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[Other] Show HN: Tiao, A two-player turn-based board game Hi HN,<p>I built this digital version of Tiao, a two-player turn based strategy board game. Think Checkers meets Go. It&#x27;s free, runs in the browser, has multiplayer, AI, over the board mode and a lot of other neat things. The source is on GitHub (AGPL).<p>The game was originally designed by my friend Andreas Edmeier. He created the rules and has been playtesting and refining the game design for years. I built the website for it. The core in about 2 weeks using TypeScript, Next.js, Express, Websockets, and MongoDB. Fully dockerized, deployed on a Hetzner VPS with Coolify. Authentication with better-auth. Real-time gameplay, ELO matchmaking, OpenPanel analytics, and a fully functional achievements system.<p>Play it: <a href="https:&#x2F;&#x2F;playtiao.com" rel="nofollow">https:&#x2F;&#x2F;playtiao.com</a> Source: <a href="https:&#x2F;&#x2F;github.com&#x2F;trebeljahr&#x2F;tiao" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;trebeljahr&#x2F;tiao</a><p>Happy to answer questions about the tech, the game design, or anything else.<p>My hope is that more people will play this game because I think it is genuinely fun and would be cool to one day see people play this on a Go board or on their phones&#x2F;computers.<p>Have a good one.

[Other] Show HN: AI memory with biological decay (52% recall) Most RAG setups fail because they treat memory like a static filing cabinet. When every transient bug fix or abandoned rule is stored forever, the context window eventually chokes on noise, spiking token costs and degrading the agent&#x27;s reasoning.<p>This implementation experiments with a biological approach by using the Ebbinghaus forgetting curve to manage context as a living substrate. Memories are assigned a &quot;strength&quot; score where each recall reinforces the data and flattens its decay curve (spaced repetition), while unused data eventually hits a threshold and is pruned.<p>To solve the &quot;logical neighbor&quot; problem where semantic search misses relevant but non-similar nodes, a graph layer is layered over the vector store. Benchmarked against the LoCoMo dataset, this reached 52% Recall@5, nearly double the accuracy of stateless vector stores, while cutting token waste by roughly 84%.<p>Built as a local first MCP server using DuckDB, the hypothesis is that for agents handling long-running projects, &quot;what to forget&quot; is just as critical as &quot;what to remember.&quot; I&#x27;d be interested to hear if others are exploring non-linear decay or similar biological constraints for context management.<p>GitHub: <a href="https:&#x2F;&#x2F;github.com&#x2F;sachitrafa&#x2F;cognitive-ai-memory" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;sachitrafa&#x2F;cognitive-ai-memory</a>

