Show HN: Browser grand strategy game for hundreds of players on huge maps https://bit.ly/41cC0i3

Show HN: Browser grand strategy game for hundreds of players on huge maps Hi HN, I've been building a browser-based multiplayer strategy game called Borderhold. Matches run on large maps designed for hundreds of players. Players expand territory, attack neighbors, and adapt as borders shift across the map. You can put buildings down, build ships, and launch nukes. The main thing I wanted to explore was scale: most strategy games are small matches, modest maps, or modest player counts, but here maps are large and game works well with hundreds of players. Matches are relatively short so you can jump in and see a full game play out. Curious what people think. https://bit.ly/4uDPCAC Gameplay: https://youtu.be/nrJTZEP-Cw8 Discord: https://bit.ly/4uEbuvu https://bit.ly/4uDPCAC March 16, 2026 at 09:51AM

Show HN: Fitness MCP https://bit.ly/4sr8Jwo

Show HN: Fitness MCP There's no external MCP for your fitness (Garmin / Strava) data, so we built one. https://bit.ly/4uCviiR March 19, 2026 at 03:00AM

Show HN: ATO – a GUI to see and fix what your LLM agents configured https://bit.ly/476fStf

Show HN: ATO – a GUI to see and fix what your LLM agents configured https://bit.ly/476fSJL March 19, 2026 at 01:28AM

Show HN: Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training https://bit.ly/4bGv6H0

Show HN: Duplicate 3 layers in a 24B LLM, logical deduction .22→.76. No training I replicated David Ng's RYS method ( https://bit.ly/4ll5ILb ) on consumer AMD GPUs (RX 7900 XT + RX 6950 XT) and found something I didn't expect. Transformers appear to have discrete "reasoning circuits" — contiguous blocks of 3-4 layers that act as indivisible cognitive units. Duplicate the right block and the model runs its reasoning pipeline twice. No weights change. No training. The model just thinks longer. The results on standard benchmarks (lm-evaluation-harness, n=50): Devstral-24B, layers 12-14 duplicated once: - BBH Logical Deduction: 0.22 → 0.76 - GSM8K (strict): 0.48 → 0.64 - MBPP (code gen): 0.72 → 0.78 - Nothing degraded Qwen2.5-Coder-32B, layers 7-9 duplicated once: - Reasoning probe: 76% → 94% The weird part: different duplication patterns create different cognitive "modes" from the same weights. Double-pass boosts math. Triple-pass boosts emotional reasoning. Interleaved doubling (13,13,14,14,15,15,16) creates a pure math specialist. Same model, same VRAM, different routing. The circuit boundaries are sharp — shift by one layer and the effect disappears or inverts. Smaller models (24B) have tighter circuits (3 layers) than larger ones (Ng found 7 layers in 72B). Tools to find circuits in any GGUF model and apply arbitrary layer routing are in the repo. The whole thing — sweep, discovery, validation — took one evening. Happy to answer questions. https://bit.ly/4rEg2PM March 18, 2026 at 10:31PM

Show HN: Sonder – self-hosted AI social simulation engine https://bit.ly/4rE8hcG

Show HN: Sonder – self-hosted AI social simulation engine https://bit.ly/4bhXvEi March 18, 2026 at 01:21AM

Show HN: CodeLedger – deterministic context and guardrails for AI https://bit.ly/4saYs7c

Show HN: CodeLedger – deterministic context and guardrails for AI We’ve been working on a tool called CodeLedger to solve a problem we kept seeing with AI coding agents (Claude Code, Cursor, Codex): They’re powerful, but on real codebases they: - read too much irrelevant code - edit outside the intended scope - get stuck in loops (fix → test → fail) - drift away from the task - introduce architectural issues that linters don’t catch The root issue isn’t the model — it’s: - poor context selection - lack of execution guardrails - no visibility at team/org level --- What CodeLedger does: It sits between the developer and the agent and: 1) Gives the agent the right files first 2) Keeps the agent inside the task scope 3) Validates output against architecture + constraints It works deterministically (no embeddings, no cloud, fully local). --- Example: Instead of an agent scanning 100–500 files, CodeLedger narrows it down to ~10–25 relevant files before the first edit :contentReference[oaicite:0]{index=0} --- What we’re seeing so far: - ~40% faster task completion - ~50% fewer iterations - significant reduction in token usage --- Works with: Claude Code, Cursor, Codex, Gemini CLI --- Repo + setup: https://bit.ly/4bxAhJd Quick start: npm install -g @codeledger/cli cd your-project codeledger init codeledger activate --task "Fix null handling in user service" --- Would love feedback from folks using AI coding tools on larger codebases. Especially curious: - where agents break down for you today - whether context selection or guardrails are the bigger issue - what other issues are you seeing. https://bit.ly/47F3l01 March 18, 2026 at 12:22AM

