Exemples de lead magnets LinkedIn en product development

𝗔𝗜 𝗣𝗿𝗼𝗱𝘂𝗰𝘁 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 𝗶𝘀 𝗼𝘃𝗲𝗿𝗵𝘆𝗽𝗲𝗱. That is what I told myself when I first started working on AI products. Turns out, I had no idea what an AI PM really did. After spending years watching world-class AI PMs, building AI products at scale, making 100s of bad decisions, I have a much better definition of the role. But, sadly, even today, most PMs trying to work on AI are in the same boat: confused and clueless about "what does an AI PM actually do." Here's the simplest mental model to think of an AI PM's role: An AI PM is responsible for finding answers to these 7 questions. 1. What problem should we solve to maximize impact? 2. Does this need AI? 3. Do we have the right data? 4. How do we turn data into something useful? 5. How will users experience it? 6. How do we know it works before launch? 7. How do we keep making it better? Let's understand in detail: 𝗪𝗵𝗮𝘁 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 𝘀𝗵𝗼𝘂𝗹𝗱 𝘄𝗲 𝘀𝗼𝗹𝘃𝗲 The problem must be specific, validated with real users, and solution-agnostic. If you get this wrong, the model does not matter. 𝗗𝗼𝗲𝘀 𝘁𝗵𝗶𝘀 𝗿𝗲𝗮𝗹𝗹𝘆 𝗻𝗲𝗲𝗱 𝗔𝗜 This is the most important question an AI PM asks. And the answer is usually no. Saying no to AI when the situation does not call for it is not a failure. It is the job. 𝗗𝗼 𝘄𝗲 𝗵𝗮𝘃𝗲 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗱𝗮𝘁𝗮 "We have data" is not a strategy. An explicit data plan that includes what data we need, what we have, and what is missing is the right strategy. AI is only as good as the data behind it. 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝘁𝘂𝗿𝗻 𝘁𝗵𝗲 𝗱𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝘀𝗼𝗺𝗲𝘁𝗵𝗶𝗻𝗴 𝘂𝘀𝗲𝗳𝘂𝗹 Simple prompt, ML model, RAG, or agents. Each has a different use case, cost profile, and failure modes. The PM who skips this hands those decisions to engineering. 𝗛𝗼𝘄 𝘄𝗶𝗹𝗹 𝘂𝘀𝗲𝗿𝘀 𝗲𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 𝗶𝘁 Users need trust, control, and recovery. Design for when the AI fails, not only for when it works. 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗸𝗻𝗼𝘄 𝗶𝘁 𝘄𝗼𝗿𝗸𝘀 𝗯𝗲𝗳𝗼𝗿𝗲 𝗹𝗮𝘂𝗻𝗰𝗵 There is no binary pass/fail. Build an eval framework. Define good, bad, and edge cases. Ship only when the product clears your threshold. 𝗛𝗼𝘄 𝗱𝗼 𝘄𝗲 𝗺𝗮𝗸𝗲 𝗶𝘁 𝗯𝗲𝘁𝘁𝗲𝗿 𝗮𝗳𝘁𝗲𝗿 𝗹𝗮𝘂𝗻𝗰𝗵 AI products degrade in production if you stop watching them. Sample live conversations. Add new failure modes to your test set. Never stop monitoring. -- Want more details? I just published a full document covering everything I know about AI Product Management--the role, mental models, the mistakes I made and much more. Linked in the comments. Free. No email required, no paywall. I will keep it updated as the field evolves.

