Show HN: Continuous Nvidia CUDA PC Sampling Profiler https://ift.tt/R1rlsmk

Show HN: Continuous Nvidia CUDA PC Sampling Profiler Blog post about how we extended our open source profiler to include support for continuous production PC sampling. https://ift.tt/GyJPTSd June 15, 2026 at 09:19PM

Show HN: PostgreSQL MCP Server with 135 tools for various purpose https://ift.tt/AUzZ3qo

Show HN: PostgreSQL MCP Server with 135 tools for various purpose https://ift.tt/OaSVRgo June 20, 2026 at 12:42AM

Show HN: NanoEuler – GPT-2 scale model in pure C/CUDA from scratch https://ift.tt/qpd4VQU

Show HN: NanoEuler – GPT-2 scale model in pure C/CUDA from scratch https://ift.tt/K1cvMG0 June 19, 2026 at 11:48PM

Show HN: I built a daily flag quiz in honor of the World Cup https://ift.tt/IGNAcYy

Show HN: I built a daily flag quiz in honor of the World Cup https://orbisearth.web.app/ June 19, 2026 at 01:45AM

Show HN: Run Agent Skills with mistral.rs v0.8.10: /v1/skills support and more https://ift.tt/OFbKrzN

Show HN: Run Agent Skills with mistral.rs v0.8.10: /v1/skills support and more Hey all! I'm the maintainer of mistral.rs. I just landed support for OpenAI-compatible Agent Skills via a /v1/skills endpoint, and it works with local open models. Until now Skills have basically been locked to closed models, and with the ability to have private, local intelligence becoming increasingly important, but this feature allows you to do XYZ with local models. It's fully compatible with OpenAI's /v1/skills API, so you can drop mistral.rs into your existing code with minimal difficulty. We support the accompanying tools too: /v1/files or input_file for attaching files to your prompts, and mistral.rs also allows models to send generated files back using the OpenAI-compatible method. It's also easier than ever to try mistral.rs: we are including prebuilt binaries for NVIDIA CUDA, Apple Silicon, and CPU! # Linux/Mac > curl --proto '=https' --tlsv1.2 -sSf https://ift.tt/L2OIdhJ... | sh # Windows > irm https://ift.tt/L2OIdhJ... | iex Then: mistralrs serve --agent --isq 4 -m google/gemma-4-E4B-it Super excited for you to try this out and any feedback! Do you have any suggestions for what you would like to see in the next releases? Check out the GitHub: https://ift.tt/choOgPQ Docs & Quickstart: https://ericlbuehler.github.io/mistral.rs/ June 18, 2026 at 12:33PM

Show HN: NGB, an open-source .NET platform for document-driven business apps https://ift.tt/wOAsXtz

Show HN: NGB, an open-source .NET platform for document-driven business apps https://ift.tt/hoYmpEy June 18, 2026 at 11:20PM

Show HN: Reyn – local-first AI that journals and recalls your work https://ift.tt/8T1CW4Z

Show HN: Reyn – local-first AI that journals and recalls your work Hey HN, I built Reyn - which I like to describe as "granola but for everything". You're probably thinking another screen capture AI tool (which is true). Same as always, the biggest question that comes up is privacy, so I'll talk about that first 1. raw screen data is never stored in the cloud 2. user controlled filters are granular to the point that you're able to configure specific apps, windows, websites, or even keywords to be discarded immediately (once again never leaving your mac) and never captured down the pipeline I personally built it because I find it useful and always had the problem of organizing my day (not note taking or task management), as well as sharing context on things that just happened to go undocumented throughout my day. As I was building it I decided to go even further and see if I could collect useful insights and find room for improvements in my day to day workflow. This led to the current version of Reyn and its differentiating factor being the fact that it has a proactive layer. Most tools in this space are reactive - you ask, they retrieve. Reyn surfaces insights on its own and sends a daily recap of what you worked on, what's still open, and what deserves attention. The journal feature also lets you search across basically anything you've done on your Mac. The proactive insights work by first having you configure what your ideal workday looks like — whether that's hours worked or the type of work being done. We have a few broad categories that tasks fall under, with more customization coming. Current integrations: Obsidian (available now, improvements in progress) Gmail, calendar, web search via a floating window with some agentic functionality Notion (coming soon) BYOK for LLM API requests (on the roadmap) ... and more It's still early, but the journal and insights features are the strongest parts right now. Would love some feedback especially on the privacy model. My personal take - I think with enough safeguards in place, the data aggregated about your work is fully in your control. A lot of these data sources already store your data. If you're using Notion, Claude, or just browsing a website, that data is already being stored somewhere. Reyn is just aggregating it and putting it to work for you. Happy to answer any questions about how it works usereyn.com (public beta) https://ift.tt/0nSvKg7 June 18, 2026 at 04:01AM

