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Show HN: Remotely use my guitar tuner

Launch HN: RunAnywhere (YC W26) – Faster AI Inference on Apple Silicon

Hi HN, we're Sanchit and Shubham (YC W26). We built a fast inference engine for Apple Silicon. LLMs, speech-to-text, text-to-speech – MetalRT beats llama.cpp, Apple's MLX, Ollama, and sherpa-onnx on every modality we tested. Custom Metal shaders, no framework overhead.<p>Also, we've open-sourced RCLI, the fastest end-to-end voice AI pipeline on Apple Silicon. Mic to spoken response, entirely on-device. No cloud, no API keys.<p>To get started:<p><pre><code> brew tap RunanywhereAI/rcli https://github.com/RunanywhereAI/RCLI.git brew install rcli rcli setup # downloads ~1 GB of models rcli # interactive mode with push-to-talk </code></pre> Or:<p><pre><code> curl -fsSL https://raw.githubusercontent.com/RunanywhereAI/RCLI/main/install.sh | bash </code></pre> The numbers (M4 Max, 64 GB, reproducible via `rcli bench`):<p>LLM decode – 1.67x faster than llama.cpp, 1.19x faster than Apple MLX (same model files): - Qwen3-0.6B: 658 tok/s (vs mlx-lm 552, llama.cpp 295) - Qwen3-4B: 186 tok/s (vs mlx-lm 170, llama.cpp 87) - LFM2.5-1.2B: 570 tok/s (vs mlx-lm 509, llama.cpp 372) - Time-to-first-token: 6.6 ms<p>STT – 70 seconds of audio transcribed in 101 ms. That's 714x real-time. 4.6x faster than mlx-whisper.<p>TTS – 178 ms synthesis. 2.8x faster than mlx-audio and sherpa-onnx.<p>We built this because demoing on-device AI is easy but shipping it is brutal. Voice is the hardest test: you're chaining STT, LLM, and TTS sequentially, and if any stage is slow, the user feels it. Most teams fall back to cloud APIs not because local models are bad, but because local inference infrastructure is.<p>The thing that's hard to solve is latency compounding. In a voice pipeline, you're stacking three models in sequence. If each adds 200ms, you're at 600ms before the user hears a word, and that feels broken. You can't optimize one stage and call it done. Every stage needs to be fast, on one device, with no network round-trip to hide behind.<p>We went straight to Metal. Custom GPU compute shaders, all memory pre-allocated at init (zero allocations during inference), and one unified engine for all three modalities instead of stitching separate runtimes together.<p>MetalRT is the first engine to handle all three modalities natively on Apple Silicon. Full methodology:<p>LLM benchmarks: <a href="https://www.runanywhere.ai/blog/metalrt-fastest-llm-decode-engine-apple-silicon">https://www.runanywhere.ai/blog/metalrt-fastest-llm-decode-e...</a><p>Speech benchmarks: <a href="https://www.runanywhere.ai/blog/metalrt-speech-fastest-stt-tts-apple-silicon">https://www.runanywhere.ai/blog/metalrt-speech-fastest-stt-t...</a><p>How: Most inference engines add layers between you and the GPU: graph schedulers, runtime dispatchers, memory managers. MetalRT skips all of it. Custom Metal compute shaders for quantized matmul, attention, and activation - compiled ahead of time, dispatched directly.<p>Voice Pipeline optimizations details: <a href="https://www.runanywhere.ai/blog/fastvoice-on-device-voice-ai-pipeline-apple-silicon">https://www.runanywhere.ai/blog/fastvoice-on-device-voice-ai...</a> RAG optimizations: <a href="https://www.runanywhere.ai/blog/fastvoice-rag-on-device-retrieval-augmented-voice-ai">https://www.runanywhere.ai/blog/fastvoice-rag-on-device-retr...</a><p>RCLI is the open-source voice pipeline (MIT) built on MetalRT: three concurrent threads with lock-free ring buffers, double-buffered TTS, 38 macOS actions by voice, local RAG (~4 ms over 5K+ chunks), 20 hot-swappable models, and a full-screen TUI with per-op latency readouts. Falls back to llama.cpp when MetalRT isn't installed.<p>Source: <a href="https://github.com/RunanywhereAI/RCLI" rel="nofollow">https://github.com/RunanywhereAI/RCLI</a> (MIT)<p>Demo: <a href="https://www.youtube.com/watch?v=eTYwkgNoaKg" rel="nofollow">https://www.youtube.com/watch?v=eTYwkgNoaKg</a><p>What would you build if on-device AI were genuinely as fast as cloud?