Most people think Claude code only needs good prompting. Unfortunately, that is not true. The real unlock is giving it a structure that is coherent and can last from start to end. If there is no structure, Claude code is just another LLM If there is proper structure, Claude code is the smartest engineer on the planet. Here are the 6 files you should include for every AI-assisted build: #𝟭 𝗣𝗥𝗗 This is your master plan. What you're building, who it's for, what good output looks like. Created once. Iterated rarely, only if absolutely necessary. The LLM refers to it after very major milestone for context, so it doesn't guess your intent. #𝟮 𝗣𝗹𝗮𝗻𝗻𝗶𝗻𝗴 This is the product's blueprint. User flows, screens, navigation, system diagrams. It defines all the details and the structure so the LLM doesn't invent pages you never asked for. #𝟯 𝗧𝗮𝘀𝗸𝘀 This is the to-do list. Milestones and tasks with checkboxes. The LLM reads this to know what's done and what to build next. Updated every session. This helps the LLM know exactly what is done and what is pending, so you don't have to remind it. #𝟰 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 (𝗜 𝘂𝘀𝗲 𝗖𝗹𝗮𝘂𝗱𝗲[.]𝗺𝗱 𝗳𝗼𝗿 𝘁𝗵𝗶𝘀) This your product's soul. Limit to 300-500 words. Include one-paragraph product summary, tech stack, design preferences, "never do" list. The LLM reads it before every action. When context gets lost in a long session, this file is what keeps it on track. It ensures that every action is taken in the right direction. #𝟱 𝗗𝗲𝗰𝗶𝘀𝗶𝗼𝗻𝘀 Your learning log. It incldues every critical decision, every tradeoff, every "we went with X because Y." It logs what you decided to do and why, so you can refer back reflect / learn from your decisions and get better every time. This is the only file that's 𝗙𝗢𝗥 𝗬𝗢𝗨, not the LLM. This is what makes you a better AI builder. #𝟲 𝗠𝗲𝗺𝗼𝗿𝘆 This your product's long-term memory. It includes critical context that must survive across sessions. Bugs that were fixed, patterns that worked, things that broke. Claude Code reads this so it never repeats a mistake or forgets a lesson you already paid for. Knowledge tells it who your product is. Memory tells it what your product has been through. -- This is the shift you need to make. Prompting is necessary. But it is not enough. Aim to create a structure that AI understands Once you do that, Claude automatically becomes the best collaborater and executes like a high performing engineering team. Reply "structure" and I will create a detailed post on each of the 6 files with real examples and snippets.

I asked Daxesh to sleep at 12:30 AM. Around 2:45 AM he was awake… working. I again told him to sleep. Morning at 7 AM, when I woke up, he was already working. He said he slept. I still don’t know when. He was working at the airport too. This is what happens when he finds a real problem worth solving. ⚙️ Problem he is working on... 1. SSH, monitoring, deployments, logs, AI insights… everything lives in separate tools. No shared context. No single workflow. 2. VPS deployments still rely on manual commands, custom scripts, tribal knowledge, trial and error. 3. Most tools rely on cloud agents, API keys, or third-party infra access. With the help of AI, he built a working POC in 1 week. 🤖 We started using it internally. Built MVP. Testing in cycles. Sharing with beta users. Our goal is, Manage entire DevOps workflow from a single screen. Server access, Logs visibility, Deployments, Monitoring, AI assistance. All connected. All contextual. Planned launch within a week. 🚀 If you want 2 months free early access, comment "DevOps". We will include you in the beta.

Product Managers, give me 6 minutes to make you understand: The true meaning of AI Product Sense In product management, problems are ambiguous, information is limited, 2+2 is not always 4. Hence, it’s hard to know the “right” answer Yet some PMs know the "right" thing to do, despite the ambiguity. And that is product sense: Ability to find the right solution for the users and the business, despite limited and ambiguous information. And fundamentally, the definition hasn’t changed (even with AI in the picture). But designing AI products adds a layer extra complexity which requires PMs to answer a few extra questions. Create/clarify the overall goal User Discovery Problem discovery Solution discovery (this is different with AI) Alignment with larger goals ---------------------------------- Building and delivering the solution Measure success and collect feedback Let’s talk about the added complexity that AI brings with it. Here are the questions you should be thinking about 1. Which layers do we need: determine what powers the heart of your solution. Is it data, model, UX, Product. Do this early on so you know exactly where to focus your energy on. 2. Define each layer’s job: for all practical reasons your product would use all of the above layers. So it’s important to define what each layer is responsible for and how you define success for each of them. This is imp because when your product breaks at scale, you’ll know exactly where to look. 3. Guardrails: most PMs are very good at defining what the product should do. But with AI, it’s important to also define what it shouldn’t do. You don’t want a customer service chat bot reveal trade secrets or offer full refunds to every customer. 4 and 5 Define good and bad quality: it’s critical to define what good quality looks like, create a golden set, run tests against an EVAL framework so you know how good or bad your product is doing. At the same time also think what bad quality looks like, so your model knows exactly what not to do. 6. Design how user experiences the product: what does the user do when she receives a good response? Does she end the session? Does she start a new session? Similarly, what does the user do when she receives a bad or inaccurate answer? Can she rerun the same query? Can she course correct? All of these are very imp questions to answer before you start building. 7. How will you collect feedback: will you use explicit signals like thumbs up? Or implicit signals like engagement, clicks, time spent? Whatever you choose, you need to know upfront so you design the solution to enable the right feedback loops. in a few hours I will break this down with a lot more details in my masterclass. We’re going to tear down perplexity as a product and understand how the PMs there answered these questions. session is free to attend, but seats are limited. See you there (sign up link in comments) date: 4th april time: 12pm BST / 430pm IST

