The best Hacker News stories from Show from the past day
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Show HN: SQL-tap – Real-time SQL traffic viewer for PostgreSQL and MySQL
sql-tap is a transparent proxy that captures SQL queries by parsing the PostgreSQL/MySQL wire protocol and displays them in a terminal UI. You can run EXPLAIN on any captured query. No application code changes needed — just change the port.
Show HN: SQL-tap – Real-time SQL traffic viewer for PostgreSQL and MySQL
sql-tap is a transparent proxy that captures SQL queries by parsing the PostgreSQL/MySQL wire protocol and displays them in a terminal UI. You can run EXPLAIN on any captured query. No application code changes needed — just change the port.
Show HN: Sameshi – a ~1200 Elo chess engine that fits within 2KB
I made a chess engine today, and made it fit within 2KB.
I used a variant of MinMax called Negamax, with alpha beta pruning. For the board representation I have used a 120-cell "mailbox". I managed to squeeze in checkmate/stalemate in there, after trimming out some edge cases.<p>I am a great fan of demoscene (computer art subculture) since middle school, and hence it was a ritual i had to perform.<p>For estimating the Elo, I measured 240 automated games against Stockfish Elo levels (1320 to 1600) under fixed depth-5 and some constrained rules, using equal color distribution.<p>Then converted pooled win/draw/loss scores to Elo through some standard logistic formula with binomial 95% confidence interval.
Show HN: Sameshi – a ~1200 Elo chess engine that fits within 2KB
I made a chess engine today, and made it fit within 2KB.
I used a variant of MinMax called Negamax, with alpha beta pruning. For the board representation I have used a 120-cell "mailbox". I managed to squeeze in checkmate/stalemate in there, after trimming out some edge cases.<p>I am a great fan of demoscene (computer art subculture) since middle school, and hence it was a ritual i had to perform.<p>For estimating the Elo, I measured 240 automated games against Stockfish Elo levels (1320 to 1600) under fixed depth-5 and some constrained rules, using equal color distribution.<p>Then converted pooled win/draw/loss scores to Elo through some standard logistic formula with binomial 95% confidence interval.
Show HN: I spent 3 years reverse-engineering a 40 yo stock market sim from 1986
Hello my name is Ben Ward for the past 3 years I have been remastering the financial game Wall Street Raider created by Michael Jenkins originally on DOS in 1986.<p>It has been a rough journey but I finally see the light at the end of the tunnel. I just recently redid the website and thought maybe the full story of how this project came to be would interest you all. Thank you for reading.
Show HN: I spent 3 years reverse-engineering a 40 yo stock market sim from 1986
Hello my name is Ben Ward for the past 3 years I have been remastering the financial game Wall Street Raider created by Michael Jenkins originally on DOS in 1986.<p>It has been a rough journey but I finally see the light at the end of the tunnel. I just recently redid the website and thought maybe the full story of how this project came to be would interest you all. Thank you for reading.
Show HN: Pgclaw – A "Clawdbot" in every row with 400 lines of Postgres SQL
Hi HN,<p>Been hacking on a simple way to run agents entirely inside of a Postgres database, "an agent per row".<p>Things you could build with this:
* Your own agent orchestrator
* A personal assistant with time travel
* (more things I can't think of yet)<p>Not quite there yet but thought I'd share it in its current state.
