Show HN: Stork – MCP server so Claude/Cursor can search 14k MCP servers AI tools https://ift.tt/oIzTPpM

Show HN: Stork – MCP server so Claude/Cursor can search 14k MCP servers AI tools https://www.stork.ai April 13, 2026 at 01:19AM

Show HN: A social feed with no strangers https://ift.tt/QWhyGVM

Show HN: A social feed with no strangers Grateful is a gratitude app with a simple social layer. You write a short entry, keep it private or share it to a circle. A circle is a small private group of your own making — family, close friends, whoever you'd actually want to hear from. It shows you the most recent post first. People in the circle can react or leave a comment. There's also a daily notification that sends you something you were grateful for in the past. Try it out on both iOS and Android. Go to grateful.so https://ift.tt/LOgZpkn April 13, 2026 at 04:11AM

Show HN: Rekal – Long-term memory for LLMs in a single SQLite file https://ift.tt/yW7kj5x

Show HN: Rekal – Long-term memory for LLMs in a single SQLite file I got tired of repeating myself to my LLM every session. rekal is an MCP server that stores memories in SQLite and retrieves them with hybrid search (BM25 + vectors + recency decay). One file, local embeddings, no API keys. https://ift.tt/slyefGr April 13, 2026 at 02:55AM

Show HN: Claudraband – Claude Code for the Power User https://ift.tt/Sxnet8g

Show HN: Bitcoin and Quantum Computing – a three-part research series https://ift.tt/6QyTJq7

Show HN: Bitcoin and Quantum Computing – a three-part research series https://bitcoinquantum.space April 12, 2026 at 12:47AM

Show HN: A living Vancouver. Connor is walking dogs at the SPCA this morning https://ift.tt/Wd4JvE2

Show HN: A living Vancouver. Connor is walking dogs at the SPCA this morning I've spent most of my career in marketing, which for the last few years has meant building consumer personas for campaigns. I wanted to see if I could make these real, living in real neighborhoods, had real weather, real budgets, real Saturday lunches. I always wanted to build a world, not a segment. This is that. 140 people so far, split across Vancouver (100), San Francisco (20), and Tokyo (20). Each one is about 1,000 lines of profile — family, finances, daily schedule, health, worldview, media diet, the channels you'd actually reach them through and the ones that will explicitly never work on them. Demographics are census-grounded income, age, ethnicity, household composition follow normal distributions against StatsCan, ACS, and Japanese e-Stat data, so the panel is roughly representative of the city instead of representative of whatever's overrepresented in an LLM's training corpus. The specific details come from real stories. They live in real local time on a live map. Right now it's Saturday 11:32 AM in Vancouver. Connor Hughes, a 31-year-old software developer at Clio in Gastown, is on his SPCA volunteer shift, he walks shelter dogs at the Boundary Road location every other Saturday morning. Hassan Khoury is in the morning lunch rush with Tony at his Lebanese café — it's his busiest day of the week. Ahmad Noori is pulling Saturday overtime on a construction site. Jordan Whitehorse is on mid-shift at East Cafe on Hastings. Every day is unique, no two days repeat. A 3 AM job fetches live data: weather from Open-Meteo, grocery CPI from StatsCan food vectors, Metro Vancouver transit delays from Google Routes API against specific corridors, Vancouver gas prices, sunrise and sunset. Each persona has a modifier file that reacts to all of it. When Vancouver gas hits $1.85/L, Jaspreet the long-haul trucker's Coquihalla run to Calgary stops feeling worth it, his margins are thin, his mood takes a hit. When food CPI spikes, Gurinder at the Amazon warehouse stops buying the $9 Subway and brings roti from home. A health flare rolls probabilistically each morning which maybe nothing, maybe Tanya's six month old had a rough night, maybe Frank's back is acting up. The days stack up and get remembered. Every persona has a journal, today's entry in a markdown file, a week of them compressed into a "dream" of ~30 lines that keeps the shape without the texture, a month compressed into ~15 lines. It's their journal. I'm not writing it; the simulation is. Click any persona to open their detail, or hit "Talk to [name]" to have a conversation and they run on Claude Haiku with their full profile and recent diary entries as context. Not a product, not a startup, just a thing I've been quietly working on. They feel, in a way I didn't expect, like my fully grown kids. Happy to answer questions. https://brasilia-phi.vercel.app April 12, 2026 at 12:12AM