SRS → Enterprise level Production ready code! We are Building AI Team! Most teams are using AI for coding. Tools like Claude Code, Cursor, GitHub Copilot, and similar tools help developers write code faster. But orchestration is still human. Planning done manually. Tickets created manually. Grooming done manually. Code review coordination manual. Documentation written later. AI writes code. Humans still manage the process. That is the slowest part. We are working on building a full AI team. Not just an AI coding assistant. AI project execution system. ⚙️ The idea is simple: Human gives problem statement or SRS. System prepares: • Tickets automatically • Grooming automatically • Technical approach automatically • Best practice checklist automatically • Documentation automatically Human only reviews. 👀 Design → automated Code → automated Test cases → automated Documentation → automated Human stays in the loop only where judgment is required. Result is, Communication between departments has reduced. No need for a 7–8 person coordination chain. One lead can monitor and verify output. Because AI agents communicate faster than humans. ⚡ Agents coordinate with each other. Planner agent → creates structure Architect agent → decides technical approach Developer agent → writes implementation QA agent → validates logic Documentation agent → prepares knowledge base There are leader agents as well that monitor quality and improvement. Agents operate at different levels and continuously improve shared knowledge. Documentation, grooming, ticket creation, and execution flow become automated. Human effort shifts from execution → validation. Speed increases. Clarity increases. Consistency increases. AI does not replace the team. It removes operational friction between team members. If you are exploring how to build an AI team for development workflow, comment AI TEAM 🤖

I spent more time naming our product than talking to my fiancée. 😅 We're building a tool that replaces PuTTY, WinSCP, ChatGPT tabs, and manual git pull deploys. SSH management. AI console. One-click deployments in under 30 seconds. 100% local. No cloud. No credentials shared. Ever. The product was ready. The name was not. 🥲 We tried everything: ❌ Descriptive names - "ABS"Ops, DevLaunch (labels, not brands) ❌ Random abstract words - no criteria = no way to evaluate ❌ Copying competitors - without understanding WHY they chose what they chose We studied 40+ competitors across SSH tools, deployment platforms, and monitoring tools. Broke down every naming pattern in the industry. Literal action → Render, Fly Power metaphor → Forge, Vault Invented abstract → Heroku, Vercel Latin/Greek roots → Termius, Prometheus Then we asked 25-30 questions we had never thought to ask before. What emotion should the name carry for a developer at 2 AM with a broken server? What does our USP feel like — speed, trust, or calm control? What does a first-time user need to FEEL before they read a single feature? How does the name sound in English, Hindi, Mandarin, Spanish, Arabic? Does the name still make sense when we're 10x bigger? We ran a full matrix across 5 dimensions 👇 🔹 Language origin 🔹 Meaning source 🔹 Brand feel 🔹 Industry signal 🔹 Memorability - can someone repeat it after hearing it once? We generated 20 scored candidates. Each one tested for what it conveys to a customer without saying a word, whether it carries the USP subtly, whether it would survive a 5-year expansion, and whether it was globally ownable. Here's the approach we chose 👇 A name rooted in an ancient concept that mirrors exactly what our product does. It carries speed, precision, and calm intelligence — without saying any of those words. A developer hears it and feels something before they read anything. A CTO puts it on a contract without hesitation. A founder remembers it after hearing it once. That's the bar a name should clear. Nothing less. We're not revealing it yet. 👀 Launching very soon, and the name will make sense the moment you see the product. If you're a founder stuck on naming, what's the hardest part for you? If you want Beta + 1 months of free early access, comment "DevOps".