Show HN: How I topped the HuggingFace open LLM leaderboard on two gaming GPUs

I found that duplicating a specific block of 7 middle layers in Qwen2-72B, without modifying any weights, improved performance across all Open LLM Leaderboard benchmarks and took #1. As of 2026, the top 4 models on that leaderboard are still descendants.<p>The weird finding: single-layer duplication does nothing. Too few layers, nothing. Too many, it gets worse. Only circuit-sized blocks of ~7 layers work. This suggests pretraining carves out discrete functional circuits in the layer stack that only work when preserved whole.<p>The whole thing was developed on 2x RTX 4090s in my basement. I'm now running current models (GLM-4.7, Qwen3.5, MiniMax M2.5) on a dual GH200 rig (see my other post). Code and new models coming soon.<p>Happy to answer questions.

Show HN: How I topped the HuggingFace open LLM leaderboard on two gaming GPUs

I found that duplicating a specific block of 7 middle layers in Qwen2-72B, without modifying any weights, improved performance across all Open LLM Leaderboard benchmarks and took #1. As of 2026, the top 4 models on that leaderboard are still descendants.<p>The weird finding: single-layer duplication does nothing. Too few layers, nothing. Too many, it gets worse. Only circuit-sized blocks of ~7 layers work. This suggests pretraining carves out discrete functional circuits in the layer stack that only work when preserved whole.<p>The whole thing was developed on 2x RTX 4090s in my basement. I'm now running current models (GLM-4.7, Qwen3.5, MiniMax M2.5) on a dual GH200 rig (see my other post). Code and new models coming soon.<p>Happy to answer questions.

Show HN: I built a site where strangers leave kind voice notes for each other

Show HN: VS Code Agent Kanban: Task Management for the AI-Assisted Developer

Agent Kanban has 4 main features:<p>GitOps & team friendly kanban board integration inside VS Code Structured plan / todo / implement via @kanban commands Leverages your existing agent harness rather than trying to bundle a built in one .md task format provides a permanent (editable) source of truth including considerations, decisions and actions, that is resistant to context rot

Show HN: VS Code Agent Kanban: Task Management for the AI-Assisted Developer

Agent Kanban has 4 main features:<p>GitOps & team friendly kanban board integration inside VS Code Structured plan / todo / implement via @kanban commands Leverages your existing agent harness rather than trying to bundle a built in one .md task format provides a permanent (editable) source of truth including considerations, decisions and actions, that is resistant to context rot