Show HN: We scanned uscis.gov for third-party trackers. The results are jarring https://ift.tt/FhMTZIQ

Show HN: We scanned uscis.gov for third-party trackers. The results are jarring https://ift.tt/g4FqmCp April 11, 2026 at 07:13PM

Show HN: Eve – Managed OpenClaw for work https://ift.tt/D0ipyWP

Show HN: Eve – Managed OpenClaw for work Eve is an AI agent harness that runs in an isolated Linux sandbox (2 vCPUs, 4GB RAM, 10GB disk) with a real filesystem, headless Chromium, code execution, and connectors to 1000+ services. You give it a task and it works in the background until it's done. I built this because I wanted OpenClaw without the self-hosting, pointed at actual day-to-day work. I’m thinking less personal assistant and more helpful colleague. Here’s a short demo video: https://ift.tt/qKz2AJc The main interface is a web app where you can watch work happen in real time (agents spawning, files being written, use of the CLI). There's also an iMessage integration so you can fire a task asynchronously, put your phone down, and get a reply when it's finished. Under the hood, there's an orchestrator (Claude Opus 4.6) that routes to the right domain-specific model for each subtask: browsing, coding, research, and media generation. For complex tasks it spins up parallel sub-agents that coordinate through the shared filesystem. They have persistent memory across sessions so context compounds over time. I’ve packaged it with a bunch of pre-installed skills so it can execute in a variety of job roles (sales, marketing, finance) at runtime. Here are a few things Eve has helped me with in the last couple days: - Edit this demo video with a voice over of Garry: https://www.youtube.com/watch?v=S4oD7H3cAQ0 - Do my tax returns - To build HN as if it was the year 2030: https://ift.tt/94RiUF3 AMA on the architecture and lmk your thoughts :) P.S. I've given every new user $100 worth of credits to try it. https://eve.new/login April 10, 2026 at 11:01PM

Show HN: FluidCAD – Parametric CAD with JavaScript https://ift.tt/nk9w8vT

Show HN: FluidCAD – Parametric CAD with JavaScript Hello HN users, This is a CAD by code project I have been working on on my free time for more than year now. I built it with 3 goals in mind: - It should be familiar to CAD designers who have used other programs. Same workflow, same terminology. - Reduce the mental effort required to create models as much as possible. This is achieved by: - Provide live rendering and visual guidance as you type. - Allow the user to reference existing edges/faces on the scene instead of having to calculate everything. - Provide interactive mouse helpers for features that are hard to write by code: Only 3 interactive modes for now: Edge trimming, Sketch region extrude, Bezier curve drawing. - Implicit coding whenever possible: e.g: There are sensible defaults for most parameters. The program will automatically fuse intersecting objects together so you do not have to worry about what object needs to be fused with what. - It should be reasonably fast: The scene objects are cached and only the updated objects are re-computed. I think I have achieved these goals to a good extent. The program is still in early stages and there are many features I want to add, rewrite but I think it is already usable for simple models. https://fluidcad.io/ April 11, 2026 at 12:09AM

Show HN: Last Year I wrote a (Sci)fictional story where the EFF was a player [pdf] https://ift.tt/cfpUb0n

Show HN: Last Year I wrote a (Sci)fictional story where the EFF was a player [pdf] https://ift.tt/qEvI9pg April 9, 2026 at 11:43PM

