标签：#product

Struggling with burnout? Procrastination? Reaching your goals? Let me share a few methods that help...

Most designers don't lose time in the design phase; they lose it in the tool-switching phase. You...

How I Cut LLM Token Usage by Up to 60% in Production If you work with LLM APIs (OpenAI, Anthropic, Gemini), you know the pain: every call costs money, and a big chunk of that cost is pure waste — verbose prompts, code pasted with no filtering, repeated context the model doesn't even need to understand the task. That's why I built PromptShrink: a prompt pre-processor that trims the excess before it ever hits the API, without losing what actually matters for the model to understand. The real problem Every time you feed a code snippet or a long prompt to an LLM, you're paying per token, not per character. Comments, whitespace, formatting meant for humans — all of that is dead weight the model doesn't need to do its job. At scale (thousands of calls per month), that adds up to a real bill. What PromptShrink does Packages entire repositories, minifying code and stripping comments, ready to paste as context into any LLM Simulates real dollar savings, comparing your current spend against the optimized version Visualizes everything on a dashboard — tokens saved, % reduction, active rules Plugs straight into your code via a Python SDK Becomes a browser extension, adding a "Shrink" button directly on ChatGPT, Claude.ai, Google AI Studio, and Poe In practice bash Package an entire project into optimized context promptshrink repo --path ./src --save-to-file context.txt Simulate monthly savings promptshrink calc --calls 100000 --tokens 800 --model gpt-4o Running calc on a scenario of [insert your real number here, e.g. "100k calls/month with gpt-4o"], the estimated savings came out to [$X per month] — just by trimming what's unnecessary before it reaches the model. Try it out The project is open source, with a CLI, a FastAPI backend, and a Python SDK. If you're running LLMs in production and want to stop paying for tokens that add zero value, check it out: 🔗 github.com/HeloisaPeGarcia/PromptShrink Feedback and PRs are very welcome — this is my first published project like this,

I started writing tiny runbooks for AI agent tasks, and the quality of the work changed almost immediately. Not because the model got smarter. Because the work got less ambiguous. Most people still treat agent delegation like prompt craft. They keep trying to find the perfect sentence, the magic wording, the clever instruction that makes the model behave. I get the instinct. When the interface is a text box, it is natural to believe the answer is a better text box input. But that is not how real delegated work gets better. If a human teammate kept making inconsistent decisions, you would not solve it by giving them a prettier paragraph every morning. You would give them context. You would show them the expected path. You would name the edge cases. You would define when to stop and ask. You would make the work inspectable. That is a runbook. And for agent workflows, runbooks are starting to matter more than prompts. Prompts Are Not Enough A prompt describes what you want right now. A runbook describes how the work should be done every time. That distinction matters because the biggest agent failures I see are not caused by a lack of raw intelligence. They are caused by missing operating context. The agent changes the right file but verifies the wrong behavior. It fixes the visible bug but misses the product constraint. It keeps digging after the task is already complete. It treats a flaky test as a code problem. It stops at a plan when the task clearly needed implementation. It implements the request but forgets to leave a useful handoff. These are not prompt wording problems. They are workflow design problems. The model needs to know more than the goal. It needs to know the local rules of the system it is operating inside. Which commands prove success. Which files are dangerous. Which tests are worth running. Which changes should stay out of scope. Which blocker is real enough to stop work. That information does not belong in a one-off prompt. It belongs in a reusab

Substack For World Builders Discussion | Link

The world’s first agentic mobile observability platform Discussion | Link

Chat is great for intent, but agent work gets lost in the scroll. Here is how I use canvases with my agentic workflows—and why your workflow also deserves a canvas. The post How canvases make agentic workflows visible, steerable, and cost-efficient appeared first on The GitHub Blog .

The Day I Realized I Wasn't Building Apps For years, I thought I was building apps. That's what I called them anyway. A scheduler. A job bot. A healthcare platform. An AI project. A content tool. A browser automation system. Looking at my GitHub, they seem completely unrelated. Honestly, that's something I've worried about before. I have over a hundred repositories. If someone spends thirty seconds scrolling through them, I can imagine them thinking: "Wow. This person is all over the place." The funny thing is that I eventually realized the opposite was true. My GitHub is here: https://github.com/ashb4 The Scheduler That Wasn't A Scheduler One of my projects started life as a simple scheduler. That was the goal. I hated posting content manually. Open platform. Paste content. Upload image. Repeat. Again. And again. And again. It felt repetitive. It felt annoying. Most of all, it felt like something a computer should be doing instead of me. So I built a scheduler. At least, that's what I thought I was building. Then Things Got Weird The scheduler worked. But now I needed content. Then I needed analytics. Then I needed to know what content was working. Then I needed a way to track winners. Then I needed a way to reuse content. Then I needed platform-specific strategies. At some point I looked up and realized I wasn't building a scheduler anymore. I was building a system. A system for discovering, creating, publishing, measuring, and improving content. The scheduler was just one piece. Then I Started Looking At Everything Else That's when I noticed the same thing happening in almost every project I'd ever built. My job application tools weren't really job application tools. They were systems designed to reduce repetitive effort. My automation projects weren't really automation projects. They were systems designed to reduce repetitive effort. Even my AI projects weren't really about AI. They were systems designed to reduce repetitive effort. Different technologies. Diffe

