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AI 资讯

How I Rebuilt My Entire User Feedback Workflow with FeedLog (And Why I Ditched Canny)

Six months into running my SaaS, my "feedback system" was three browser tabs, a starred Gmail folder, and a sticky note on my monitor that said "check Discord." That was the whole system. It held together until the day I found a three-paragraph email from a paying user — a genuinely detailed feature request with a real use case — sitting unread for 24 days. His last line was: "Happy to pay more if you can support this." I replied the same afternoon I found it. His reply: "Switched last week, thanks anyway." That was the moment I stopped treating feedback management as a nice-to-have. Why the usual fixes didn't fix anything I tried the obvious things first. I want to document them because I see a lot of people cycling through the same failed solutions. Notion database 🪦 Built a beautiful one. Color-coded tags, priority columns, status tracking. It lasted 11 days before nobody — including me — was maintaining it. The friction of "open Notion, find the right database, fill in six fields" is invisible when you're designing the system and fatal when you're in the middle of a support conversation. Airtable form 🪦 Better entry point, still disconnected from where users actually were when they had feedback. Nobody bookmarks your Airtable form. They DM you on Discord and you think "I'll add that later" and you don't. Canny — this one actually worked, for a while I genuinely liked Canny. Clean interface, users could upvote requests, I could see what was popular. It felt like a real system. Then our user count grew and the pricing tier jumped. I was looking at $99/month for a feedback board for a product still finding its footing. That's not a moral judgment on Canny — it's a fair product — but for a bootstrapped indie dev, it started feeling like a tax on momentum. The deeper problem with all three solutions was the same: they were inboxes, not loops. User submits → enters the void → user never knows if anyone saw it → user assumes nobody did → trust erodes → churn. I had bui

2026-06-01 原文 →
AI 资讯

public-apis: what 438k stars actually buy you, and what they don't

Repository: public-apis/public-apis What public-apis actually is public-apis is a community-curated directory of free and public APIs, maintained by contributors together with staff at APILayer. It is not a library, SDK, or gateway: there is no package to import and nothing to run in production. The repository is essentially one very large, structured README that catalogs APIs across roughly fifty categories, from Animals and Anime to Finance, Machine Learning, Security, and Weather. Each entry is a row in a table with five columns: the API, a short description, the authentication model ( apiKey , OAuth , or none), whether it serves over HTTPS, and whether it sets permissive CORS headers. That last detail is the part most engineers undervalue. Why engineers keep coming back to it The star count, now past 438,000, is less interesting than the metadata discipline. When you are prototyping and need a currency-exchange or geocoding endpoint, the Auth/HTTPS/CORS columns let you filter candidates before you ever open a browser tab. "No auth, HTTPS yes, CORS yes" tells you that you can call the endpoint directly from a front-end spike without standing up a proxy or registering for a key. For throwaway demos, hackathons, internal tools, and teaching material, that triage saves real time. The category index doubles as a map of what kinds of public data are actually available, which helps when you are scoping whether an idea is even feasible. How it is maintained Curation is manual and community-driven: changes arrive as pull requests against the README, governed by a contributing guide, with issues and PRs as the moderation surface. The project's primary language is Python, reflecting validation tooling that checks entries rather than any runtime you would consume. There is also a separate companion project that exposes the list itself as an API. The model is simple and has clearly scaled, but "manually curated" is both the strength and the weakness. Limitations worth statin