Show HN: Vpod – Tiny Linux sandbox running in WASM https://ift.tt/uXc7zsa

Show HN: Vpod – Tiny Linux sandbox running in WASM Hi HN, I spent the last few months reading the RISC‑V specification to build the lightest possible sandboxes. The idea behind a vpod is to quickly spin up a Linux sandbox from snapshots (Alpine by default) without any setup or subsystem required. The trade-off for portability and security is raw CPU speed. So we don't expect it to match native workloads with Python or pip, for example. More info is in the README https://ift.tt/YygiS9H Happy to answer any questions! https://ift.tt/YygiS9H June 17, 2026 at 10:11PM

Show HN: Sabela – A Reactive Notebook for Haskell https://ift.tt/J0S1c8N

Show HN: Sabela – A Reactive Notebook for Haskell Sabela is a reactive notebook for Haskell. The name is the Ndebele word for "to respond." Cells respond to each other on change. Initially it was meant as a tool for working with data but it has turned out to have a lot of pedagogical value outside of data analysis work. There is a gallery to read through on the website and a number of examples in the repo showcasing things like: * Python interop * Widgets and animation * Exploratory data analysis If you find any of this interesting please try it out. Any feedback is welcome. https://ift.tt/6QnF0UZ June 14, 2026 at 02:03PM

Show HN: Ctx, save tokens by loading only the relevant tools https://ift.tt/9Wcobtv

Show HN: Ctx, save tokens by loading only the relevant tools Hi HN! Token cost has started to become a high topic of concern to all of us. I tried a few (awesome) tools such as rtk, caveman, and the recent (hillarious but effective) ponytail. What they usually do, is in-line token reduction, e.g. try to compress requests / responses as much as possible. But then it hit me (and I’m sure others had similar ideas) - just like we have routers that pick the right model, why not have something that will also narrow down the amount of available tools, skills and mcps based on repo/context? People usually accumulate skills, agents, MCP servers, harnesses, prompts, repo instructions, and local scripts. I’m not saying we are all hoarders, but we sort of are. When did you remove a skill recently? After a while, the model has way too many options to choose from. ctx tries to fix that by selecting context before the session gets bloated.So no, it doesn’t cleanup your messy garage, but it gives you magic glasses that let you focus only on the tools you need. It does it by watching the repo and task, walks a graph of available tooling, and recommends a small top-scored bundle of skills, agents, MCP servers, and harnesses. How does it know? To make sure results are not hallucinated, and repeatable, I curated a list of 91k+ skills, 467 agents, 10.7k MCP servers, 207 harnesses, and built a graph to help ctx make decisions on what to recommend. While I used AI to generate it of course, I curated it and revised it to make sure the data is up to date. So how this is different from rtk, caveman, ponytail, and similar token-saving tools? As mentioned above those tools mostly reduce tokens after something is already being used. rtk compresses command output. caveman-style tools make the assistant respond with fewer words. ponytail, is, well, awesome, but again it focuses more on reducing code (YAGNI) ctx is upstream. It tries to avoid loading irrelevant skills, agents, MCPs, and harnesses into context at all. So it is not really a replacement. It should work side by side with them! Use ctx to choose the right tools. Use rtk to reduce terminal-output noise. Use terse-output tools if you want shorter responses. The goal is simple: save tokens without forcing the user to manually test and compare thousands of possible skills, agents, MCP servers, and harnesses. Repo: https://ift.tt/zjWi0xF https://ift.tt/zjWi0xF June 16, 2026 at 11:44PM

Show HN: StarScope – Free astronomy dashboard for observers outside the US/UK https://ift.tt/Xpmth4l

Show HN: StarScope – Free astronomy dashboard for observers outside the US/UK https://starscope.live/feed June 16, 2026 at 12:51AM

Show HN: Understand and reduce token usage with ContextSpy context profiler https://ift.tt/Tkxo9Pn

Show HN: Understand and reduce token usage with ContextSpy context profiler https://ift.tt/Qk5ExfP June 16, 2026 at 12:59AM