Our AI agent designs the entire GTM strategy for a new product launch. We feed it high-signal content - the best podcasts, top notch books, and foundational articles in the space. Then we give it product knowledge. USPs. Use cases. Real context. From there, it gets to work. It studies competitors. Maps pricing. Reviews compliances. Identifies market gaps most teams miss during a launch. Then it starts making decisions: → Which marketing channels deserve your attention → Where your actual MOAT sits — not what you assume it is → Which communities to enter and how to show up in them (Slack, Discord, Reddit, Inc. Communities) → Which platforms to launch on and in what sequence Then it executes. Writes the blogs. Drafts the posts. Creates documentation. Builds personalised outreach messages. All aligned to the strategy it just built. No 6-week agency engagement. No consultant decks. No back-and-forth. One agent. Full GTM. Ready to move. We recently used this for a developer operations platform. Think of it as your AI-powered command centre for DevOps teams. It connects your entire dev workflow - pipelines, deployments, incidents, team activity into one place. Instead of jumping between tools to figure out what's broken, what's delayed, or who's blocked, everything surfaces automatically. Your team spends less time managing the process and more time shipping. No complex setup. No new workflow to learn. It plugs into what you already use and starts giving you visibility from day one. This is what AI-led product growth looks like in practice and what the right tooling makes possible. If you want early access to the DevOps platform comment "DevOps" below.

𝗧𝗵𝗲𝗿𝗲'𝘀 𝗮𝗻 𝗔𝗜 𝗮𝗴𝗲𝗻𝘁 𝘁𝗵𝗮𝘁 𝗶𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝘀 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝗿𝘂𝗻𝘀 𝘁𝗲𝘀𝘁𝘀, 𝗮𝗻𝗱 𝗰𝗼𝗺𝗺𝗶𝘁𝘀 𝗰𝗼𝗱𝗲. 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀𝗹𝘆. 𝗢𝗻 𝗮 𝗹𝗼𝗼𝗽. 𝗥𝗮𝗹𝗽𝗵 runs on Amp Code and does something wild: → Takes your PRD → Breaks it into atomic stories → Ships them one by one autonomously → Fresh AI instance each iteration (memory via git + progress.txt) 𝗧𝗵𝗲 𝗶𝗻𝗱𝘂𝘀𝘁𝗿𝘆 𝘀𝗽𝗹𝗶𝘁 𝗶𝘀 𝗳𝗮𝘀𝗰𝗶𝗻𝗮𝘁𝗶𝗻𝗴: Tools like 𝗖𝘂𝗿𝘀𝗼𝗿 𝗽𝗹𝗮𝘆 𝗶𝘁 𝘀𝗮𝗳𝗲; 𝗮𝘀𝗸 𝗽𝗲𝗿𝗺𝗶𝘀𝘀𝗶𝗼𝗻 𝗯𝗲𝗳𝗼𝗿𝗲 𝗲𝘃𝗲𝗿𝘆 𝗲𝗱𝗶𝘁. Makes sense for most devs today. But 𝗹𝗼𝗻𝗴-𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗹𝗼𝗼𝗽𝘀 like Ralph are the future for advanced users. 𝗖𝗼𝗱𝗲𝘅 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘀𝘂𝗽𝗽𝗼𝗿𝘁𝘀 𝗹𝗼𝗻𝗴-𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝘁𝗮𝘀𝗸𝘀. The pattern is emerging. The key: 𝗮𝘁𝗼𝗺𝗶𝗰 𝘂𝘀𝗲𝗿 𝘀𝘁𝗼𝗿𝗶𝗲𝘀. Not "build the dashboard" but "add filter dropdown to user list." 𝗘𝗮𝗰𝗵 𝘁𝗮𝘀𝗸 𝗳𝗶𝘁𝘀 𝗼𝗻𝗲 𝗰𝗼𝗻𝘁𝗲𝘅𝘁 𝘄𝗶𝗻𝗱𝗼𝘄. 𝗠𝘆 𝘁𝗮𝗸𝗲: This is where coding is heading. Two paths:  • 𝗜𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝘃𝗲 𝘁𝗼𝗼𝗹𝘀 (𝗖𝘂𝗿𝘀𝗼𝗿) - safe, collaborative, human-in-loop  • 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗮𝗴𝗲𝗻𝘁𝘀 (Ralph/Codex) - ship while you sleep, review PRs later Both valid. Different use cases. The autonomous pattern 𝗺𝗮𝗸𝗲𝘀 𝘀𝗲𝗻𝘀𝗲 for power users who have:  • Solid test coverage  • Good CI/CD pipeline  • Ability to write clear PRDs Ralph proves the pattern works. Amp Code makes it real. 𝗖𝗵𝗮𝗻𝗴𝗶𝗻𝗴 𝘄𝗵𝗮𝘁 "𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴" 𝗺𝗲𝗮𝗻𝘀. Link of Github in first comment 👇 If this helped clarify where 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝗰𝗼𝗱𝗶𝗻𝗴 𝗶𝘀 𝗵𝗲𝗮𝗱𝗶𝗻𝗴, share it with someone building real products 🔁 Follow Dhruv Bansal for practical insights on AI development tools and emerging patterns.