2026-06-01 原文 →
开发者

🌐OS May Recap: Learning to Navigate the Open-Source Galactica

In May, I continued my "One Commit a Day" Challenge and spent more time contributing across different open-source projects. Compared to April, I was able to contribute a bit more and explore a wider variety of repositories. Repositories That Stood Out Some of the projects that left the biggest impression on me were: python-odpt Huggin Face Context Course Human Signal ML ScribeSVG A Stable Checkpoint One milestone I was happy about this month was reaching a stable checkpoint for my Tokyo MCP Server project. It is still a work in progress, but getting to a point where the project feels stable enough to build upon was a satisfying moment. Documentation Matters Another contribution that stood out was helping improve a python-odpt README documentation . It wasn't a large technical contribution, but it reminded me that making a project easier for others to understand can be just as valuable as writing code. Good documentation lowers the barrier for future contributors. Sometimes, a clearer README can help more people than a small code change. Learning Beyond Python One practical lesson I learned this month was that being a Python-focused contributor doesn't mean I can ignore the JavaScript ecosystem . While working with different repositories, I finally installed Node.js and started using npm . Many modern open-source projects rely on TypeScript-based tooling, build systems, or development workflows, and understanding those tools makes contributing much easier. The Biggest Challenge: Finding Information And Communication Matters The biggest challenge I faced wasn't coding. It was documentation. Every repository has its own way of organizing information. There are definitely common patterns, but every project also develops its own style over time. Sometimes the information I need is in the README. Sometimes it's in a wiki. Sometimes it's buried in a docs folder several levels deep. And sometimes it's spread across all three. Open Source Is Also About Navigation As a contri

2026-06-01 原文 →
AI 资讯

UbuCon26 Kenya

Stepping up to give my first-ever presentation at UbuCon 2026 was a massive milestone, and honestly, it was pretty intimidating. The stakes felt high, especially with the live demo. It was a race against the clock to get everything running, and it only finally came together exactly ten minutes before I went on stage. Talk about a close call. While I am proud of what I delivered, I originally wanted to pack even more into the session. I had planned to showcase a simulated mission, Gazebo visualizations and RViz path simulations. While time caught up with me for the presentation, these features are still actively in the works over at the aeronix project. My goal is to have the entire end-to-end setup completed and ready by the end of the year. I connected with some incredible engineers and industry peers and I am looking forward to building on those conversations for future professional collaborations. This experience proved that the best way to grow is to just put yourself out there. Moving forward, I plan to keep speaking on topics that challenge me. It is the ultimate way to deepen my own technical understanding share what I have learned with the community and grow professionally.

2026-06-01 原文 →
AI 资讯

Your RL Agent Failed a 12-Step Task. Which Step Was Wrong? (The Supervision Problem in Agentic RL)

About this series. I'm going to take a fresh paper - Self-Distilled Agentic Reinforcement Learning (SDAR, arXiv:2605.15155 ) - and architect it end to end on AWS: the system design, the actual gate code, the evaluation plan, and a brutally honest cost model. What I'm not going to do is wave a benchmark number around. Reproducing a paper like this costs thousands in GPU time, and I'd rather show you the machinery than a screenshot you can't audit. The design is the deliverable. This is Part 1. A small, infuriating problem Picture an LLM agent working a web-shopping task. It reads the goal, searches, clicks a category, filters, opens a product, compares, adds to cart - twelve steps in all. At the end, it bought the wrong thing. So you do what reinforcement learning tells you to do: you score the trajectory. Reward = 0. Bad agent. Now answer this: which of the twelve steps was actually wrong? Maybe step 3, the search query, was fine and step 9, a filter choice, doomed everything. Maybe steps 1–11 were brilliant and step 12 fat-fingered the wrong button. Your single scalar reward has no idea. It punishes all twelve equally, including the eight that were correct. That's the supervision problem in agentic RL, and it's the thing this whole series is about. Why "just use RL" isn't enough for agents RL has become the default way to post-train LLM agents. The catch is that the reward usually lands at the trajectory level - one number for the entire multi-step episode. For a single-turn task ("answer this question"), that's tolerable; the action and the outcome are close together. For a long-horizon agent - ten, twenty, fifty turns of searching, calling tools, and reacting to an environment - it's a disaster of credit assignment . The signal is too coarse to tell the model which decisions earned the reward and which torpedoed it. You can throw more episodes at it and let statistics sort the credit out eventually. But "eventually" on a 30-turn task burns a lot of expensive comp

2026-05-31 原文 →
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