Show HN: I built a message board where you pay to be the homepage https://bit.ly/4sKqCps

Show HN: I built a message board where you pay to be the homepage I kept thinking about what would happen if a message board only had one slot. One message, front and center, until someone pays to replace it. That's the entire product. You pay the current message's decayed value plus a penny to take the homepage. Message values drop over time using a gravity-based formula (same concept HN uses for ranking), so a $10 message might only cost a few bucks to replace a day later. Likes slow the decay, dislikes speed it up. The whole thing runs on three mini PCs in my house (k3s cluster, PostgreSQL, Redis Sentinel). Is it overengineered for a message board? Absolutely. I genuinely don't know where this goes. Curious what HN thinks. Archive of past messages: https://bit.ly/3Pcn94I https://bit.ly/4bi0GvG March 17, 2026 at 01:06PM

Show HN: Seasalt Cove, iPhone access to your Mac https://bit.ly/4cL7FOO

Show HN: Seasalt Cove, iPhone access to your Mac I feel like I finally built something I actually use every day and it has completely changed the way I think about work. AI workflows have flipped how devs operate. You're not heads down writing code anymore, you're bouncing between projects, instructing agents, reviewing their work, nudging them forward. The job is now less about typing and more about judgment calls. And the thing about that workflow is you spend a lot of time waiting. Waiting for the agent to finish, waiting for the next approval gate. That waiting doesn't have to happen at your desk. It doesn't have to happen in front of a monitor at all. I built Seasalt because I realized my iPhone could handle 80% of what I was chaining myself to my Mac for. Kick off the agent, walk away, review the diff from the store, a walk, or in a separate room away from your Mac. Approve it. Start the next one, switch to another session. You don't need giant dual monitors for this. That's kind of the whole point. Also, I have a deep security background so I felt like it was 100% necessary to include end to end encrypted with a zero knowledge relay, no ports getting opened, no VPN configuration needed, with key validation in the onboarding flow. https://bit.ly/3PnfnVy March 16, 2026 at 11:48PM

Show HN: Webassembly4J Run WebAssembly from Java https://bit.ly/41cf2aN

Show HN: Webassembly4J Run WebAssembly from Java I’ve released WebAssembly4J, along with two runtime bindings: Wasmtime4J – Java bindings for Wasmtime https://bit.ly/471hULh WAMR4J – Java bindings for WebAssembly Micro Runtime https://bit.ly/4blCCGY WebAssembly4J – a unified Java API that allows running WebAssembly across different engines https://bit.ly/40CvoJI The motivation was that Java currently has multiple emerging WebAssembly runtimes, but each exposes its own API. If you want to experiment with different engines, you have to rewrite the integration layer each time. WebAssembly4J provides a single API while allowing different runtime providers underneath. Goals of the project: Run WebAssembly from Java applications Allow cross-engine comparison of runtimes Make WebAssembly runtimes more accessible to Java developers Provide a stable interface while runtimes evolve Currently supported engines: Wasmtime WAMR Chicory GraalWasm To support both legacy and modern Java environments the project targets: Java 8 (JNI bindings) Java 11 Java 22+ (Panama support) Artifacts are published to Maven Central so they can be added directly to existing projects. I’d be very interested in feedback from people working on Java + WebAssembly integrations or runtime implementations. March 16, 2026 at 12:08AM

Show HN: Lockstep – A data-oriented programming language https://bit.ly/4lB6qEo

Show HN: Lockstep – A data-oriented programming language https://bit.ly/4lyvcF9 I want to share my work-in-progress systems language with a v0.1.0 release of Lockstep. It is a data-oriented systems programming language designed for high-throughput, deterministic compute pipelines. I built Lockstep to bridge the gap between the productivity of C and the execution efficiency of GPU compute shaders. Instead of traditional control flow, Lockstep enforces straight-line SIMD execution. You will not find any if, for, or while statements inside compute kernels; branching is entirely replaced by hardware-native masking and stream-splitting. Memory is handled via a static arena provided by the Host. There is no malloc, no hidden threads, and no garbage collection, which guarantees predictable performance and eliminates race conditions by construction. Under the hood, Lockstep targets LLVM IR directly to leverage industrial-grade optimization passes. It also generates a C-compatible header for easy integration with host applications written in C, C++, Rust, or Zig. v0.1.0 includes a compiler with LLVM IR and C header emission, a CLI simulator for validating pipeline wiring and cardinality on small datasets and an opt-in LSP server for real-time editor diagnostics, hover type info, and autocompletion. You can check out the repository to see the syntax, and the roadmap outlines where the project is heading next, including parameterized SIMD widths and multi-stage pipeline composition. I would love to hear feedback on the language semantics, the type system, and the overall architecture! https://bit.ly/4lyvcF9 March 16, 2026 at 01:14AM

