Show HN: TBD, a Mac-native CLI-forward coding agent multiplexer https://ift.tt/2I3TKB7

Show HN: TBD, a Mac-native CLI-forward coding agent multiplexer Inspired by Conductor, dmux, claude-squad, agent-deck, and Git Tower ## What makes it different: (Aside from GUI) A core tenet is -- everything a user can do manually, must be exposed via CLI for agents/automation Best paired with something that lets agents in different worktrees talk to each other (e.g. https://ift.tt/HTjYahr ) ## Background: I used and loved Conductor for months starting around January, but hit some persistent issues that made me realize that a core tool that I'm actively using for most of my waking hours sits too close to my skin to produce itches that I can't scratch myself After realizing I needed to switch to something hackable, I went through a few week-ish long trials of dmux, claude-squad, and agent-deck. They were all great, but I then realized I really didn't want to memorize keyboard shortcuts, and I've managed to put off learning how to drive tmux for over a decade, didn't want to end that streak XD So TBD happened in March. In the months since, it's gotten stable enough to the point where a few former and current colleagues have switched to using it as their daily drivers as well. It's been kind of like a fun little club house we contribute to The architecture is a daemon that handles the bulk of state management and actual work, and CLI and GUI clients as two interfaces. Users go through GUI, LLMs and scripts go through CLI. It works best for Claude Code (our shared daily drivers) but two of us also use Codex on the side, so there's some basic support there as well The only way to run it is to clone and build from source, partially b/c I imagine the main appeal is for people who need to hack on the thing they're using (but also b/c didn't want to shell out for an Apple dev license) I think it's now a good enough starting point for similarly minded folks to use as a base to fork and build your own variants, tailored to your own workflows https://ift.tt/Pmz4Fkp June 26, 2026 at 10:29PM

Show HN: Mantis, A self-hosted LLM gateway https://ift.tt/9uEkyB0

Show HN: Mantis, A self-hosted LLM gateway Hey HNers - Riz here. I got together with a few guys and we built an LLM gateway. It's designed for small teams working on early-stage products, and can be deployed to AWS using a single command (i.e. `mantis deploy`). It's self-hosted, and is designed to belong to you. https://ift.tt/tLYE9eq June 27, 2026 at 12:45AM

Show HN: Puzzle with Strangers. A free multiplayer jigsaw https://ift.tt/es4avwt

Show HN: Puzzle with Strangers. A free multiplayer jigsaw I built this over the last few days. Me and handful of friends are successfully hooked. I recently went to a — for lack of a better word – social/collaborative performance at an art gallery in Berlin where a group of artists filled a huge industrial hall with wooden 10x10cm cubes for people to build structures with. It was beautiful how universal the concept of playing with wooden blocks is and how ephemeral the structures were, people of all ages were put back into a childlike play. The thought about what kind of games need zero explanation stuck with me and i built an anonymous multiplayer jigsaw. We've already spent hours in there and you're invited now as well. Hope you enjoy. https://ift.tt/okCpys9 June 26, 2026 at 10:17PM

Show HN: I created a Scrabble-like word game with simple rules and fun combos https://ift.tt/GayFv3W

Show HN: I created a Scrabble-like word game with simple rules and fun combos When I was in school, my teacher used to play this game to our class. You add one letter turnwise and try to make a word. Later, I tried searching for this game but didn't find the exact match anywhere. The closest was Scrabble, but it was too complicated. So, I decided to build my own. I did make some modifications to make the game more challenging and fun. Back then, we would start with a blank board and also score 2 letter words. Here, the game gets prefilled with random letters so the game becomes more different each time. No scoring for two letter words. The best thing that I added was the combos. If your letter makes 2 or more words, you will get a multiplier for each subsequent word, so the challenge becomes finding a way to score more combos. Initially, I wanted to assign values to each letter like Scrabble, but after running multiple AI-to-AI experiments, I concluded that having flat values per letter increases variances in the game and also reduces the first turn advantage to 0. I still added the weighted game mode if you would like to give that a try as well. And I also added daily puzzles where you get 5 boards, and you need to find the best spot and best letter that scores the most. You can share the Wordle-like result to your friends. You can also play directly on the web at https://ift.tt/1RAp0La or free download in the App Store at https://ift.tt/gPnUha1 https://letterphile.com June 26, 2026 at 03:37AM