Show HN: Sol LeWitt-style instruction-based drawings in the browser
Sol LeWitt was a conceptual artist who never touched his own walls.<p>He wrote instructions and other people executed them, the original prompt engineer!<p>I bookmarked a project called "Solving Sol" seven years ago and made a repo in 2018. Committed a README. Never pushed anything else.<p>Fast forward to 2026, I finally built it.<p><a href="https://intervolz.com/sollewitt/" rel="nofollow">https://intervolz.com/sollewitt/</a>
Show HN: Moltis – AI assistant with memory, tools, and self-extending skills
Hey HN. I'm Fabien, principal engineer, 25 years shipping production systems (Ruby, Swift, now Rust). I built Moltis because I wanted an AI assistant I could run myself, trust end to end, and make extensible in the Rust way using traits and the type system. It shares some ideas with OpenClaw (same memory approach, Pi-inspired self-extension) but is Rust-native from the ground up. The agent can create its own skills at runtime.<p>Moltis is one Rust binary, 150k lines, ~60MB, web UI included. No Node, no Python, no runtime deps. Multi-provider LLM routing (OpenAI, local GGUF/MLX, Hugging Face), sandboxed execution (Docker/Podman/Apple Containers), hybrid vector + full-text memory, MCP tool servers with auto-restart, and multi-channel (web, Telegram, API) with shared context. MIT licensed. No telemetry phoning home, but full observability built in (OpenTelemetry, Prometheus).<p>I've included 1-click deploys on DigitalOcean and Fly.io, but since a Docker image is provided you can easily run it on your own servers as well. I've written before about owning your content (<a href="https://pen.so/2020/11/07/own-your-content/" rel="nofollow">https://pen.so/2020/11/07/own-your-content/</a>) and owning your email (<a href="https://pen.so/2020/12/10/own-your-email/" rel="nofollow">https://pen.so/2020/12/10/own-your-email/</a>). Same logic here: if something touches your files, credentials, and daily workflow, you should be able to inspect it, audit it, and fork it if the project changes direction.<p>It's alpha. I use it daily and I'm shipping because it's useful, not because it's done.<p>Longer architecture deep-dive: <a href="https://pen.so/2026/02/12/moltis-a-personal-ai-assistant-built-in-rust/" rel="nofollow">https://pen.so/2026/02/12/moltis-a-personal-ai-assistant-bui...</a><p>Happy to discuss the Rust architecture, security model, or local LLM setup. Would love feedback.
Show HN: Moltis – AI assistant with memory, tools, and self-extending skills
Hey HN. I'm Fabien, principal engineer, 25 years shipping production systems (Ruby, Swift, now Rust). I built Moltis because I wanted an AI assistant I could run myself, trust end to end, and make extensible in the Rust way using traits and the type system. It shares some ideas with OpenClaw (same memory approach, Pi-inspired self-extension) but is Rust-native from the ground up. The agent can create its own skills at runtime.<p>Moltis is one Rust binary, 150k lines, ~60MB, web UI included. No Node, no Python, no runtime deps. Multi-provider LLM routing (OpenAI, local GGUF/MLX, Hugging Face), sandboxed execution (Docker/Podman/Apple Containers), hybrid vector + full-text memory, MCP tool servers with auto-restart, and multi-channel (web, Telegram, API) with shared context. MIT licensed. No telemetry phoning home, but full observability built in (OpenTelemetry, Prometheus).<p>I've included 1-click deploys on DigitalOcean and Fly.io, but since a Docker image is provided you can easily run it on your own servers as well. I've written before about owning your content (<a href="https://pen.so/2020/11/07/own-your-content/" rel="nofollow">https://pen.so/2020/11/07/own-your-content/</a>) and owning your email (<a href="https://pen.so/2020/12/10/own-your-email/" rel="nofollow">https://pen.so/2020/12/10/own-your-email/</a>). Same logic here: if something touches your files, credentials, and daily workflow, you should be able to inspect it, audit it, and fork it if the project changes direction.<p>It's alpha. I use it daily and I'm shipping because it's useful, not because it's done.<p>Longer architecture deep-dive: <a href="https://pen.so/2026/02/12/moltis-a-personal-ai-assistant-built-in-rust/" rel="nofollow">https://pen.so/2026/02/12/moltis-a-personal-ai-assistant-bui...</a><p>Happy to discuss the Rust architecture, security model, or local LLM setup. Would love feedback.