Most people using AI have no idea how LLMs are actually evaluated. And that’s a problem. Because LLM evaluation & benchmarking is becoming one of the most important areas in AI right now. This week, I went deep into: • How LLMs are evaluated in real-world scenarios • What benchmarking actually means (beyond buzzwords) • How companies are building products around it • Career opportunities emerging in this space • How you can even monetize this as a side or full-time business I’ve summarized everything into a simple guide 👇 (Screenshot below — save it for a quick overview) If you want the full deep-dive PDF with step-by-step details, comment “LLM” and I’ll send it to you. #llm #eval #ai

𝗔𝗜 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝗱𝗼𝗲𝘀𝗻’𝘁 𝗯𝗿𝗲𝗮𝗸 𝗰𝗼𝗱𝗲. 𝗜𝘁 𝗮𝗺𝗽𝗹𝗶𝗳𝗶𝗲𝘀 𝘄𝗵𝗮𝘁𝗲𝘃𝗲𝗿 𝗶𝘀 𝗮𝗹𝗿𝗲𝗮𝗱𝘆 𝘁𝗵𝗲𝗿𝗲. A new research paper studied real repositories using: • IDE AI tools • Fully autonomous coding agents Here is what they found: → Autonomous agents create a sharp early boost in delivery speed → Especially when they are the first AI introduced in a project → But code complexity and static analysis warnings increase → And those quality issues persist even after velocity gains flatten The key insight is not “agents are bad”. It is this: By comparing IDE AI tools with autonomous agents, the paper shows that 𝗔𝗜 𝗮𝗱𝗼𝗽𝘁𝗶𝗼𝗻 𝗮𝘁 𝘁𝗵𝗲 𝗹𝗼𝘄𝗲𝘀𝘁 𝗹𝗲𝘃𝗲𝗹𝘀 𝗼𝗳 𝘁𝗵𝗲 𝗦𝗗𝗟𝗖 𝗶𝘀 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗮 𝘁𝗼𝗼𝗹𝗶𝗻𝗴 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻, 𝗶𝘁 𝗶𝘀 𝗮 𝗽𝗿𝗼𝗰𝗲𝘀𝘀 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻. Autonomous agents shift execution away from humans and into systems. If tests, CI, task boundaries, and review loops do not evolve at the same time, autonomy converts speed into long-term drag. 𝗖𝗼𝗻𝗰𝗹𝘂𝘀𝗶𝗼𝗻 𝗳𝗿𝗼𝗺 𝘁𝗵𝗲 𝗽𝗮𝗽𝗲𝗿: Autonomous agents should be introduced intentionally, after teams have built the discipline to support them. Otherwise, IDE-level assistance remains the safer default. A few weeks back, I posted about autonomous agents that can implement features, run tests, and commit code on a loop, and how tools like Cursor took a more human-in-the-loop approach (which has also started evolving in recent releases… more on that later) 𝗔𝘂𝘁𝗼𝗻𝗼𝗺𝘆 𝗶𝘀 𝗻𝗼𝘁 𝘁𝗵𝗲 𝗲𝗻𝗱 𝘀𝘁𝗮𝘁𝗲. 𝗜𝘁 𝗶𝘀 𝗮 𝗿𝗲𝘀𝗽𝗼𝗻𝘀𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝘂𝗽𝗴𝗿𝗮𝗱𝗲. 🔗 I will link that post and the research paper in the comments to connect the dots 👇 If this helped clarify where AI coding is really heading, share it with someone building real products 🔁 Follow Dhruv Bansal for practical insights on AI development tools and emerging patterns.

NEVER hire market researchers again. What they charge $15K for over 3 weeks, this system delivers in 12 minutes. Most DTC brands are flying blind with competitor research: → Paying $8K-$25K for agency reports full of surface-level fluff → Waiting 2-4 weeks for outdated market analysis → Getting generic insights that don't translate to action → Missing the exact words customers use to describe problems This Claude skills + Amazon MCP rips that apart completely. * Here's what it does: Six Intelligence Filters running simultaneously: → Product Gap Analysis (what % of customers want specific improvements) → Conversion Copywriting Intel (exact customer language that sells) → Competitor Vulnerability Mapping (repeated failure points to attack) → Unexpected Use Cases (untapped market segments) → Review Timing Patterns (when satisfaction degrades, optimal review timing) → Price Anchoring Data (value perception triggers with justification language) Everything quantified. Everything actionable. Everything verbatim from real customers. * You get: → Visual HTML infographic with executive dashboard → 3+ insights you can act on within 48 hours → Exact marketing copy pulled from customer voices → Prioritized product roadmap based on demand frequency * Want the complete Amazon Reviews Intelligence System? 1. Connect with me 2. Comment "AMAZON" below (Connect with me so I'm able to DM the system your way) ♻️ Repost if your product team deserves real customer intelligence

