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[Other] Anonymous GitHub account mass-dropping undisclosed 0-days

[Other] Spec-driven development (SDD) for AI coding assistants.

[Other] The open source coding agent.

[Other] DSpark: Speculative decoding accelerates LLM inference [pdf]

[Other] Show HN: Hacker News on a train station-style flip board Although the page itself is more just fun to have made and look at (I like the flip sound), the fun part is how I made it to verify the (and I hate to say it) vibe host service I&#x27;ve been working on. The recent flip board back and forth&#x27;s on Twitter (X) are what inspired me.<p>The idea here is that people (like me or you) can create something neat like this, and others can remix it, change it and publish their own version. This is that all in action and it worked great. I wrote a blog about it (the blog is dogfooding, it&#x27;s just an app hosted on quickish that uses the built in db lib).<p>For the HN version of this flip board I use their firebase api via the built in quickish server functions that make use of the fact that the front-end can get realtime updates (now that you mention firebase) from cloud function db updates. Of course that&#x27;s over-kill but I wanted to show something fun. You can remix and host your own version for free, just need a google oauth login that&#x27;s it.<p>OG flip board I built (Portland Based - Current Weather): <a href="https:&#x2F;&#x2F;popflame.quickish.space&#x2F;flipboard-preview" rel="nofollow">https:&#x2F;&#x2F;popflame.quickish.space&#x2F;flipboard-preview</a><p>Blog post that dives a tiny bit deeper: <a href="https:&#x2F;&#x2F;popflame.quickish.space&#x2F;blog&#x2F;hacker-news-on-a-split-flap-board&#x2F;" rel="nofollow">https:&#x2F;&#x2F;popflame.quickish.space&#x2F;blog&#x2F;hacker-news-on-a-split-...</a>

[DevOps] Show HN: Mantis, A self-hosted LLM gateway Hey HNers - Riz here.<p>I got together with a few guys and we built an LLM gateway.<p>It&#x27;s designed for small teams working on early-stage products, and can be deployed to AWS using a single command (i.e. `mantis deploy`).<p>It&#x27;s self-hosted, and is designed to belong to you.

[Other] Slisp: Simple Lisp compiler (Linux/amd64)

[Other] How to Corrupt an SQLite Database File

[Other] Previewing GPT‑5.6 Sol: a next-generation model System card: <a href="https:&#x2F;&#x2F;deploymentsafety.openai.com&#x2F;gpt-5-6-preview" rel="nofollow">https:&#x2F;&#x2F;deploymentsafety.openai.com&#x2F;gpt-5-6-preview</a>

[API/SDK] Show HN: Smart model routing directly in Claude, Codex and Cursor We built a model router that plugs into coding agents (e.g. Claude Code, Codex, Cursor, etc.) and intelligently sends requests to the best model to serve them. Here&#x27;s a quick demo of running it locally: <a href="https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=isKhAyivtfM" rel="nofollow">https:&#x2F;&#x2F;www.youtube.com&#x2F;watch?v=isKhAyivtfM</a>.<p>At Weave, we write most of our code with AI, and it&#x27;s been getting more expensive. This came to a head when Opus 4.7 was released and, thanks to its tokenizer changes, our costs shot up. We knew we didn&#x27;t need Opus for <i>everything</i> but we didn&#x27;t want to lose out on the intelligence for the cases where you really need it. So we decided to build a model router to handle this for us.<p>The Weave Router acts as an Anthropic&#x2F;OpenAI endpoint specifically for coding agents. It looks at every inference request and intelligently (more on that in a sec) decides what model to send it to, handling all the translations required along the way. So it can use faster&#x2F;cheaper models (e.g. DeepSeek v4, GLM 5.2, Kimi K2.6) when possible, and frontier models (Opus 4.8 &amp; GPT 5.5 (&amp; Fable whenever it&#x27;s back)) when necessary.<p>How do we know what model to route to? We trained an RL model on tens of thousands (so far!) of agent traces. We reward the routing model when it selects an LLM that successfully completes the given task.<p>Here&#x27;s an example: if you ask the router to plan a complex change, it will (probably) route that request to Opus 4.8. Subagents exploring the codebase to gather context will be routed to more suitable models (e.g. DeepSeek V4 Flash). Then when you have the plan ready to implement, it will be (most likely) be handed to a quicker model (e.g. GLM 5.2) to carry it out.<p>We&#x27;ve been using this internally for the last month or so. We&#x27;ve saved 40% on tokens vs. what we otherwise would have paid, with no noticeable differences in quality or velocity.<p>The router is source-available under Elastic License 2.0, so you can self-host it. Or if you prefer, you can also use our hosted version: weaverouter.com.<p>I&#x27;ll be here to answer any questions you may have!