Show HN: Skill that lets Claude Code/Codex spin up VMs and GPUs
I've been working on CloudRouter, a skill + CLI that gives coding agents like Claude Code and Codex the ability to start cloud VMs and GPUs.<p>When an agent writes code, it usually needs to start a dev server, run tests, open a browser to verify its work. Today that all happens on your local machine. This works fine for a single task, but the agent is sharing your computer: your ports, RAM, screen. If you run multiple agents in parallel, it gets a bit chaotic. Docker helps with isolation, but it still uses your machine's resources, and doesn't give the agent a browser, a desktop, or a GPU to close the loop properly. The agent could handle all of this on its own if it had a primitive for starting VMs.<p>CloudRouter is that primitive — a skill that gives the agent its own machines. The agent can start a VM from your local project directory, upload the project files, run commands on the VM, and tear it down when it's done. If it needs a GPU, it can request one.<p><pre><code> cloudrouter start ./my-project
cloudrouter start --gpu B200 ./my-project
cloudrouter ssh cr_abc123 "npm install && npm run dev"
</code></pre>
Every VM comes with a VNC desktop, VS Code, and Jupyter Lab, all behind auth-protected URLs. When the agent is doing browser automation on the VM, you can open the VNC URL and watch it in real time. CloudRouter wraps agent-browser [1] for browser automation.<p><pre><code> cloudrouter browser open cr_abc123 "http://localhost:3000"
cloudrouter browser snapshot -i cr_abc123
# → @e1 [link] Home @e2 [link] Settings @e3 [button] Sign Out
cloudrouter browser click cr_abc123 @e2
cloudrouter browser screenshot cr_abc123 result.png
</code></pre>
Here's a short demo: <a href="https://youtu.be/SCkkzxKBcPE" rel="nofollow">https://youtu.be/SCkkzxKBcPE</a><p>What surprised me is how this inverted my workflow. Most cloud dev tooling starts from cloud (background agents, remote SSH, etc) to local for testing. But CloudRouter keeps your agents local and pushes the agent's work to the cloud. The agent does the same things it would do locally — running dev servers, operating browsers — but now on a VM. As I stopped watching agents work and worrying about local constraints, I started to run more tasks in parallel.<p>The GPU side is the part I'm most curious to see develop. Today if you want a coding agent to help with anything involving training or inference, there's a manual step where you go provision a machine. With CloudRouter the agent can just spin up a GPU sandbox, run the workload, and clean it up when it's done. Some of my friends have been using it to have agents run small experiments in parallel, but my ears are open to other use cases.<p>Would love your feedback and ideas. CloudRouter lives under packages/cloudrouter of our monorepo <a href="https://github.com/manaflow-ai/manaflow" rel="nofollow">https://github.com/manaflow-ai/manaflow</a>.<p>[1] <a href="https://github.com/vercel-labs/agent-browser" rel="nofollow">https://github.com/vercel-labs/agent-browser</a>
Show HN: Skill that lets Claude Code/Codex spin up VMs and GPUs
I've been working on CloudRouter, a skill + CLI that gives coding agents like Claude Code and Codex the ability to start cloud VMs and GPUs.<p>When an agent writes code, it usually needs to start a dev server, run tests, open a browser to verify its work. Today that all happens on your local machine. This works fine for a single task, but the agent is sharing your computer: your ports, RAM, screen. If you run multiple agents in parallel, it gets a bit chaotic. Docker helps with isolation, but it still uses your machine's resources, and doesn't give the agent a browser, a desktop, or a GPU to close the loop properly. The agent could handle all of this on its own if it had a primitive for starting VMs.<p>CloudRouter is that primitive — a skill that gives the agent its own machines. The agent can start a VM from your local project directory, upload the project files, run commands on the VM, and tear it down when it's done. If it needs a GPU, it can request one.<p><pre><code> cloudrouter start ./my-project
cloudrouter start --gpu B200 ./my-project
cloudrouter ssh cr_abc123 "npm install && npm run dev"
</code></pre>
Every VM comes with a VNC desktop, VS Code, and Jupyter Lab, all behind auth-protected URLs. When the agent is doing browser automation on the VM, you can open the VNC URL and watch it in real time. CloudRouter wraps agent-browser [1] for browser automation.<p><pre><code> cloudrouter browser open cr_abc123 "http://localhost:3000"
cloudrouter browser snapshot -i cr_abc123
# → @e1 [link] Home @e2 [link] Settings @e3 [button] Sign Out
cloudrouter browser click cr_abc123 @e2
cloudrouter browser screenshot cr_abc123 result.png
</code></pre>