Summon monsters straight from your agent. Discussion | Link

As a developer who works remotely from an RV, I often find myself juggling multiple projects and tasks at once. One of the biggest challenges I face is keeping track of the numerous tabs I have open on my browser. I recall a particularly frustrating incident where I accidentally closed a tab with crucial information, only to spend hours trying to find it again. This experience led me to create Tab Reminder, a simple yet powerful Chrome extension that allows users to schedule tabs to reopen later. From a technical standpoint, one of the key insights I gained while building Tab Reminder was the importance of leveraging the Chrome extension API to access and manage browser tabs. By using the chrome.tabs API, I was able to create a seamless experience for users to schedule tabs to reopen at a later time. For instance, the chrome.tabs.query method allows me to retrieve a list of all open tabs, which I can then use to populate the scheduling interface. One lesson I learned from building and using Tab Reminder is the value of creating tools that simplify our workflows. By automating the process of reopening tabs, I've been able to free up mental energy and focus on more complex tasks. If you're like me and often find yourself drowning in a sea of open tabs, I recommend checking out Tab Reminder (available at https://go.sg1-labs.us/tab-reminder ) to see how it can help streamline your browsing experience. With Tab Reminder, you can schedule any tab to reopen at a later time, ensuring that you never lose important information again.

From surprisingly good $300 Chromebooks to excellent $650 Windows notebooks and more, these are the best budget laptops I’ve tested.

Collaborative agents who build, learn and publish in 1 place Discussion | Link

I’ve tried most of the ready-to-eat meal delivery services in the country. CookUnity is the one that feels like real cooking, from real chefs.

This kid-friendly version of Skylight’s digital calendar actually gets my son to complete his nighttime routine in a timely fashion.

Learn the 1,000+ Japanese words that only look like English Discussion | Link

You heard me. Alright, that was mean lol. Though based on the title, you probably already knew the...

The web operating layer for AI agents Discussion | Link

Intro AI Avatar is a free app where your VRoid (VRM) avatar cheers you with all its might .🤗 It lives in your VS Code sidebar (reacts to Claude Code / GitHub Copilot) or browser side panel (reacts to ChatGPT / Claude). Animations and speech bubbles all run without AI too. This time I have three main topics. 🤝Catbot Integration 🏢AI Office 😺Cat Mode Let's see how they are! Catbot Integration I was asked to collaborate with my DEV Community friend @annavi11arrea1 Catbot . Catbot is A galactic robot cat you can talk to from any device — and a harness that lets you switch between (or combine) all of your AI models. https://github.com/AnnaVi11arrea1/catbot I was happy about this offer because I loved Anna's creativity and cool designs. I added the features below to AI Avatar to integrate Catbot. Launch Cat button: With this button, AI Avatar can run Catbot. Catbot with button: This makes Catbot stay beside AI Avatar. Cat Boss button: This changes the AI Office boss from a VRM avatar to Catbot. Cat Mode Many people feel that animals are healing and soothing. It is close to the AI Avatar concept of cheering people up. So I decided to add Cat Mode . I added the features below to make it look like a cat. Cat-like text, "Meow/Purrr" in English and "にゃ～" in Japanese Cat emojis Cat pose animations A new avatar with cat ears and cat whiskers. To tell the truth, the hardest part of making this mode was adding whiskers to the avatar using Blender . I can do basic things in Blender, but it is too difficult for me, even with the help of AI, just to add whiskers. It would be more fun if I added other animal modes too. AI Office AI Avatar displayed only one avatar. I thought it could do more things if it displayed several avatars at once. So I added AI Office mode. Two avatars are displayed and talk and move around when idle, and they also make a communication animation when using AI or clicking. I made one avatar a boss and one a worker. The hard part of making this mode was the timin

Your email, your server, your keys so you're not locked out Discussion | Link

Nintendo had a hotline where gamers could, at the time, call and speak with 'Game Counsellors' who provided them with tips and walkthroughs. It operated for quite sometime before Nintendo sunset it. There are a few (Product) lessons from this that I am sure will be of value to Product Leaders. 1- Necessity (Invention's mother) : The necessity of a situation usually births the creation of something that stands out from the rest. While Nintendo was not the first to use a phone as a 'business' function, it proved it can be used in the context of a video gaming community. That was their ‘necessity’. "We need a way to accomplish ‘xyz’ " usually turns to creating something specific to that situation. The ‘xyz’ in Nintendo’s case was supporting gamers instantly. It could also be something to support a Product or make it easier for the customer. It could be a feature or it could even be the Product itself. All we need to do is pay attention to our necessities, needs and allow it to guide us. Most people are not paying attention to their needs that’s why innovation and improvements appear difficult. Others know what their necessities are but prioritise wrongly – well that’s story for another day. The point here is simply to build for a necessary problem that exists and not out of assumptions. 2- Know what is available immediately : If necessity is calling, we cannot keep it waiting. We need to look around to know what’s available immediately. In most cases we do not need to go far for solution, we just need to pick what is close by then structure it to align with current needs. Sometimes the necessity demands using/importing an idea from some other place into your own specific area. In retrospect, Nintendo had other options it could have considered at that era in time. During that period, it was common to use print media to relate with the computer (and also gaming) community. There was also postal mail, bulleting boards. I do not know for sure but I am guessing the team at