Show HN:Every Team Is Building the Same Cache https://ift.tt/zr2pVnw

Show HN:Every Team Is Building the Same Cache https://ift.tt/bOijAHJ June 26, 2026 at 03:10AM

Show HN: Full featured language that compiles to binary https://ift.tt/pKmvXM2

Show HN: Full featured language that compiles to binary Features: 1. Self-hosting compiler 2. C99 backend 3. Built-in dependency injection / IoC 4. Typed business-rule features like decision tables 5. Native binaries + WASM 6. Real app built with it: eXstream https://ift.tt/ERkhTPy June 26, 2026 at 12:45AM

Show HN: OpenKnowledge – open source AI-first alternative to Obsidian/Notion https://ift.tt/IBxm5cE

Show HN: OpenKnowledge – open source AI-first alternative to Obsidian/Notion https://ift.tt/8hStksB June 25, 2026 at 09:34PM

Show HN: Dspyer – self-correcting, optimizable LLM steps for DSPy and LangGraph https://ift.tt/KpYFV74

Show HN: Dspyer – self-correcting, optimizable LLM steps for DSPy and LangGraph https://ift.tt/jwiQgJO June 25, 2026 at 02:38AM

Show HN: LookAway, a Mac break reminder that knows when not to interrupt https://ift.tt/swgeKXU

Show HN: LookAway, a Mac break reminder that knows when not to interrupt Hello, I'm Kushagra and I am the indie developer behind LookAway (I've posted about it earlier but it has received quite a lot of updates since the last time so I am posting it again). LookAway is a native break reminder for macOS that doesn't interrupt. I built it because I work from home and I spend a lot of time in front of my screens. It's very easy for me to get lost in the flow and I can end up sitting for hours. Due to this, I started facing issues like eye strain and back pain by the end of the day. The solution to this was simply taking enough breaks throughout the day. But remembering to take breaks was difficult, especially when I was in the flow. I tried some reminder apps but the problem with those was that they always interrupted me at the worst moments. So I ended up not using them. LookAway is designed not to interrupt. It gives enough heads up before a break so that you're not caught off-guard. It's also context-aware and it automatically pauses when you go into a meeting, start watching a video, record screen, and much more. It even waits for you to finish typing or dictating when a break is due. One thing worth mentioning is the free iOS counterpart LookAway Mirror. When your Mac goes on a break, your iOS devices can also mirror the same break so you don't end up scrolling your phone screen during the Mac break. I've spent a lot of time in making LookAway the least annoying break reminder app and I would love to know your thoughts. It's a native Swift app so it doesn't take much resources (150MB RAM and <1% CPU when idle). It's available to download from the website (lookaway.com), Setapp, and the App Store. Thank you! https://lookaway.com June 24, 2026 at 06:59PM

Show HN: The Cascade Graph – An interactive map of AI and energy constraints https://ift.tt/wJGe0Ed

Show HN: The Cascade Graph – An interactive map of AI and energy constraints Hello, I wanted to share with you all a interactive map of the economics and physics constraints of the AI buildout. It has macro drivers, industrial chokepoints, and where that shows up in markets. I've added 393 nodes and 562 edges to capture other supply / physics constraints as well. There's no sign up, and no pay wall, it's all free. Please let me know what you think! https://ift.tt/4CfYr19 June 23, 2026 at 08:52PM

