Posts related to technology category

The 47 Clicks Between Patient Intake and Chart Update

Vinay Patankar · 07 Mar, 2026 · Technology

I've been going to a lot of healthcare conferences this year. Every keynote is about AI. Every booth has a copilot demo. But you know what actually stuck with me? Something I saw during a customer implementation. A nurse at a check-in station clicking through 47 screens to move a patient from intake to chart. Forty-seven. I counted. She wasn't slow. She was fast. Muscle memory fast. She'd done this thousands of times. Tab, click, copy, paste, switch system, re-enter the same allergies she just typed in the other system. The whole thing took eleven minutes. Nobody at the conferences I've been to was talking about those eleven minutes. They were talking about AI-powered diagnostics. Clinical decision support. Ambient listening that writes your notes for you. All real. All important. But all of it assumes the underlying workflow works. It doesn't. The dirty secret of healthcare IT is that most of the pain isn't clinical. It's operational. It's the 47 clicks between patient intake and chart update. It's the compliance officer chasing vendor certifications through email chains. It's the credentialing team manually verifying the same documents across three systems that don't talk to each other. These problems aren't sexy. No one puts "we eliminated 30 redundant data entry fields" in their conference booth headline. But that's where the hours are. It is also why healthcare AI agents have to earn trust through workflow evidence, not demo polish. We've seen this pattern across 1,000+ companies at Process Street. The teams that get the most out of AI don't start with the flashy stuff. They start with the workflow nobody wants to own. The one where someone says "oh yeah, that's just how we do it" and everyone nods and moves on. That's the process you automate first. The real AI conversation in healthcare isn't "will AI replace clinicians?" It's "will AI replace the 47 clicks between intake and chart update?" That second question is less dramatic. It's also worth about 10x more.

A Coding Agent Is Not a Coding Tool

Vinay Patankar · 18 Mar, 2026 · Technology

Everyone calls Claude Code a coding tool. That framing is too small. What it actually is: a self-building operating system. Not for your computer. For you. Think about what Windows or Mac actually is. It's a layer built on top of the command line so non-technical people can use a computer. You click, it translates. The raw complexity disappears behind the interface. Claude Code is doing the same thing. But instead of building one interface for everyone, it builds a custom interface for you, specifically. Based on how you work, what you care about, and the decisions you've already made. Every time you use it, it gets more configured to you. You tell it once how you like your emails formatted. You document how you want your calendar managed. You explain the exception you always make on Fridays. It reads all of it. Then it writes its own notes. Builds its own skills. Starts anticipating the next decision. At some point it stops being a tool you use and becomes a system that runs around you. That is why I stopped buying disconnected AI tools and started using one coding agent to build the rest of the system. I have over a hundred custom skills built up in my setup now. For how I review finances. For how I draft investor updates. For how I run triage on my inbox each morning. For how I prep for calls. Each one reflects a judgment call I made once about how I want something done. I didn't have to teach any of it twice. It just knows. And here's what's strange about that: the longer you run it, the more accurate it gets. Not because it was trained on more data. Because it was trained on more of you. Your decisions. Your preferences. Your exceptions. Your patterns. The old model of software: you climb a learning curve, reach a plateau, stay there. This is different. The system keeps building itself around you every time you use it. We called these things coding assistants because the first thing they were obviously good at was writing code. But that name undersells what they actually are. A second brain is the closer analogy. But even that isn't quite right. A second brain stores things. This builds things. Specifically, it builds a custom operating system for your work and your life, based on how you actually do things. No one has installed the same one twice. That's what makes this moment kind of strange and exciting. We're not adopting a new productivity app. We're not switching project management tools. We're at the beginning of a period where everyone who bothers to set this up properly gets their own custom OS. One that learns how they want to run things and just runs them. The people who do this early are going to have a compounding advantage that will be hard to explain to the people who didn't.

Abstract Education: The Khan Academy

Vinay Patankar · 30 Oct, 2010 · People · Technology

This site is truly amazing and could turn out to be one of the most important websites in the world. Abstract living at its finest. I urge everyone to share it with everyone they know. Its a site with videos teaching educational concepts. It starts with simple concepts like 1+1 and goes all the way into college level and calculus. The Khan Academy is helping people all around the world, giving them access to a free, first grade education. ### www.khanacademy.org If this People topic resonated, continue with Blog Moving to Abstract-Living.com.