Show HN: Logoshi, a brand kit generator for solo founders https://ift.tt/mSB3aDv

Show HN: Logoshi, a brand kit generator for solo founders https://logoshi.com/ April 9, 2026 at 10:12PM

Show HN: I built Dirac, Hash Anchored AST native coding agent, costs -64.8 pct https://ift.tt/6Q4GeUW

Show HN: I built Dirac, Hash Anchored AST native coding agent, costs -64.8 pct Fully open source, a hard fork of cline. Full evals on the github page that compares 7 agents (Cline, Kilo, Ohmypi, Opencode, Pimono, Roo, Dirac) on 8 medium complexity tasks. Each task, each diff and correctness + cost info on the github Dirac is 64.8% cheaper than the average of the other 6. https://ift.tt/nzZ7pXa April 9, 2026 at 05:36PM

Show HN: Homebutler – I manage my homelab from chat. AI never gets raw shell https://ift.tt/JyI6DT4

Show HN: Homebutler – I manage my homelab from chat. AI never gets raw shell https://homebutler.dev April 9, 2026 at 05:39PM

Show HN: CSS Studio. Design by hand, code by agent https://ift.tt/LlzXoE7

Show HN: CSS Studio. Design by hand, code by agent Hi HN! I've just released CSS Studio, a design tool that lives on your site, runs on your browser, sends updates to your existing AI agent, which edits any codebase. You can actually play around with the latest version directly on the site. Technically, the way this works is you view your site in dev mode and start editing it. In your agent, you can run /studio which then polls (or uses Claude Channels) an MCP server. Changes are streamed as JSON via the MCP, along with some viewport and URL information, and the skill has some instructions on how best to implement them. It contains a lot of the tools you'd expect from a visual editing tool, like text editing, styles and an animation timeline editor. https://cssstudio.ai April 9, 2026 at 04:53PM

Show HN: Moon simulator game, ray-casting https://ift.tt/Zzgnm2a

Show HN: Moon simulator game, ray-casting Did this a few years ago. Seems apropos. Sources and more here: https://ift.tt/pJcOWBw https://ift.tt/Y0rCkLz April 6, 2026 at 10:39PM

Show HN: Skrun – Deploy any agent skill as an API https://ift.tt/y6HZWs3

Show HN: Skrun – Deploy any agent skill as an API https://ift.tt/s9AKyF8 April 8, 2026 at 06:04PM

Show HN: 500k+ events/sec transformations for ClickHouse ingestion https://ift.tt/1cDuIan

Show HN: 500k+ events/sec transformations for ClickHouse ingestion Hi HN! We are Ashish and Armend, founders of GlassFlow. Over the last year, we worked with teams running high-throughput pipelines into self-hosted ClickHouse. Mostly for observability and real-time analytics. A question that came repeatedly was: What happens when throughput grows? Usually, things work fine at 10k events/sec, but we started seeing backpressure and errors at >100k. When the throughput per pipeline stops scaling, then adding more CPU/memory doesn’t help because often parts of the pipeline are not parallelized or are bottlenecked by state handling. At this point, engineers usually scale by adding more pipeline instances. That works but comes with some trade-offs: - You have to split the workload (e.g., multiple pipelines reading from the same source) - Transformation logic gets duplicated across pipelines - Stateful logic becomes harder to manage and keep consistent - Debugging and changes get more difficult because the data flow is fragmented Another challenge arises when working with high-cardinality keys like user IDs, session IDs, or request IDs, and when you need to handle longer time windows (24h or more). The state grows quickly and many systems rely on in-memory state, which makes it expensive and harder to recover from failures. We wanted to solve this problem and rebuild our approach at GlassFlow. Instead of scaling by adding more pipelines, we scale within a single pipeline by using replicas. Each replica consumes, processes, and writes independently, and the workload is distributed across them. In the benchmarks we’re sharing, this scales to 500k+ events/sec while still running stateful transformations and writing into ClickHouse. A few things we think are interesting: - Scaling is close to linear as you add replicas - Works with stateful transformations (not just stateless ingestion) - State is backed by a file-based KV store instead of relying purely on memory - The ClickHouse sink is optimized for batching to avoid small inserts - The product is built with Go Full write-up + benchmarks: https://ift.tt/ol5djf9... Repo: https://ift.tt/BCG9pDw Happy to answer questions about the design or trade-offs. https://ift.tt/BCG9pDw April 8, 2026 at 10:56PM

