Show HN: Readn – Feed reader with Hacker News support https://ift.tt/7E6k348

Show HN: Readn – Feed reader with Hacker News support This feed reader can fetch and display discussion threads from Hacker News and Lobste.rs, making it convenient to follow both articles and the conversations around them. It’s a fork of the original Yarr project, whose author considers it feature-complete and is no longer accepting feature requests. https://ift.tt/RD5l18F August 30, 2025 at 12:01AM

Prioritizing Safety at Schools Citywide: An Update on Our Crossing Guard Program

Prioritizing Safety at Schools Citywide: An Update on Our Crossing Guard Program
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Our teams work year-round to improve safety at San Francisco schools. That includes crossing guards who support more than 90 campuses citywide. As students across the city head back to class, our teams have been working hard to make sure their trips are safe and reliable. Our Crossing Guard Program is a service we provide to schools and an important part of our work to help students get to and from school safely. Crossing guards are beloved by students, parents, caregivers and neighbors – and right now, we are facing a shortage. While there have been no cuts to the roughly $4 million crossing...

Published August 29, 2025 at 05:30AM
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Show HN: An open source implementation of OpenStreetMap in Electron https://ift.tt/3OPuQSd

Show HN: An open source implementation of OpenStreetMap in Electron https://ift.tt/dzpgb4O August 30, 2025 at 02:14AM

Show HN: Magic links – Get video and dev logs without installing anything https://ift.tt/Bscligu

Show HN: Magic links – Get video and dev logs without installing anything Hey HN, For a while now, our team has been trying to solve a common problem: getting all the context needed to debug a bug report without the endless back-and-forth. It’s hard to fix what you can't see, and console logs, network requests, and other dev data are usually missing from bug reports. We’ve been working on a new tool called Recording Links. The idea is simple: you send a link to a user or teammate, and when they record their screen to show an issue, the link automatically captures a video of the problem along with all the dev context, like console logs and network requests. Our goal is to make it so you can get a complete, debuggable bug report in one go. We think this can save a ton of time that's normally spent on follow-up calls and emails. We’re a small team and would genuinely appreciate your thoughts on this. Is this a problem you face? How would you improve this? Any and all feedback—positive or critical—would be incredibly helpful as we continue to build. PS - you can try it out from here: https://ift.tt/eOFStpA August 27, 2025 at 10:21AM

Show HN: Smart Buildings Powered by SparkplugB, Aklivity Zilla, and Kafka https://ift.tt/h9McNAC

Show HN: Smart Buildings Powered by SparkplugB, Aklivity Zilla, and Kafka https://ift.tt/T4fsynM August 29, 2025 at 03:03AM

Show HN: A private, flat monthly subscription for open-source LLMs https://ift.tt/erkdGPB

Show HN: A private, flat monthly subscription for open-source LLMs Hey HN! We've run our privacy-focused open-source inference company for a while now, and we're launching a flat monthly subscription similar to Anthropic's. It should work with Cline, Roo, KiloCode, Aider, etc — any OpenAI-compatible API client should do. The rate limits at every tier are higher than the Claude rate limits, so even if you prefer using Claude it can be a helpful backup for when you're rate limited, for a pretty low price. Let me know if you have any feedback! https://ift.tt/gkUeArj August 29, 2025 at 12:33AM

Show HN: Persistent Mind Model (PMM) – Update: an model-agnostic "mind-layer" https://ift.tt/gNDEK3X

Show HN: Persistent Mind Model (PMM) – Update: an model-agnostic "mind-layer" A few weeks ago I shared the Persistent Mind Model (PMM) — a Python framework for giving an AI assistant a durable identity and memory across sessions, devices, and even model back-ends. Since then, I’ve added some big updates: - DevTaskManager — PMM can now autonomously open, track, and close its own development tasks, with event-logged lifecycle (task_created, task_progress, task_closed). - BehaviorEngine hook — scans replies for artifacts (e.g. Done: lines, PR links, file references) and uto-generates evidence events; commitments now close with confidence thresholds instead of vibes. - Autonomy probes — new API endpoints (/autonomy/tasks, /autonomy/status) expose live metrics: open tasks, commitment close rates, reflection contract pass-rate, drift signals. - Slow-burn evolution — identity and personality traits evolve steadily through reflections and “drift,” rather than resetting each session. Why this matters: Most agent frameworks feel impressive for a single run but collapse without continuity. PMM is different: it keeps an append-only event chain (SQLite hash-chained), a JSON self-model, and evidence-gated commitments. That means it can persist identity and behavior across LLMs — swap OpenAI for a local Ollama model and the “mind” stays intact. In simple terms: PMM is an AI that remembers, stays consistent, and slowly develops a self-referential identity over time. Right now the evolution of it "identity" is slow, for stability and testing reasons, but it works. I’d love feedback on: What you’d want from an “AI mind-layer” like this. Whether the probes (metrics, pass-rate, evidence ratio) surface the right signals. How you’d imagine using something like this (personal assistant, embodied agent, research tool?). https://ift.tt/ackJWNR August 29, 2025 at 12:04AM