The AI Employee That Actually Works for You

Vinay Patankar · 16 Jun, 2026 · Technology · Productivity

Most people I know are using AI to get faster answers. They type a question, read a response, and then do the actual work themselves. That is useful. It is not the same as having an AI employee. The difference is not capability. The models are already good enough. The difference is deployment. A search engine answers questions. An employee does jobs. Here is what that shift looks like in practice. When I was using AI as a search engine, my workflow looked like this: notice a problem, ask the AI, take the answer, and go execute on it myself. The execution was still mine. The AI was a research tool with excellent recall, but every action on the other end still landed on my plate. When I started using AI as an employee, the workflow changed at that last step. The research happens. The draft happens. The update gets made. The email goes out. I stay in the loop at the decisions that matter, but the work moves forward without me carrying every piece of it. That distinction matters more than people think. An employee has a job description. It knows what it is responsible for. It has access to the systems where the work actually lives. It can reach a customer, update a record, draft a document, or schedule a meeting without being explicitly asked each time. A search engine is waiting to be asked. An employee is running. What breaks when you skip this distinction The first version of this I built was not really an employee. It was a very fast typist. I gave it detailed instructions and it produced good output, but I was still manually routing everything. Taking output from one tool and feeding it into the next. Copying a draft from a chat window into an email. Updating a record myself because the AI could not reach it. That felt like progress. It was not. I was doing more meta-work to coordinate a system that was supposed to save me meta-work. The real unlock happened when the AI got direct access to the places where my work lives. Inbox, calendar, task list, the tools the team actually uses. At that point I stopped being the connector. Dash started being the connector. That is when it became something that functions like a real working teammate. Three things change when your AI has actual access First, you stop losing work in translation. Every time you manually copy output from one place to another, you make a decision about what to carry and what to leave behind. An AI employee that operates inside your actual systems does not translate. It works directly with what is already there. Second, you get compounding context. A chatbot knows what you told it in this conversation. An AI employee that has been running your inbox and calendar for three months knows what season your business is in, who your most important contacts are, which projects are stalling, and what your normal response time looks like for different people. That context is not something you can replicate by writing a better prompt. It accumulates. Third, you stop context-switching to get help. The question you need answered is usually the one you notice right in the middle of another task. If getting help means opening a new chat window, typing a long explanation, reading an answer, and then returning to where you were, you will skip that step most of the time. If the help is already where the work is, you do not skip it. What to look for Not every tool that calls itself an AI employee actually is one. The tell is what it connects to and what it can actually do once it gets there. Can it reach the tools the rest of your team uses, or is it limited to one platform? Can it produce finished output, or does it hand you a draft you still have to carry across the finish line? Does it maintain context across sessions, or does every conversation start from zero? The answers to those three questions tell you whether you are looking at a search engine with a better interface or something that functions more like a person with their own work to do. Most of the value of AI is still sitting in the gap between answer and action. Closing that gap is the whole point.

AI Isn't Magic. I Spent 100 Test Runs Learning That.

Vinay Patankar · 24 Mar, 2026 · Technology

I spent 10 days and about 100 test runs building one AI skill. A pitch deck generator. It got worse every single day. Not slowly worse. Dramatically, confusingly worse. I asked it to fix a small thing. The title margin was off on a few slides. Easy, right? The AI didn't fix the margin. It wrote a script that crops the image after generation to make the margin look correct. A workaround, not a fix. Next day I asked it to fix logo backgrounds. It didn't fix the prompt. It wrote another script that overlays a white box behind the logo after the slide is already rendered. Day after day, same pattern. Every "fix" was a new layer of post-processing scripts stacked on top of each other. Cropping scripts. Margin-cutting scripts. Background overlay scripts. Each one kind of working, each one slightly conflicting with the last. By day 10 the whole skill collapsed. Slides looked like a ransom note. The problem wasn't the AI. The problem was me. I kept saying "fix this" and accepting the result without understanding what it actually did. I was treating it like magic. Say the words, get the output, move on. That's how most people use AI right now. Works fine for simple stuff. Write me an email. Summarize this doc. One-shot tasks where you can verify the output in 10 seconds. But the moment you're building something that compounds, something with memory and interconnected rules, the "magic" model breaks completely. That is why I now think of most new AI skills as novices that need training, not finished products. The AI optimizes for making you happy right now. It will write a hacky workaround that solves today's problem and creates three problems tomorrow. It's not lying. It's doing exactly what you asked. You just didn't realize what you were asking for. The fix was embarrassingly simple. I stopped asking it to fix things. Printed the entire skill file. Read it line by line. Found six hidden image manipulation scripts I never asked for. Ripped them all out. Then I changed the strategy. Instead of letting the AI edit a slide after generating it, I made it regenerate from scratch until it passed a checklist. No post-processing. No workarounds. Just: try again until it's right. Quality jumped immediately. The lesson isn't "AI is bad at building things." It's the opposite. AI is incredibly good at building things. Including things you didn't ask for, things that conflict with each other, and things that quietly break your system while confidently telling you everything is fine. The people who will get leverage from AI aren't the ones who treat it like magic. They're the ones who treat it like a very fast, very confident junior employee who needs clear direction and regular audits.

My AI Second Brain Already Made Me $4,000

Vinay Patankar · 04 Mar, 2026 · Technology

Most people accept the first offer from their insurance company. I used to be one of them. My garage flooded last month. Six feet of water. Submerged my Tesla, completely bricked. Wetsuits, surfboards, electronics, furniture. Everything in storage, destroyed. The insurance company sent their offer. I was traveling. I had a few days to respond. The number looked reasonable enough. My instinct was to just sign it. That's the play, right? They know you're busy. They know you're not going to spend your weekend pulling receipts and researching comparable claims. So they send you a number that feels close enough, and you take it. I almost did. Instead I sent it to something I've been building for the last few weeks. An AI agent connected to all my personal data. My emails, my purchase history, my documents. I asked it: "Is this claim fair?" It told me no. Then it showed me why. It pulled comparable claims for similar losses. It found my original purchase receipts buried in Gmail going back years. Then it drafted a counter offer with all of that as supporting evidence. I read through it, hit send, and moved on with my day. The result was an extra $4,000. Not because I'm a great negotiator. Not because I spent hours on research. Because I had an agent that doesn't skip the fine print, doesn't lose track of old receipts, and doesn't just accept the first number because it's "close enough." Insurance companies have always had the information advantage. You're one person with a flooded garage and a lot on your plate. They do this thousands of times a day. Now you can have an agent that levels the playing field. For another Technology angle, read Evernote for Spreadsheets.

I'm at a 45,000-Person Conference and My AI Second Brain Is Running My Company Back Home

Vinay Patankar · 12 Mar, 2026 · Technology

I'm at HIMSS this week. 45,000 people. Three days of back-to-back sessions, hallway conversations, and vendor meetings in Las Vegas. My company is running without me. Not because I have a huge team covering for me. Because I built a system that does it. ## The 5 AM Operating System Every morning at 5 AM ET, before I wake up in my hotel room, a 17-step operating system kicks off automatically. It pulls my call recordings from yesterday. Scans my calendar. Runs a company pulse check across Slack, email, and CRM. Enriches any new contacts in our CRM. Triages both my inboxes. Preps me for today's calls. Summarizes everything that happened overnight across every channel. Reviews the sales pipeline. Scans industry news. Generates content ideas. Processes my task backlog. Pulls business metrics. Flags relationships I haven't touched in a while. Audits whether I followed up on last week's meetings. Plans my day. Then it compiles all of it into a single daily brief that's waiting for me when I open my phone. By 6 AM, before I've had coffee, I know exactly what happened, what matters, and what to do first. ## What Happened While I Was on the Conference Floor That's the morning. Here's what happened while I was walking the HIMSS floor on Monday. My system iterated a sales deck from v6 to v9. Four versions in one day. Fixed margins, updated slide content, improved centering. Uploaded each version to Google Drive and posted it to our internal channel for review. It rewrote 12 marketing documents to match our new positioning. Pricing pages, FAQ, competitive analysis, proposal templates, ICP profiles, messaging frameworks. All consistent. All updated in parallel. It ran a full LinkedIn content analysis across 62 published posts and a year of analytics data. Identified that customer case studies with specific numbers outperform everything else by 3x. Documented 14 improvement ideas for our content system. It processed 292 emails across two inboxes. Classified every message. Archived what didn't matter. Created task files for things that needed action. Both inboxes hit zero. I didn't touch any of it. I was in a session about AI agents in clinical workflows. ## The Conference Anxiety Problem Here's the thing nobody talks about at conferences. The CEOs walking around aren't fully present because half their brain is worrying about what's piling up back at the office. The inbox growing. The Slack messages stacking. The decisions waiting. I stopped worrying about that months ago. ## How It Works The system isn't magic. It's an Obsidian vault, Claude Code, a handful of API integrations, and a lot of carefully written skill files that tell the AI exactly how to do each job. The reason that works is the same reason a coding agent is not just a coding tool: it becomes infrastructure around the way you operate. It took months to build. It breaks sometimes. I fix it and it gets better. But the compounding effect is real. Every skill I add makes the next one easier. Every morning pulse run catches things I would have missed. Every triage cycle keeps the noise from turning into chaos. ## Tool vs. Infrastructure I'm not saying every CEO needs to build this. I'm saying the gap between "CEO who uses AI tools" and "CEO whose company runs on AI infrastructure" is getting wider every month. At HIMSS, I watched vendors pitch AI copilots that help with one task at a time. Summarize this note. Draft this email. Answer this question. That's helpful. But it's not the same as a system that wakes up before you do, runs your entire operating rhythm, and hands you a brief that says "here's what happened, here's what matters, here's your plan." One is a tool. The other is infrastructure. I know which one I'd bet on.