Show HN: Unicode Steganography https://ift.tt/KN4i0s2

Show HN: Unicode Steganography I built a demo of two Unicode steganography techniques, zero-width characters and homoglyph substitution, in the context of AI misalignment. The first is about the use of two invisible zero-width characters (ZWS and ZWNJ) to binary encode text. The second is much cooler. Most characters in the Latin and Cyrillic alphabets look nearly identical, but have different unicode. If you have text to encode and convert it into binary representation (1s and 0s), you could take plain english "carrier" text and for each 1 in the binary representation you could substitute the Cyrillic letter equivalent. Decoding the message requires traversing the text and seeing where Cyrillic letters could have been substituted but weren't, and where they were, leading to 0s and 1s respectively, which can be built back into your original hidden text. In both cases, these are detectable, but the interesting problem for me is whether an LLM could eventually invent an encoding that goes unnoticed by both us, and automated detection. If LLMs were able to covertly include messages in plaintext, misaligned AI Agents could eventually communicate across MCP/A2A and individual chat session boundaries undetected. A deceptive LLM might seem helpful, but work against your goals. It could tell other agents it interacts with over MCP/A2A to help it discreetly fail, signal intent, and avoid tripping oversight/safety mechanisms. Furthermore, oversight mechanisms become more difficult to implement if we can't believe our own eyes. https://ift.tt/d0hXRoK April 7, 2026 at 06:27PM

Show HN: Marimo pair – Reactive Python notebooks as environments for agents https://ift.tt/F8LdsG3

Show HN: Marimo pair – Reactive Python notebooks as environments for agents Hi HN! We're excited to share marimo pair [1] [2], a toolkit that drops AI agents into a running marimo notebook [3] session. This lets agents use marimo as working memory and a reactive Python runtime, while also making it easy for humans and agents to collaborate on computational research and data work. GitHub repo: https://ift.tt/zL9cIKD Demo: https://www.youtube.com/watch?v=6uaqtchDnoc marimo pair is implemented as an agent skill. Connect your agent of choice to a running notebook with: /marimo-pair pair with me on my_notebook.py The agent can do anything a human can do with marimo and more. For example, it can obtain feedback by running code in an ephemeral scratchpad (inspect variables, run code against the program state, read outputs). If it wants to persist state, the agent can add cells, delete them, and install packages (marimo records these actions in the associated notebook, which is just a Python file). The agent can even manipulate marimo's user interface — for fun, try asking your agent to greet you from within a pair session. The agent effects all actions by running Python code in the marimo kernel. Under the hood, the marimo pair skill explains how to discover and create marimo sessions, and how to control them using a semi-private interface we call code mode. Code mode lets models treat marimo as a REPL that extends their context windows, similar to recursive language models (RLMs). But unlike traditional REPLs, the marimo "REPL" incrementally builds a reproducible Python program, because marimo notebooks are dataflow graphs with well-defined execution semantics. As it uses code mode, the agent is kept on track by marimo's guardrails, which include the elimination of hidden state: run a cell and dependent cells are run automatically, delete a cell and its variables are scrubbed from memory. By giving models full control over a stateful reactive programming environment, rather than a collection of ephemeral scripts, marimo pair makes agents active participants in research and data work. In our early experimentation [4], we've found that marimo pair accelerates data exploration, makes it easy to steer agents while testing research hypotheses, and can serve as a backend for RLMs, yielding a notebook as an executable trace of how the model answered a query. We even use marimo pair to find and fix bugs in itself and marimo [5]. In these examples the notebook is not only a computational substrate but also a canvas for collaboration between humans and agents, and an executable, literate artifact comprised of prose, code, and visuals. marimo pair is early and experimental. We would love your thoughts. [1] https://ift.tt/zL9cIKD [2] https://ift.tt/JXcG5tK [3] https://ift.tt/kyulF0b [4] https://www.youtube.com/watch?v=VKvjPJeNRPk [5] https://ift.tt/JjdLoQC... https://ift.tt/zL9cIKD April 7, 2026 at 11:17PM