Show HN: Wordit – Change One Letter, Keep the Chain Going https://ift.tt/hu9sB16

Show HN: Wordit – Change One Letter, Keep the Chain Going Hi everyone, I got this idea for a game where, starting from a four letter word you need to go as deep as you can in your vocabulary, changing only one letter per word. bear -> beer -> peer... Each correct word gives you 1 point Each incorrect word takes one life away from you, you start with 3 https://ift.tt/0qdxLFf June 24, 2026 at 12:27AM

Show HN: I scanned every YC Spring 2026 startup for what AI crawlers see https://ift.tt/XPyvnFL

Show HN: I scanned every YC Spring 2026 startup for what AI crawlers see Used 'potatometer.com' to scan and analyze all All 197 YC Spring 2026 startups on their SEO / GEO / AEO technical setup. I scanned the URL each startup lists in YC's directory. Most are readable by AI crawlers. Most don't tell a crawler what they are. Read more in the blog above. https://ift.tt/U5lhE0q June 23, 2026 at 08:10AM

Show HN: Durable Agent Sessions API (Preview) https://ift.tt/HhVZfFd

Show HN: Durable Agent Sessions API (Preview) https://ift.tt/0hdVuFL June 23, 2026 at 07:07AM

Show HN: Kitcat 2.0 – A Matplotlib back end for terminal plotting https://ift.tt/873ML5y

Show HN: Kitcat 2.0 – A Matplotlib back end for terminal plotting https://ift.tt/OWo1wsm June 22, 2026 at 11:00PM

Show HN: Pure Effect – Reproduce production bugs on your laptop without a DB https://ift.tt/R3IV6Mz

Show HN: Pure Effect – Reproduce production bugs on your laptop without a DB Hi HN, I think it's safe to say that the majority of developers don't give a second thought to writing code with I/O tangled in business logic. It's all too common to see code like: const user = findUser(email); if (!user) await saveUser(user); Now, you may ask: what's the big deal? When we write code like this, two things happen: 1. It gets harder to debug production bugs. Unless you have the exact same database and remote API services to connect to, you may fail to reproduce the bug. 2. You have to use mocks and fakes in your tests, or use test containers, which only help somewhat, and they are slow! To solve these issues, I built Pure Effect, a tiny TypeScript/JavaScript effect library. The core idea is simple: if a function performs I/O, it isn't pure. But if it returns a description of the I/O it wants to perform, it is. So instead of await findUser(email), you return a Command object that says, "I would like to call this function, and when it finishes, here's what to do next." Your business logic becomes a pure function. Same input, same output, every time. The database never gets touched until the interpreter (runEffect) runs. When I first started the library, I didn't expect just how far that one idea would stretch. Once your pipelines are just data, a lot of wonderful things become possible: - No need for mocking libraries. You walk the tree in tests and assert on its structure: assert.equal(flow.cmd.name, 'cmdFindUser'). Nothing is executed. - Wrap any effect with Retry(effect, { attempts: 3, delay: 200, backoff: 2 }). The configuration is plain data, so you can assert on it in tests. - Every command's input and output flows through the interpreter, so you get a full execution trace for free. You can write a simple timeTravel() function that replays it locally without touching any I/O. Perfect for debugging complex production bugs. - An onBeforeCommand hook sits between your business logic and the interpreter. Since it sees every intended side effect before it fires, it can be used to enforce runtime guardrails. You can quarantine destructive calls before they happen for example. - You can review AI-generated code before it runs. Since Pure Effect pipelines are plain data, you can inspect what the generated code intends to do before it touches anything. There are just six primitives: Success, Failure, Command, Ask, Retry, and Parallel, plus effectPipe and runEffect. Zero dependencies. Under 1 KB minified and gzipped. How it compares to Effect-TS Effect-TS is the full-featured option in this space and has a large ecosystem. Pure Effect offers a different tradeoff. It covers the 80% case: testable pipelines, dependency injection, retry, and OpenTelemetry hooks, all in under 1 KB with zero dependencies and no new vocabulary to learn. Effect-TS is a framework you build around. Pure Effect, on the other hand, is a pattern you drop into existing code. I've been using Pure Effect in production since December. It's at v0.8.0, not 1.0 yet, but stable enough that I wanted to put it out there and hear what people think. GitHub: https://ift.tt/9zJMtsD I wrote five posts that document how Pure Effect evolved. They are tagged at https://ift.tt/YUyn48K if you want the longer story. https://pure-effect.org June 21, 2026 at 11:06PM