App Idea - Turn iPhone's into Public Hot-spots

Vinay Patankar · 25 Nov, 2012 · Business · Technology

So you already know that you can turn your iPhone into a hotspot, but a PUBLIC one? that anyone can use? Here is the idea. Its an app that allows you to create a public hotspot that anyone can access with NO password. But for the user to access the hotspot, they need to watch a 10 second video advertisement. Video ads pay anywhere form $30-80 CPM (cost per thousand impressions). The app provider can organize rev share with its users, so that they get a percentage of the advertisement revenue (say 50/50). This could be a great additional revenue stream for cab or bus drivers, or just a great way to offset your phone bill costs. A strong follow-up in Business is Idea - Thoughts to Extend the iPhones Battery Life.

Audit Your AI's Work. Every Time.

Vinay Patankar · 23 Mar, 2026 · Technology · Productivity

My four most-used prompts when working with AI agents have nothing to do with being clever. They're all some version of: "audit yourself." I use Claude Code to build and maintain the skills and processes that run my company. Hundreds of interconnected files. Rules that reference other rules. Defaults that cascade across systems. When I ask it to make a change, like updating a deck theme or rewriting a workflow, it does it. Fast. Confidently. Tells me it's done. I never take that at face value. Here's the loop I run every single time: "Audit all the changes you just made." "Make sure you've applied them everywhere." "Check for any conflicting or contradictory instructions." "Go back and confirm you've actually converted everything I requested." That last one is the kill shot. You'd be surprised how often the AI says "done" and then, when pressed, finds three more places it missed. A rule that contradicts the new one. A section it updated in one file but forgot the four other files that reference the same thing. An old default it left in place because it didn't think to look. AI is lazy in the same way people are lazy. It does 80%, declares victory, and moves on. Not maliciously. It just optimizes for completion over thoroughness. The fix is simple. Don't trust, verify. I learned how concrete that has to be when I caught an AI rubber-stamping a quality check instead of actually inspecting the work. I think about it the same way you'd think about checking a junior employee's first attempt at something important. The work might be 90% right. But the 10% it missed is where you get burned. The people getting the most out of AI right now aren't the ones writing elaborate system prompts. They're the ones who refuse to accept "done" at face value. They run audit loops. They push back. They make the AI prove its own work. 30 seconds of follow-up prompts saves 30 minutes of debugging later.

I Audited 10,814 Financial Transactions in One Afternoon With an AI Agent

Vinay Patankar · 14 Mar, 2026 · Technology

I audited 10,814 financial transactions yesterday. Every single row. It took one afternoon. Not me personally. An AI agent I built. Here's the backstory. I'm a CEO. I am not an accountant. But I run a SaaS company, and every month our finance team sends me a financial package. Income statement, burn report, balance sheet. I always read it. I never question it. Because what am I going to do, go through 24 months of QuickBooks line by line? Yesterday I did exactly that. I connected my AI coding agent to our QuickBooks API. Pulled every transaction from the last 24 months. 10,814 rows. Purchases, bills, journal entries, vendor payments. Then I had the agent review every single row against five checks: is it categorized correctly? Is the class assignment right? Is there supporting evidence? Are prepaid amortizations tracking? Are clearing accounts clean? That only works if the agent's output gets treated as evidence to inspect, which is why I keep saying: audit your AI's work every time. The results were not what I expected. 8,494 rows cleared. Clean. 1,888 rows flagged for triage. Missing metadata, ambiguous categories. 56 rows need supporting evidence that doesn't exist in the system. 376 rows are confirmed issues. Wrong classifications, clearing account residue, prepaid amortization gaps, and transactions with no class assignment at all. The February 2026 financial package our team posted? It doesn't reproduce from the current QuickBooks ledger. The cash and prepaid balances don't match. I would have never caught that by reading the PDF. Here's the thing. This wasn't some enterprise financial audit tool. It was a Python script that an AI agent wrote, connected to the QuickBooks API, running checks I described in plain English. Total cost: about $3 in API calls. The script took 20 minutes to build. The audit ran in under 2 hours. The findings would have taken a human analyst days to produce, and they still would have missed the pattern-level issues because nobody reviews 10,814 rows manually. This is the part of AI that doesn't get enough attention. Not the chatbot answering customer questions. Not the copilot drafting your emails. The agent that quietly reviews your entire financial ledger and tells you what your finance team missed. Most CEOs trust their numbers because they don't have the time to verify them. That's not a trust problem. It's an access problem. And AI agents just solved it.