Telegram bot sets up meetings, does coding, web search, and anything we want. Imagine a bot that can do real work. A bot that can execute tasks with accuracy. 🤖 We configured an OpenClaw agent with: • Web search capability • Coding skill • Persistent memory • Self-improvement ability over time Then we connected it to Telegram. Now instructions can be given directly from mobile 📱 and the bot can access the laptop 💻 to perform tasks. No need to sit in front of the system. Just send a message, task gets executed. Feels like Iron Man’s Jarvis. ⚡ Because of its self-improvement capability, the bot keeps learning general skills with minimal guidance. Some real tasks have already been performed: • Scraped AIBoom attendee data with a single Telegram query • Created reminders for important work automatically • Performed a web search to find travel locations and useful information • Executed coding-related tasks through a connected environment Instead of switching between tools, The bot becomes the interface. Mobile becomes the control panel for your AI agent team. Instruction, execution, result. No manual follow-ups. Key idea is, LLM is not enough. AI becomes powerful when it has: • Memory • Tools • Execution ability • Feedback loop That is when it starts behaving like an assistant, not a chatbot. One important note 📌 We only give laptop access in a controlled way. We store memories of workflows and approaches I use. We do not give personal bank details or critical sensitive information. And we prefer using a separate device for this kind of setup. Daxesh always does super work! If you want to build your own Jarvis-style workflow, comment JARVIS ⚙️

3 months building an AI system with 10+ features... we abandoned it before landing a single client Mid 2025, a client and I went into a partnership to build an AI system for B2B sales teams. It would handle the full sales pipeline: → Lead research and qualification → Personalized outreach messaging → Sales call prep And it was genuinely good. Here's what it actually did under the hood: → Pulled LinkedIn data for every prospect automatically → Built full company and prospect profiles → Analyzed competitors, tech stack, job postings, websites, recent news and social media → Generated a full research report per prospect → Scored and qualified leads based on custom criteria → Created personalized outreach emails and interview scripts → Saved everything to the client CRM The system was tailored to each client's needs. Different qualification criteria. Different research depth.  Different sales process. We even built client ready dashboards on top of it. It looked exactly like a finished product. There was just one problem. We built all of it before talking to a single potential client. Here's the part I don't see people talk about though. We didn't skip validation because we were lazy or naive. We skipped it because my client felt like proof enough. He knew B2B sales inside out, he knew the pain firsthand. Being that deep in the problem together felt like we were already talking to the market. We weren't. We were just talking to each other. Nobody wanted to pay for 100% of a solution to 30% of their problem. We couldn't find a single client whose needs matched the full scope. So we shut it down. Here are the lessons I learned from this: 1- Start with one feature only, the most valuable one, ignore everything else for now 2- Your first client should shape version one, not inherit it. 3- What you think is needed and what people will pay for are rarely the same thing, only way to know is to ask. 4- Prove the ROI on version one before you even think about expanding We skipped all four steps. → If you're a founder building AI products, you'll waste months on features nobody asked for. → If you're a business owner trying to adopt AI, you'll end up paying for a system that's 80% irrelevant to your actual problem. Same mistake. Different seat. Have you ever over built or over bought before validating the actual need? 👇 P.S. If you wanna access to the dashboard + full breakdown of how the agentic research system worked, just Comment "SYSTEM" below