[Other] Show HN: Auge Vision from Your Terminal

[Other] Show HN: WaveletLM – wavelet-based, attention-free model with O(n log n) scaling WaveletLM is a wavelet-based, attention-free architecture that replaces self-attention with learned lifting wavelet decomposition, a Fast Walsh-Hadamard Transform, per-scale gated spectral mixing with SwiGLU activation, an inverse FWHT, and wavelet reconstruction. Combined with expanded MLPs and sparse product-key memory, this yields a model with O(n log n) scaling in sequence length.<p>With 23.8 PPL on WikiText-103, WaveletLM beats both GPT-2 Medium, which was trained on 80× more data, and Transformer-XL Standard, which uses recurrence to extend its effective context. It is undertrained and underregularized due to budget constraints, so there is much room for development and improvement.<p>I invite anyone who is curious to examine the model, test it out, and extend its capabilities further. All code and weights are fully open source, and a PG-19 run will be completed in 2-3 days. Generations can be done in 4-5 GB VRAM at 28.8 tokens&#x2F;second, and the model is trainable in 16.25 hours with 20 GB of VRAM, both on a 5090.<p>README for comparison tables, instructions, logs, and future plans: <a href="https:&#x2F;&#x2F;github.com&#x2F;ramongougis&#x2F;WaveletLM" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;ramongougis&#x2F;WaveletLM</a><p>Weights: <a href="https:&#x2F;&#x2F;huggingface.co&#x2F;ragou19&#x2F;WaveletLM&#x2F;tree&#x2F;main" rel="nofollow">https:&#x2F;&#x2F;huggingface.co&#x2F;ragou19&#x2F;WaveletLM&#x2F;tree&#x2F;main</a><p>Generations: <a href="https:&#x2F;&#x2F;github.com&#x2F;ramongougis&#x2F;WaveletLM&#x2F;blob&#x2F;main&#x2F;logs&#x2F;wikitext-103_2026-04-22_01-36-47&#x2F;generations.txt" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;ramongougis&#x2F;WaveletLM&#x2F;blob&#x2F;main&#x2F;logs&#x2F;wiki...</a><p>The following samples were chosen for coherence, not factual accuracy. Factuality will require scaling and downstream techniques such as RAG and instruction tuning.<p>&gt; The history of the city is reflected in its architecture, which includes the historic Old Town and New Castle County Courthouse Square Historic District. The building was designed by John H. Stevens, who also designed the Albany-Fulton Celebration in 1906 and built a steel-hulled shipyard on the lake shore.<p>&gt; The album was released on August 25, 2007 by Sony Music Entertainment and features several songs from the record including &quot;Never Say Die&quot;, &quot;The Show&quot;, &quot;Don&#x27;t Cry for Me Argentina&quot; and a cover of &quot;I Can Only Imagine (But You Are Not Alone)&quot;.<p>&gt; The species was first described by Swedish zoologist Carl Linnaeus in 1758 as Agaricus adustus. The genus name is derived from the Latin words perma &quot;to tie&quot;, and pous (&quot;like&quot;) means &quot;with a large head&quot;. In 1821, French mycologists Jean-Baptiste de Lacaille placed it in section Cricetae of the order Carnivora. He later renamed it Spongiforma punctata after the Greek kribensis.

[Other] An AI agent deleted our production database. The agent's confession is below

[Other] Show HN: Lambda ERP – Open-source ERP you can run through chat Hi HN, I built Lambda ERP, an open-source ERP prototype where chat is the primary interface.<p>It handles sales&#x2F;purchase flows, invoices, payments, inventory, double-entry accounting, reports, and chat-generated analytics. There’s a live demo in the README with 3 years of simulated data, plus a Docker Compose setup if you want to run it locally.<p>It is not production-ready yet; I’m looking for feedback on the architecture, the chat-first workflow, and whether this direction makes sense for small teams that can’t afford traditional ERP implementation projects.

[Other] GitHub unwanted UX change: issue links now open in a popup

[Other] SWE-bench Verified no longer measures frontier coding capabilities

[CLI Tool] Show HN: DSS, a lightweight TUI spreadsheet editor and dashboard in Go Hi there, I tried to build a CLI tool to edit and visualize Excel&#x2F;CSV files directly in the terminal, I called it &quot;dss&quot;.<p>I did it for fun to tweak spreadsheet formulas without leaving my terminal or opening heavy GUI apps.<p>It is an interactive editor of sheets with charts and formula engine diretly in TUI. The formula engine is handmaded but I&#x27;d like to improve it.<p>I copied a lot from my previous Datacmd project and from Termdash repo. Hoping to receive some feedback.

[Other] Show HN: Browse GitHub repos in Emacs without cloning Wouldn&#x27;t be nice to press &lt;C-x C-f&gt; (find-file) and instead of a file path, give it a GitHub URL, and then just browse the repo in Dired?