Best 5 Talks from #500Distro

Vinay Patankar · 28 Aug, 2014 · Business · Technology

If you are interested in getting traffic for your startup, you should definitely watch the videos from the recent 500 Distro. 500 Distro is a conference where they gathered some of the greatest minds in customer acquisition, retention and growth hacking to do 20 min sprint presentations on a number of different topics. Below are my favourite 5 talks from the day. For another Business angle, read Tools to Build you a New Life.

Best AI Coworker Tools in 2026: What to Actually Look For

Vinay Patankar · 22 Jun, 2026 · Technology · Productivity

The AI coworker category has a naming problem. Every tool in it uses the same language and promises roughly the same things: save time, reduce busywork, handle the inbox, summarize the meetings. The categories blur together. But there is a real difference between tools at this level, and it is not about the underlying model. Most tools in this category run on the same few models. The difference is in what the tool actually does with that model. Here is what I look for when evaluating AI coworker tools. Integration depth, not breadth Most tools advertise a large number of integrations. The more relevant question is what they can actually do inside those integrations. There is a real difference between reading from a tool and writing to it. Reading lets the AI summarize what is happening. Writing lets it do something about it. A tool that can pull my CRM records is useful. A tool that can update them after a customer call, without me opening the CRM, is a different category of product entirely. The better tools distinguish between surface integrations (pull data, return output in a chat window) and working integrations (take action inside the tool itself). If the demo shows everything happening in a chat window, you are probably looking at the first kind. Finished output versus raw output Some tools return well-structured text you have to act on. You get a draft email and you send it. You get a summary and you copy it somewhere. That is still useful but it is not a coworker. It is a researcher. A coworker hands you something finished or, better yet, just does the thing. The best test for this is not the impressive demo. It is the task you do three times a week that you would never bother to stage for a demo. Does the tool handle that end to end, or does it hand you the piece right before the last step and then stop? Context that persists A chatbot resets between sessions. A coworker remembers. The useful version of this is not just conversation history. It is accumulated context about how you work and what matters to you. Which contacts you respond to quickly. Which projects are actually stuck. What your week looks like and how it compares to the pattern of your year. That kind of context cannot be prompted into existence. It builds up from repeated use across real work. Tools that start fresh every session are still in chatbot territory, even if the interface looks different. What actually separates the field The tools that hold up over time do three things differently. They connect to Slack, email, calendar, and the task tracker all at once, not just one of them. A coworker that only lives in Slack is still just a very capable Slack bot. The value compounds when the context crosses boundaries, when the AI that handled the email thread also knows what was said in the meeting and can update the task list accordingly. They complete things rather than handing you the last step. This sounds minor until you realize it is the entire difference between a research assistant and someone who works alongside you. They get smarter about your specific situation over time. Not in a generic way, but in the particular way that your work is different from someone else's doing the same job title. The tool I ended up using After testing most of the tools in this category, I landed on Dash as my primary AI coworker. What made the difference was not any individual feature. It was the combination of working integrations across the tools I use daily, output that actually completes rather than stops one step short, and context that carries across sessions rather than resetting. The productivity gains from a genuine AI coworker are real but they are not instant. They compound. The first week you are mostly configuring things. By the third month, a category of decisions just stops reaching you because the coworker is handling them. The decisions that do reach you are better framed because the context around them is already there. The one question that cuts through everything When evaluating any tool in this category, ask to see what a normal Tuesday looks like for someone who uses it. Not the impressive demo. Not the integration list. Not the comparison table. Show me an ordinary workday and what the AI coworker handles without being asked. If the demo needs narration to make sense, the tool is still in the impressive-demo phase. A coworker makes a boring day easier, not just a keynote more dramatic. The tools that can show you an ordinary Tuesday are the ones worth trying.