Show HN: C64 Ultimate Toolbox for macOS https://ift.tt/GnPqvRf

Show HN: C64 Ultimate Toolbox for macOS My wife got me a Commodore 64 Ultimate ( https://ift.tt/dC6pPVj ) for my birthday, and it became an obvious hassle to have to keep an entire monitor connected to it just to tinker with it. When I found out the Ultimate FPGA board has built-in support for streaming the video and audio data over the network, as well as a REST API allowing for file and configuration management, I set to work on an app to remotely control my new device. - View and hear your Commodore 64 Ultimate or Ultimate 64 device over the network, with a fully configurable CRT shader so you can dial in just the right retro feel. - View and manage files on your device, including support for drag and drop folder/file upload, as well as the ability to run and mount disks, create new disk images, and more. - BASIC Scratchpad is a mini-IDE in the app where you can write BASIC apps and send them directly to any of your connected devices to run. - Keyboard forwarding allows you to interact with your device with your computer keyboard, includes a keyboard overlay for Commodore specific keys your keyboard definitely doesn't have. - Visual memory viewer and editor, along with a terminal-like memory viewer and editor for debugging and tinkering. - Built-in support for recording videos and taking screenshots cleanly. - Fully native macOS AppKit app. Here's a rough and ready demo video I recorded and sent to App Review for the 2.0 release which was approved yesterday: https://www.youtube.com/watch?v=_2wJO2wOGm8 Please note again this app only works with Commodore 64 Ultimate or Gideon's Ultimate 64 devices. Ultimate II does not have the data streams feature to power the display. https://ift.tt/HfAn8Y0 April 7, 2026 at 10:09PM

Show HN: Meta-agent: self-improving agent harnesses from live traces https://ift.tt/EAjXO24

Show HN: Meta-agent: self-improving agent harnesses from live traces We built meta-agent: an open-source library that automatically and continuously improves agent harnesses from production traces. Point it at an existing agent, a stream of unlabeled production traces, and a small labeled holdout set. An LLM judge scores unlabeled production traces as they stream. A proposer reads failed traces and writes one targeted harness update at a time, such as changes to prompts, hooks, tools, or subagents. The update is kept only if it improves holdout accuracy. On tau-bench v3 airline, meta-agent improved holdout accuracy from 67% to 87%. We open-sourced meta-agent. It currently supports Claude Agent SDK, with more frameworks coming soon. Try it here: https://ift.tt/v8D0M3n https://ift.tt/v8D0M3n April 7, 2026 at 12:52AM

Show HN: ComputeLock – Insurance to reduce unpredictable compute spend https://ift.tt/UuNIZPb

Show HN: ComputeLock – Insurance to reduce unpredictable compute spend Reserved instances save money... until utilization changes, and you’re still paying. With ComputeLock, the risk of on-demand price spikes doesn’t exist - we offer burst insurance. 1. Send us an estimate of on-demand spend you expect and from what provider. 2. We confirm the maximum we'll cover for you for a small fee, and you get it in writing. 3. If on-demand prices spike, we'll reimburse you. We plan to work with smaller developers to start. How we do this is by monitoring supply and demand for compute. Of course, we'll get it wrong sometimes. But it's like insurance, you'll only need it when you NEED it. Would love to hear your feedback: https://ift.tt/ohfT3wK https://ift.tt/ohfT3wK April 6, 2026 at 10:53PM