[API/SDK] Show HN: ZeroGate – API gateway to scale cloud GPUs to zero when idle

[Other] Incident CVE-2026-LGTM

[Other] Show HN: Jargo – a Golang port of Pipecat for conversational-AI apps A WebRTC-native, audio-first conversational-AI framework for Go.<p>Pipecat is great, and jargo is a port of it — the architecture and many design decisions are Pipecat&#x27;s.<p>But, I prefer Golang.

[Monitoring/Observability] The open and composable observability and data visualization platform. Visualize metrics, logs, and traces from multiple sources like Prometheus, Loki, Elasticsearch, InfluxDB, Postgres and many more.

[Other] Show HN: Git-lazy-mount mount a repo without cloning it. Works with ordinary Git Hello!<p>This is an attempt to make google3 style repo clones work with Git. In a HN thread a few days ago the idea sparked for me.<p>It can be super useful for very large repos that need to be cloned for AI coding sessions that might only need a subset of files to accomplish something.<p>Similar to google3, files appear to be there and can be read and edited but they are only fetched when they are needed.<p>It works with normal Git commands so there is no need for a new CLI.<p>On huge catch is, running grep will force fetch all files that grep glob matches. AI coding sessions run the Grep tool quite often. To mitigate this, git-lazy-mount comes with sgrep that offloads grepping to a remote code search engine like SourceGraph.<p>With this, microVMs that run AI sessions can stay lean and start up much faster.<p>I am guessing this is probably faster than baking in the git repo in the image but I have not measured performance of it yet. It is definitely useful if the microVM is spun up with unknown repositories (something like Claude on web).<p>Curious to hear your thoughts and criticism<p>Thanks!

[Other] A format specification for describing a visual identity to coding agents. DESIGN.md gives agents a persistent, structured understanding of a design system.

[Other] Show HN: DeepSeek Flash inverted the economics of agent products There is an adversarial relationship between developers and big model labs.<p>Model labs charged developers higher API prices to subsidize their own agent harness offerings. Think Anthropic charging 5x higher Claude API prices to subsidize consumer subscriptions. So Cursor in a way was subsidizing their own direct competitor.<p>DeepSeek V4 Flash totally inverted this relationship. Now you have a model that beats even Sonnet in some benchmarks and is totally opensourced. Now inference providers are racing to the bottom to optimize and give cheaper hosting. Every player with a non-SOTA is now racing to swap over to stop paying the big model lab tax, even Microsoft is switching Copilot to use DeepSeek.<p>On switching over to Deepseek:<p>- we noticed over a 100x cost decrease while similar or better performance then Gemini 3 Flash<p>- insane saving from the cached input tokens: $0.002&#x2F;1 Million tokens<p>- both DeepSeek Flash and GLM 5.2 are text-only models, so clearly multimodal training is not worth the additional cost. Language is just a much more efficient sparse representation of the world&#x2F;reasoning than vision<p>- we had a early bet on a text-only web agent harness, and now with DeepSeek this results in unique cost advantages.<p>- we rewrote our harness as a callable DSL library that a model can generate code to execute on. DeepSeek has proven phenomenal on code generation to drive an agent harness.<p>- I would highly recommend everyone to rewrite their harness to be text-only and callable via executable code leveraging DeepSeek V4 Flash.

