After years of following @lennysan's wonderful takes on product, I finally had the opportunity to chat with him about AI products! piped.video/watch?v=qbvY0dQg… 1. Many AI product problems aren’t because of AI. It’s usually because of user experience, data quality, or organizational structure. A chatbot failed to get traction because their targeted users simply couldn’t type (because their hands were usually busy — taking care of kids or driving), so showing pre-populated questions and adding a voice option significantly improved traction. Another team told me their lead scoring model was broken. It turns out that it’s because the marketing team wasn’t asking the right questions to get data. The biggest product improvements still come from understanding your users, preparing your data, and investing in your team! 2. Senior engineers see the most productivity improvement with AI coding because they have more experience with writing design docs and API specs, which help them write better instructions. However, they’re also more resistant to using AI for coding. Senior folks are often more opinionated and get frustrated easily when AI doesn’t do what they want. 3. Many teams spend a lot of time debating which tool to use, which can be counter-productive. When teams ask me which of the 2 tools to use, I usually ask 2 questions: “How much performance improvement will the optional tool give over the less optimal one?” –> If the improvement is small, then spend less time debating. “How hard is it to change from one tool to another once you’ve adopted it?” –> If the tool is new and not yet battle tested, I’d think twice about adopting something that I can’t get out later. 4. Many people know that the most effective way to learn AI is to build with AI. Yet, people keep asking me: “But what should I build?” We seem to be having an “idea crisis”. We have all these wonderful tools to help us build things, and no idea what to build. An exercise I often recommend is to spend a week noticing what frustrates you in your daily work, then build small tools to solve those specific pain points.
@chipro
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AI Model Struggles with Following Simple Em Dash Instructions
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this seems to have reached a corner of the internet that my innocent soul wasn't ready for. "why do you hate em dashes so much?" this isn't about punctuation. this is about getting AI to follow simple instructions "what model was it?" 4o "why not use a thinking model? it works fine on o3" i don't want to have to use an expensive, slow model just to fix some typos. there's also a limit for o3 usage. "just add the instruction to exclude em dash to every message" yes, i can, but we shouldn't have to "it's not that hard to remove the em dashes yourself" not the point "write your own words lol" also not the point
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ChatGPT’s excessive use of em-dashes frustrates user
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$100 for anyone who can show me how to get ChatGPT to stop using emdashes. it's driving me insane
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Manus AI: Context Engineering Over Model Development Strategy
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Manus AI Manus AI chose to focus on context engineering rather than developing models. If you were to start an agentic company today, which would you invest in?
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Dynamic Few Shot Prompting to Prevent Agent Overfitting
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3. Dynamic few shot prompting They cautioned against using traditional few shot prompting for agents. Seeing the same few examples repeatedly will cause the agent to overfit to these examples. Ex: if you ask the agent to process a batch of 20 resumes, and one example in the prompt visits the job description, the agent might visit the same job description 20 times for these 20 resumes. Their solution is to introduce small structured variations each time an example is used: different phrasing, minor noise in formatting, etc. [Translated from EN to English]
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Tool Use in Agents: Managing Complexity and Forcing Tool Selection
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2. Tool use Given how easy it is to add new tools (e.g., with MCP servers), the number of tools a user adds to an agent can explode. Too many tools make it easier for the agent to choose the wrong action, making them dumber. They caution against removing tools mid-iteration. Instead, you can force an agent to choose certain tools with response prefilling. Ex: starting your response with <|im_start|>assistant<tool_call>{"name": “browser_ forces the agent to choose a browser. Name your tools so that related tools have the same prefix. Eg: browser tools should start with `browser_`, and command line tools should start with `shell_`
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Context Engineering: Reversible Compact Summaries for Memory Management
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Very useful tips on tool use and memory from Manus's context engineering blog post. Key takeaways. 1. Reversible compact summary Most models allow 128K context, which can easily fill up after a few turns when working with data like PDFs or web pages. When the context gets full, they have to compact it. It’s important to compact the context so that it’s reversible. Eg, removing the content of a file/web page if the path/URL is kept.
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Human Brain Bottleneck Limits AI Coding Agent Productivity
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I’m slowly beginning to accept that my productivity, when working with AI coding agents, is limited by my human brain. AI can do many tasks in parallel, but I can only track the context of a few, so I only run a few tasks at a time. I am the bottleneck.
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Claude Code’s File Function Name Memory Limitations
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if you ask it to do something, it usually starts with: let me find where in the code base this is implemented. it'd guess what a name a file/function should be, and search for it. it surprises me that Claude Code doesn't keep track of all the file/function names. feels like a
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Sniffly: Open-Source AI Instruction Management Tool
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4. Sniffly also allows me to walk through all my previous instructions and model’s responses, and I can also share them with my collaborators if needed. Sniffly is open-sourced and can be used without installation uvx sniffly@latest init GitHub: