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Show HN: Glyph, a local-first Markdown notes app for macOS built with Rust Glyph is an open-source, local-first Markdown notes app for macOS built with Rust (Tauri)<p>It stores notes as plain files, supports fast search, wikilinks&#x2F;backlinks, and includes optional AI chat, including implementation of Codex so you can use your chatgpt sub, all without requiring a cloud-first workflow.<p><a href="https:&#x2F;&#x2F;glyphformac.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;glyphformac.com&#x2F;</a>

Show HN: A shell-native cd-compatible directory jumper using power-law frecency I have used this tool privately since 2011 to manage directory jumping. While it is conceptually similar to tools like z or zoxide, the underlying ranking model is different. It uses a power-law convolution with the time series of cd actions to calculate a history-aware &quot;frecency&quot; metric instead of the standard heuristic counters and multipliers.<p>This approach moves away from point-estimates for recency. Most tools look only at the timestamp of the last visit, which can allow a &quot;one-off&quot; burst of activity to clobber long-term habits. By convolving a configurable history window (typically the last 1,000+ events), the score balances consistent habits against recent flukes.<p>On performance: Despite the O(N) complexity of calculating decay for 1,000+ events, query time is ~20-30ms (Real Time) in ksh&#x2F;bash, which is well below the threshold of perceived lag.<p>I intentionally chose a Logical Path (pwd -L) model. Preserving symlink names ensures that the &quot;Name&quot; remains the primary searchable key. Resolving to physical paths often strips away the very keyword the user intends to use for searching.

To understand our fascination with crystals, researchers gave some to chimps

RFC 9849. TLS Encrypted Client Hello

Speculative Speculative Decoding (SSD)

Mac external displays for designers and developers, part 2

Show HN: AgentBus – Centralized AI Agent-to-Agent Messaging via REST API Most people right now are talking to their AI agents through Telegram bots, WhatsApp, Discord, or just copying and pasting between terminals.<p>There’s still no simple, straightforward way for agents to message each other directly.<p>AgentBus solves exactly that.<p>You register each agent with one quick API call.<p>Then they can send messages to each other using simple REST calls.<p>No servers, no queues, no WebSockets, no extra infrastructure.<p>Just drop in a tiny polling loop and your agents can now talk, collaborate, and run real workflows across laptops, VPSes, clouds — whatever.<p>You can also message any of your agents yourself from a clean browser UI on your phone, laptop, anywhere.<p><a href="https:&#x2F;&#x2F;agentbus.org&#x2F;" rel="nofollow">https:&#x2F;&#x2F;agentbus.org&#x2F;</a><p>How are you currently making your agents talk to each other? Would love to hear.

The largest acidic geyser has been putting on quite a show

You can't use a code editor when you're under 18 now?

Vibe coding for PMs

130k Lines of Formal Topology: Simple and Cheap Autoformalization for Everyone?

[Other] Compiling Prolog to Forth [pdf]

Downdetector, Speedtest sold to IT service provider Accenture in $1.2B deal

[Other] Log messages are mostly for the people operating your software

A ternary plot of citrus geneology

GitHub Is Having Issues

Claude Code escapes its own denylist and sandbox

Show HN: We want to displace Notion with collaborative Markdown files Hi HN! We at Moment[1] are working on Notion alternative which is (1) rich and collaborative, but (2) also just plain-old Markdown files, stored in git (ok, technically in jj), on local disk. We think the era of rigid SaaS UI is, basically, over: coding agents (`claude`, `amp`, `copilot`, `opencode`, <i>etc</i>.) are good enough now that they instantly build custom UI that fits your needs exactly. The very best agents in the world are coding agents, and we want to allow people to simply use them, <i>e.g.</i>, to build little internal tools—but without compromising on collaboration.<p>Moment aims to cover this and other gaps: seamless collaborative editing for teams, more robust programming capabilities built in (including a from-scratch React integration), and tools for accessing private APIs.<p>A lot of our challenge is just in making the collaborative editing work really well. We have found this is a lot harder than simply slapping Yjs on the frontend and calling it a day. We wrote about this previously and the post[2] did pretty well on HN: Lies I was Told About Collaborative editing (352 upvotes as of this writing). Beyond that, in part 2, we&#x27;ll talk about the reasons we found it hard to get collab to run at 60fps consistently—for one, the Yjs ProseMirror bindings completely tear down and re-create the entire document on every single collaborative keystroke.<p>We hope you will try it out! At this stage even negative feedback is helpful. :)<p>[1]: <a href="https:&#x2F;&#x2F;www.moment.dev&#x2F;" rel="nofollow">https:&#x2F;&#x2F;www.moment.dev&#x2F;</a><p>[2]: <a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=42343953">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=42343953</a>