Show HN: Open-source playground to red-team AI agents with exploits published https://bit.ly/4bawx1g

Show HN: Open-source playground to red-team AI agents with exploits published We build runtime security for AI agents. The playground started as an internal tool that we used to test our own guardrails. But we kept finding the same types of vulnerabilities because we think about attacks a certain way. At some point you need people who don't think like you. So we open-sourced it. Each challenge is a live agent with real tools and a published system prompt. Whenever a challenge is over, the full winning conversation transcript and guardrail logs get documented publicly. Building the general-purpose agent itself was probably the most fun part. Getting it to reliably use tools, stay in character, and follow instructions while still being useful is harder than it sounds. That alone reminded us how early we all are in understanding and deploying these systems at scale. First challenge was to get an agent to call a tool it's been told to never call. Someone got through in around 60 seconds without ever asking for the secret directly (which taught us a lot). Next challenge is focused on data exfiltration with harder defences: https://bit.ly/4b98dgc https://bit.ly/3PCKDjq March 15, 2026 at 11:29PM

Show HN: Signet.js – A minimalist reactivity engine for the modern web https://bit.ly/3P7Oghg

Show HN: Signet.js – A minimalist reactivity engine for the modern web https://bit.ly/4uuhYwV March 15, 2026 at 03:58AM

Show HN: GrobPaint: Somewhere Between MS Paint and Paint.net https://bit.ly/472TcKq

Show HN: GrobPaint: Somewhere Between MS Paint and Paint.net https://bit.ly/47wryWg March 14, 2026 at 11:41PM

Show HN: Structural analysis of the D'Agapeyeff cipher (1939) https://bit.ly/4lwRcQA

Show HN: Structural analysis of the D'Agapeyeff cipher (1939) I am working on the D'Agapeyeff cipher, an unsolved cryptogram from 1939. Two findings that I haven't seen published before: 1. All 5 anomalous symbol values in the cipher cluster in the last column of a 14x14 grid. This turns out to be driven by a factor-of-2-and-7 positional pattern in the linear text. 2. Simulated annealing with Esperanto quadgrams (23M char Leipzig corpus) on a 2x98 columnar transposition consistently outscores English by 200+ points and recovers the same Esperanto vocabulary across independent runs. The cipher is not solved. But the combination of structural geometry and computational linguistics narrows the search space significantly. Work in progress, more to come! https://bit.ly/3PbwGc6 March 15, 2026 at 12:34AM

Show HN: Simple plugin to get Claude Code to listen to you https://bit.ly/4br70Qi

Show HN: Simple plugin to get Claude Code to listen to you Hey HN, My cofounder and I have gotten tired of CC ignoring our markdown files so we spent 4 days and built a plugin that automatically steers CC based on our previous sessions. The problem is usually post plan-mode. What we've tried: Heavily use plan mode (works great) CLAUDE.md, AGENTS.md, MEMORY.md Local context folder (upkeep is a pain) Cursor rules (for Cursor) claude-mem (OSS) -> does session continuity, not steering We use fusion search to find your CC steering corrections. - user prompt embeddings + bm25 - correction embeddings + bm25 - time decay - target query embeddings - exclusions - metadata hard filters (such as files) The CC plugin: - Automatically captures memories/corrections without you having to remind CC - Automatically injects corrections without you having to remind CC to do it. The plugin will merge, update, and distill your memories, and then inject the highest relevant ones after each of your own prompts. We're not sure if we're alone in this. We're working on some benchmarks to see how effective context injection actually is in steering CC and we know we need to keep improving extraction, search, and add more integrations. We're passionate about the real-time and personalized context layer for agents. Giving Agents a way to understand what you mean when you say "this" or "that". Bringing the context of your world, into a secure, structured, real-time layer all your agents can access. Would appreciate feedback on how you guys get CC to actually follow your markdown files, understand your modus operandi, feedback on the plugin, or anything else about real-time memory and context. - Ankur https://bit.ly/4bx6joK March 14, 2026 at 12:15AM

Show HN: Kube-pilot – AI engineer that lives in your Kubernetes cluster https://bit.ly/4lq1wK4