Here's a short demo: <a href="https://youtu.be/SCkkzxKBcPE" rel="nofollow">https://youtu.be/SCkkzxKBcPE</a><p>What surprised me is how this inverted my workflow. Most cloud dev tooling starts from cloud (background agents, remote SSH, etc) to local for testing. But CloudRouter keeps your agents local and pushes the agent's work to the cloud. The agent does the same things it would do locally — running dev servers, operating browsers — but now on a VM. As I stopped watching agents work and worrying about local constraints, I started to run more tasks in parallel.<p>The GPU side is the part I'm most curious to see develop. Today if you want a coding agent to help with anything involving training or inference, there's a manual step where you go provision a machine. With CloudRouter the agent can just spin up a GPU sandbox, run the workload, and clean it up when it's done. Some of my friends have been using it to have agents run small experiments in parallel, but my ears are open to other use cases.<p>Would love your feedback and ideas. CloudRouter lives under packages/cloudrouter of our monorepo <a href="https://github.com/manaflow-ai/manaflow" rel="nofollow">https://github.com/manaflow-ai/manaflow</a>.<p>[1] <a href="https://github.com/vercel-labs/agent-browser" rel="nofollow">https://github.com/vercel-labs/agent-browser</a>
Show HN: Data Engineering Book – An open source, community-driven guide
Hi HN! I'm currently a Master's student at USTC (University of Science and Technology of China). I've been diving deep into Data Engineering, especially in the context of Large Language Models (LLMs).<p>The Problem:
I found that learning resources for modern data engineering are often fragmented and scattered across hundreds of medium articles or disjointed tutorials. It's hard to piece everything together into a coherent system.<p>The Solution:
I decided to open-source my learning notes and build them into a structured book. My goal is to help developers fast-track their learning curve.<p>Key Features:<p>LLM-Centric: Focuses on data pipelines specifically designed for LLM training and RAG systems.<p>Scenario-Based: Instead of just listing tools, I compare different methods/architectures based on specific business scenarios (e.g., "When to use Vector DB vs. Keyword Search").<p>Hands-on Projects: Includes full code for real-world implementations, not just "Hello World" examples.<p>This is a work in progress, and I'm treating it as "Book-as-Code". I would love to hear your feedback on the roadmap or any "anti-patterns" I might have included!<p>Check it out:<p>Online: <a href="https://datascale-ai.github.io/data_engineering_book/" rel="nofollow">https://datascale-ai.github.io/data_engineering_book/</a><p>GitHub: <a href="https://github.com/datascale-ai/data_engineering_book" rel="nofollow">https://github.com/datascale-ai/data_engineering_book</a>
Show HN: Data Engineering Book – An open source, community-driven guide
Hi HN! I'm currently a Master's student at USTC (University of Science and Technology of China). I've been diving deep into Data Engineering, especially in the context of Large Language Models (LLMs).<p>The Problem:
I found that learning resources for modern data engineering are often fragmented and scattered across hundreds of medium articles or disjointed tutorials. It's hard to piece everything together into a coherent system.<p>The Solution:
I decided to open-source my learning notes and build them into a structured book. My goal is to help developers fast-track their learning curve.<p>Key Features:<p>LLM-Centric: Focuses on data pipelines specifically designed for LLM training and RAG systems.<p>Scenario-Based: Instead of just listing tools, I compare different methods/architectures based on specific business scenarios (e.g., "When to use Vector DB vs. Keyword Search").<p>Hands-on Projects: Includes full code for real-world implementations, not just "Hello World" examples.<p>This is a work in progress, and I'm treating it as "Book-as-Code". I would love to hear your feedback on the roadmap or any "anti-patterns" I might have included!<p>Check it out:<p>Online: <a href="https://datascale-ai.github.io/data_engineering_book/" rel="nofollow">https://datascale-ai.github.io/data_engineering_book/</a><p>GitHub: <a href="https://github.com/datascale-ai/data_engineering_book" rel="nofollow">https://github.com/datascale-ai/data_engineering_book</a>
Show HN: 20+ Claude Code agents coordinating on real work (open source)
Single-agent LLMs suck at long-running complex tasks.<p>We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress.<p>How it works:
1. Orchestrator agent that manages task decomposition
2. Sub-agents for parallel work
3. Subscriptions to task state and progress
4. Real-time sharing of intermediate discoveries between agents<p>We tested this on a Putnam-level math problem, but the pattern generalizes to things like refactors, app builds, and long research.