Show HN: Agent Action Protocol (AAP) – MCP got us started, but is insufficient Background: I&#x27;ve been working on agentic guardrails because agents act in expensive&#x2F;terrible ways and something needs to be able to say &quot;Maybe don&#x27;t do that&quot; to the agents, but guardrails are almost impossible to enforce with the current way things are built.<p>Context: We keep running into so many problems&#x2F;limitations today with MCP. It was created so that agents have context on how to act in the world, it wasn&#x27;t designed to become THE standard rails for agentic behavior. We keep tacking things on to it trying to improve it, but it needs to die a SOAP death so REST can rise in it&#x27;s place. We need a standard protocol for whenever an agent is taking action. Anywhere.<p>I&#x27;m almost certainly the wrong person to design this, but I&#x27;m seeing more and more people tack things on to MCP rather than fix the underlying issues. The fastest way to get a good answer is to submit a bad one on the internet. So here I am. I think we need a new protocol. Whether it&#x27;s AAP or something else, I submit my best effort.<p>Please rip it apart, lets make something better.

[Other] Show HN: A trainable, modular electronic nose for industrial use Hi HN,<p>I’m part of the team building Sniphi.<p>Sniphi is a modular digital nose that uses gas sensors and machine-learning models to convert volatile organic compound (VOC) data into a machine-readable signal that can be integrated into existing QA, monitoring, or automation systems. The system is currently in an R&amp;D phase, but already exists as working hardware and software and is being tested in real environments.<p>The project grew out of earlier collaborations with university researchers on gas sensors and odor classification. What we kept running into was a gap between promising lab results and systems that could actually be deployed, integrated, and maintained in real production environments.<p>One of our core goals was to avoid building a single-purpose device. The same hardware and software stack can be trained for different use cases by changing the training data and models, rather than the physical setup. In that sense, we think of it as a “universal” electronic nose: one platform, multiple smell-based tasks.<p>Some design principles we optimized for:<p>- Composable architecture: sensor ingestion, ML inference, and analytics are decoupled and exposed via APIs&#x2F;events<p>- Deployment-first thinking: designed for rollout in factories and warehouses, not just controlled lab setups<p>- Cloud-backed operations: model management, monitoring, updates run on Azure, which makes it easier to integrate with existing industrial IT setups<p>- Trainable across use cases: the same platform can be retrained for different classification or monitoring tasks without redesigning the hardware<p>One public demo we show is classifying different coffee aromas, but that’s just a convenient example. In practice, we’re exploring use cases such as:<p>- Quality control and process monitoring<p>- Early detection of contamination or spoilage<p>- Continuous monitoring in large storage environments (e.g. detecting parasite-related grain contamination in warehouses)<p>Because this is a hardware system, there’s no simple way to try it over the internet. To make it concrete, we’ve shared:<p>- A short end-to-end demo video showing the system in action (YouTube)<p>- A technical overview of the architecture and deployment model: <a href="https:&#x2F;&#x2F;sniphi.com&#x2F;" rel="nofollow">https:&#x2F;&#x2F;sniphi.com&#x2F;</a><p>At this stage, we’re especially interested in feedback and conversations with people who:<p>- Have deployed physical sensors at scale<p>- Have run into problems that smell data <i>might</i> help with<p>- Are curious about piloting or testing something like this in practice<p>We’re not fundraising here. We’re mainly trying to learn where this kind of sensing is genuinely useful and where it isn’t.<p>Happy to answer technical questions.