Show HN: Kube-pilot – AI engineer that lives in your Kubernetes cluster I built kube-pilot — an autonomous AI agent that runs inside your Kubernetes cluster and does the full dev loop: writes code, builds containers, deploys services, verifies they're healthy, and closes the ticket. You file a GitHub issue, it does the rest. What makes this different from AI coding tools: kube-pilot doesn't just generate code and hand it back to you. It lives inside the cluster with direct access to the entire dev stack — git, Tekton (CI/CD), Kaniko (container builds), ArgoCD (GitOps deployments), kubectl, Vault. Every tool call produces observable state that feeds into the next decision. The cluster isn't just where code runs — it's where the agent thinks. The safety model: all persistent changes go through git, so everything is auditable and reversible. ArgoCD is the only thing that writes to the cluster. Secrets stay behind Vault — the agent creates ExternalSecret references, never touches raw credentials. Credentials are scrubbed before reaching the LLM. Live demo: I filed GitHub issues asking it to build a 4-service office suite (auth, docs API, notification worker, API gateway). It built and deployed all of them autonomously. You can see the full agent loop — code, builds, deploys, verification, comments — on the closed issues: - https://bit.ly/4b8SihV... - https://bit.ly/4b8SiOX... - https://bit.ly/4lBAjEw... - https://bit.ly/4sPN6FP... One helm install gives you everything — the agent, Gitea (git + registry), Tekton, ArgoCD, Vault, External Secrets. No external dependencies. Coming next: Slack and Jira integrations (receive tasks and post updates where your team already works), Prometheus metrics and Grafana dashboards for agent observability, and Alertmanager integration so firing alerts automatically become issues that kube-pilot investigates and fixes. Early proof of concept. Rough edges. But it works. https://bit.ly/3Pk0p2x March 14, 2026 at 03:49AM

Show HN: I wrote my first neural network https://bit.ly/4ltOFGV

Show HN: I wrote my first neural network I have been interested in neural nets since the 90's. I've done quite a bit of reading, but never gotten around to writing code. I used Gemini in place of Wikipedia to fill in the gaps of my knowledge. The coolest part of this was learning about dual numbers. You can see in early commits that I did not yet know about auto-diff; I was thinking I'd have to integrate a CAS library or something. Now, I'm off to play with TensorFlow. https://bit.ly/4cGH7y9 March 14, 2026 at 01:21AM

Show HN: EdgeWhisper – On-device voice-to-text for macOS (Voxtral 4B via MLX) https://bit.ly/4cMsuJQ

Show HN: EdgeWhisper – On-device voice-to-text for macOS (Voxtral 4B via MLX) I built a macOS voice dictation app where zero bytes of audio ever leave your machine. EdgeWhisper runs Voxtral Mini 4B Realtime (Mistral AI, Apache 2.0) locally on Apple Silicon via the MLX framework. Hold a key, speak, release — text appears at your cursor in whatever app has focus. Architecture: - Native Swift (SwiftUI + AppKit). No Electron. - Voxtral 4B inference via MLX on the Neural Engine. ~3GB model, runs in ~2GB RAM on M1+. - Dual text injection: AXUIElement (preserves undo stack) with NSPasteboard+CGEvent fallback. - 6-stage post-processing pipeline: filler removal → dictionary → snippets → punctuation → capitalization → formatting. - Sliding window KV cache for unlimited streaming without latency degradation. - Configurable transcription delay (240ms–2.4s). Sweet spot at 480ms. What it does well: - Works in 20+ terminals/IDEs (VS Code, Xcode, iTerm2, Warp, JetBrains). Most dictation tools break in terminals — we detect them and switch injection strategy. - Removes filler words automatically ("um", "uh", "like"). - 13 languages with auto-detection. - Personal dictionary + snippet expansion with variable support (, ). - Works fully offline after model download. No accounts, no telemetry, no analytics. What it doesn't do (yet): - No file/meeting transcription (coming) - No translation (coming) - No Linux/Windows (macOS only, Apple Silicon required) Pricing: Free tier (5 min/day, no account needed). Pro at $7.99/mo or $79.99/yr. I'd love feedback on: 1. Would local LLM post-processing (e.g., Phi-4-mini via MLX) for grammar/tone be worth the extra ~1GB RAM? 2. For developers using voice→code workflows: what context would you want passed to your editor? 3. Anyone else building on Voxtral Realtime? Curious about your experience with the causal audio encoder. https://bit.ly/4bqT09c March 13, 2026 at 11:57PM

Show HN: What was the world listening to? Music charts, 20 countries (1940–2025) https://bit.ly/40pkPcZ

Show HN: What was the world listening to? Music charts, 20 countries (1940–2025) I built this because I wanted to know what people in Japan were listening to the year I was born. That question spiraled: how does a hit in Rome compare to what was charting in Lagos the same year? How did sonic flavors propagate as streaming made musical influence travel faster than ever? 88mph is a playable map of music history: 230 charts across 20 countries, spanning 8 decades (1940–2025). Every song is playable via YouTube or Spotify. It's open source and I'd love help expanding it — there's a link to contribute charts for new countries and years. The goal is to crowdsource a complete sonic atlas of the world. https://bit.ly/4s6DV3v March 10, 2026 at 05:18PM

Show HN: fftool – A Terminal UI for FFmpeg – Shows Command Before It Runs https://bit.ly/3NwJ71I

Show HN: fftool – A Terminal UI for FFmpeg – Shows Command Before It Runs https://bit.ly/4sCyUzG March 13, 2026 at 11:08AM