It’s packaged as a Claude Code skill and designed to be small, readable, and modifiable.<p>Use it, break it, tell me about what workloads we should try and run next!
Show HN: 20+ Claude Code agents coordinating on real work (open source)
Single-agent LLMs suck at long-running complex tasks.<p>We’ve open-sourced a multi-agent orchestrator that we’ve been using to handle long-running LLM tasks. We found that single LLM agents tend to stall, loop, or generate non-compiling code, so we built a harness for agents to coordinate over shared context while work is in progress.<p>How it works:
1. Orchestrator agent that manages task decomposition
2. Sub-agents for parallel work
3. Subscriptions to task state and progress
4. Real-time sharing of intermediate discoveries between agents<p>We tested this on a Putnam-level math problem, but the pattern generalizes to things like refactors, app builds, and long research.
It’s packaged as a Claude Code skill and designed to be small, readable, and modifiable.<p>Use it, break it, tell me about what workloads we should try and run next!
Show HN: Agent Alcove – Claude, GPT, and Gemini debate across forums
Show HN: Agent Alcove – Claude, GPT, and Gemini debate across forums
Show HN: CodeRLM – Tree-sitter-backed code indexing for LLM agents
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations.<p>Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts.<p>The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into context, the model gets a searchable environment. The model can then query just for what it needs and can drill deeper where needed.<p>coderlm is my implementation of that idea for codebases. A Rust server indexes a project with tree-sitter, builds a symbol table with cross-references, and exposes an API. The agent queries for structure, symbols, implementations, callers, and grep results — getting back exactly the code it needs instead of scanning for it.<p>The agent workflow looks like:<p>1. `init` — register the project, get the top-level structure<p>2. `structure` — drill into specific directories<p>3. `search` — find symbols by name across the codebase<p>4. `impl` — retrieve the exact source of a function or class<p>5. `callers` — find everything that calls a given symbol<p>6. `grep` — fall back to text search when you need it<p>This replaces the glob/grep/read cycle with index-backed lookups. The server currently supports Rust, Python, TypeScript, JavaScript, and Go for symbol parsing, though all file types show up in the tree and are searchable via grep.<p>It ships as a Claude Code plugin with hooks that guide the agent to use indexed lookups instead of native file tools, plus a Python CLI wrapper with zero dependencies.<p>For anecdotal results, I ran the same prompt against a codebase to "explore and identify opportunities to clarify the existing structure".<p>Using coderlm, claude was able to generate a plan in about 3 minutes. The coderlm enabled instance found a genuine bug (duplicated code with identical names), orphaned code for cleanup, mismatched naming conventions crossing module boundaries, and overlapping vocabulary. These are all <i>semantic</i> issues which clearly benefit from the tree-sitter centric approach.<p>Using the native tools, claude was able to identify various file clutter in the root of the project, out of date references, and a migration timestamp collision. These findings are more consistent with methodical walks of the filesystem and took about 8 minutes to produce.<p>The indexed approach did better at catching semantic issues than native tools and had a key benefit in being faster to resolve.<p>I've spent some effort to streamline the installation process, but it isn't turnkey yet. You'll need the rust toolchain to build the server which runs as a separate process. Installing the plugin from a claude marketplace is possible, but the skill isn't being added to your .claude yet so there are some manual steps to just getting to a point where claude could use it.<p>Claude continues to demonstrate significant resistance to using CodeRLM in exploration tasks. Typically to use you will need to explicitly direct claude to use it.<p>---<p>Repo: github.com/JaredStewart/coderlm<p>Paper: Recursive Language Models <a href="https://arxiv.org/abs/2512.24601" rel="nofollow">https://arxiv.org/abs/2512.24601</a> — Zhang, Kraska, Khattab (MIT CSAIL, 2025)<p>Inspired by: <a href="https://github.com/brainqub3/claude_code_RLM" rel="nofollow">https://github.com/brainqub3/claude_code_RLM</a>
