on my machine." The reason early-stage founders and visionaries don't realize this is because all they care about is that they have a product idea and they want to ship it to the public. With the leverage of AI today, people are jumping straight into these tools to build out their dreams. And when they do, they put on the software engineer cap. Actually not even engineer, I'd say the software developer cap. Then they fall into this rabbit hole where they're working with code they've never really learned from day one. Suddenly they want to build out their whole stack and feel like they understand everything from backend to middle end to frontend. This is good to some extent when you're putting together an MVP. But it isn't feasible for you as a founder, as a visionary, as an early individual, to actually grow your app idea this way. You, as an early founder, should be focused on the growth of your idea, your strategy, your marketing. You can still be part of the technical layer, but you don't want that to be your bread and butter day and night, where you're building on and on and never actually getting to market. Because at the end of the day, sales and engineering work hand in hand. It's not that you finish the engineering and then sales takes over on its own time. That's not how businesses work. That's how products fail. That's why at Blinkz, we want you to understand that we care about the focus and growth of your app idea and your product. We care about that. And while we care about that, we also have a technical team that helps you build out these applications, no matter what sector you're in. Our goal is to provide you with the technical capability so that your vision isn't just an MVP. It's completely ready for production. To show you what happens when you put on the engineering cap, we made this short film for you to visualize it, from an idea on a napkin to launch :)