Show HN: The Mog Programming Language

Hi, Ted here, creator of Mog.<p>- Mog is a statically typed, compiled, embedded language (think statically typed Lua) designed to be written by LLMs -- the full spec fits in 3,200 tokens. - An AI agent writes a Mog program, compiles it, and dynamically loads it as a plugin, script, or hook. - The host controls exactly which functions a Mog program can call (capability-based permissions), so permissions propagate from agent to agent-written code. - Compiled to native code for low-latency plugin execution -- no interpreter overhead, no JIT, no process startup cost. - The compiler is written in safe Rust so the entire toolchain can be audited for security. Even without a full security audit, Mog is already useful for agents extending themselves with their own code. - MIT licensed, contributions welcome.<p>Motivations for Mog:<p>1. Syntax Only an AI Could Love: Mog is written for AIs to write, so the spec fits easily in context (~3200 tokens), and it's intended to minimize foot-guns to lower the error rate when generating Mog code. This is why Mog has no operator precedence: non-associative operations have to use parentheses, e.g. (a + b) * c. It's also why there's no implicit type coercion, which I've found over the decades to be an annoying source of runtime bugs. There's also less support in Mog for generics, and there's absolutely no support for metaprogramming, macros, or syntactic abstraction.<p>When asking people to write code in a language, these restrictions could be onerous. But LLMs don't care, and the less expressivity you trust them with, the better.<p>2. Capabilities-Based Permissionsl: There's a paradox with existing security models for AI agents. If you give an agent like OpenClaw unfettered access to your data, that's insecure and you'll get pwned. But if you sandbox it, it can't do most of what you want. Worse, if you run scripts the agent wrote, those scripts don't inherit the permissions that constrain the agent's own bash tool calls, which leads to pwnage and other chaos. And that's not even assuming you run one of the many OpenClaw plugins with malware.<p>Mog tries to solve this by taking inspiration from embedded languages. It compiles all the way to machine code, ahead of time, but the compiler doesn't output any dangerous code (at least it shouldn't -- Mog is quite new, so that could still be buggy). This allows a host program, such as an AI agent, to generate Mog source code, compile it, and load it into itself using dlopen(), while maintaining security guarantees.<p>The main trick is that a Mog program on its own can't do much. It has no direct access to syscalls, libc, or memory. It can basically call functions, do heap allocations (but only within the arena the host gives it), and return something. If the host wants the Mog program to be able to do I/O, it has to supply the functions that the Mog program will call. A core invariant is that a Mog program should never be able to crash the host program, corrupt its state, or consume more resources than the host allows.<p>This allows the host to inspect the arguments to any potentially dangerous operation that the Mog program attempts, since it's code that runs in the host. For example, a host agent could give a Mog program a function to run a bash command, then enforce its own session-level permissions on that command, even though the command was dynamically generated by a plugin that was written without prior knowledge of those permission settings.<p>(There are a couple other tricks that PL people might find interesting. One is that the host can limit the execution time of the guest program. It does this using cooperative interrupt polling, i.e. the compiler inserts runtime checks that check if the host has asked the guest to stop. This causes a roughly 10% drop in performance on extremely tight loops, which are the worst case. It could almost certainly be optimized.)<p>3. Self Modification Without Restart: When I try to modify my OpenClaw from my phone, I have to restart the whole agent. Mog fixes this: an agent can compile and run new plugins without interrupting a session, which makes it dynamically responsive to user feedback (e.g., you tell it to always ask you before deleting a file and without any interruption it compiles and loads the code to... actually do that).<p>Async support is built into the language, by adapting LLVM's coroutine lowering to our Rust port of the QBE compiler, which is what Mog uses for compilation. The Mog host library can be slotted into an async event loop (tested with Bun), so Mog async calls get scheduled seamlessly by the agent's event loop. Another trick is that the Mog program uses a stack inside the memory arena that the host provides for it to run in, rather than the system stack. The system tracks a guard page between the stack and heap. This design prevents stack overflow without runtime overhead.<p>Lots of work still needs to be done to make Mog a "batteries-included" experience like Python. Most of that work involves fleshing out a standard library to include things like JSON, CSV, Sqlite, and HTTP. One high-impact addition would be an `llm` library that allows the guest to make LLM calls through the agent, which should support multiple models and token budgeting, so the host could prevent the plugin from burning too many tokens.<p>I suspect we'll also want to do more work to make the program lifecycle operations more ergonomic. And finally, there should be a more fully featured library for integrating a Mog host into an AI agent like OpenClaw or OpenAI's Codex CLI.