Show HN: DebugBrief – turn debugging sessions into reports, no AI https://ift.tt/jkSQhn8

Show HN: DebugBrief – turn debugging sessions into reports, no AI https://ift.tt/QFh1C0x June 22, 2026 at 01:27AM

Show HN: CleverCrow: give tokens to your favorite projects https://ift.tt/9K2XU3a

Show HN: CleverCrow: give tokens to your favorite projects Howdy all. I'm Zack :wave:. I've been thinking about the problem of misguided AI pull requests and figured I'd throw a possible solution out there for feedback. Basically, CleverCrow lets supporters give tokens to a GitHub repo (or set of issues in that repo) for the maintainers to use to build/fix stuff. The fun implementation challenges have been around implementing the pooling dynamics and keeping the maintainers in charge while the backers are motivated to support their work. https://clevercrow.io June 22, 2026 at 12:36AM

Show HN: We post-trained a model that pen tests instead of refusing https://ift.tt/W4x1YnM

Show HN: We post-trained a model that pen tests instead of refusing Anthropic and OpenAI's publicly available models are explicitly guard-railed so that they refuse offensive tasks. And their cyber-focussed models are gated for enterprises. This leaves SMEs and mid market open to major vulnerabilities. AI can be used as both an adversarial and defensive tool in the world of cyber. A worst case outcome is if only the adversaries have access. Meanwhile, most existing AI cyber tools are just wrappers. The problem is that they still have all the guardrails on from the foundation model where they will inherit its refusals. For this project we've post-trained a specific model on a decade of capture-the-flag contests. This won't be made available to anyone and everyone, but we do believe that responsible SMEs and midmarket companies also need access to these tools in order to identify key vulnerabilities in their systems; not just enterprises. We have developed two modes that run over a CLI: • Security scan: a read-only audit of your local codebase for vulnerabilities. It only reports what it can tie to a specific file and line, so you're not wading through vibes-based findings. • Pen test: an active adversarial mode that will try to break a live system in a sandboxed environment. It proves each vulnerability by running the exploit and showing the request it sent and the response your code gave back, not a confidence score. Currently gated. To show what the scan does, we pointed it at Bank of Anthos and it found an integer overflow in the transfer path: amount is an int, and amount + fee can overflow negative, so the balance check passes and you move funds you don't have. Plus the usual auth and secrets issues. (Bank of Anthos is Google's open-source bank. It's a known app and some of it is intentionally weak, which is the point: you can clone it and re-run the scan yourself instead of trusting a screenshot) The base model is a Kimi K2.6 (open weights). We didn't pretrain from scratch. We post-trained it ourselves, SFT on CTF writeups, then RL with verifiable rewards against actual exploit checks. How the harness works: Along with the model we built the harness to support this. The harness runs on a multi-agent swarm: an orchestrator splits the job across subagents running in parallel, each owning a slice, then synthesising one report. The CLI is a local binary (brew/curl). It reads your code locally, then sends context to our inference API over TLS tcpdump it and you'll see exactly what leaves and where. Install is free; and you can run a scan for free up to 2m tokens, then need to pay for tokens beyond this. For full disclosure this is a product part of Cosine (YC W23) Up for debate: tool safety, e.g. domain verification is one method that proves control but not necessarily permission. How would you gate a pen-test tool given that? https://ift.tt/MbV9LWz June 20, 2026 at 07:19PM

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