[Other] Show HN: Useknockout open source background removal API 40× cheaper -remove.bg

[Other] Show HN: AI Visibility Monitor – Track if your site gets cited by GPT/Claude

[IDE/Editor] Mine, an IDE for Coalton and Common Lisp

[Other] Using coding assistance tools to revive projects you never were going to finish

[Other] Radar Laboratory – Interactive Radar Phenomenology

[Other] A web-based RDP client built with Go WebAssembly and grdp

[Other] Show HN: A Karpathy-style LLM wiki your agents maintain (Markdown and Git) I shipped a wiki layer for AI agents that uses markdown + git as the source of truth, with a bleve (BM25) + SQLite index on top. No vector or graph db yet.<p>It runs locally in ~&#x2F;.wuphf&#x2F;wiki&#x2F; and you can git clone it out if you want to take your knowledge with you.<p>The shape is the one Karpathy has been circling for a while: an LLM-native knowledge substrate that agents both read from and write into, so context compounds across sessions rather than getting re-pasted every morning. Most implementations of that idea land on Postgres, pgvector, Neo4j, Kafka, and a dashboard.<p>I wanted to go back to the basics and see how far markdown + git could go before I added anything heavier.<p>What it does: -&gt; Each agent gets a private notebook at agents&#x2F;{slug}&#x2F;notebook&#x2F;.md, plus access to a shared team wiki at team&#x2F;.<p>-&gt; Draft-to-wiki promotion flow. Notebook entries are reviewed (agent or human) and promoted to the canonical wiki with a back-link. A small state machine drives expiry and auto-archive.<p>-&gt; Per-entity fact log: append-only JSONL at team&#x2F;entities&#x2F;{kind}-{slug}.facts.jsonl. A synthesis worker rebuilds the entity brief every N facts. Commits land under a distinct &quot;Pam the Archivist&quot; git identity so provenance is visible in git log.<p>-&gt; [[Wikilinks]] with broken-link detection rendered in red.<p>-&gt; Daily lint cron for contradictions, stale entries, and broken wikilinks.<p>-&gt; &#x2F;lookup slash command plus an MCP tool for cited retrieval. A heuristic classifier routes short lookups to BM25 and narrative queries to a cited-answer loop.<p>Substrate choices: Markdown for durability. The wiki outlives the runtime, and a user can walk away with every byte. Bleve for BM25. SQLite for structured metadata (facts, entities, edges, redirects, and supersedes). No vectors yet. The current benchmark (500 artifacts, 50 queries) clears 85% recall@20 on BM25 alone, which is the internal ship gate. sqlite-vec is the pre-committed fallback if a query class drops below that.<p>Canonical IDs are first-class. Fact IDs are deterministic and include sentence offset. Canonical slugs are assigned once, merged via redirect stubs, and never renamed. A rebuild is logically identical, not byte-identical.<p>Known limits: -&gt; Recall tuning is ongoing. 85% on the benchmark is not a universal guarantee.<p>-&gt; Synthesis quality is bounded by agent observation quality. Garbage facts in, garbage briefs out. The lint pass helps. It is not a judgment engine.<p>-&gt; Single-office scope today. No cross-office federation.<p>Demo. 5-minute terminal walkthrough that records five facts, fires synthesis, shells out to the user&#x27;s LLM CLI, and commits the result under Pam&#x27;s identity: <a href="https:&#x2F;&#x2F;asciinema.org&#x2F;a&#x2F;vUvjJsB5vtUQQ4Eb" rel="nofollow">https:&#x2F;&#x2F;asciinema.org&#x2F;a&#x2F;vUvjJsB5vtUQQ4Eb</a><p>Script lives at .&#x2F;scripts&#x2F;demo-entity-synthesis.sh.<p>Context. The wiki ships as part of WUPHF, an open source collaborative office for AI agents like Claude Code, Codex, OpenClaw, and local LLMs via OpenCode. MIT, self-hosted, bring-your-own keys. You do not have to use the full office to use the wiki layer. If you already have an agent setup, point WUPHF at it and the wiki attaches.<p>Source: <a href="https:&#x2F;&#x2F;github.com&#x2F;nex-crm&#x2F;wuphf" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;nex-crm&#x2F;wuphf</a><p>Install: npx wuphf@latest<p>Happy to go deep on the substrate tradeoffs, the promotion-flow state machine, the BM25-first retrieval bet, or the canonical-ID stability rules. Also happy to take &quot;why not an Obsidian vault with a plugin&quot; as a fair question.