A chocolate company was struggling with 8,000+ SKUs. 🍫 Price range constraint Weight constraint SKU diversity constraint Expiry constraint Profit constraint Now try solving that manually across 8,000+ SKUs. 👇 So we built an algorithm to automatically generate mixed chocolate boxes that: • Stay within the combo price range, for example 98–135 • Keep total weight within packaging limits • Maintain product variety, flavor, vendor, cocoa % • Prioritize expiring inventory • Maximize margin • Still feel curated, not random This is not recommendation logic. This is constraint optimization. ⚙️ How the system works: Step 1, Filter layer Remove SKUs that do not meet hard conditions • Price limit per SKU • Minimum weight requirement • Cocoa % constraints • Inventory availability • Expiry buffer window • Category rules Step 2, Greedy selection SKUs are sorted using a priority score Risk score = expiry risk + financial exposure + sell-through gap The algorithm starts picking the highest priority SKUs first while respecting: • Vendor diversity rules • Flavor balance • SKU count rules • Price band constraint Step 3, Backtracking adjustment Greedy alone cannot satisfy minimum constraints. Let’s take an example: • Minimum 2 dark chocolates required • Minimum 4 SKUs required • Maximum 1 SKU per vendor allowed Backtracking swaps low priority SKUs with better candidates while maintaining price and weight limits. 🔁 Step 4, Price band optimization The system continuously validates: 98 ≤ bundle price ≤ 135 If the price goes above range, replace a high-price SKU If the price goes below range, upgrade a SKU Final output, a constraint-satisfied bundle generated automatically in seconds. 🚀 Key insight: Greedy gives speed Backtracking gives feasibility Together, they solve real-world combinatorial problems. If you are solving SKU mix, pricing optimization, or bundle generation problems, comment ALGO 🤝

Ramp just hit $1B ARR. Outbound is their number 1 growth engine. Here are 5 things they do differently: 1. Run on Speed 48-hour MVP rule. Weekly experiment cadence. Live "days since founding" timer for urgency. Short feedback loops = lower cost of being wrong. 2. Dual Outbound Funnels Funnel 1: Sales team (Tier 1 manual, Tier 2 AI-assisted, Tier 3 sequences) Funnel 2: Separate domains/IPs as benchmark + deliverability hedge Most companies run one funnel. And wonder why it breaks. 3. Treat Outbound Like Product Growth people + engineers on outbound. 200 experiments per quarter. Track one metric: qualified pipeline per payback. Not just "more dials." 4. Flexible Tech Stack Composable tools. No vendor lock-in. Contact data waterfalls. Multi-channel orchestration. Start simple. Scale into supporting hundreds of reps. 5. AI as Force Multiplier Use AI for repetitive work: - Write messaging - CRM autofill - Reply triage - Risk alerts So humans spend more time selling. Not replacing reps. Most companies fail at outbound because they underinvest. Ramp treats it like product development. With dedicated engineering resources. Result: $1B ARR. - 📌 I've broken down exactly how Ramp scaled to $1B ARR with outbound. The complete playbook with tactical details. Want it? 1. Like this post 2. Comment "outbound". I'll send you the link ASAP. -- Sources: Not Boring (’21–’22), TechCrunch (Mar ’25), Ramp PR (Aug ’25).

90% des outils d'IA de prospection sont nazes. Parce qu'ils sont codés par des devs qui n'ont jamais prospecté. Romàn Czerny, lui, a fait l'inverse. Avant de coder GojiberryAI, il a prospecté à la main. Pendant des mois. Des messages LinkedIn écrits un par un. Des mails envoyés un par un. Des démos en PowerPoint. Et la livraison du travail, faite par lui! Il a appliqué la bonne vieille méthode. Le test and learn. Fastidieux. Long au début. La seule efficace à moyen terme. Et il a fini par dire : "c'est devenu un bordel, on ne pouvait plus tenir". C'est là qu'il a codé l'outil. 220k de revenu mensuel aujourd'hui. 2000 clients. En 10 mois. Tout le monde va retenir "il a vendu avant de construire". Perso je retiens autre chose. Le seul outil d'IA de prospection qui marche vraiment, aura été construit par un mec qui s'est pris des non, qui a galéré, puis qui a livré. Pas par un dev enfermé qui "automatise un workflow". C'est pour ça que je l'utilise. Pas parce que c'est de l'IA. Parce que c'est de l'IA pensée par quelqu'un qui sait ce que c'est de prospecter. Et c'est aussi pour ça que mon métier n'est pas mort. L'IA qui marche, c'est celle qui sait ce que c'est de passer 80 appels dans une journée et d'envoyer des DM Linkedin Pas celle qui te vend "remplace ton SDR" (la blague carambar) Le jour où tu choisis un outil construit par quelqu'un qui connaît le terrain tu sens la différence dès la première semaine. PS : Si tu veux le lien pour tester (7 jours gratuits), dis-le en commentaire.