Show HN: The Mog Programming Language

Hi, Ted here, creator of Mog.<p>- Mog is a statically typed, compiled, embedded language (think statically typed Lua) designed to be written by LLMs -- the full spec fits in 3,200 tokens. - An AI agent writes a Mog program, compiles it, and dynamically loads it as a plugin, script, or hook. - The host controls exactly which functions a Mog program can call (capability-based permissions), so permissions propagate from agent to agent-written code. - Compiled to native code for low-latency plugin execution -- no interpreter overhead, no JIT, no process startup cost. - The compiler is written in safe Rust so the entire toolchain can be audited for security. Even without a full security audit, Mog is already useful for agents extending themselves with their own code. - MIT licensed, contributions welcome.<p>Motivations for Mog:<p>1. Syntax Only an AI Could Love: Mog is written for AIs to write, so the spec fits easily in context (~3200 tokens), and it's intended to minimize foot-guns to lower the error rate when generating Mog code. This is why Mog has no operator precedence: non-associative operations have to use parentheses, e.g. (a + b) * c. It's also why there's no implicit type coercion, which I've found over the decades to be an annoying source of runtime bugs. There's also less support in Mog for generics, and there's absolutely no support for metaprogramming, macros, or syntactic abstraction.<p>When asking people to write code in a language, these restrictions could be onerous. But LLMs don't care, and the less expressivity you trust them with, the better.<p>2. Capabilities-Based Permissionsl: There's a paradox with existing security models for AI agents. If you give an agent like OpenClaw unfettered access to your data, that's insecure and you'll get pwned. But if you sandbox it, it can't do most of what you want. Worse, if you run scripts the agent wrote, those scripts don't inherit the permissions that constrain the agent's own bash tool calls, which leads to pwnage and other chaos. And that's not even assuming you run one of the many OpenClaw plugins with malware.<p>Mog tries to solve this by taking inspiration from embedded languages. It compiles all the way to machine code, ahead of time, but the compiler doesn't output any dangerous code (at least it shouldn't -- Mog is quite new, so that could still be buggy). This allows a host program, such as an AI agent, to generate Mog source code, compile it, and load it into itself using dlopen(), while maintaining security guarantees.<p>The main trick is that a Mog program on its own can't do much. It has no direct access to syscalls, libc, or memory. It can basically call functions, do heap allocations (but only within the arena the host gives it), and return something. If the host wants the Mog program to be able to do I/O, it has to supply the functions that the Mog program will call. A core invariant is that a Mog program should never be able to crash the host program, corrupt its state, or consume more resources than the host allows.<p>This allows the host to inspect the arguments to any potentially dangerous operation that the Mog program attempts, since it's code that runs in the host. For example, a host agent could give a Mog program a function to run a bash command, then enforce its own session-level permissions on that command, even though the command was dynamically generated by a plugin that was written without prior knowledge of those permission settings.<p>(There are a couple other tricks that PL people might find interesting. One is that the host can limit the execution time of the guest program. It does this using cooperative interrupt polling, i.e. the compiler inserts runtime checks that check if the host has asked the guest to stop. This causes a roughly 10% drop in performance on extremely tight loops, which are the worst case. It could almost certainly be optimized.)<p>3. Self Modification Without Restart: When I try to modify my OpenClaw from my phone, I have to restart the whole agent. Mog fixes this: an agent can compile and run new plugins without interrupting a session, which makes it dynamically responsive to user feedback (e.g., you tell it to always ask you before deleting a file and without any interruption it compiles and loads the code to... actually do that).<p>Async support is built into the language, by adapting LLVM's coroutine lowering to our Rust port of the QBE compiler, which is what Mog uses for compilation. The Mog host library can be slotted into an async event loop (tested with Bun), so Mog async calls get scheduled seamlessly by the agent's event loop. Another trick is that the Mog program uses a stack inside the memory arena that the host provides for it to run in, rather than the system stack. The system tracks a guard page between the stack and heap. This design prevents stack overflow without runtime overhead.<p>Lots of work still needs to be done to make Mog a "batteries-included" experience like Python. Most of that work involves fleshing out a standard library to include things like JSON, CSV, Sqlite, and HTTP. One high-impact addition would be an `llm` library that allows the guest to make LLM calls through the agent, which should support multiple models and token budgeting, so the host could prevent the plugin from burning too many tokens.<p>I suspect we'll also want to do more work to make the program lifecycle operations more ergonomic. And finally, there should be a more fully featured library for integrating a Mog host into an AI agent like OpenClaw or OpenAI's Codex CLI.