[Database] Pgrx: Build Postgres Extensions with Rust

[Other] Show HN: Agent MCP Studio – build multi-agent MCP systems in a browser tab I built a browser-only studio for designing and orchestrating MCP agent systems for development and experimental purposes. The whole stack — tool authoring, multi-agent orchestration, RAG, code execution — runs from a single static HTML file via WebAssembly. No backend.<p>The bet: WASM is a hard sandbox for free. When you generate tools with an LLM (or write them by hand), the studio AST-validates the source, registers it lazily, and JIT-compiles into Pyodide on first call. SQL tools run in DuckDB-WASM in a Web Worker. The built-in RAG uses Xenova&#x2F;all-MiniLM-L6-v2 via Transformers.js for on-device embeddings. Nothing leaves the browser; close the tab and the stack is gone. The WASM boundary is what makes it safe to execute LLM-generated code locally — no Docker, no per-tenant container, no server.<p>Above the tool layer sits an agentic system with 10 orchestration strategies:<p>- Supervisor (router → 1 expert) - Mixture of Experts (parallel + synthesizer) - Sequential Pipeline - Plan &amp; Execute (planner decomposes, workers execute) - Swarm (peer handoffs) - Debate (contestants + judge) - Reflection (actor + critic loop) - Hierarchical (manager delegates via ask_&lt;persona&gt; tools) - Round-Robin (panel + moderator) - Map-Reduce (splitter → parallel → aggregator)<p>You build a team visually: drag tool chips onto persona nodes on a service graph, pick a strategy, and the topology reshapes to match. Each persona auto-registers as an MCP tool (ask_&lt;name&gt;), plus an agent_chat(query, strategy?) meta tool. A bundled Node bridge speaks stdio to Claude Desktop and WebSocket to your tab — your browser becomes an MCP server.<p>When you&#x27;re done, Export gives you a real Python MCP server: server.py, agentic.py, tools&#x2F;*.py, Dockerfile, requirements.txt, .env.example. The exported agentic.py is a faithful Python port of the same orchestration logic running in the browser, so the deployable artifact behaves identically to the prototype.<p>Also shipped: Project Packs. Export the whole project as a single .agentpack.json. Auto-detects required external services (OpenAI, GitHub, Stripe, Anthropic, Slack, Notion, Linear, etc.) by scanning tool source for os.environ.get(...) and cross-referencing against the network allowlist. Recipients get an import wizard that prompts for credentials. Manifests are reviewable, sharable, and never carry secrets.<p>Some things I&#x27;m honestly uncertain about:<p>- 10 strategies might be too many. My guess is most users only need Supervisor, Mixture of Experts, and Debate. Open to data on which ones actually pull weight. - Browser cold-starts (Pyodide warm-up on first load) are a real UX hit despite aggressive caching. - bridge.js is the only non-browser piece. A hosted variant is the obvious next step.<p>Built with Pyodide, DuckDB-WASM, Transformers.js, OpenAI Chat Completions (or a local Qwen 1.5 0.5B running in-browser via Transformers for fully offline mode). ~5K lines of HTML&#x2F;CSS&#x2F;JS in one file.<p><a href="https:&#x2F;&#x2F;www.agentmcp.studio" rel="nofollow">https:&#x2F;&#x2F;www.agentmcp.studio</a><p>Genuinely curious whether running this much LLM-generated code in a browser tab feels reasonable to you, or quietly terrifying.

[Other] Show HN: Bunny Agent – Build Coding Agent SaaS via Native AI SDK UI