Claude shipped 83 things in 2026. It is only May. This should scare you! Here is why: 1. This is a clear signal of how fast Anthropic (and the industry) is moving. If you don't know what they're building and why they're building it, you're already behind. 2. Every release is solving a problem you didn't even know existed. If you're not building and experimenting and failing with AI, you will not know how to add value to the world. You will NOT have the skills the world needs. 3. There has been no better time to build real, functional, useful products from scratch without knowing how to code and make a real difference (while making money from it) If you haven't built anything yet, you have no idea what is even possible (and what you're missing on) Every week you're not building with AI, others are moving further ahead. You NEED to know how to use AI to your advantage. -- Get started here. I'm launching 𝗕𝘂𝗶𝗹𝗱 𝗪𝗶𝘁𝗵 𝗔𝗜: A FREEEE course taught in Claude Code that g̶i̶v̶e̶s̶ shows you how Claude Code goes from idea to launched proudct. Launch date: May 20th. Reply with BUILD. I will DM you as soon as my newest FREE course launches:

Why the real AI agent era is still 5 years away: (Based on Abhishek Das, co-founder of Ytorii and AI researcher with 20,000+ citation papers) The patterns separating real agents from demos: Compounding Errors > Single Step Accuracy A 10-step workflow at 90% accuracy per step does not produce 90% overall success. → Long-horizon agentic tasks are not ready for production. The math works against you. Backtracking > Brute Force Retry The agents that work recognise mistakes, backtrack, and go down a different branch. → Recovery intelligence matters more than raw capability at any single step. Evals > Vibes Every production query at Ytorii goes through a comprehensive eval set identifying where agents succeed and where they fail. → Shipping without evals means normalising failure and calling it a beta. Reliability > Raw Performance 100 agent products claim they can do anything. Most work three out of ten tries. → If it does not work on the first try it is not good enough. Proof of Work > Final Answer Users need to inspect what the agent did. Which websites. Which steps. → An agent that cannot show its work is asking users to trust a black box. Digital > Physical (Timeline) The near-term opportunity is web agents handling digital chores reliably. → Not general intelligence. Not physical robots. Routine web tasks done right. Dogfooding > Assumptions Tens of experiments run at Ytorii every week. Maybe one ships to production. → Taste is built through repetition not theory. 80/20 > Feature Lists The best features feel like they were built by someone who watched you use the product. → Intuition plus user signal beats a hundred items on a roadmap. Experts worth following: Yann Dine (Building Conigma) Adam Robinson (Founder led playbook) Boris Cherny (Claude Code) What to watch: Evals infrastructure as the signal of a serious product vs a demo Backtracking as the technical differentiator Proof of work UI as the trust layer P.S. What is the longest workflow you have tried to run before it broke down? P.P.S.: We run a free Slack community with 100+ GTM playbook resources. Comment "CONIGMA" to join.

You want a promotion But you’re behaving like you want job security You say you want to grow. But your behavior says you want to stay safe. Promotion doesn’t go to the most loyal person. It goes to the most ready person. Here’s what’s quietly delaying your growth especially if you’re not thinking strategically. 1. You’re too attached to your current role 🎯 If you’re irreplaceable, you’re unpromotable. ✅ Build a successor before you ask for elevation. 2. You think your manager owns your career 🎯 No one is more invested in your growth than you. ✅ Bring a promotion plan to your manager, not a hope. 3. You don’t have a Plan B 🎯 Frustration grows when you feel stuck and dependent. ✅ Build options inside and outside your team. 4. You focus on tasks, not positioning 🎯 Checklists don’t signal leadership readiness. ✅ Own problems that affect multiple teams. 5. You underestimate visibility 🎯 You can’t be promoted by people who don’t see you. ✅ Speak where decisions happen, not just where work gets done. 6. Your network is shallow 🎯 Promotions accelerate when others vouch for you. ✅ Build real relationships beyond your immediate team. 7. You never aligned on promotion criteria 🎯 You can’t hit a target you haven’t defined. ✅ Ask your manager, “What would make you confident promoting me?” 8. You communicate effort instead of impact 🎯 Hard work doesn’t signal readiness. ✅ Outcomes do. What to do: Frame your contributions in business results. 9. You wait for the annual review cycle 🎯 By then, the narrative is already formed.  ✅ Shape the narrative months before decisions are made. Promotion is rarely about merit alone. It’s about leverage, positioning, and perception. If you’re serious about accelerating your growth: 👇 Comment or DM “Mentor” or 📧 Send me a connection request with a note "Mentor”. I’ll personally share the next step. 🔁 Repost this if you believe careers should be built deliberately. Follow Harit Bhasin for more leadership & career insights.