Show HN: The Mog Programming Language

Hi, Ted here, creator of Mog.<p>- Mog is a statically typed, compiled, embedded language (think statically typed Lua) designed to be written by LLMs -- the full spec fits in 3,200 tokens. - An AI agent writes a Mog program, compiles it, and dynamically loads it as a plugin, script, or hook. - The host controls exactly which functions a Mog program can call (capability-based permissions), so permissions propagate from agent to agent-written code. - Compiled to native code for low-latency plugin execution -- no interpreter overhead, no JIT, no process startup cost. - The compiler is written in safe Rust so the entire toolchain can be audited for security. Even without a full security audit, Mog is already useful for agents extending themselves with their own code. - MIT licensed, contributions welcome.<p>Motivations for Mog:<p>1. Syntax Only an AI Could Love: Mog is written for AIs to write, so the spec fits easily in context (~3200 tokens), and it's intended to minimize foot-guns to lower the error rate when generating Mog code. This is why Mog has no operator precedence: non-associative operations have to use parentheses, e.g. (a + b) * c. It's also why there's no implicit type coercion, which I've found over the decades to be an annoying source of runtime bugs. There's also less support in Mog for generics, and there's absolutely no support for metaprogramming, macros, or syntactic abstraction.<p>When asking people to write code in a language, these restrictions could be onerous. But LLMs don't care, and the less expressivity you trust them with, the better.<p>2. Capabilities-Based Permissionsl: There's a paradox with existing security models for AI agents. If you give an agent like OpenClaw unfettered access to your data, that's insecure and you'll get pwned. But if you sandbox it, it can't do most of what you want. Worse, if you run scripts the agent wrote, those scripts don't inherit the permissions that constrain the agent's own bash tool calls, which leads to pwnage and other chaos. And that's not even assuming you run one of the many OpenClaw plugins with malware.<p>Mog tries to solve this by taking inspiration from embedded languages. It compiles all the way to machine code, ahead of time, but the compiler doesn't output any dangerous code (at least it shouldn't -- Mog is quite new, so that could still be buggy). This allows a host program, such as an AI agent, to generate Mog source code, compile it, and load it into itself using dlopen(), while maintaining security guarantees.<p>The main trick is that a Mog program on its own can't do much. It has no direct access to syscalls, libc, or memory. It can basically call functions, do heap allocations (but only within the arena the host gives it), and return something. If the host wants the Mog program to be able to do I/O, it has to supply the functions that the Mog program will call. A core invariant is that a Mog program should never be able to crash the host program, corrupt its state, or consume more resources than the host allows.<p>This allows the host to inspect the arguments to any potentially dangerous operation that the Mog program attempts, since it's code that runs in the host. For example, a host agent could give a Mog program a function to run a bash command, then enforce its own session-level permissions on that command, even though the command was dynamically generated by a plugin that was written without prior knowledge of those permission settings.<p>(There are a couple other tricks that PL people might find interesting. One is that the host can limit the execution time of the guest program. It does this using cooperative interrupt polling, i.e. the compiler inserts runtime checks that check if the host has asked the guest to stop. This causes a roughly 10% drop in performance on extremely tight loops, which are the worst case. It could almost certainly be optimized.)<p>3. Self Modification Without Restart: When I try to modify my OpenClaw from my phone, I have to restart the whole agent. Mog fixes this: an agent can compile and run new plugins without interrupting a session, which makes it dynamically responsive to user feedback (e.g., you tell it to always ask you before deleting a file and without any interruption it compiles and loads the code to... actually do that).<p>Async support is built into the language, by adapting LLVM's coroutine lowering to our Rust port of the QBE compiler, which is what Mog uses for compilation. The Mog host library can be slotted into an async event loop (tested with Bun), so Mog async calls get scheduled seamlessly by the agent's event loop. Another trick is that the Mog program uses a stack inside the memory arena that the host provides for it to run in, rather than the system stack. The system tracks a guard page between the stack and heap. This design prevents stack overflow without runtime overhead.<p>Lots of work still needs to be done to make Mog a "batteries-included" experience like Python. Most of that work involves fleshing out a standard library to include things like JSON, CSV, Sqlite, and HTTP. One high-impact addition would be an `llm` library that allows the guest to make LLM calls through the agent, which should support multiple models and token budgeting, so the host could prevent the plugin from burning too many tokens.<p>I suspect we'll also want to do more work to make the program lifecycle operations more ergonomic. And finally, there should be a more fully featured library for integrating a Mog host into an AI agent like OpenClaw or OpenAI's Codex CLI.