Show HN: A pure-Ruby X11 terminal https://ift.tt/QURWcHO

Show HN: A pure-Ruby X11 terminal I use this as my regular xterm replacement... Why? Because I can. It's pure-Ruby down to the font-renderer, and the X11-bindings. (I also run a Ruby WM, a Ruby editor, file manager, and more, so this is just par for the course of my descent into madness) It supports double-width and double-height text, unicode (but double-width characters may currently be rescaled down), layering fonts, special rendering of box-drawing characters (to ensure they seamlessly scale and connect, and has reasonably complete vt-100/vt-102 emulation. The whole thing is available as a Rubygem and comes with an ANSI text backend, so you can run your terminal in your terminal. The bulk was written manually, but the last few days I had Claude write a test harness to shake out a bunch of bugs, and start refactoring and cleaning up the code base (it's still full of warts). https://ift.tt/EiGAcSP June 15, 2026 at 11:45PM

Show HN: Solaris the Thinking Ocean Simulator https://ift.tt/hRDxMIN

Show HN: Solaris the Thinking Ocean Simulator https://ift.tt/sa3XIQg June 15, 2026 at 02:47AM

Show HN: Trace – Offline Mac meeting transcripts you can flag mid-call https://ift.tt/hBWCkLn

Show HN: Trace – Offline Mac meeting transcripts you can flag mid-call I'm the developer of Trace, a non-intrusive, shortcut-driven Mac app that records and transcribes your meetings on-device. I know, another meeting transcription app. Please bear with me though, I'm confident that this is at least a little novel. I primarily built Trace for myself. I'd been using MacWhisper, but there was enough fiddling before each call that I'd forget to start it and walk out of an hour-long meeting with nothing written down. So the things I cared about most were that it's quick to activate and stays out of the way. You activate Trace by pressing a global shortcut (configurable), which reveals a small bar at the bottom of your screen (there's also a keystroke and/or option to hide it entirely if you'd rather not see it at all). As I was building it I wanted to bake in a couple of workflows I'd wished for in other transcription apps. 1. Mid-meeting you can press another global shortcut to mark a "key moment" and type a note. The note shows up in the resulting transcript inline at that timestamp. I wanted to add this because I kept catching myself thinking "wait, that bit matters" in meetings and reaching to jot it down in a separate app like Obsidian, which I then needed to add context to, which took me out of the meeting. I use it all the time. If I paste the transcript into an LLM afterwards (which I find myself doing more and more these days) the important moments are flagged so it doesn't gloss over them. This is more noticeable in longer meetings with lots of topics. 2. With another keyboard shortcut you can summon a rough live recap (subtitles, basically) to quickly recap what's just been said. Trace uses standard macOS microphone and system recording APIs to capture both sides of the conversation as two separate tracks and then runs the system side through on-device diarization to identify speakers. Right now we only label them as "Speaker 1", "Speaker 2", etc but there are plans for speaker labelling in the future. You can also show a "live recap" as the call is happening to review what someone just said. All transcription models run on your machine. To be clear though, Trace doesn't do any of the summarising itself, it just produces a markdown transcript, so if you want summaries then you need to pass the output to an AI. The app is sandboxed and your audio/transcripts are never uploaded anywhere - they just exist as audio files and markdown on disk. The only network call Trace is required to make is on the first run to download the speech and speaker models (around 500MB) from Hugging Face, and after that it can be used fully offline. If enabled, a Google Calendar integration can auto-name sessions but that needs a network connection. The app is £9.99 on the macOS App Store. I've been using it every day for months now and I'm super happy with how it's improved my workflow. Feedback very welcome. https://traceapp.info June 14, 2026 at 02:11AM

Show HN: Philosophy for Kids https://ift.tt/VLfw7X9

Show HN: Philosophy for Kids Sometimes my son asks me 'why' questions that could be answered well by a kid-friendly philosophy article. But I don't know where to find those, so I ask Claude or ChatGPT, and have a specific workflow for getting the type of output I want. I figured other people might find those AI-generated articles helpful, so I put them here: https://ift.tt/ENz5J17 There's a search box at the top. https://ift.tt/ENz5J17 June 14, 2026 at 11:45PM

Show HN: Slopsome – a VRAM fit calculator and tok/s database for local LLMs https://ift.tt/LD9XYTk

Show HN: Slopsome – a VRAM fit calculator and tok/s database for local LLMs https://slopsome.com June 14, 2026 at 01:14AM

Show HN: Galdor – a Go LLM agent framework with built-in tracing and replay https://ift.tt/QrVPWmE

Show HN: Galdor – a Go LLM agent framework with built-in tracing and replay https://ift.tt/QGUDtoC June 14, 2026 at 12:34AM