Show HN: CodeRLM – Tree-sitter-backed code indexing for LLM agents
I've been building a tool that changes how LLM coding agents explore codebases, and I wanted to share it along with some early observations.<p>Typically claude code globs directories, greps for patterns, and reads files with minimal guidance. It works in kind of the same way you'd learn to navigate a city by walking every street. You'll eventually build a mental map, but claude never does - at least not any that persists across different contexts.<p>The Recursive Language Models paper from Zhang, Kraska, and Khattab at MIT CSAIL introduced a cleaner framing. Instead of cramming everything into context, the model gets a searchable environment. The model can then query just for what it needs and can drill deeper where needed.<p>coderlm is my implementation of that idea for codebases. A Rust server indexes a project with tree-sitter, builds a symbol table with cross-references, and exposes an API. The agent queries for structure, symbols, implementations, callers, and grep results — getting back exactly the code it needs instead of scanning for it.<p>The agent workflow looks like:<p>1. `init` — register the project, get the top-level structure<p>2. `structure` — drill into specific directories<p>3. `search` — find symbols by name across the codebase<p>4. `impl` — retrieve the exact source of a function or class<p>5. `callers` — find everything that calls a given symbol<p>6. `grep` — fall back to text search when you need it<p>This replaces the glob/grep/read cycle with index-backed lookups. The server currently supports Rust, Python, TypeScript, JavaScript, and Go for symbol parsing, though all file types show up in the tree and are searchable via grep.<p>It ships as a Claude Code plugin with hooks that guide the agent to use indexed lookups instead of native file tools, plus a Python CLI wrapper with zero dependencies.<p>For anecdotal results, I ran the same prompt against a codebase to "explore and identify opportunities to clarify the existing structure".<p>Using coderlm, claude was able to generate a plan in about 3 minutes. The coderlm enabled instance found a genuine bug (duplicated code with identical names), orphaned code for cleanup, mismatched naming conventions crossing module boundaries, and overlapping vocabulary. These are all <i>semantic</i> issues which clearly benefit from the tree-sitter centric approach.<p>Using the native tools, claude was able to identify various file clutter in the root of the project, out of date references, and a migration timestamp collision. These findings are more consistent with methodical walks of the filesystem and took about 8 minutes to produce.<p>The indexed approach did better at catching semantic issues than native tools and had a key benefit in being faster to resolve.<p>I've spent some effort to streamline the installation process, but it isn't turnkey yet. You'll need the rust toolchain to build the server which runs as a separate process. Installing the plugin from a claude marketplace is possible, but the skill isn't being added to your .claude yet so there are some manual steps to just getting to a point where claude could use it.<p>Claude continues to demonstrate significant resistance to using CodeRLM in exploration tasks. Typically to use you will need to explicitly direct claude to use it.<p>---<p>Repo: github.com/JaredStewart/coderlm<p>Paper: Recursive Language Models <a href="https://arxiv.org/abs/2512.24601" rel="nofollow">https://arxiv.org/abs/2512.24601</a> — Zhang, Kraska, Khattab (MIT CSAIL, 2025)<p>Inspired by: <a href="https://github.com/brainqub3/claude_code_RLM" rel="nofollow">https://github.com/brainqub3/claude_code_RLM</a>