Show HN: DenchClaw – Local CRM on Top of OpenClaw

Hi everyone, I am Kumar, co-founder of Dench (<a href="https://denchclaw.com" rel="nofollow">https://denchclaw.com</a>). We were part of YC S24, an agentic workflow company that previously worked with sales floors automating niche enterprise tasks such as outbound calling, legal intake, etc.<p>Building consumer / power-user software always gave me more joy than FDEing into an enterprise. It did not give me joy to manually add AI tools to a cloud harness for every small new thing, at least not as much as completely local software that is open source and has all the powers of OpenClaw (I can now talk to my CRM on Telegram!).<p>A week ago, we launched Ironclaw, an Open Source OpenClaw CRM Framework (<a href="https://x.com/garrytan/status/2023518514120937672?s=20" rel="nofollow">https://x.com/garrytan/status/2023518514120937672?s=20</a>) but people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw (<a href="https://denchclaw.com" rel="nofollow">https://denchclaw.com</a>).<p>OpenClaw today feels like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode was the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt.<p>That is how we think about DenchClaw. We are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world.<p>Demo: <a href="https://www.youtube.com/watch?v=pfACTbc3Bh4#t=43" rel="nofollow">https://www.youtube.com/watch?v=pfACTbc3Bh4#t=43</a><p><pre><code> npx denchclaw </code></pre> I use DenchClaw daily for almost everything I do. It also works as a coding agent like Cursor - DenchClaw built DenchClaw. I am addicted now that I can ask it, “hey in the companies table only show me the ones who have more than 5 employees” and it updates it live than me having to manually add a filter.<p>On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill.<p>The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. Thank you DuckDB team!<p>It creates a new OpenClaw profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA.<p>Think of it as Cursor for your Mac (also works on Linux and Windows) which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do linkedin outreach using MY browser.<p>DenchClaw finds your Chrome Profile and copies it fully into its own, so you won’t have to log in into all your websites again. DenchClaw sees what you see, does what you do. It’s an everything app, that sits locally on your mac.<p>Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use.<p>We would love you all to break it, stress test its CRM capabilities, how it streams subagents for lead enrichment, hook it into your Apollo, Gmail, Notion and everything there is. Looking forward to comments/feedback!

Show HN: DenchClaw – Local CRM on Top of OpenClaw

Hi everyone, I am Kumar, co-founder of Dench (<a href="https://denchclaw.com" rel="nofollow">https://denchclaw.com</a>). We were part of YC S24, an agentic workflow company that previously worked with sales floors automating niche enterprise tasks such as outbound calling, legal intake, etc.<p>Building consumer / power-user software always gave me more joy than FDEing into an enterprise. It did not give me joy to manually add AI tools to a cloud harness for every small new thing, at least not as much as completely local software that is open source and has all the powers of OpenClaw (I can now talk to my CRM on Telegram!).<p>A week ago, we launched Ironclaw, an Open Source OpenClaw CRM Framework (<a href="https://x.com/garrytan/status/2023518514120937672?s=20" rel="nofollow">https://x.com/garrytan/status/2023518514120937672?s=20</a>) but people confused us with NearAI’s Ironclaw, so we changed our name to DenchClaw (<a href="https://denchclaw.com" rel="nofollow">https://denchclaw.com</a>).<p>OpenClaw today feels like early React: the primitive is incredibly powerful, but the patterns are still forming, and everyone is piecing together their own way to actually use it. What made React explode was the emergence of frameworks like Gatsby and Next.js that turned raw capability into something opinionated, repeatable, and easy to adopt.<p>That is how we think about DenchClaw. We are trying to make it one of the clearest, most practical, and most complete ways to use OpenClaw in the real world.<p>Demo: <a href="https://www.youtube.com/watch?v=pfACTbc3Bh4#t=43" rel="nofollow">https://www.youtube.com/watch?v=pfACTbc3Bh4#t=43</a><p><pre><code> npx denchclaw </code></pre> I use DenchClaw daily for almost everything I do. It also works as a coding agent like Cursor - DenchClaw built DenchClaw. I am addicted now that I can ask it, “hey in the companies table only show me the ones who have more than 5 employees” and it updates it live than me having to manually add a filter.<p>On Dench, everything sits in a file system, the table filters, views, column toggles, calendar/gantt views, etc, so OpenClaw can directly work with it using Dench’s CRM skill.<p>The CRM is built on top of DuckDB, the smallest, most performant and at the same time also feature rich database we could find. Thank you DuckDB team!<p>It creates a new OpenClaw profile called “dench”, and opens a new OpenClaw Gateway… that means you can run all your usual openclaw commands by just prefixing every command with `openclaw --profile dench` . It will start your gateway on port 19001 range. You will be able to access the DenchClaw frontend at localhost:3100. Once you open it on Safari, just add it to your Dock to use it as a PWA.<p>Think of it as Cursor for your Mac (also works on Linux and Windows) which is based on OpenClaw. DenchClaw has a file tree view for you to use it as an elevated finder tool to do anything on your mac. I use it to create slides, do linkedin outreach using MY browser.<p>DenchClaw finds your Chrome Profile and copies it fully into its own, so you won’t have to log in into all your websites again. DenchClaw sees what you see, does what you do. It’s an everything app, that sits locally on your mac.<p>Just ask it “hey import my notion”, “hey import everything from my hubspot”, and it will literally go into your browser, export all objects and documents and put it in its own workspace that you can use.<p>We would love you all to break it, stress test its CRM capabilities, how it streams subagents for lead enrichment, hook it into your Apollo, Gmail, Notion and everything there is. Looking forward to comments/feedback!