[IDE/Editor] Show HN: OpenKnowledge – open source AI-first alternative to Obsidian/Notion Hi HN, Nick here. We’re launching OpenKnowledge (<a href="https:&#x2F;&#x2F;openknowledge.ai&#x2F;" rel="nofollow">https:&#x2F;&#x2F;openknowledge.ai&#x2F;</a>), a “what you see is what you get” markdown editor that has direct integrations with Claude, Codex, and Cursor. Available as MacOS app or CLI. Fully free&#x2F;local and OSS (<a href="https:&#x2F;&#x2F;github.com&#x2F;inkeep&#x2F;open-knowledge" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;inkeep&#x2F;open-knowledge</a>).<p>We built this because we wanted a “Google docs” like experience for writing and sharing markdown files across our team. Obsidian is the best alternative we tried, but found it doesn’t have a true “what you see is what you get” UI and it didn’t integrate well with Claude&#x2F;Codex outside of community plugins.<p>So we built OpenKnowledge. It takes shape as:<p>1. A MacOS app with a file navigator, the WYSIWYG editor, and link explorer.<p>2. Integrations with the Claude, Codex, and Cursor desktop apps. The agents can open an OpenKnowledge editor within their embedded web browsers for a side-by-side experience.<p>3. Built-in mcps, skills, and RAG for LLM-wiki and “AI Second Brain” scenarios + spec writing<p>4. An embedded terminal and CLI for TUI-first users<p>OSS stack includes: Tiptap&#x2F;prosemirror, CodeMirror, yjs (CRDT), Electron (MacOS app), Orama, remark&#x2F;rehype&#x2F;micromark&#x2F;mdast, @pierre&#x2F;trees<p>On the architecture side, the interesting eng. challenges included:<p>1. A pipeline to convert ProseMirror to markdown in a bidirectional lossless way. ProseMirror uses ASTs, which are not designed to have byte-fidelity.<p>2. A dual-observer CRDT to keep the ProseMirror and markdown state in-sync.<p>The CRDT + git also power a collaborative experience that shows what Agents are doing in the markdown, have undo&#x2F;redo, and version history. The “Share” and cloud-sync functionality are geared for team collaboration. They feel “no-code” but leverage git&#x2F;GitHub under the hood, which also means data stays fully private.<p>In that spirit, we made OpenKnowledge open source for anybody who’s curious or who’d like to contribute.<p>We’re actively thinking about plugins&#x2F;extensibility and what’s next. If you have suggestions or feedback, would love to hear it.

[Other] Show HN: Autofit2 – End-to-end pipeline for multilingual text classification Hi HN, Stefan here. autofit2 is a project I have been using at my previous company and is now opensourced. It has been used extensively in automated text moderation, but can be applied to any text&#x2F;document classification task. We had success modeling offensive texts in 20+ languages (cf. github.com&#x2F;neospe&#x2F;dataload for all the datasets).<p>It&#x27;s an integrated pipeline for lightweight multilingual text classification, covering preprocessing, training, and evaluation. It implements SetFit, a few-shot learning technique that works well for low-data regimes (down to a few dozen examples), and offers high throughput on CPUs, since it&#x27;s based on Sentence Transformers. Dependencies are kept lean, but of course PyTorch itself isn&#x27;t exactly small.<p>autofit2 takes a base model and a JSON config as input, and outputs a TorchServe model archive as well as a model card. The model card includes any benchmarks you have for your task, self-consistency tests, estimated CO2 emissions of the finetune, as well as an entropy-based bias analysis. For the bias eval, small test corpora for 50 languages are included. It works best with my EAR (Entropy-based Attention Regularization) fork of Sentence Transformers.<p>Feedback is welcome.

[Other] A Tiny Compiler for Data-Parallel Kernels