Show HN: Mcp2cli – One CLI for every API, 96-99% fewer tokens than native MCP

Every MCP server injects its full tool schemas into context on every turn — 30 tools costs ~3,600 tokens/turn whether the model uses them or not. Over 25 turns with 120 tools, that's 362,000 tokens just for schemas.<p>mcp2cli turns any MCP server or OpenAPI spec into a CLI at runtime. The LLM discovers tools on demand:<p><pre><code> mcp2cli --mcp https://mcp.example.com/sse --list # ~16 tokens/tool mcp2cli --mcp https://mcp.example.com/sse create-task --help # ~120 tokens, once mcp2cli --mcp https://mcp.example.com/sse create-task --title "Fix bug" </code></pre> No codegen, no rebuild when the server changes. Works with any LLM — it's just a CLI the model shells out to. Also handles OpenAPI specs (JSON/YAML, local or remote) with the same interface.<p>Token savings are real, measured with cl100k_base: 96% for 30 tools over 15 turns, 99% for 120 tools over 25 turns.<p>It also ships as an installable skill for AI coding agents (Claude Code, Cursor, Codex): `npx skills add knowsuchagency/mcp2cli --skill mcp2cli`<p>Inspired by Kagan Yilmaz's CLI vs MCP analysis and CLIHub.<p><a href="https://github.com/knowsuchagency/mcp2cli" rel="nofollow">https://github.com/knowsuchagency/mcp2cli</a>

Show HN: I open-sourced my Steam game, 100% written in Lua, engine is also open

Homebrew engine <a href="https://github.com/willtobyte/carimbo" rel="nofollow">https://github.com/willtobyte/carimbo</a>

Show HN: Eyot, A programming language where the GPU is just another thread

Show HN: Eyot, A programming language where the GPU is just another thread

Show HN: Curiosity – DIY 6" Newtonian Reflector Telescope

A DIY Newtonian reflector telescope with dobsonian mount. A fun to do hobby project taking us closer to the moon and beyond. A lot of plans ahead on how to make it much better and portable but this was the first time me and my friend implemented or rather built a telescope.<p>Have a look at what all we have captured.

Show HN: Curiosity – DIY 6" Newtonian Reflector Telescope

A DIY Newtonian reflector telescope with dobsonian mount. A fun to do hobby project taking us closer to the moon and beyond. A lot of plans ahead on how to make it much better and portable but this was the first time me and my friend implemented or rather built a telescope.<p>Have a look at what all we have captured.

Show HN: ANSI-Saver – A macOS Screensaver

Hi, I've been working on something I've been thinking for long time but since I had no experience with macOS screen savers I always posponed. Now thanks to Claude I was able to create a screensaver that scroll ANSI files while your computer is idle.<p>It allow to use local ANS files or packs directly from 16colo.rs.<p>Repo: <a href="https://github.com/lardissone/ansi-saver" rel="nofollow">https://github.com/lardissone/ansi-saver</a>

Show HN: Kula – Lightweight, self-contained Linux server monitoring tool

Zero dependencies. No external databases. Single binary. Just deploy and go. I needed something that would allow for real-time monitoring, and installation is as simple as dropping a single file and running it. That's exactly what Kula is. Kula is the Polish word for "ball," as in "crystal ball." The project is in constant development, but I'm already using it on multiple servers in production. It still has some rough edges and needs to mature, but I wanted to share it with the world now—perhaps someone else will find it useful and be willing to help me develop it by testing or providing feedback. Cheers! Github: <a href="https://github.com/c0m4r/kula" rel="nofollow">https://github.com/c0m4r/kula</a>